pax_global_header 0000666 0000000 0000000 00000000064 13565313113 0014513 g ustar 00root root 0000000 0000000 52 comment=be25570a09191471676779c4d6586fc701419a97 scw-be25570a09191471676779c4d6586fc701419a97/ 0000775 0000000 0000000 00000000000 13565313113 0016621 5 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/README.md 0000664 0000000 0000000 00000000042 13565313113 0020074 0 ustar 00root root 0000000 0000000 # SCW Software carpentry workshop scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/ 0000775 0000000 0000000 00000000000 13565313113 0017646 5 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints/ 0000775 0000000 0000000 00000000000 13565313113 0023437 5 ustar 00root root 0000000 0000000 Defensive Programming-filled-checkpoint.ipynb 0000664 0000000 0000000 00000770627 13565313113 0034203 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Defensive Programming" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Novice | Expert |
---|---|
1) Writes a fucntion range_overlap | 1) Writes a short function for each test |
2) Call it in 2 or 3 inputs | 2) Writes a range_overlap function that should pass those tests |
3) If wrong, fix and re-run test | 3) If wrong, fix and re-run test functions |
Novice | Expert |
---|---|
1) Writes a fucntion range_overlap | 1) Writes a short function for each test |
2) Call it in 2 or 3 inputs | 2) Writes a range_overlap function that should pass those tests |
3) If wrong, fix and re-run test | 3) If wrong, fix and re-run test functions |
Category: Some more info
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Where Categories are:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Syntax Errors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You forget a colon, add too many spaces, forget a parenthesis etc... Makes Python not to know how to read your program. That gives you:\n",
"SyntaxError
"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (Category: Some more info
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Where Categories are:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Syntax Errors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You forget a colon, add too many spaces, forget a parenthesis etc... Makes Python not to know how to read your program. That gives you:\n",
"SyntaxError
"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"%whos
shows memory stored variables defined along the notebook:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"What does the following program print out?\n",
"\n",
"\n",
"first, second = 'Grace', 'Hopper'\n",
"third, fourth = second, first\n",
"print(third, fourth)\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using libraries (modules)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we will load a library. Let's import Numpy. Numpy is important for fancy number manipulations:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Libraries provide additional functionality to the basic Python package. Importing too many libraries can sometimes complicate and slow down your programs - so we only import what we need for each program.\n",
"\n",
"Now we read the first file where patient data are stored. \n",
"\n",
"We will use functions which belong to a certain library and perform \"something\" for us. In this case we want to load a data file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The expression ``numpy.loadtxt(...)`` is a function call that asks Python to run the function loadtxt which belongs to the numpy library. This dotted notation is used everywhere in Python: the thing that appears before the dot contains the thing that appears after."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"``numpy.loadtxt`` has two parameters: the name of the file we want to read, and the delimiter that separates values on a line. These both need to be character strings (or strings for short), so we put them in quotes."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We did not assign the output of the function to any variable, so the notebook just dumped the ouptut to an ``Out[]`` cell. But, just the same way we asigned values to a variable, we can assing the array of values to an variable using the same syntax:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"No output here, as we just assigned, as we did before with ``weight_kg``. To print out the variable value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check what type of \"thing\" ``data`` variable refers to:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we create a variable, we also create information about it, as the attribute shape! That's very important!!\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we have 60 rows and 40 columns in ``data`` variable."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to get a single number from the array we provide an index in square brackets, just as we do in maths"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Selecting intervals of data by using ranges : . Caution here!:ranges seem closed but they are [) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is how index is assigned by python:\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is no need to include upper and lower bounds:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"what does ``data[3:3, 4:4]`` produce? \n",
"What about ``data[3:3, :]``?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Some array math\n",
"Basic arithmetics: The operation is performed in every element of the array"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Array to Array operations: The operation is made on each corresponding element of the arrays:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"More complex operations:\n",
"
The following is a method. Method are actions that can be made on the variable, Python knows how to!. The actions available depend on the variable type:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"Reminder: method vs attribute\n",
"
\n",
"\n",
"\n",
"\n",
"- attributes are descriptions of variable properties\n",
"
- methods are actions that Python knows to perform on the variable\n",
"
\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Several important methods for numpy variables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes is useful to make some partial statistics. For instance max inflammation for first patient of all days: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Oh wait... maybe I am creating to many variables... let's check:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes we can avoid the use of some variables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What if we need max inflammation for all patients at once?. Operations along all columns or rows at once:
\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then the calculation for the average inflammation over all patients per day is:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A quick check that the array is ok:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or if preferred, the average inflammation over all days per patient:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data visualization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ploting is great for insight! Matplotlib is the facto standar. Some basics:\n",
"
\n",
"Just pick the pyplot library of Matplotlib:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"IPython Magic!\n",
"
\n",
"\n",
"\n",
"The % indicates an IPython magic function - a funciton that is valid only within the notebook environment.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot data array (the average inflammation per day):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Average inflammation over time. Linear plot:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This plot does not correspond with the expected. The model predices a smooth sharper rise and a slower after fall. Let's have a look to the other statistics:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"None of them seems very likely to fit the model. Something could be wrong with the data\n",
"
\n",
"
\n",
"We can further improve the visualization by grouping plots in a multiplot:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional Exercises \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Slicing strings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is the value of element[:4]? What about element[4:]? Or element[:]?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is element[-1]? What is element[-2]?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given those answers, explain what element[1:-1] does."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
ProgPy_Analyzing_Patient_Data_filled-checkpoint.ipynb 0000664 0000000 0000000 00000303462 13565313113 0035750 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyzing Patient Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are studying inflammation in patients who have been given a new treatment for arthritis, and need to analyze the first dozen data sets of their daily inflammation. The data sets are stored in comma-separated values (CSV) format: each row holds information for a single patient, and the columns represent successive days"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want to:\n",
"\n",
" * load that data into memory,\n",
" * calculate the average inflammation per day across all patients, and\n",
" * plot the result.\n",
"\n",
"To do all that, we’ll have to learn a little bit about programming."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python as a calculator\n",
"\n",
"Any Python interpreter can be used as a calculator:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"3 + 5 * 4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here you have an overview of math operators:\n",
"\n",
"| Operators | Operations | Examples |\n",
"| :--------:| :---------------- | ---------- |\n",
"| + | Addition | 2 + 3 = 5 |\n",
"| - | Substraction | 3 - 2 = 1 |\n",
"| * | Multiplication | 2 * 3 = 6 |\n",
"| / | Division | 3 / 2 = 1.5|\n",
"| // | Integer division | 7 // 3 = 2 |\n",
"| % | remainder division| 7 % 2 = 1 |\n",
"| ** | Exponent | 2**3 = 8 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This only outputs a value. To use values in any manner, we have to access them in memory by using a variable.\n",
"\n",
"In Python, variable names:\n",
"\n",
"* can include letters, digits, and underscores\n",
"* cannot start with a digit\n",
"* are case sensitive.\n",
"\n",
"Let's assign a value 55 to variable named ``weight_kg``:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"weight_kg = 55 # we assign the value 55 to the variable weight_kg using ="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use ``print`` to output the value of a variable. Is a built-in function:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"55\n"
]
}
],
"source": [
"print(weight_kg) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can do some arithmetics with variables:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Weight in pounds: 121.00000000000001\n"
]
}
],
"source": [
"print(\"Weight in pounds: \", 2.2 * weight_kg) # Don't forget to explain about printing\n",
" # types, strings and comma separation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What the ...?? why is not simply giving 121.0???
\n",
"(Pro tip: Because 2.2 is a number with no exact float representation, so it multiplies by the closest number with a float representation. That produces a 1 at the last decimal position so Python realices that there is something non-zero at the end and plots it.)
\n",
"Well it looks 121 enought to me.... Ok let's continue. \n",
"\n",
"We can change the value of a variable by re-assigning it:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Weight in kilograms is now: 57.5\n"
]
}
],
"source": [
"weight_kg = 57.5\n",
"print('Weight in kilograms is now:', weight_kg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Variables as sticky notes with the name put in a particular value.
\n",
"\n",
"\n",
"We can create new variables from others:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"weight in kilograms: 57.5 and in pounds: 126.50000000000001\n"
]
}
],
"source": [
"weight_lb = 2.2 * weight_kg\n",
"\n",
"print( 'weight in kilograms: ', weight_kg, 'and in pounds: ', weight_lb) #concatenating complex expressions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What happens to ``weight_lb`` if you change ``weight_kg``?:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"weight in kilograms: 100 and in pounds: 126.50\n"
]
}
],
"source": [
"weight_kg = 100 # we change the sticky note of weight to 100 \n",
"print( 'weight in kilograms: ', weight_kg, 'and in pounds: %.2f' %weight_lb) #Do it as an exercise"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"Command %whos
shows memory stored variables defined along the notebook:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variable Type Data/Info\n",
"------------------------------\n",
"weight_kg int 100\n",
"weight_lb float 126.50000000000001\n"
]
}
],
"source": [
"%whos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"What does the following program print out?\n",
"\n",
"\n",
"first, second = 'Grace', 'Hopper'\n",
"third, fourth = second, first\n",
"print(third, fourth)\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hopper Grace\n"
]
}
],
"source": [
"first, second = 'Grace', 'Hopper'\n",
"third, fourth = second, first\n",
"print(third, fourth)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using libraries (modules)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we will load a library. Let's import Numpy. Numpy is important for fancy number manipulations:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Libraries provide additional functionality to the basic Python package. Importing too many libraries can sometimes complicate and slow down your programs - so we only import what we need for each program.\n",
"\n",
"Now we read the first file where patient data are stored. \n",
"\n",
"We will use functions which belong to a certain library and perform \"something\" for us. In this case we want to load a data file"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 1., ..., 3., 0., 0.],\n",
" [ 0., 1., 2., ..., 1., 0., 1.],\n",
" [ 0., 1., 1., ..., 2., 1., 1.],\n",
" ..., \n",
" [ 0., 1., 1., ..., 1., 1., 1.],\n",
" [ 0., 0., 0., ..., 0., 2., 0.],\n",
" [ 0., 0., 1., ..., 1., 1., 0.]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy.loadtxt(fname=\"inflammation-01.csv\", delimiter=\",\") #Explain what is a function call\n",
" # and what are parameters briefly\n",
"# Do not forget to explain why the elipsis below "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The expression ``numpy.loadtxt(...)`` is a function call that asks Python to run the function loadtxt which belongs to the numpy library. This dotted notation is used everywhere in Python: the thing that appears before the dot contains the thing that appears after."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"``numpy.loadtxt`` has two parameters: the name of the file we want to read, and the delimiter that separates values on a line. These both need to be character strings (or strings for short), so we put them in quotes."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We did not assign the output of the function to any variable, so the notebook just dumped the ouptut to an ``Out[]`` cell. But, just the same way we asigned values to a variable, we can assing the array of values to an variable using the same syntax:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"No output here, as we just assigned, as we did before with ``weight_kg``. To print out the variable value:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0. 0. 1. ..., 3. 0. 0.]\n",
" [ 0. 1. 2. ..., 1. 0. 1.]\n",
" [ 0. 1. 1. ..., 2. 1. 1.]\n",
" ..., \n",
" [ 0. 1. 1. ..., 1. 1. 1.]\n",
" [ 0. 0. 0. ..., 0. 2. 0.]\n",
" [ 0. 0. 1. ..., 1. 1. 0.]]\n"
]
}
],
"source": [
"print(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check what type of \"thing\" ``data`` variable refers to:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"print(type(data)) #You should explain numpy arrays at this point"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we create a variable, we also create information about it, as the attribute shape! That's very important!!\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(60, 40)\n"
]
}
],
"source": [
"print(data.shape) # Explain here that once we created \n",
" # data we also created a information about \n",
" # the array called members or attributes that describe \n",
" # data in the same way an adjetive describes a noun"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we have 60 rows and 40 columns in ``data`` variable."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to get a single number from the array we provide an index in square brackets, just as we do in maths"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"first value in data: 0.0\n"
]
}
],
"source": [
"print('first value in data:', data[0,0]) # You should explain here than in many \n",
" # languages arrays start by 0"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"middle value in data: 13.0\n"
]
}
],
"source": [
"print('middle value in data:', data[30,20])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Selecting intervals of data by using ranges : . Caution here!:ranges seem closed but they are [) "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0. 0. 1. 2. 2. 4. 2. 1. 6. 4.]\n",
" [ 0. 0. 2. 2. 4. 2. 2. 5. 5. 8.]\n",
" [ 0. 0. 1. 2. 3. 1. 2. 3. 5. 3.]\n",
" [ 0. 0. 0. 3. 1. 5. 6. 5. 5. 8.]\n",
" [ 0. 1. 1. 2. 1. 3. 5. 3. 5. 8.]]\n"
]
}
],
"source": [
"print(data[5:10, 0:10]) # Selecting rows 0,1,2,3 and cols from 0 to 9. Important! notice upper limits are not \n",
" # included in the counting\n",
" # You don't have to start slices at 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is how index is assigned by python:\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is no need to include upper and lower bounds:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"small is: \n",
"[[ 2. 3. 0. 0.]\n",
" [ 1. 1. 0. 1.]\n",
" [ 2. 2. 1. 1.]]\n"
]
}
],
"source": [
"small = data[:3, 36:] # There is no need to include upper and lower bounds\n",
"print ('small is: ')\n",
"print (small)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"what does ``data[3:3, 4:4]`` produce? \n",
"What about ``data[3:3, :]``?\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n",
"[]\n"
]
}
],
"source": [
"print(data[3:3, 4:4])\n",
"print(data[3:3, :])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Some array math\n",
"Basic arithmetics: The operation is performed in every element of the array"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"original:\n",
"[[ 2. 3. 0. 0.]\n",
" [ 1. 1. 0. 1.]\n",
" [ 2. 2. 1. 1.]]\n",
"doubledata: \n",
"[[ 4. 6. 0. 0.]\n",
" [ 2. 2. 0. 2.]\n",
" [ 4. 4. 2. 2.]]\n"
]
}
],
"source": [
"doubledata = 2 * data # The x2 operation is made in every element of the array\n",
"print('original:')\n",
"print(data[:3,36:])\n",
"print('doubledata: ')\n",
"print(doubledata[:3,36:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Array to Array operations: The operation is made on each corresponding element of the arrays:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tripledata:\n",
"[[ 6. 9. 0. 0.]\n",
" [ 3. 3. 0. 3.]\n",
" [ 6. 6. 3. 3.]]\n"
]
}
],
"source": [
"tripledata = doubledata + data #Array-Array operations are made on each corresponding element\n",
"print('tripledata:')\n",
"print(tripledata[:3,36:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"More complex operations:\n",
"
The following is a method. Method are actions that can be made on the variable, Python knows how to!. The actions available depend on the variable type:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.14875\n"
]
}
],
"source": [
"print(data.mean()) # method: Difference with attribute->\n",
" # they can take parameters that modify the behavior\n",
" # Note the parenthesis. They are \n",
" # actions related to the object, whereas the attributes \n",
" # are descriptions of the object in \n",
" # question. Maybe is better to explain tab completion here...!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"Reminder: method vs attribute\n",
"
\n",
"\n",
"\n",
"\n",
"- attributes are descriptions of variable properties\n",
"
- methods are actions that Python knows to perform on the variable\n",
"
\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Several important methods for numpy variables:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"maximum inflammation: 20.0\n",
"minimum inflammation: 0.0\n",
"standard deviation: 4.61383319712\n"
]
}
],
"source": [
"print('maximum inflammation:', data.max())\n",
"print('minimum inflammation:', data.min())\n",
"print('standard deviation:', data.std())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes is useful to make some partial statistics. For instance max inflammation for first patient of all days: "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"maximum inflammation for patient 0: 18.0\n"
]
}
],
"source": [
"patient_0 = data[0,:] #One possibility is creating an array\n",
"print('maximum inflammation for patient 0:', patient_0.max())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Oh wait... maybe I am creating to many variables... let's check:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variable Type Data/Info\n",
"---------------------------------\n",
"data ndarray 60x40: 2400 elems, type `float64`, 19200 bytes\n",
"doubledata ndarray 60x40: 2400 elems, type `float64`, 19200 bytes\n",
"numpy module kages/numpy/__init__.py'>\n",
"patient_0 ndarray 40: 40 elems, type `float64`, 320 bytes\n",
"small ndarray 3x4: 12 elems, type `float64`, 96 bytes\n",
"weight_kg int 100\n",
"weight_lb float 126.50000000000001\n"
]
}
],
"source": [
"%whos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes we can avoid the use of some variables:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"maximum inflamation for patient 2: 19.0\n"
]
}
],
"source": [
"print('maximum inflamation for patient 2:', data[2, :].max())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What if we need max inflammation for all patients at once?. Operations along all columns or rows at once:
\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then the calculation for the average inflammation over all patients per day is:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 0.45 1.11666667 1.75 2.43333333 3.15\n",
" 3.8 3.88333333 5.23333333 5.51666667 5.95 5.9\n",
" 8.35 7.73333333 8.36666667 9.5 9.58333333\n",
" 10.63333333 11.56666667 12.35 13.25 11.96666667\n",
" 11.03333333 10.16666667 10. 8.66666667 9.15 7.25\n",
" 7.33333333 6.58333333 6.06666667 5.95 5.11666667 3.6\n",
" 3.3 3.56666667 2.48333333 1.5 1.13333333\n",
" 0.56666667]\n"
]
}
],
"source": [
"print(data.mean(axis=0)) #columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A quick check that the array is ok:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40,)\n"
]
}
],
"source": [
"print(data.mean(axis=0).shape) #Quick check everything if fine"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or if preferred, the average inflammation over all days per patient:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 5.45 5.425 6.1 5.9 5.55 6.225 5.975 6.65 6.625 6.525\n",
" 6.775 5.8 6.225 5.75 5.225 6.3 6.55 5.7 5.85 6.55\n",
" 5.775 5.825 6.175 6.1 5.8 6.425 6.05 6.025 6.175 6.55\n",
" 6.175 6.35 6.725 6.125 7.075 5.725 5.925 6.15 6.075 5.75\n",
" 5.975 5.725 6.3 5.9 6.75 5.925 7.225 6.15 5.95 6.275 5.7\n",
" 6.1 6.825 5.975 6.725 5.7 6.25 6.4 7.05 5.9 ]\n"
]
}
],
"source": [
"print(data.mean(axis=1)) # mean of each row"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(60,)\n"
]
}
],
"source": [
"print(data.mean(axis=1).shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data visualization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ploting is great for insight! Matplotlib is the facto standar. Some basics:\n",
"
\n",
"Just pick the pyplot library of Matplotlib:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot # Diffrence with numpy: Matplotlib is a extense package.\n",
" # We only want pyplot at the moment\n",
" # You may get a warning here about rendering fonts. If we re-run it not htere anymore."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"IPython Magic!\n",
"
\n",
"\n",
"\n",
"The % indicates an IPython magic function - a funciton that is valid only within the notebook environment.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot data array (the average inflammation per day):"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"image = matplotlib.pyplot.imshow(data) # Remider: explain %matplotlib inline Note: Ipython!!\n",
" # otherwise matplotlib.pyplot.show() is mandatory!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Average inflammation over time. Linear plot:"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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SXG11FNVDtNDVcW0truZwUwt7D9Rx40vZHX5IpaXVcOdrOewqr+WZK0YRG6qTcFll4sAY\nQKcB8CR2FbqI/FxENonIRhF5VUT0t9dN5BRWAnDveYNYvaeC+9/K7dCf7n/5eCufbS3l4ZmnMDY1\nurtjqpOICwtgUFxou7M0KvfR5UIXkT7A7UCmMWYo4A1c5qhgylo5+ZVEh/hx06QB/Pzsgby1toi5\ny3ae9GvezC7k31/s4srTk7nqjJSeCapOauLAaNbsPsjhRp0GwBPYO+TiAwSKiA8QBOyzP5JyBjkF\nB8lIjEBEuH1KKheNSOCvH2/jvQ3H/0+8Nv8g97+Vyxn9e/HQzCE9nFadyIS0GBpbWlm5+4DVUVQP\n6HKhG2OKgMeAfKAYOGSM+cRRwZR1quqb2FlWy4ikCABEhEcvGc7ovpHc/b/1rMv//hwh+yoPM+fF\nbHqHBzD3ilH4euulGWcxpl8U/j5efKnTAHgEe4ZcIoELgX5AAhAsIlce53VzRCRLRLLKynQszxVs\nKDgEwIjkiCPbAny9mXfVaGLD/Ln+xWwKD7Ytc1bX2Mz1L2ZR39TC/GsyiQz2sySzOr4AX2/G9IvS\ncXQPYc+p1NnAbmNMmTGmCXgLGHvsi4wx84wxmcaYzJiYGDt2p3rKetsF0eGJEd/b3ivEn//+5FQa\nmlv42QtZVNU38YvXN7C5uIonZ48gLS7UiriqHZMGxpBXWsO+ysNWR1HdzJ5CzwdOF5EgERFgCrDF\nMbGUldblV9I/JpjwwB8+EJQaG8ozV4xmR2kN5/19Oe/nFnPfeemclR5nQVLVERPS9PZFT2HPGPoq\n4A1gLZBr+17zHJRLWcQYQ05B5ZHx8+MZnxbNIxcOYd+hen40qg9zdI1QpzYwLoS4MH+W79BxdHdn\n12yLxpiHgIcclEU5gX2H6imvaThpoQNccVpfRveNJDUmhLY/0JSzEhEmpMXw6ZYSWloN3jqNsdvS\n2xHU9+Tkt42ft1foAOm9w/DRO1pcwsSBMVTWNZFbdMjqKKob6W+j+p6cgoP4+XiR3jvM6ijKgcan\nRiMCX+rsi25NC119z/qCQwxJCNP5y91MVLAfw/qE6+2Lbk5/a9URzS2t5BYd6tBwi3I9E9KiWZtf\nSXV903E/f+hwExuLDtHU0trDyZSj6BJ06ohtJW0zLGqhu6eJaTE8vXQn728oJjEyiJ1lNeSV2t7K\naiirbgDgitOS+cPFwyxOq7pCC10dsf7bJ0S10N3SyORIgv28ue+t3CPbQv19GBAbwqSBMaTGhrCj\npIaXV+UzZXCsPlvggrTQ1RE5BQeJCvYjOSrI6iiqG/j5ePHU5aMoOFhHakwIqbEhxIT6f++204bm\nFjbtO8S9b+Ty8Z0R9ArRBaZdiY6hqyNyCirJSAzX+8rd2OT0WK4+I4WxqdHEhgX84L+1v483T1w2\ngqrDTR2eA185Dy10BUBNQzM7SmvI0OEWj5feO4xfnDuITzaX8HpWodVxVCdooSsANhRWYoyOn6s2\n143vx+n9o/jt4k3kH6izOo7qIC10BbQNt4AWumrj5SU8fukIvES46385tLTq0Isr0EJXAKwvqKRf\ndDARQTqfuWrTJyKQRy4aQtbeg/zri5MvP6icgxa6Ar67IKrU0S4a0Yfpw+P5+5LtbNR5YJyeFrqi\n+NBhSqran2FReR4R4Q8XDaVXiB93LsyhvkkXm3ZmWuiK9d+OnydHWpxEOaOIID8em5VBXmkNj364\n1eo46iS00BXrCirx8/ZicLwuIaeOb0JaDD8Zm8LzX+/hzWy9ldFZ6ZOiipz8SgYnhOHv4211FOXE\n7puWzo7Sau55Yz0txnBpZpLVkdQx9Azdw7W0GnKLDjFSx89VOwJ8vZl/zamMT43ml29uYOGafKsj\nqWNooXu4HaXV1DW2kJGkd7io9gX4evPs1ZlMSIvhl2/m8soqLXVnooXu4b5bck4viKqOCfD1Zt5V\no5k8KIZfvZ3LS9/ssTqSstFC93DrCysJD/QlpZfOsKg6LsDXm39dNZqzB8fy63c38cLXe6yOpLCz\n0EUkQkTeEJGtIrJFRM5wVDDVM9blV5KRFKEzLKpO8/fxZu4Vo5l6ShwPLdrEcyt2Wx3J49l7hv4P\n4CNjTDqQAWyxP5LqKXWNzWwvqdYHilSX+fl48fTlozh3SByPvLeZJz/bQWlVvdWxPFaXb1sUkXBg\nIvATAGNMI9DomFiqJ+QWHqLVoHe4KLt8u3DGna/l8Lcl2/nbku0kRgYyKjmSUckRjOobyeD4MHy9\ndYS3u9lzH3o/oAz4r4hkANnAHcaY2qNfJCJzgDkAycnJduxOddbq3RV8uLGYqCA/YsP8iQn1JzY0\ngJhQf3oF+x2ZYXG4zuGi7OTr7cU/Z4/k2vH9WJd/kLX5B1m1+wCL1u8DIMDXi+GJEfzf6ES9f70b\nSVdXJBGRTGAlMM4Ys0pE/gFUGWN+faKvyczMNFlZWV1LqjrlpZV7eXjRJrxFaDzOKu4i4OvlRe/w\nAJbfO9mChMrdGWPYd6ietXvbCv6rvHJ2lNbw1k1jGanTTHSKiGQbYzLbe509Z+iFQKExZpXt4zeA\n++z4fsoBmlpaeWTxZl5auZcp6bE8cdkIfL29KK9poLS6gbLq7/4tq65n7IBoqyMrNyUi9IkIpE9E\nIDMzEqiub2Lq35Zz/1u5LL5tvA7BdIMuF7oxZr+IFIjIIGPMNmAKsNlx0VRnVdY1cvPLa/l65wFu\nmNSfe89Nx9ur7e6VxMggEiP11kRlndAAXx6+YAg3Lshm/ord3DhpgNWR3I69c7ncBrwsIn7ALuCn\n9kdSXZFXWs3PXshiX2U9j8/K4JLRiVZHUuoHzhvam6mnxPHEp9uZPiyepCg9yXAku/7mMcbkGGMy\njTHDjTEXGWMOOiqY6rhl20q5+OmvqWlo5tU5p2uZK6f22wuG4C3Cg+9spKvX8NTx6SCWCzPGMH/F\nbq59fg1JUUG8e+t4RvfVi03KuSVEBHL3OYP4YnsZizcUWx3HrWihu6icgkoum7eS3723mXNO6c0b\nN51Bn4hAq2Mp1SHXjE1heGI4jyzexKG6JqvjuA0tdBezu7yWm1/O5qKnv2JnWQ2/u2goc68YRZCf\nTm2vXIe3l/DHi4dRUdvIox/pKkiOoi3gIsqqG3jysx28ujofPx8v7jw7jZ9N6E+Iv/4nVK5paJ9w\nrh3Xj/+s2M2PRvXh1JQoqyO5PG0DJ1fT0Myzy3fx7Je7aGxuZfaYZG6fkkZMqL/V0ZSy28+nDuTD\njfv51Vu5vH/7BPx8dNDAHlroTqy8poEZT65gf1U95w/rzS/OTadfdLDVsZRymGB/H3530RCufT6L\nect3cutZaVZHcmla6E7sjexC9lfV88rPTmNsqj7RqdzTWelxTB8Wz5Of5zF9eIKetNhB/75xUsYY\n/remgMy+kVrmyu09NPMU/L29eOyTbVZHcWla6E5qzZ6D7Cqv5dJTdWY65f5iwwK4eFQfPt1cQk1D\ns9VxXJYWupNauKaAEH8fpg+LtzqKUj3igowEGppbWbJ5v9VRXJYWuhOqrm/ig9xiZmbEE6y3JSoP\nMSo5koTwABav16dHu0oL3QktXl/M4aYWXQhAeRQvL2FGRgLLt5dRWaeLn3WFFroTWphVwMC4EF3r\nU3mcCzISaG41fLRRh126QgvdyWzdX8X6gkp+fGoyImJ1HKV61JCEMPpFBx9Zuk51jha6k1m4pgBf\nb+HikX2sjqJUjxMRZg6P55tdByitqrc6jsvRQnciDc0tvL2uiHNO6U1UsJ/VcZSyxMyMBIyBD3L1\n4mhnaaE7kSWbS6isa9J7z5VHS4sLJb13qA67dIEWuhNZuKaAhPAAxuuTocrDzcxIYG1+JQUVdVZH\ncSla6E6i8GAdK/LKmZWZdGRhZ6U81czhCQC8r8MunaKF7iRezyoEYFamrgeqVHKvIDKSIliswy6d\nooXuBFpaDW9kFzI+NZrESF0FXSlouyd9074qdpbVWB3FZdhd6CLiLSLrROQ9RwTyRF/llVNUeVif\nDFXqKNOHxSOCnqV3giPO0O8Atjjg+3ishVkFRAT5cs6QOKujKOU0eocHMCYlisXr92GMsTqOS7Cr\n0EUkEZgO/McxcTxPRW0jSzaVcPHIPvj7eFsdRymncsGIBHaW1bKluNrqKC7B3jP0J4B7gdYTvUBE\n5ohIlohklZWV2bk79/P2uiIaW1r5sd57rtQPTBsaj7eX6D3pHdTluVlFZAZQaozJFpEzT/Q6Y8w8\nYB5AZmamR//dZIyhrLqBvNIadpbVkFdawwcb95ORGE567zCr4ynldKKC/RifGs3i9fv45XmDdH6j\ndtgz2fY44AIROR8IAMJEZIEx5krHRHMPX+4oY1HOPvJsBV5d/91qLCH+PgyICeaB6adYmFAp5zYz\nI4F7Xl/PuoJKRiVHWh3HqXW50I0x9wP3A9jO0O/RMv++7L0Hue75LIL9vRnUO5QLRySQGhNCamwo\nA2KD6R0WoGccSrXjnCFx+L3txaKcfVro7dDlcLpJSVU9Ny3Ipnd4AItuHUdEkE62pVRXhAX4MnlQ\nDO/nFvPrGafok9Qn4ZAHi4wxy4wxMxzxvdxBQ3MLNy7IpqahmXlXj9YyV8pOMzMSKKtusGu9UU+4\n9VGfFHUwYwy/eWcT6/IreXxWhl7sVMoBpqTHkRgZyI0L1nLX/3Io6cRc6evyD3LV/FWc+ofPKKtu\n6MaU1tNCd7AFq/JZmFXArZNTmTYs3uo4SrmFQD9vPrxjAjdOGsB764uZ/Ngynvp8B/VNLSf8mtzC\nQ1z7/Bounvs1m/ZVUVHbwPwVu3swdc/TQnegVbsO8NtFmzgrPZafTx1odRyl3EpogC/3TUvn07sm\nMTEthsc+2c6Ux7/gvQ3ff5J0874q5ryYxcynVpC99yC/OHcQX947mfOHxfPSN3vcegFq6clxpczM\nTJOVldVj++tJ+yoPM/OfKwgP9OXtW8YRHuhrdSSl3NrXO8t5ZPFmtu6vZkxKFD+b0I93cor4IHc/\noQE+XD+hPz8dl0JoQNvv4pbiKqb940vuPDuNO892rRMuEck2xmS2+zotdPvVN7Uw61/fsLu8lndu\nGUtqbKjVkZTyCC2thoVrCnj8k20cqG0kxN+Ha8elcN34/oQH/fCk6voXs1i9u4IVv5x8pOhdQUcL\nXW9btJMxhvvfyiW36BDPXp2pZa5UD/L2Ei4/LZkZGfEs317GuAHRRJ5kPd5bJ6dy4eavWLAyn5vO\nHNCDSXuGjqHb6YWv9/D2uiJ+fvZApp6isyUqZYWwAF9mDE84aZkDZCRFMCEtmvkrdnG48cQXVF2V\nFrodNhYd4o8fbGVKeiy3nZVqdRylVAfcOjmV8ppGXluTb3UUh9NC76LahmZue3UdUcF+PDYrAy99\nek0pl3Ba/16MSYli3vJdNDS711m6FnoX/ebdTew5UMsTl41o9888pZRzufWsVIoP1fPW2iKroziU\nFnoXvLOuiDfXFnLb5FRO79/L6jhKqU6akBbN8MRwnlm2k+aWEy7n4HK00DtpT3ktD7ydy6kpkdw+\nJc3qOEqpLhARbp2cSn5FHYs3uM/iGVrondDY3Mrtr63Dx9uLJy4biY+3Hj6lXNXZg+MYFBfKU5/n\n0drqHhN3aSN1wmOfbGND4SH+fMlw+kQEWh1HKWUHLy/hlrNS2VlWy0ebuj6LozPRQu+gZdtKmbd8\nF1eensx5Q3tbHUcp5QDTh8XTLzqYpz7Pc4vpdbXQO6C0up57Xl9Peu9QHtTl4pRyG95ewk1nDmBz\ncRVLt5VaHcduWujtaG013LVwPTUNzfxz9kgCfL2tjqSUcqCLR/ahT0QgT37m+mfpWujteOaLnazI\nK+ehmUNIi9N5WpRyN77eXtx6Vio5BZV8usW1z9K10E/i653lPP7JNi7ISOCyU5OsjqOU6iazRifS\nPzqYv368lRYXvuNFC/0ESqrquf3VdfSPCeFPPxqGiD7ar5S78vH24u5zBrG9pIZ31rnu06NdLnQR\nSRKRpSKyWUQ2icgdjgxmpaaWVm57ZR21DS08c8Uogv11lmGl3N20ob0Z1iecvy3Z7rJzvNhzht4M\n3G2MOQU4HbhFRNziFpDHPt7G6j0VPHrJMB03V8pDeHkJ9543iKLKw7y80jVnYuxyoRtjio0xa23v\nVwNbgD7wkD14AAAKGklEQVSOCmaVjzft59+2+80vHOHy/3OUUp0wPjWasQN68dTSPGoamq2O02kO\nGUMXkRRgJLDKEd/PKnsP1HLP6+sZnhjOr2e4xR8bSqlOEBF+eV46FbWN/OfLXVbH6TS7C11EQoA3\ngTuNMVXH+fwcEckSkayysjJ7d9dt6ptauGnBWgR4+vJR+Pvo/eZKeaKMpAimDe3Ns8t3caCmweo4\nnWJXoYuIL21l/rIx5q3jvcYYM88Yk2mMyYyJibFnd93qt4s3sbm4ir9dOoKkqCCr4yilLHT3OYM4\n3NTC00t3Wh2lU+y5y0WA+cAWY8zfHBep572RXcirqwu46cwBnK3rgirl8VJjQ5g1OokFK/dSeLDO\n6jgdZs8Z+jjgKuAsEcmxvZ3voFw9ZtO+Qzz4Ti6n9Yvi7qkDrY6jlHISd5ydBgJPfLrD6igdZs9d\nLiuMMWKMGW6MGWF7+8CR4bpbWXUD17+QRUSgH/+8XOc3V0p9JyEikJ+MTeGttYVsL6m2Ok6HeGyD\nNTS3cMNLWVTUNfLs1ZnEhgZYHUkp5WRumjSAYD8f/vrxNqujdIhHFroxhgfe3sja/Eoem5XBsMRw\nqyMppZxQZLAfN0zqz5LNJWTvPWh1nHZ5ZKHPX7GbN7ILuX1KGjOGJ1gdRynlxK4d34+YUH9uXJDN\nF9ud99Zr8MBCX7qtlD9+sIVpQ3tzpy7yrJRqR5CfDy9eO4bIIF+ueW41D727kfom55zrxaMKPa+0\nmttfWUd67zAevzQDLy+dQVEp1b7B8WEsunU8Px2Xwgvf7GXGP1ewseiQ1bF+wGMKvbKuketeyMLf\n14tnr8kkyE9nUFRKdVyArzcPzRzCS9eNobq+iYvnfsXcZXlONX+6RxR6U0srt7yyluLKev591Wj6\nRARaHUkp5aImpMXw8Z0TmXpKHH/5aBuz562koMI5Hj7yiEL//Xub+SrvAH+4eCij+0ZZHUcp5eIi\ngvx4+vJRPD4rg83FVUz7x5cs3Wr98nVuX+ivZxXwwjd7+dn4fszK1GXklFKOISJcMjqRD++YQN9e\nQdy4IJvVuysszeTWhb6x6BAPvrORM/r34r5p6VbHUUq5oaSoIF68dgx9IgO57vk1bNpn3cVSty30\ng7WN3Lggm6hgfaxfKdW9eoX4s+C60wgN8OGa59awp7zWkhxu2XItrYY7FuZQWtXAM1eOJjrE3+pI\nSik3lxARyIvXnUZLaytXzl9FSVV9j2dwy0J/4tPtLN9exsMXDGFEUoTVcZRSHiI1NoTnfzqGg7WN\nXD1/NZV1jT26f7cr9CWbS/jn53lcmpnI7DF6EVQp1bMykiKYd3Umu8trufb5NdQ19tzapG5V6LvL\na7lrYQ7D+oTzyIVDaVuDQymleta41GienD2CnIJKblywlsbm1h7Zr9sUel1jMze+lI23t/DMlaMI\n8NU1QZVS1jlvaDx/+tEwlm8v467/5fTIE6VuUejGGO57M5ftpdU8edlIEiN1TVCllPV+fGoy901L\n570NxXyQW9zt+3OLCU2e/XIXi9bv4xfnDmLiQOddiFop5XlunDSAU+LDmJAW3e37culCb201PPbJ\nNuYu28m0ob25adIAqyMppdQP9NSJpssWen1TC3e/vp73NxQze0wyj1w4RKfDVUp5NJcs9AM1DVz/\nYhZr8yu5f1o6cyb21ztalFIez66LoiJynohsE5E8EbnPUaFOJq+0hovnfs2mfVXMvWIUN0waoGWu\nlFLYcYYuIt7A08BUoBBYIyKLjDGbHRXuWN/sPMCNC7Lx9RZem3M6I5Mju2tXSinlcuw5Qx8D5Blj\ndhljGoHXgAsdE+uH3swu5OrnVhET6s/bN4/TMldKqWPYM4beByg46uNC4DT74hzfU5/v4LFPtjN2\nQC+euWI04UG+3bEbpZRyad3+YJGIzBGRLBHJKisr69L36B8TwqWZiTz/0zFa5kopdQL2nKEXAUfP\nfpVo2/Y9xph5wDyAzMzMLj37ev6weM4fFt+VL1VKKY9hzxn6GiBNRPqJiB9wGbDIMbGUUkp1VpfP\n0I0xzSJyK/Ax4A08Z4zZ5LBkSimlOsWuB4uMMR8AHzgoi1JKKTu4xWyLSimltNCVUsptaKErpZSb\n0EJXSik3oYWulFJuQozp/nXujuxMpAzY28UvjwbKHRjHkTRb12i2rtFsXePK2foaY9pdJaNHC90e\nIpJljMm0OsfxaLau0Wxdo9m6xhOy6ZCLUkq5CS10pZRyE65U6POsDnASmq1rNFvXaLaucftsLjOG\nrpRS6uRc6QxdKaXUSbhEoVuxGHVHicgeEckVkRwRybI4y3MiUioiG4/aFiUiS0Rkh+1fS9buO0G2\nh0WkyHbsckTkfIuyJYnIUhHZLCKbROQO23bLj91Jsll+7EQkQERWi8h6W7bf2rY7w3E7UTbLj5st\nh7eIrBOR92wfO+SYOf2Qi20x6u0ctRg1MLs7F6PuDBHZA2QaYyy/v1VEJgI1wIvGmKG2bX8BKowx\nj9r+zzDSGPNLJ8n2MFBjjHmsp/Mcky0eiDfGrBWRUCAbuAj4CRYfu5NkuxSLj52ICBBsjKkREV9g\nBXAH8COsP24nynYezvEzdxeQCYQZY2Y46vfUFc7Qe3QxaldmjFkOVByz+ULgBdv7L9BWBj3uBNmc\ngjGm2Biz1vZ+NbCFtjVzLT92J8lmOdOmxvahr+3N4BzH7UTZLCciicB04D9HbXbIMXOFQj/eYtRO\n8QNtY4BPRSRbROZYHeY44owxxbb39wNxVoY5jttEZINtSMaS4aCjiUgKMBJYhZMdu2OygRMcO9vQ\nQQ5QCiwxxjjNcTtBNrD+uD0B3Au0HrXNIcfMFQrd2Y03xowApgG32IYWnJJpG19zirMUm2eA/sAI\noBh43MowIhICvAncaYypOvpzVh+742RzimNnjGmx/fwnAmNEZOgxn7fsuJ0gm6XHTURmAKXGmOwT\nvcaeY+YKhd6hxaitYowpsv1bCrxN2xCRMymxjcN+Ox5banGeI4wxJbZfulbgWSw8drZx1jeBl40x\nb9k2O8WxO142Zzp2tjyVwFLaxqid4rgdL5sTHLdxwAW2a2+vAWeJyAIcdMxcodCddjFqEQm2XahC\nRIKBc4CNJ/+qHrcIuMb2/jXAuxZm+Z5vf4BtLsaiY2e7gDYf2GKM+dtRn7L82J0omzMcOxGJEZEI\n2/uBtN24sBXnOG7HzWb1cTPG3G+MSTTGpNDWZZ8bY67EUcfMGOP0b8D5tN3pshN4wOo8R+XqD6y3\nvW2yOhvwKm1/RjbRdq3hOqAX8BmwA/gUiHKibC8BucAG2w90vEXZxtP2J+4GIMf2dr4zHLuTZLP8\n2AHDgXW2DBuB39i2O8NxO1E2y4/bURnPBN5z5DFz+tsWlVJKdYwrDLkopZTqAC10pZRyE1roSinl\nJrTQlVLKTWihK6WUm9BCV0opN6GFrpRSbkILXSml3MT/A28p6Ke22pdeAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ave_inflammation = data.mean(axis=0)\n",
"ave_plot = matplotlib.pyplot.plot(ave_inflammation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This plot does not correspond with the expected. The model predices a smooth sharper rise and a slower after fall. Let's have a look to the other statistics:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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bG5OOHEnaOQBkad2fftVVYfX0blU6udDNAfQqlJ5OQ29BNwfQrxB6Og09xX5zAIOIdX96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\nxP8DpFAe9vF8qV0AAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"max_plot = matplotlib.pyplot.plot(data.max(axis=0))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"min_plot = matplotlib.pyplot.plot(data.min(axis=0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"None of them seems very likely to fit the model. Something could be wrong with the data\n",
"
\n",
"
\n",
"We can further improve the visualization by grouping plots in a multiplot:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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cO97pcuLamAIP180u56n3aq07TWyMAl4TkQ+AZfj3ID/ncE1xI9iuzVtgK8gH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\nxvRjE2RjjDHGGGP6+f8skxfhNmbXPwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0)) # Figure canvas, size in inches!!\n",
"\n",
"\n",
"axes1 = fig.add_subplot(1,3,1) # Means: add subplot to a 1x3 grid in the first place\n",
"axes2 = fig.add_subplot(1,3,2) # Means: add subplot to a 1x3 grid in the second place\n",
"axes3 = fig.add_subplot(1,3,3) # Means: add subplot to a 1x3 grid in the third place\n",
"\n",
"axes1.set_ylabel('average')\n",
"axes1.plot(data.mean(axis=0))\n",
"axes2.set_ylabel('max')\n",
"axes2.plot(data.max(axis=0))\n",
"axes3.set_ylabel('min')\n",
"axes3.plot(data.min(axis=0))\n",
"\n",
"fig.tight_layout() # Adjust the subplots to fit the margins\n",
"\n",
"matplotlib.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional Exercises "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Slicing strings"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"first three characters: oxi\n",
"last three characters: gen\n"
]
}
],
"source": [
"element = 'oxigen'\n",
"print('first three characters:', element[0:3])\n",
"print('last three characters:', element[-3:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is the value of element[:4]? What about element[4:]? Or element[:]?"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'en'"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"element[4:]"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'oxig'"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"element[:4]\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'oxigen'"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"element[:]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is element[-1]? What is element[-2]?"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'n'"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"element[-1]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'e'"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"element[-2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given those answers, explain what element[1:-1] does."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'xige'"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"element[1:-1]"
]
}
],
"metadata": {
"kernelspec": {
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ProgPy_Functions-checkpoint.ipynb 0000664 0000000 0000000 00001740236 13565313113 0032035 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions\n",
"\n",
"What if?\n",
" We have thousand of datasets and do not want to make a plot for every single one\n",
" We want to reuse the code in other different datasets or parts of the program\n",
" We are collaborating with other people working in the same code\n",
"It is a bad idea:\n",
" Commenting in and out lines in the plotting code\n",
" Copy and paste chuncks of code here and there\n",
"
\n",
"
\n",
"That makes the code illegible, redundant and prone to errors. And impossible to share it with others.\n",
"
\n",
"\n",
"### The Solution: Package and reuse code as functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let’s start by defining a function fahr_to_celsius that converts temperatures from Fahrenheit to Kelvin:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
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{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"- Definition of the function contains the name and the parameters needed by it to do its job
\n",
"- Body is the part that is compiled and has the instructions to make the task done
\n",
"- Return statement returns the result of the compilation of the function's body
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calling the function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just call the name "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Composing functions \n",
"\n",
"We make now a function to convert Kelvin to Celsius:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What about Farh to Celsius? One of the main proposes of functions is reusability. So let's reuse one of them to build some \"neat\" code:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Exercise\n",
"\n",
"#### The old switcheroo\n",
"Which of the following would be printed if you were to run this code? Why did you pick this answer?\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"1) 7 3\n",
"2) 3 7\n",
"3) 3 3\n",
"4) 7 7\n",
"\n",
"a = 3\n",
"b = 7\n",
"\n",
"def swap(a, b):\n",
" temp = a\n",
" a = b\n",
" b = temp\n",
"\n",
"swap(a, b)\n",
"\n",
"print(a, b)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tidying up"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now make to functions to wrap the code and make the analysis of our inflammation data easier to read and reuse:\n",
"
\n",
"First we create a function to plot the data by feeding a filename"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#First function: Makes avg,max,min plots of a file. Note: No return or void function\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Second we build a function that detects anomalies in the data. Also needs a filename as parameter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Second function: Find anomalies in a file. Again void function\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we use them (Do not forget to import numpy and matplotlib.pyplot because they are needed by the code)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"#### Rescaling an array\n",
"\n",
"Write a function rescale that takes an array as input and returns a corresponding array of values scaled to lie in the range 0.0 to 1.0. (Hint: If $L$ and $H$ are the lowest and highest values in the original array, then the replacement for a value $v$ should be $(v-L) / (H-L)$.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When using functions we need to test they are working as expected. By following the example we will see how it is"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To test the function we used controlled data instead real one:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks great. Let's center the real data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can make further tests to see if results are correct:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also test that the standard deviation has not changed showing the difference between the two:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Documentation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Is important to document each function to remember ourselves and let others know what the function does"
]
},
{
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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"![image_crawling.PNG](attachment:image_crawling.PNG)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#You can use this kind of comment but there is a better way\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"attachments": {
"good_comments.PNG": {
"image/png": 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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"![good_comments.PNG](attachment:good_comments.PNG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining Defaults "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes we like our parameters to be optional and if in case they are not specified then default to a chosen value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can ommit the optional parameter, in that case it takes the given default:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's add another optional parameter to comment the result:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imagine you want only to comment it:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Arguments are matched from left to right:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How we can omit the default desired value then?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that:\n",
"\n",
"- data must be provided because it do not have any defaults.\n",
"
- Given no name to the parameter makes the assumption that it is the next non-default parameter ordered from left to right.\n",
"
- It can be avoided it specifying the name of the optional parameter. \n",
"
\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"#### Mixing Default and Non-Default Parameters\n",
"Given the following code:"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"def numbers(one, two=2, three, four=4):\n",
" n = str(one) + str(two) + str(three) + str(four)\n",
" return n\n",
"\n",
"print(numbers(1, three=3))\n",
"what do you expect will be printed? What is actually printed? What rule do you think Python is following?\n",
"\n",
"1) 1234\n",
"2) one2three4\n",
"3) 1239\n",
"4) SyntaxError"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### All required parameters must be placed before any default arguments. Simply because they are mandatory, whereas default arguments are not. Syntactically, it would be impossible for the interpreter to decide which values match which arguments if mixed modes were allowed. A SyntaxError is raised if the arguments are not given in the correct order:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given that, what does the following piece of code display when run?"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"def func(a, b=3, c=6):\n",
" print('a: ', a, 'b: ', b, 'c:', c)\n",
"\n",
"func(-1, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Readable functions\n",
"\n",
"Functions are supposed to pack code to reuse and for readability to yourself or others... Check these two functions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"It its important to keep code readable!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Combining strings\n",
"“Adding” two strings produces their concatenation: 'a' + 'b' is 'ab'. Write a function called fence that takes two parameters called original and wrapper and returns a new string that has the wrapper character at the beginning and end of the original. A call to your function should look like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Selecting characters from strings\n",
"If the variable s refers to a string, then s[0] is the string’s first character and s[-1] is its last. Write a function called outer that returns a string made up of just the first and last characters of its input. A call to your function should look like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing and documenting your function\n",
"Run the commands help(numpy.arange) and help(numpy.linspace) to see how to use these functions to generate regularly-spaced values, then use those values to test your rescale function. Once you’ve successfully tested your function, add a docstring that explains what it does."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding docstrings:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Variables inside and outside functions\n",
"What does the following piece of code display when run - and why?\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"f = 0\n",
"k = 0\n",
"\n",
"def f2k(f):\n",
" k = ((f-32)*(5.0/9.0)) + 273.15\n",
" return k\n",
"\n",
"f2k(8)\n",
"f2k(41)\n",
"f2k(32)\n",
"\n",
"print(k)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Readable code"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
ProgPy_Functions_filled-checkpoint.ipynb 0000664 0000000 0000000 00000332773 13565313113 0033356 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions\n",
"\n",
"What if?\n",
" We have thousand of datasets and do not want to make a plot for every single one\n",
" We want to reuse the code in other different datasets or parts of the program\n",
" We are collaborating with other people working in the same code\n",
"It is a bad idea:\n",
" Commenting in and out lines in the plotting code\n",
" Copy and paste chuncks of code here and there\n",
"
\n",
"
\n",
"That makes the code illegible, redundant and prone to errors. And impossible to share it with others.\n",
"
\n",
"\n",
"### The Solution: Package and reuse code as functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let’s start by defining a function fahr_to_celsius that converts temperatures from Fahrenheit to Kelvin:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Building a function to convert fahr_to_kelvin: This is a repetitive tedious task\n",
"\n",
"def fahr_to_kelvin(temp): # this first line is the definition of the function\n",
" return ((temp -32) * (5/9)) + 273.15 # this is the body, in this case made of a single line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"- Definition of the function contains the name and the parameters needed by it to do its job
\n",
"- Body is the part that is compiled and has the instructions to make the task done
\n",
"- Return statement returns the result of the compilation of the function's body
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calling the function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just call the name "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"freezing point of water: 273.15\n",
"boiling point of water: 373.15\n"
]
}
],
"source": [
"print('freezing point of water:', fahr_to_kelvin(32)) \n",
"print('boiling point of water:', fahr_to_kelvin(212))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Composing functions \n",
"\n",
"We make now a function to convert Kelvin to Celsius:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"absolute zero in Celsius: -273.15\n"
]
}
],
"source": [
"def kelvin_to_celsius(temp_k):\n",
" return temp_k - 273.15\n",
"\n",
"print('absolute zero in Celsius:', kelvin_to_celsius(0.0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What about Farh to Celsius? One of the main proposes of functions is reusability. So let's reuse one of them to build some \"neat\" code:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"freezing point of water in Celsius: 0.0\n"
]
}
],
"source": [
"#Fahrenheit to Celsius. We can reuse the function f->k instead of copypasting the code\n",
"\n",
"def fahr_to_celsius(temp_f):\n",
" return kelvin_to_celsius(fahr_to_kelvin(temp_f))\n",
"print ('freezing point of water in Celsius:', fahr_to_celsius(32.0))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Exercise\n",
"\n",
"#### The old switcheroo\n",
"Which of the following would be printed if you were to run this code? Why did you pick this answer?\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"1) 7 3\n",
"2) 3 7\n",
"3) 3 3\n",
"4) 7 7\n",
"\n",
"a = 3\n",
"b = 7\n",
"\n",
"def swap(a, b):\n",
" temp = a\n",
" a = b\n",
" b = temp\n",
"\n",
"swap(a, b)\n",
"\n",
"print(a, b)\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3 7\n"
]
}
],
"source": [
"a = 3\n",
"b = 7\n",
"def swap(a, b):\n",
" temp = a\n",
" a = b\n",
" b = temp\n",
"swap(a, b)\n",
"print(a, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tidying up"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now make to functions to wrap the code and make the analysis of our inflammation data easier to read and reuse:\n",
"
\n",
"First we create a function to plot the data by feeding a filename"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#First function: Makes avg,max,min plots of a file. Note: No return or void function. Explain it\n",
"\n",
"def analyze(filename):\n",
" data = numpy.loadtxt(fname=filename, delimiter=',')\n",
"\n",
" fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
"\n",
" axes1 = fig.add_subplot(1, 3, 1)\n",
" axes2 = fig.add_subplot(1, 3, 2)\n",
" axes3 = fig.add_subplot(1, 3, 3)\n",
"\n",
" axes1.set_ylabel('average')\n",
" axes1.plot(data.mean(axis=0))\n",
"\n",
" axes2.set_ylabel('max')\n",
" axes2.plot(data.max(axis=0))\n",
"\n",
" axes3.set_ylabel('min')\n",
" axes3.plot(data.min(axis=0))\n",
"\n",
" fig.tight_layout()\n",
" matplotlib.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Second we build a function that detects anomalies in the data. Also needs a filename as parameter"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Second function: Find anomalies in a file. Again void function\n",
"\n",
"def detect_problems(filename):\n",
"\n",
" data = numpy.loadtxt(fname=filename, delimiter=',')\n",
"\n",
" if data.max(axis=0)[0] == 0 and data.max(axis=0)[20] == 20:\n",
" print('Suspicious looking maxima!')\n",
" elif data.min(axis=0).sum() == 0:\n",
" print('Minima add up to zero!')\n",
" else:\n",
" print('Seems OK!')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we use them (Do not forget to import numpy and matplotlib.pyplot because they are needed by the code)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"inflammation-01.csv\n"
]
},
{
"data": {
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J1eCsdG44czw/enoNL6+vs21T7jIbuEhEzgeygMEi8qCqXuVwXQmlurEdEf87LpHWn/ss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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Suspicious looking maxima!\n",
"------------------------------------\n",
"\n",
"\n",
"inflammation-02.csv\n"
]
},
{
"data": {
"image/png": 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sgc8r0C0WrwHzgau9H68A7wI7gcdDUlmMenTpp6QnJ/DFEwc6XUrEOmd0H1aX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\nNwJ25PsQ/9m6m7Xl+7julAI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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Suspicious looking maxima!\n",
"------------------------------------\n",
"\n",
"\n",
"inflammation-03.csv\n"
]
},
{
"data": {
"image/png": 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IsH9LnOOXmhfAH/32cX78z7/b9HlFUZSNQDc8yPcBnwEeAh6z13DXWq+jmzx5fo5Hz7Qe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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minima add up to zero!\n",
"------------------------------------\n",
"\n",
"\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import numpy\n",
"import matplotlib.pyplot\n",
"import glob\n",
"\n",
"filenames = glob.glob('inflammation-*.csv')\n",
"\n",
"for f in filenames[:3]:\n",
" print(f)\n",
" analyze(f)\n",
" detect_problems(f)\n",
" print('------------------------------------\\n\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"#### Rescaling an array\n",
"Write a function rescale that takes an array as input and returns a corresponding array of values scaled to lie in the range 0.0 to 1.0. (Hint: If $L$ and $H$ are the lowest and highest values in the original array, then the replacement for a value $v$ should be $(v-L) / (H-L)$.)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0. 0. 0.05 ..., 0.15 0. 0. ]\n",
" [ 0. 0.05 0.1 ..., 0.05 0. 0.05]\n",
" [ 0. 0.05 0.05 ..., 0.1 0.05 0.05]\n",
" ..., \n",
" [ 0. 0.05 0.05 ..., 0.05 0.05 0.05]\n",
" [ 0. 0. 0. ..., 0. 0.1 0. ]\n",
" [ 0. 0. 0.05 ..., 0.05 0.05 0. ]]\n"
]
}
],
"source": [
"def rescale(data_orig):\n",
" max = data_orig.max()\n",
" min = data_orig.min()\n",
" return (data_orig - min)/(max - min)\n",
"data = numpy.loadtxt('inflammation-01.csv', delimiter=',')\n",
"print(rescale(data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When using functions we need to test they are working as expected. By following the example we will see how it is"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# This function takes data and center them around certain desired value\n",
"def center(data, desired):\n",
" return (data - data.mean()) + desired"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To test the function we used controlled data instead real one:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"z before: \n",
" [[ 0. 0.]\n",
" [ 0. 0.]]\n",
"\n",
" z after: \n",
" [[ 3. 3.]\n",
" [ 3. 3.]]\n"
]
}
],
"source": [
"z = numpy.zeros((2,2)) # We create a 2x2 0's matrix\n",
"print(\"z before: \\n\", z)\n",
"print(\"\\n z after: \\n\",center(z,3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks great. Let's center the real data:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-6.14875 -6.14875 -5.14875 ..., -3.14875 -6.14875 -6.14875]\n",
" [-6.14875 -5.14875 -4.14875 ..., -5.14875 -6.14875 -5.14875]\n",
" [-6.14875 -5.14875 -5.14875 ..., -4.14875 -5.14875 -5.14875]\n",
" ..., \n",
" [-6.14875 -5.14875 -5.14875 ..., -5.14875 -5.14875 -5.14875]\n",
" [-6.14875 -6.14875 -6.14875 ..., -6.14875 -4.14875 -6.14875]\n",
" [-6.14875 -6.14875 -5.14875 ..., -5.14875 -5.14875 -6.14875]]\n"
]
}
],
"source": [
"data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')\n",
"print(center(data,0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can make further tests to see if results are correct:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"original min, mean, and max are: 0.0 6.14875 20.0\n",
"min, mean, and and max of centered data are: -6.14875 2.84217094304e-16 13.85125\n"
]
}
],
"source": [
"print('original min, mean, and max are:', data.min(), data.mean(), data.max())\n",
"centered = center(data, 0)\n",
"print('min, mean, and and max of centered data are:', centered.min(), centered.mean(), centered.max())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also test that the standard deviation has not changed showing the difference between the two:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"difference in standard deviations before and after: 0.0\n"
]
}
],
"source": [
"print('difference in standard deviations before and after:', data.std() - centered.std())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Documentation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Is important to document each function to remember ourselves and let others know what the function does"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#You can use this kind of comment but there is a better way\n",
"\n",
"def center(data, desired):\n",
" '''Return a new array containing the original data centered around the desired value.''' \n",
" return (data - data.mean()) + desired"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function center in module __main__:\n",
"\n",
"center(data, desired)\n",
" Return a new array containing the original data centered around the desired value.\n",
"\n"
]
}
],
"source": [
"help(center)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function center in module __main__:\n",
"\n",
"center(data, desired)\n",
" Return a new array containing the original data centered around the desired value.\n",
" Example: center([1, 2, 3], 0) => [-1, 0, 1]\n",
"\n"
]
}
],
"source": [
"def center(data, desired):\n",
" '''Return a new array containing the original data centered around the desired value.\n",
" Example: center([1, 2, 3], 0) => [-1, 0, 1]''' #It is advisable to use examples using previous known results\n",
" return (data - data.mean()) + desired\n",
"\n",
"help(center)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining Defaults "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes we like our parameters to be optional and if in case they are not specified then default to a chosen value:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def center(data, desired=0.0):\n",
" '''Return a new array containing the original data centered around the desired value (0 by default).\n",
" Example: center([1, 2, 3], 0) => [-1, 0, 1]'''\n",
" return (data - data.mean()) + desired"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 3. 3.]\n",
" [ 3. 3.]]\n"
]
}
],
"source": [
"test_data = numpy.zeros((2, 2))\n",
"print(center(test_data, 3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can ommit the desired value, in that case it takes the given default:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data before centering:\n",
"[[ 5. 5.]\n",
" [ 5. 5.]]\n",
"centered data:\n",
"[[ 0. 0.]\n",
" [ 0. 0.]]\n"
]
}
],
"source": [
"more_data = 5 + numpy.zeros((2, 2))\n",
"print('data before centering:')\n",
"print(more_data)\n",
"print('centered data:')\n",
"print(center(more_data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's add another optional parameter to comment the result:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def center(data, desired=0.0, comment='#'):\n",
" '''Return a new array containing the original data centered around the desired value (0 by default) \n",
" with the possibility of commenting something before the result.\n",
" Example: center([1, 2, 3], 0) => [-1, 0, 1]'''\n",
" print(comment)\n",
" return (data - data.mean()) + desired"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imagine you want only to comment it:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#\n"
]
},
{
"ename": "TypeError",
"evalue": "ufunc 'add' did not contain a loop with signature matching types dtype('\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0meven_more_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m7\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcenter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0meven_more_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'centered data:'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m\u001b[0m in \u001b[0;36mcenter\u001b[1;34m(data, desired, comment)\u001b[0m\n\u001b[0;32m 3\u001b[0m Example: center([1, 2, 3], 0) => [-1, 0, 1]'''\n\u001b[0;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcomment\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdesired\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: ufunc 'add' did not contain a loop with signature matching types dtype('\n",
" data must be provided because it do not have any defaults.\n",
" Given no name to the parameter makes the assumption that it is the next non-default parameter ordered from left to right.\n",
" It can be avoided it specifying the name of the optional parameter. \n",
"\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"#### Mixing Default and Non-Default Parameters\n",
"Given the following code:"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"def numbers(one, two=2, three, four=4):\n",
" n = str(one) + str(two) + str(three) + str(four)\n",
" return n\n",
"\n",
"print(numbers(1, three=3))\n",
"what do you expect will be printed? What is actually printed? What rule do you think Python is following?\n",
"\n",
"1) 1234\n",
"2) one2three4\n",
"3) 1239\n",
"4) SyntaxError"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "non-default argument follows default argument (, line 1)",
"output_type": "error",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m def numbers(one, two=2, three, four=4):\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m non-default argument follows default argument\n"
]
}
],
"source": [
"def numbers(one, two=2, three, four=4):\n",
" n = str(one) + str(two) + str(three) + str(four)\n",
" return n\n",
"\n",
"print(numbers(one = 1, two =1, three= 3, four = 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### All required parameters must be placed before any default arguments. Simply because they are mandatory, whereas default arguments are not. Syntactically, it would be impossible for the interpreter to decide which values match which arguments if mixed modes were allowed. A SyntaxError is raised if the arguments are not given in the correct order:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given that, what does the following piece of code display when run?"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"def func(a, b=3, c=6):\n",
" print('a: ', a, 'b: ', b, 'c:', c)\n",
"\n",
"func(-1, 2)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a: -1 b: 2 c: 6\n"
]
}
],
"source": [
"def func(a, b = 3, c = 6):\n",
" print('a: ', a, 'b: ', b,'c:', c)\n",
"\n",
"func(-1, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Readable functions\n",
"\n",
"Functions are supposed to pack code to reuse and for readability to yourself or others... Check these two functions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def s(p):\n",
" a = 0\n",
" for v in p:\n",
" a += v\n",
" m = a / len(p)\n",
" d = 0\n",
" for v in p:\n",
" d += (v - m) * (v - m)\n",
" return numpy.sqrt(d / (len(p) - 1))\n",
" \n",
"#It is not very to say what it does...though it does something very well known. Check this one:\n",
"\n",
"def std_deviation(sample):\n",
" sample_sum = 0\n",
" for value in sample:\n",
" sample_sum += value\n",
" \n",
" sample_mean = sample_sum / len(sample)\n",
" sum_squared_devs = 0\n",
" for value in sample:\n",
" sum_squared_devs += (value - sample_mean) * (value - sample_mean)\n",
" return numpy.sqrt(sum_squared_devs / (len(sample) - 1))\n",
"\n",
"#Much more easy to read!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It its important to keep code readable!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Combining strings\n",
"“Adding” two strings produces their concatenation: 'a' + 'b' is 'ab'. Write a function called fence that takes two parameters called original and wrapper and returns a new string that has the wrapper character at the beginning and end of the original. A call to your function should look like this:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"*emilio*\n"
]
}
],
"source": [
"def fence(word, wrapper):\n",
" return wrapper + word + wrapper\n",
"print(fence('emilio','*'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Selecting characters from strings\n",
"If the variable s refers to a string, then s[0] is the string’s first character and s[-1] is its last. Write a function called outer that returns a string made up of just the first and last characters of its input. A call to your function should look like this:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"eo\n"
]
}
],
"source": [
"def outer(word):\n",
" return word[0] + word[-1]\n",
"print(outer('emilio'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing and documenting your function\n",
"Run the commands help(numpy.arange) and help(numpy.linspace) to see how to use these functions to generate regularly-spaced values, then use those values to test your rescale function. Once you’ve successfully tested your function, add a docstring that explains what it does."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"help(numpy.arange)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"help(numpy.linspace) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = numpy.linspace(10, 30, 30)\n",
"print('original data :', data)\n",
"print('rescaled data :', rescale(data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding docstrings:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def rescale(data_orig):\n",
" '''Returns a rescaled array from the original data_orig, in interval [0:1]'''\n",
" max = data_orig.max()\n",
" min = data_orig.min()\n",
" return (data_orig - min)/(max - min)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"help(rescale)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Variables inside and outside functions\n",
"What does the following piece of code display when run - and why?\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"f = 0\n",
"k = 0\n",
"\n",
"def f2k(f):\n",
" k = ((f-32)*(5.0/9.0)) + 273.15\n",
" return k\n",
"\n",
"f2k(8)\n",
"f2k(41)\n",
"f2k(32)\n",
"\n",
"print(k)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"259.81666666666666\n",
"278.15\n",
"273.15\n",
"0\n"
]
}
],
"source": [
"f = 0\n",
"k = 0\n",
"\n",
"def f2k(f):\n",
" k = ((f-32)*(5.0/9.0)) + 273.15\n",
" return k\n",
"\n",
"print(f2k(8))\n",
"print(f2k(41))\n",
"print(f2k(32))\n",
"\n",
"print(k)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Readable code"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
ProgPy_Lists-checkpoint.ipynb 0000664 0000000 0000000 00000014320 13565313113 0031146 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lists\n",
"They are used to store many values. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Difference with array?\n",
"They are very similar but they do not belong to numpy, and do not have some of the attributes of numpy arrays, such as mean sd max min etc... Beside this they are pretty similar in concept\n",
" We create a list the same "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Selecting elements of a list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can loop over a list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List are also pretty similar to strings. There is an IMPORTANT difference between strings and lists: Elements in a list can be modified but not ones in a string:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### List of Lists or nested lists\n",
"Making a list whose elements are also lists."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example: Products in the shelves of a shop. We can print the results of the index operations in the images:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can print the first element of the list x
:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Printing first element of x[0]
list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Modifying list contents\n",
"Many ways to change a string"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Caution here!: List are treated slightly conterintuitive:
\n",
"Pro-tip: Technically it is said that lists are copied \"by reference\" instead of \"by value\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"#### Turn string into a list\n",
"\n",
"Use a for-loop to convert the string “hello” into a list of letters:\n",
"\n",
"\n",
"[\"h\", \"e\", \"l\", \"l\", \"o\"]\n",
"
\n",
"\n",
"Hint: You can create an empty list like this:\n",
"\n",
"``my_list = []``"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can make a single copy of the original list using list built-in"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Reminder: When not to use \"list\" method?
\n",
"\n",
"\n",
"Reference copy uses less resources \n",
" Is the default when relating lists through assignment\n",
" But whether you need to do a reference or a copy is your choice!!\n",
" \n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"#### Overloading\n",
"\n",
"`+` usually means addition, but when used on strings or lists, it means “concatenate”. Given that, what do you think the multiplication operator * does on lists? In particular, what will be the output of the following code?\n",
"\n",
"\n",
"counts = [2, 4, 6, 8, 10]\n",
"repeats = counts * 2\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
ProgPy_Lists_filled-checkpoint.ipynb 0000664 0000000 0000000 00000024557 13565313113 0032502 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lists\n",
"They are used to store many values. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Difference with array?\n",
"They are very similar but they do not belong to numpy, and do not have some of the attributes of numpy arrays, such as mean sd max min etc... Beside this they are pretty similar in concept\n",
" We create a list the same "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"odds are: [1, 3, 5, 7]\n"
]
}
],
"source": [
"odds = [1, 3, 5, 7]\n",
"print('odds are:', odds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Selecting elements of a list:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"first and last: 1 7\n"
]
}
],
"source": [
"print('first and last:', odds[0], odds[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can loop over a list:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"3\n",
"5\n",
"7\n"
]
}
],
"source": [
"for number in odds: #explain iterables\n",
" print(number)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List are also pretty similar to strings. There is an IMPORTANT difference between strings and lists: Elements in a list can be modified but not ones in a string:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"names originally: ['Newton', 'Darwing', 'Turing']\n",
"names corrected: ['Newton', 'Darwin', 'Turing']\n"
]
}
],
"source": [
"names = ['Newton', 'Darwing', 'Turing']\n",
"print('names originally:', names)\n",
"names[1]='Darwin'\n",
"print('names corrected:', names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'str' object does not support item assignment",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Bell'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
]
}
],
"source": [
"name = 'Bell' #Explain mutability\n",
"name[1] = 'a'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### List of Lists or nested lists\n",
"Making a list whose elements are also lists."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example: Products in the shelves of a shop. We can print the results of the index operations in the images:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = [['salt', 'zuchini', 'onion'], ['cabbage', 'lettuce', 'garlic'],['apple','pear','banana']]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[['salt', 'zuchini', 'onion']]\n"
]
}
],
"source": [
"print([x[0]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can print the first element of the list x
:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['salt', 'zuchini', 'onion']\n"
]
}
],
"source": [
"print(x[0]) # Prints x's first element, in this case again a list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Printing first element of x[0]
list:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"salt\n"
]
}
],
"source": [
"print(x[0][0]) # Prints x's first element's first element. In this case a string"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Modifying list contents\n",
"Many ways to change a string"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 3, 5, 7, 11]\n"
]
}
],
"source": [
"odds.append(11) # Add 11 at the end of the list. Also written as +=[] \n",
"print(odds) # Explain that operators have different effects depending on the nature of the operands"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 5, 7, 11]\n"
]
}
],
"source": [
"del odds[0]\n",
"print(odds)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"reversed odds: [11, 7, 5, 3]\n"
]
}
],
"source": [
"odds.reverse()\n",
"print('reversed odds:', odds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"#### Turn string into a list\n",
"\n",
"Use a for-loop to convert the string “hello” into a list of letters:\n",
"\n",
"\n",
"[\"h\", \"e\", \"l\", \"l\", \"o\"]\n",
"
\n",
"\n",
"Hint: You can create an empty list like this:\n",
"\n",
"``my_list = []``"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the list: ['h', 'e', 'l', 'l', 'o']\n"
]
}
],
"source": [
"my_list = []\n",
"\n",
"for letter in 'hello':\n",
" my_list +=[letter] \n",
"print('the list:', my_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Caution here!: List are treated slightly conterintuitive:
\n",
"Pro-tip: Technically it is said that lists are copied \"by reference\" instead of \"by value\""
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"primes: [1, 3, 5, 7, 2]\n",
"odds: [1, 3, 5, 7, 2]\n"
]
}
],
"source": [
"odds = [1, 3, 5, 7]\n",
"primes = odds # list do not behave has simple variables, \n",
" # when assigning it does not copy the whole list, but adds\n",
" # a reference to the list odds named primes\n",
"primes += [2] \n",
"print('primes:', primes)\n",
"print('odds:', odds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can make a single copy of the original list using list built-in"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"primes: [1, 3, 5, 7, 2]\n",
"odds: [1, 3, 5, 7]\n"
]
}
],
"source": [
"odds = [1, 3, 5, 7]\n",
"primes = list(odds) # This time a new copy of the list is done\n",
"primes += [2]\n",
"print('primes:', primes)\n",
"print('odds:', odds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Reminder: When not to use \"list\" method?
\n",
"\n",
"\n",
"Reference copy uses less resources \n",
" Is the default when relating lists through assignment\n",
" But whether you need to do a reference or a copy is your choice!!\n",
" \n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"#### Overloading\n",
"\n",
"`+` usually means addition, but when used on strings or lists, it means “concatenate”. Given that, what do you think the multiplication operator * does on lists? In particular, what will be the output of the following code?\n",
"\n",
"\n",
"counts = [2, 4, 6, 8, 10]\n",
"repeats = counts * 2\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2, 4, 6, 8, 10, 2, 4, 6, 8, 10]\n"
]
}
],
"source": [
"counts = [2, 4, 6, 8, 10]\n",
"repeats = counts * 2 # The * operator applied to lists replicates the elements in it a given number of times\n",
"print(repeats)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
ProgPy_Loops-checkpoint.ipynb 0000664 0000000 0000000 00000011062 13565313113 0031144 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Repeating Actions with Loops"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We found some suspicious features in our firs dataset \"inflammation-01.csv\". It is possible that other datasets are also affected by this?? We would like to check... Fast!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Repeat tasks:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have a lot of data sets and we have to tell the computer how to repeat things.\n",
"
\n",
"Let's first do something simpler: Show each character in a string!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bad approach: tedious and if the string is larger it misses some characters, and if it shorter:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### There is a better approach: Recursion\n",
"Recursion is made by for loops:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example 1. A better way:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The method supports strings of any lenght:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see it in action:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### General form of a for-loop:\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can choose any name we want for variables. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example 2. This loop repeatedly updates a variable that acts as counter. It is very usual to find this structure:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The for body is executed 5 times\n",
"\n",
"- 1st: vowel eqs a; lenght eqs 1\n",
"
- 2st: vowel eqs e; lenght eqs 2\n",
"
- etc.."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example 3. This loop overrides the value of a external variable:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Off-topic tip: Some loops frecuently used are wraped inside very useful methods, such as
len
which returns the length of the variable. The methods avaiable by default in Python without import a thing are called built-in methods"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From 1 to N"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python has a built-in function called range that creates a sequence of numbers. Using range, write a loop that uses range to print the first 3 odd natural numbers. Use the builtin help!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
ProgPy_Loops_filled-checkpoint.ipynb 0000664 0000000 0000000 00000017431 13565313113 0032471 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Repeating Actions with Loops"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We found some suspicious features in our firs dataset \"inflammation-01.csv\". It is possible that other datasets are also affected by this?? We would like to check... Fast!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Repeat tasks:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have a lot of data sets and we have to tell the computer how to repeat things.\n",
"
\n",
"Let's first do something simpler: Show each character in a string!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"l\n",
"e\n",
"a\n",
"d\n"
]
}
],
"source": [
"word = 'lead' # From wich number to wich number we had to go? Ask people\n",
"print(word[0])\n",
"print(word[1])\n",
"print(word[2])\n",
"print(word[3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bad approach: tedious and if the string is larger it misses some characters, and if it shorter:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t\n",
"i\n",
"n\n"
]
},
{
"ename": "IndexError",
"evalue": "string index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m: string index out of range"
]
}
],
"source": [
"word = 'tin' # Bad approach, if the string is larger it misses characters and if it shorter it fails!\n",
"print(word[0])\n",
"print(word[1])\n",
"print(word[2])\n",
"print(word[3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### There is a better approach: Recursion\n",
"Recursion is made by for loops:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example 1. A better way:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"l\n",
"e\n",
"a\n",
"d\n"
]
}
],
"source": [
"word = 'lead'\n",
"for char in word:\n",
" print(char)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The method supports strings of any lenght:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"o\n",
"x\n",
"i\n",
"g\n",
"e\n",
"n\n"
]
}
],
"source": [
"word = 'oxigen'\n",
"for char in word:\n",
" print (char)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see it in action:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### General form of a for-loop:\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can choose any name we want for variables. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"o\n",
"x\n",
"y\n",
"g\n",
"e\n",
"n\n"
]
}
],
"source": [
"word = 'oxygen'\n",
"for banana in word:\n",
" print(banana)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example 2. This loop repeatedly updates a variable that acts as counter. It is very usual to find this structure:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 5 vowels\n"
]
}
],
"source": [
"lenght = 0\n",
"for vowel in 'aeiou': # The process details can be explained here\n",
" lenght = lenght + 1 # Explain what happens if lenght is inside\n",
"print('There are ',lenght,'vowels')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The for body is executed 5 times\n",
"\n",
"- 1st: vowel eqs a; lenght eqs 1\n",
"
- 2st: vowel eqs e; lenght eqs 2\n",
"
- etc.."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Off-topic tip: Some loops frecuently used are wraped inside very useful methods, such as
len
which returns the length of the variable. The methods avaiable by default in Python without import a thing are called built-in methods"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"print(len('aeiou')) # built-in function to get the length of a string (and the length of many other things...). \n",
" # Is faster and much easier to read than a for loop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From 1 to N"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python has a built-in function called range that creates a sequence of numbers. Using range, write a loop that uses range to print the first 3 odd natural numbers. Use the builtin help!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"3\n",
"5\n"
]
}
],
"source": [
"for num in range(1,6,2):\n",
" print (num)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
ProgPy_Making_choices-checkpoint.ipynb 0000664 0000000 0000000 00000017517 13565313113 0032766 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Making choices\n",
"\n",
"In last section we discovered something suspicios in our inflammation data by drawing some plots. How can we use Python\n",
"to automatically detect the anomalies we found and take an action for each of them?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By writing code that runs only certain conditions:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Conditionals\n",
"\n",
"A conditional is a piece of code that takes different actions depending on a condition:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imagine we want to determine if a certain number is lower or greater than 100. The flow of this conditional:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This scheme is achieved in Python by means of the if
/else
clause:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes there is no need to use a else, it depends on the logic of the problem:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes the flow of the decision is more complex. There are still multiple choices available \n",
"when the first condition is discarded.
\n",
"As an example imagine we want to know if a given number is positive negative or zero. We make use of the elif
clause:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note the \"==\". This symbol checks equality rather than \"=\" wich means assingment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercises\n",
"#### What Is Truth?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if '':\n",
" print('empty string is true')\n",
"if 'word':\n",
" print('word is true')\n",
"if []:\n",
" print('empty list is true')\n",
"if [1, 2, 3]:\n",
" print('non-empty list is true')\n",
"if 0:\n",
" print('zero is true')\n",
"if 1:\n",
" print('one is true')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### That’s Not Not What I Meant"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if not '':\n",
" print('empty string is not true')\n",
"if not 'word':\n",
" print('word is not true')\n",
"if not not True:\n",
" print('not not True is true')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Composed conditions\n",
"We can write complex conditions in the if clause by using concatenation of simple conditions by means of and
and or
.
\n",
"As in human language an and
condition is True if all its components are True:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Whereas an or
condition is True if any of it components is True:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"#### Close Enough\n",
"\n",
"Write some conditions that print True if the variable a is within 10% of the variable b and False otherwise. Compare your implementation with your partner’s: do you get the same answer for all possible pairs of numbers?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Checking our Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we are in conditions to check our suspicious data in the inflammation tests.
\n",
"First two files max inflammation seems to rise linearly. So let's write a code that check this behavior and show warning:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We saw a different issue in file 3. All minima where 0. So we write a code to check that:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can check now several files putting all toghether:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional Exercises\n",
"\n",
"### In-place operators"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Python (and most other languages in the C family) provides in-place operators that work like this:\n",
"\n",
"```x = 1 # original value\n",
"x += 1 # add one to x, assigning result back to x\n",
"x *= 3 # multiply x by 3\n",
"print(x)```\n",
"\n",
"\n",
"``6``\n",
"\n",
"Write some code that sums the positive and negative numbers in a list separately, using in-place operators. Do you think the result is more or less readable than writing the same without in-place operators?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Counting Vowels "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"* Write a loop that counts the number of vowels in a character string.\n",
"* Test it on a few individual words and full sentences.\n",
"* Once you are done, compare your solution to your neighbor’s. Did you make the same decisions about how to handle the letter ‘y’ (which some people think is a vowel, and some do not)?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
ProgPy_Making_choices_filled-checkpoint.ipynb 0000664 0000000 0000000 00000033733 13565313113 0034303 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Making choices\n",
"\n",
"In last section we discovered something suspicios in our inflammation data by drawing some plots. How can we use Python\n",
"to automatically detect the anomalies we found and take an action for each of them?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By writing code that runs only certain conditions:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Conditionals\n",
"\n",
"A conditional is a piece of code that takes different actions depending on a condition:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imagine we want to determine if a certain number is lower or greater than 100. The flow of this conditional:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This scheme is achieved in Python by means of the if
/else
clause:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"not greater\n",
"done\n"
]
}
],
"source": [
"num = 37\n",
"if num > 100:\n",
" print('greater')\n",
"else:\n",
" print('not greater')\n",
"print('done')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes there is no need to use a else, it depends on the logic of the problem:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"before conditional....\n",
"... after conditional\n"
]
}
],
"source": [
"num = 53\n",
"print ('before conditional....')\n",
"if num > 100:\n",
" print ('53 is greater than 100')\n",
"print('... after conditional')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes the flow of the decision is more complex. There are still multiple choices available \n",
"when the first condition is discarded.
\n",
"As an example imagine we want to know if a given number is positive negative or zero. We make use of the elif
clause:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-3 is negative\n"
]
}
],
"source": [
"\n",
"num = -3\n",
"\n",
"if num > 0:\n",
" print(num, 'is positive')\n",
"elif num == 0: # note the sign == instead of =. The reason is that \"==\" tests equality whereas \"=\" means assignment\n",
" print(num, 'is zero')\n",
"else:\n",
" print(num, 'is negative')\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note the \"==\". This symbol checks equality rather than \"=\" wich means assingment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercises\n",
"\n",
"#### What Is Truth?"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"word is true\n",
"non-empty list is true\n",
"one is true\n"
]
}
],
"source": [
"if '':\n",
" print('empty string is true')\n",
"if 'word':\n",
" print('word is true')\n",
"if []:\n",
" print('empty list is true')\n",
"if [1, 2, 3]:\n",
" print('non-empty list is true')\n",
"if 0:\n",
" print('zero is true')\n",
"if 1:\n",
" print('one is true')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### That’s Not Not What I Meant"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"empty string is not true\n",
"not not True is true\n"
]
}
],
"source": [
"if not '':\n",
" print('empty string is not true')\n",
"if not 'word':\n",
" print('word is not true')\n",
"if not not True:\n",
" print('not not True is true')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Composed conditions\n",
"We can write complex conditions in the if clause by using concatenation of simple conditions by means of and
and or
.
\n",
"As in human language an and
condition is True if all its components are True:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"at least one part is false\n"
]
}
],
"source": [
"# We can add complex conditions to the if clause, concatenating sentences with \"or\" and \"and\"\n",
"\n",
"if (1 > 0) and (-1 > 0): # Note the () the are not really needed but the reading is clearer\n",
" # and the \"and\". For and \"and\" to be true every part has to be true by itself\n",
" print ('both parts are true')\n",
"else:\n",
" print('at least one part is false')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Whereas an or
condition is True if any of it components is True:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"at least one part is true\n"
]
}
],
"source": [
"if (1 > 0) or (-1 > 0): # Note that an \"or\" condition is true if any of the parts is true\n",
" print('at least one part is true')\n",
"else:\n",
" print('none of them are true')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"#### Close Enough\n",
"\n",
"Write some conditions that print True if the variable a is within 5% of the variable b and False otherwise. Compare your implementation with your partner’s: do you get the same answer for all possible pairs of numbers?"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"a = .9\n",
"b = 1\n",
"if (a > 0.9 * b) and (a < 1.1 * b): # We consider within as strictly below b+5% and above b-5%\n",
" print('True')\n",
"else:\n",
" print('False')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(abs(a - b) < 0.1 * abs(b))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"multiplication=a*b\n",
"substraction=a-b"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variable Type Data/Info\n",
"-----------------------------------\n",
"a float 0.9\n",
"b int 1\n",
"multiplication float 0.9\n",
"substraction float -0.09999999999999998\n"
]
}
],
"source": [
"%whos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Checking our Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we are in conditions to check our suspicious data in the inflammation tests.
\n",
"First two files max inflammation seems to rise linearly. So let's write a code that check this behavior and show warning:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Suspicious looking maxima\n"
]
}
],
"source": [
"import numpy\n",
"\n",
"data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')\n",
"if (data.max(axis=0)[0]==0) and (data.max(axis=0)[20]==20): # Anomalous linear behavior of the max in the data\n",
" print('Suspicious looking maxima')\n",
"elif (data.min(axis=0).sum() == 0):\n",
" print('Minima add up to zero')\n",
"else:\n",
" print('Everything seems OK')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We saw a different issue in file 3. All minima where 0. So we write a code to check that:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minima add up to zero\n"
]
}
],
"source": [
"data = numpy.loadtxt(fname='inflammation-03.csv', delimiter=',')\n",
"if (data.max(axis=0)[0]==0) and (data.max(axis=0)[20]==20): # Anomalous linear behavior of the max in the data\n",
" print('Suspicious looking maxima')\n",
"elif (data.min(axis=0).sum() == 0):\n",
" print('Minima add up to zero')\n",
"else:\n",
" print('Everything seems OK')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can check now several files putting all toghether:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import glob"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"inflammation-01.csv :\n",
"Suspicious looking maxima\n",
"inflammation-02.csv :\n",
"Suspicious looking maxima\n",
"inflammation-03.csv :\n",
"Minima add up to zero\n"
]
}
],
"source": [
"filenames = sorted(glob.glob('inflammation*.csv'))\n",
"filenames = filenames[0:3] # Get elements 0, 1 and 2 of the list corresponding to inf-01 inf-02 and inf-03\n",
"filenames\n",
"for f in filenames:\n",
" print(f,':')\n",
" data = numpy.loadtxt(fname=f, delimiter=',')\n",
" if (data.max(axis=0)[0]==0) and (data.max(axis=0)[20]==20): # Anomalous linear behavior of the max in the data\n",
" print('Suspicious looking maxima')\n",
" elif (data.min(axis=0).sum() == 0):\n",
" print('Minima add up to zero')\n",
" else:\n",
" print('Everything seems OK')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How many paths?"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"C\n"
]
}
],
"source": [
"if 4 > 5:\n",
" print('A')\n",
"elif 4 == 5:\n",
" print('B')\n",
"elif 4 < 5:\n",
" print('C')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### In-place operators"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python (and most other languages in the C family) provides in-place operators that work like this:\n",
"\n",
"```x = 1 # original value\n",
"x += 1 # add one to x, assigning result back to x\n",
"x *= 3 # multiply x by 3\n",
"print(x)```\n",
"\n",
"\n",
"``6``\n",
"\n",
"Write some code that sums the positive and negative numbers in a list separately, using in-place operators. Do you think the result is more or less readable than writing the same without in-place operators?"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"positive sum is 14 , and the negative sum is -6\n"
]
}
],
"source": [
"numbers = [-1, 2, -3, 7, 5, -2] # Given a list. Not an array!!\n",
"\n",
"pos_sum = 0\n",
"neg_sum = 0\n",
"\n",
"for number in numbers:\n",
" if number > 0:\n",
" pos_sum += number\n",
" else:\n",
" neg_sum += number\n",
"print('positive sum is', pos_sum, ', and the negative sum is', neg_sum ) \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Counting Vowels "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Write a loop that counts the number of vowels in a character string.\n",
"* Test it on a few individual words and full sentences.\n",
"* Once you are done, compare your solution to your neighbor’s. Did you make the same decisions about how to handle the letter ‘y’ (which some people think is a vowel, and some do not)?"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of vowels: 4\n"
]
}
],
"source": [
"string = 'aljdiajdi'\n",
"vowels = 0\n",
"for char in string:\n",
" if (char == 'a') or (char == 'e') or (char == 'i' ) or (char == 'o') or (char == 'u'):\n",
" vowels += 1\n",
" \n",
"print('number of vowels:', vowels )"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
ProgPy_Multiple_files-checkpoint.ipynb 0000664 0000000 0000000 00000004424 13565313113 0033031 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multiple files\n",
"\n",
"We almost have all the tools needed to process all files. We need an extra library that will make the task easier:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Glob has a function called glob
that finds files and directories whose names match a pattern:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Output is an unsorted list of filenames to loop over. We can sort it using sorted
built-in"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can bring together all loops, lists and loops over lists, and use it to process all files: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting Differences\n",
"\n",
"Plot the difference between the average of the first dataset and the average of the second dataset, i.e., the difference between the leftmost plot of the first two figures."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
ProgPy_Multiple_files_filled-checkpoint.ipynb 0000664 0000000 0000000 00000333231 13565313113 0034351 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multiple files\n",
"\n",
"We almost have all the tools needed to process all files. We need an extra library that will make the task easier:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import glob"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Glob has a function called glob
that finds files and directories whose names match a pattern:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['inflammation-02.csv', 'inflammation-09.csv', 'inflammation-01.csv', 'inflammation-06.csv', 'inflammation-04.csv', 'inflammation-10.csv', 'inflammation-08.csv', 'inflammation-12.csv', 'inflammation-07.csv', 'inflammation-05.csv', 'inflammation-03.csv', 'inflammation-11.csv']\n"
]
}
],
"source": [
"print(glob.glob('inflammation*.csv')) # * is a wildcard that matches to any string of characters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Output is an unsorted list of filenames to loop over. We can sort it using sorted
built-in"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['inflammation-01.csv', 'inflammation-02.csv', 'inflammation-03.csv', 'inflammation-04.csv', 'inflammation-05.csv', 'inflammation-06.csv', 'inflammation-07.csv', 'inflammation-08.csv', 'inflammation-09.csv', 'inflammation-10.csv', 'inflammation-11.csv', 'inflammation-12.csv']\n"
]
}
],
"source": [
"print(sorted(glob.glob('inflammation*.csv')))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can bring together all loops, lists and loops over lists, and use it to process all files: "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"inflammation-01.csv\n"
]
},
{
"data": {
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J1eCsdG44czw/enoNL6+vs21T7jIbuEhEzgeygMEi8qCqXuVwXQmlurEdEf87LpHWn/ss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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"inflammation-02.csv\n"
]
},
{
"data": {
"image/png": 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sgc8r0C0WrwHzgau9H68A7wI7gcdDUlmMenTpp6QnJ/DFEwc6XUrEOmd0H1aX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\nYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"inflammation-03.csv\n"
]
},
{
"data": {
"image/png": 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IsH9LnOOXmhfAH/32cX78z7/b9HlFUZSNQDc8yPcBnwEeAh6z13DXWq+jmzx5fo5Hz7Qe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\nnVvBrVePH+FkEgZLp48jI2KserPB7ygiEhJJ20E+fOwUL32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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy\n",
"import matplotlib.pyplot\n",
"%matplotlib inline\n",
"\n",
"filenames = sorted(glob.glob('inflammation*.csv'))\n",
"filenames = filenames[0:3] # We just only pick up three first elements in the list ( 0, 1 and 2) for brevity\n",
"\n",
"for f in filenames:\n",
" print(f) # Prints file name to keep progress\n",
" \n",
" data = numpy.loadtxt(fname=f, delimiter=',') # Read the file data\n",
" \n",
" fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
" \n",
" axes1 = fig.add_subplot(1, 3, 1)\n",
" axes2 = fig.add_subplot(1, 3, 2)\n",
" axes3 = fig.add_subplot(1, 3, 3)\n",
" \n",
" axes1.set_ylabel('average')\n",
" axes1.plot(data.mean(axis=0))\n",
" \n",
" axes2.set_ylabel('max')\n",
" axes2.plot(data.max(axis=0))\n",
" \n",
" axes3.set_ylabel('min')\n",
" axes3.plot(data.min(axis=0))\n",
" \n",
" \n",
" fig.tight_layout()\n",
" matplotlib.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting Differences\n",
"\n",
"Plot the difference between the average of the first dataset and the average of the second dataset, i.e., the difference between the leftmost plot of the first two figures."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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bW9Y3eMBMT1moaWSTEtlzZppPPnSIN2xbxZu2r0nfbrcJf/O2K/nZbav4y/tf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\nV4TjMj9JTvgAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import glob\n",
"import numpy\n",
"import matplotlib.pyplot\n",
"\n",
"filenames = glob.glob('inflammation*.csv')\n",
"\n",
"data0 = numpy.loadtxt(fname=filenames[0], delimiter=',')\n",
"data1 = numpy.loadtxt(fname=filenames[1], delimiter=',')\n",
"\n",
"#fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
"\n",
"matplotlib.pyplot.ylabel('Difference in average')\n",
"matplotlib.pyplot.plot(data0.mean(axis=0) - data1.mean(axis=0))\n",
"\n",
"#fig.tight_layout()\n",
"matplotlib.pyplot.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
jupyter-quickstart-checkpoint.ipynb 0000664 0000000 0000000 00000023522 13565313113 0032446 0 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/.ipynb_checkpoints {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![jupyter](images/jupyter-logo-2.png)\n",
"\n",
"![IPython](images/ipython_logo-s.png)\n",
"\n",
"\n",
"# Ipython / Jupyter Notebooks walkthrough"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Starting the notebook server using the command line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can start the notebook server from the command line (Terminal on Mac/Linux, CMD prompt on Windows) by running the following command: \n",
"\n",
" jupyter-notebook\n",
"\n",
"This will print some information about the notebook server in your terminal, including the URL of the web application (by default, `http://127.0.0.1:8888`). It will then open your default web browser to this URL.\n",
"\n",
"When the notebook opens, you will see the **notebook dashboard**, which will show a list of the notebooks, files, and subdirectories in the directory where the notebook server was started. Most of the time, you will want to start a notebook server in the highest directory in your filesystem where notebooks can be found. Often this will be your home directory."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic conceps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* **Notebook documents**: Notebook documents (or \"notebooks\") are documents produced by the Jupyter Notebook App which contain both computer code (e.g. Python) and rich text elements (paragraph, equations, figures, links, etc...). Notebook documents are both human-readable documents containing the analysis description and the results (figures, tables, etc..) as well as executable documents which can be run to perform data analysis.\n",
"\n",
"* **Kernels**: A notebook kernel is a “computational engine” that executes the code contained in a Notebook document and returns output back to the notebook web application. We will use here the `IPython` kernel. \n",
"\n",
"* **Notebook Dashboard**: The Notebook Dashboard is the component which is shown first when the launching Jupyter Notebook App. The Notebook Dashboard is mainly used to open notebook documents, and to manage the running kernels (visualize and shutdown).\n",
"\n",
"* **Cells**: The notebook consists of a sequence of **cells**. A cell is a multi-line text input field, and its contents can be executed by using `Shift-Enter`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modal editor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jupyter Notebooks have a modal user interface. This means that the keyboard does different things depending on which mode the Notebook is in. There are two modes: *edit* mode and *command* mode."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Edit mode\n",
"\n",
"Edit mode is indicated by a green cell border and a prompt showing in the editor area:\n",
"\n",
"\n",
"\n",
"When a cell is in edit mode, you can type into the cell, like a normal text editor.\n",
"\n",
"\n",
"Enter edit mode by pressing `ENTER` or using the mouse to click on a cell's editor area.\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Command mode\n",
"\n",
"Command mode is indicated by a grey cell border with a blue left margin:\n",
"\n",
"\n",
"\n",
"When you are in command mode, you are able to edit the notebook as a whole, but not type into individual cells. Most importantly, in command mode, the keyboard is mapped to a set of shortcuts that let you perform notebook and cell actions efficiently. For example, if you are in command mode and you press `c`, you will copy the current cell - no modifier is needed.\n",
"\n",
"\n",
"Don't try to type into a cell in command mode; unexpected things will happen!\n",
"\n",
"\n",
"\n",
"Enter command mode by pressing `ESC` or using the mouse to click *outside* a cell's editor area.\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have different types of cells. The type of a cell is selected from the dropdown menu or with a keyboard shortcut in command mode. We'll see `code` cells and `Markdown` cells. \n",
"\n",
"This is a **Markdown** cell itself and is used to add formatted text to your notebooks. You can use both [Markdown](https://en.wikipedia.org/wiki/Markdown) syntax and [LaTeX](https://en.wikipedia.org/wiki/LaTeX) syntax.\n",
"\n",
"You can include mathematical expressions both inline: $e^{i\\pi} + 1 = 0$ and displayed:\n",
"\n",
"$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Code cells** on the other hand allow us to input Python code which will be later run by the kernel. \n",
"\n",
"Run a code cell using `Shift-Enter` or pressing the button in the toolbar above. Once the cell is run (see below how), the corresponding output, if any, apperas below the cell."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.0, 4.0, 3.14]\n"
]
}
],
"source": [
"# This is a code cell. It runs code :-)\n",
"a = [1., 4., 3.14]\n",
"print (a)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.141592653589793\n",
"1.0\n"
]
}
],
"source": [
"# And another code cell\n",
"import math\n",
"print (math.pi)\n",
"print (math.sin(math.pi/2.))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running cells"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The keyboard shortcuts for running a cell, both in edit and command mode, are:\n",
"\n",
"* `Shift-Enter` runs the current cell and moves to the one below.\n",
"* `Alt-Enter` runs the current cell and inserts a new one below.\n",
"* `Ctrl-Enter` run the current cell and enters command mode in current cell.\n",
"\n",
"\n",
"Press `h` anytime in command mode for a keyboard shotcut list.\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tab completion & help (edit mode)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Tab completion**, especially for attributes, is a convenient way to explore the structure of any object you’re dealing with. Simply type in a code cell `object_name.` to view the object’s attributes. Besides Python objects and keywords, tab completion also works on file and directory names.\n",
"\n",
"For getting an object **help**, type `object_name.` and a tooltip with the object short help will open. Pressing `` twice (``) the full object help will open. Doing it four times and the full object help will go into a new frame."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Summarizing\n",
"\n",
"\n",
"* An Jupyter **notebook** is composed of **cells**.\n",
"\n",
"* The **cells** are edited by us and then executed by the **kernel**.\n",
"\n",
"* Cells can be **Markdown** (text cells accepting Markdown and LaTeX syntax), and **code** cells (for executing code).\n",
"\n",
"* We have a **modal** interface with **command** (for the whole notebook, press `ESC`) and **edit** (for a cell, `ENTER`) modes.\n",
"\n",
"* Use tab completion (`object_name.`) and inline help (`object_name.`) extensively.\n",
"\n",
"* `H` for keyboard shotcut reference (`ESC` to exit).\n",
"\n",
"Typically, you will work on a computational problem in pieces, organizing related ideas into cells and moving forward once previous parts work correctly. \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References \n",
"\n",
"* This tutorial was essentially based in [IPython's notebook-based documentation](http://nbviewer.ipython.org/github/ipython/ipython/blob/3.x/examples/Index.ipynb). Take a look at it for a more extensive introduction to IPython.\n",
"\n",
"* Official [Jupyter notebook begginer guide](http://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/index.html#).\n",
"\n",
"* Official [Jupyter user manual](https://jupyter.readthedocs.io/en/latest/index.html)\n",
"\n",
"* [A gallery of interesting IPython Notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks).\n",
"\n",
"* [IPython project page](http://ipython.org/)\n",
"\n",
"* [Jupyter project page](http://jupyter.org/)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/Defensive_Programming.ipynb 0000664 0000000 0000000 00000735125 13565313113 0025200 0 ustar 00root root 0000000 0000000 {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Defensive Programming"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- How can I make my programs more reliable?\n",
"
- OK my program does not have errors. But how to know the program is getting the right answer?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To achieve that, we need to:\n",
"\n",
"
- Write programs that check their own operation.\n",
"
- Write and run tests for widely-used functions.\n",
"
- Make sure we know what “correct” actually means."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assertions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An assertion is a statement that something must be true at a certain point in the program"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When Python finds an assertion it evaluates it and:\n",
"
- If True does nothing\n",
"
- If False throws an assertion Error and some given text\n",
"\n",
"First example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assertions fall in three categories:\n",
"
- A precondition is something that must be true at the start of a function in order for it to work correctly.\n",
"\n",
"
- A postcondition is something that the function guarantees is true when it finishes.\n",
"\n",
"
- An invariant is something that is always true at a particular point inside a piece of code."
]
},
{
"attachments": {
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}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"A more complicated example: Rectangles\n",
"\n",
"![rectangle.png](attachment:rectangle.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def normalize_rectangle(rect):\n",
" '''Normalizes a rectangle so that it is at the origin and 1.0 units long on its longest axis.'''\n",
" assert len(rect) == 4, 'Rectangles must contain 4 coordinates'\n",
" llx, llyy, urx, ury = rect\n",
" assert llx < urx, 'Invalid X coordinates'\n",
" assert lly < ury, 'Invalid Y coordinates'\n",
"\n",
" dx = urx - llx\n",
" dy = ury - lly\n",
" if dx > dy: #Rectangle in landscape mode\n",
" scaled = float(dx) / dy\n",
" upper_x, upper_y = 1.0, scaled\n",
" else: #Rectangle in portrait mode\n",
" scaled = float(dy) / dx\n",
" upper_x, upper_y = scaled, 1.0\n",
"\n",
" assert 0 < upper_x <= 1.0, 'Calculated upper X coordinate invalid'\n",
" assert 0 < upper_y <= 1.0, 'Calculated upper Y coordinate invalid'\n",
"\n",
" return (0, 0, upper_x, upper_y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"preconditions in lines 3,5 and 6 catch invalid inputs:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test-Driven Development"
]
},
{
"attachments": {
"the_art_of_programming.PNG": {
"image/png": 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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"![the_art_of_programming.PNG](attachment:the_art_of_programming.PNG)"
]
},
{
"attachments": {
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},
"cell_type": "markdown",
"metadata": {},
"source": [
"Suppose we need to find where two o more time series overlap:\n",
"\n",
"![overlap.svg](attachment:overlap.svg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Novice vs. Expert\n",
"
\n",
"Novice Expert \n",
"1) Writes a fucntion range_overlap 1) Writes a short function for each test \n",
"2) Call it in 2 or 3 inputs 2) Writes a range_overlap function that should pass those tests \n",
"3) If wrong, fix and re-run test 3) If wrong, fix and re-run test functions \n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See how it works. First test functions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now the range_overlap function version 1:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we run test function:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pre- and Post-Conditions\n",
"\n",
"Suppose you are writing a function called average that calculates the average of the numbers in a list. What pre-conditions and post-conditions would you write for it? Compare your answer to your neighbor’s: can you think of a function that will pass your tests but not his/hers or vice versa?"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fixing and Testing\n",
"\n",
"Fix range_overlap. Re-run test_range_overlap after each change you make."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
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"outputs": [],
"source": []
}
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"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/Errors_and_exceptions.ipynb 0000664 0000000 0000000 00000020171 13565313113 0025251 0 ustar 00root root 0000000 0000000 {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Errors and Exceptions\n",
"\n",
"Two main questions to be answered\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" - How Python report errors?\n",
"\n",
"
- How I can handle errors in Python programs?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Every programmer makes Errors\n",
"\n",
"Errors in Python have a very specific form called \"traceback\". Traceback are made of levels wich are marked with special symbol -->"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# This code has an intentional error. You can type it directly or\n",
"# use it for reference to understand the error message below.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Traceback with two levels. Are read from bottom to top:\n",
"
- 1) Last level is the line with the error actually is\n",
"\n",
"
- 2) Upper level(s) show what function was executed to get the next level down"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tracebacks could be very long but important info is usually at the bottom"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Error categories"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All errors at the end of a traceback, are usually in the form:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Category: Some more info
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Where Categories are:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Syntax Errors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You forget a colon, add too many spaces, forget a parenthesis etc... Makes Python not to know how to read your program. That gives you:\n",
"SyntaxError
"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" - Remember: Both SyntaxError and IntentationError indicate a problem with the syntax of the program"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Variable Name Errors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Difficult to state a general case but common mistakes are:\n",
"
- You meant to use a string but forgot quotes\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
- You forgot to create a variable before using it\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
- There is a typo in a variable name (maybe because Python's Case Sensitivity)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### IndexErrors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Typically you try to access something out of bounds:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### File Errors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Typically two:\n",
"
- Trying to access a file that does not exist:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
- You try to read(write) a write-only(read-only) file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"### Reading Error Messages "
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Read the python code and the resulting traceback below, and answer the following questions:\n",
"\n",
"How many levels does the traceback have?\n",
"What is the function name where the error occurred?\n",
"On which line number in this function did the error occurr?\n",
"What is the type of error?\n",
"What is the error message?\n",
"How to fix it?\n",
"\n",
"# This code has an intentional error. Do not type it directly;\n",
"# use it for reference to understand the error message below.\n",
"def print_message(day):\n",
" messages = {\n",
" \"monday\": \"Hello, world!\",\n",
" \"tuesday\": \"Today is tuesday!\",\n",
" \"wednesday\": \"It is the middle of the week.\",\n",
" \"thursday\": \"Today is Donnerstag in German!\",\n",
" \"friday\": \"Last day of the week!\",\n",
" \"saturday\": \"Hooray for the weekend!\",\n",
" \"sunday\": \"Aw, the weekend is almost over.\"\n",
" }\n",
" print(messages[day])\n",
"\n",
"def print_friday_message():\n",
" print_message(\"Friday\")\n",
"\n",
"print_friday_message()\n",
"---------------------------------------------------------------------------\n",
"KeyError Traceback (most recent call last)\n",
"
in ()\n",
" 14 print_message(\"Friday\")\n",
" 15\n",
"---> 16 print_friday_message()\n",
"\n",
" in print_friday_message()\n",
" 12\n",
" 13 def print_friday_message():\n",
"---> 14 print_message(\"Friday\")\n",
" 15\n",
" 16 print_friday_message()\n",
"\n",
" in print_message(day)\n",
" 9 \"sunday\": \"Aw, the weekend is almost over.\"\n",
" 10 }\n",
"---> 11 print(messages[day])\n",
" 12\n",
" 13 def print_friday_message():\n",
"\n",
"KeyError: 'Friday'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Identifying Syntax Errors"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Read the code below, and (without running it) try to identify what the errors are.\n",
"Run the code, and read the error message. Is it a SyntaxError or an IndentationError?\n",
"Fix the error.\n",
"Repeat steps 2 and 3, until you have fixed all the errors.\n",
"\n",
"def another_function \n",
" print(\"Syntax errors are annoying.\")\n",
" print(\"But at least python tells us about them!\")\n",
" print(\"So they are usually not too hard to fix.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
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"nbformat": 4,
"nbformat_minor": 1
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scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/Exercises_part1.ipynb 0000664 0000000 0000000 00000034747 13565313113 0023771 0 ustar 00root root 0000000 0000000 {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part1: Patient data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" ## 1. Check Your Understanding\n",
"\n",
" Draw diagrams showing what variables refer to what values after each statement in the following program:\n",
"\n",
" mass = 47.5\n",
" age = 122\n",
" mass = mass * 2.0\n",
" age = age - 20\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Sorting Out References\n",
"\n",
"What does the following program print out?\n",
"```python\n",
"first, second = 'Grace', 'Hopper'\n",
"third, fourth = second, first\n",
"print(third, fourth)\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Slicing Strings\n",
"\n",
"A section of an array is called a slice. We can take slices of character strings as well:\n",
"```python\n",
"element = 'oxygen'\n",
"print('first three characters:', element[0:3])\n",
"print('last three characters:', element[3:6])\n",
"\n",
"first three characters: oxy\n",
"last three characters: gen\n",
"```\n",
"What is the value of element[:4]? What about element[4:]? Or element[:]?\n",
"\n",
"What is element[-1]? What is element[-2]?\n",
"\n",
"Given those answers, explain what element[1:-1] does."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4.Thin Slices\n",
"\n",
"The expression element[3:3] produces an empty string, i.e., a string that contains no characters. If data holds our array of patient data, what does data[3:3, 4:4] produce? What about data[3:3, :]?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Make Your Own Plot\n",
"\n",
"Create a plot showing the standard deviation (numpy.std) of the inflammation data for each day across all patients."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Moving Plots Around\n",
"\n",
"Modify the program to display the three plots on top of one another instead of side by side."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Stacking Arrays\n",
"\n",
"Arrays can be concatenated and stacked on top of one another, using NumPy’s vstack and hstack functions for vertical and horizontal stacking, respectively.\n",
"```python\n",
"import numpy\n",
"\n",
"A = numpy.array([[1,2,3], [4,5,6], [7, 8, 9]])\n",
"print('A = ')\n",
"print(A)\n",
"\n",
"B = numpy.hstack([A, A])\n",
"print('B = ')\n",
"print(B)\n",
"\n",
"C = numpy.vstack([A, A])\n",
"print('C = ')\n",
"print(C)\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The output is:\n",
"\n",
"```python\n",
"A =\n",
"[[1 2 3]\n",
" [4 5 6]\n",
" [7 8 9]]\n",
"B =\n",
"[[1 2 3 1 2 3]\n",
" [4 5 6 4 5 6]\n",
" [7 8 9 7 8 9]]\n",
"C =\n",
"[[1 2 3]\n",
" [4 5 6]\n",
" [7 8 9]\n",
" [1 2 3]\n",
" [4 5 6]\n",
" [7 8 9]]\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Write some additional code that slices the first and last columns of A, and stacks them into a 3x2 array. Make sure to print the results to verify your solution."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Change In Inflammation\n",
"\n",
"This patient data is longitudinal in the sense that each row represents a series of observations relating to one individual. This means that the change in inflammation over time is a meaningful concept.\n",
"\n",
"The numpy.diff() function takes a NumPy array and returns the differences between two successive values along a specified axis. For example, a NumPy array that looks like this:\n",
"\n",
"```python\n",
" npdiff = numpy.array([ 0, 2, 5, 9, 14])\n",
"\n",
" Calling numpy.diff(npdiff) would do the following calculations and put the answers in another array.\n",
"\n",
" [ 2 - 0, 5 - 2, 9 - 5, 14 - 9 ]\n",
"\n",
" numpy.diff(npdiff)\n",
"\n",
" array([2, 3, 4, 5])\n",
"```\n",
"Which axis would it make sense to use this function along?\n",
"\n",
"If the shape of an individual data file is (60, 40) (60 rows and 40 columns), what would the shape of the array be after you run the diff() function and why?\n",
"\n",
"How would you find the largest change in inflammation for each patient? Does it matter if the change in inflammation is an increase or a decrease?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part2: Loops"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. From 1 to N\n",
"\n",
"Python has a built-in function called range that creates a sequence of numbers. range can accept 1, 2, or 3 parameters.\n",
"\n",
"If one parameter is given, range creates an array of that length, starting at zero and incrementing by 1. For example, range(3) produces the numbers 0, 1, 2.\n",
"If two parameters are given, range starts at the first and ends just before the second, incrementing by one. For example, range(2, 5) produces 2, 3, 4.\n",
"If range is given 3 parameters, it starts at the first one, ends just before the second one, and increments by the third one. For exmaple range(3, 10, 2) produces 3, 5, 7, 9.\n",
"\n",
"Using range, write a loop that uses range to print the first 3 natural numbers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9. Computing Powers With Loops\n",
"\n",
"Exponentiation is built into Python:\n",
"```python\n",
"print(5 ** 3)\n",
"\n",
"125\n",
"```\n",
"Write a loop that calculates the same result as 5 ** 3 using multiplication (and without exponentiation)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 10. Reverse a String\n",
"\n",
"Knowing that two strings can be concatenated using the + operator, write a loop that takes a string and produces a new string with the characters in reverse order, so 'Newton' becomes 'notweN'."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part3: Lists"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 11. Turn a String Into a List\n",
"\n",
"Use a for-loop to convert the string “hello” into a list of letters:\n",
"\n",
"[\"h\", \"e\", \"l\", \"l\", \"o\"]\n",
"\n",
"Hint: You can create an empty list like this:\n",
"\n",
"my_list = []\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 12. Slicing From the End\n",
"\n",
"Use slicing to access only the last four characters of a string or entries of a list.\n",
"```python\n",
"string_for_slicing = \"Observation date: 02-Feb-2013\"\n",
"list_for_slicing = [[\"fluorine\", \"F\"], [\"chlorine\", \"Cl\"], [\"bromine\", \"Br\"], [\"iodine\", \"I\"], [\"astatine\", \"At\"]]\n",
"\n",
"\"2013\"\n",
"[[\"chlorine\", \"Cl\"], [\"bromine\", \"Br\"], [\"iodine\", \"I\"], [\"astatine\", \"At\"]]\n",
"```\n",
"Would your solution work regardless of whether you knew beforehand the length of the string or list (e.g. if you wanted to apply the solution to a set of lists of different lengths)? If not, try to change your approach to make it more robust."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 13. Non-Continuous Slices\n",
"\n",
"So far we’ve seen how to use slicing to take single blocks of successive entries from a sequence. But what if we want to take a subset of entries that aren’t next to each other in the sequence?\n",
"\n",
"You can achieve this by providing a third argument to the range within the brackets, called the step size. The example below shows how you can take every third entry in a list:\n",
"```python\n",
"primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]\n",
"subset = primes[0:12:3]\n",
"print(\"subset\", subset)\n",
"\n",
"subset [2, 7, 17, 29]\n",
"```\n",
"Notice that the slice taken begins with the first entry in the range, followed by entries taken at equally-spaced intervals (the steps) thereafter. If you wanted to begin the subset with the third entry, you would need to specify that as the starting point of the sliced range:\n",
"```python\n",
"primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]\n",
"subset = primes[2:12:3]\n",
"print(\"subset\", subset)\n",
"\n",
"subset [5, 13, 23, 37]\n",
"```\n",
"Use the step size argument to create a new string that contains only every other character in the string “In an octopus’s garden in the shade”\n",
"```python\n",
"beatles = \"In an octopus's garden in the shade\"\n",
"\n",
"I notpssgre ntesae\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 14. Overloading\n",
"\n",
"'+' usually means addition, but when used on strings or lists, it means “concatenate”. Given that, what do you think the multiplication operator * does on lists? In particular, what will be the output of the following code?\n",
"```python\n",
"counts = [2, 4, 6, 8, 10]\n",
"repeats = counts * 2\n",
"print(repeats)\n",
"```\n",
"Possible answers:\n",
"```python\n",
" [2, 4, 6, 8, 10, 2, 4, 6, 8, 10]\n",
" [4, 8, 12, 16, 20]\n",
" [[2, 4, 6, 8, 10],[2, 4, 6, 8, 10]]\n",
" [2, 4, 6, 8, 10, 4, 8, 12, 16, 20]\n",
"```\n",
"The technical term for this is operator overloading: a single operator, like + or *, can do different things depending on what it’s applied to."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part4: Multiple Files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 15. Generate Composite Statistics\n",
"\n",
"Use each of the files once to generate a dataset containing values averaged over all patients. This is the structure you should be using:\n",
"```python\n",
"filenames = glob.glob('inflammation*.csv')\n",
"composite_data = numpy.zeros((60,40))\n",
"for f in filenames:\n",
" # sum each new file's data into composite_data as it's read\n",
" #\n",
"# and then divide the composite_data by number of samples\n",
"composite_data /= len(filenames)\n",
"```\n",
"Then use pyplot to generate average, max, and min for all patients."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part5: Conditionals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 16. How Many Paths?\n",
"\n",
"Consider this code:\n",
"```python\n",
"if 4 > 5:\n",
" print('A')\n",
"elif 4 == 5:\n",
" print('B')\n",
"elif 4 < 5:\n",
" print('C')\n",
"```\n",
"Which of the following would be printed if you were to run this code? Why did you pick this answer?\n",
"\n",
" A\n",
" B\n",
" C\n",
" B and C\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 17. What Is Truth?\n",
"\n",
"True and False booleans are not the only values in Python that are true and false. In fact, any value can be used in an if or elif. After reading and running the code below, explain what the rule is for which values are considered true and which are considered false.\n",
"```python\n",
" if '':\n",
" print('empty string is true')\n",
" if 'word':\n",
" print('word is true')\n",
" if []:\n",
" print('empty list is true')\n",
" if [1, 2, 3]:\n",
" print('non-empty list is true')\n",
" if 0:\n",
" print('zero is true')\n",
" if 1:\n",
" print('one is true')\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 18. That’s Not Not What I Meant\n",
"\n",
"Sometimes it is useful to check whether some condition is not true. The Boolean operator not can do this explicitly. After reading and running the code below, write some if statements that use not to test the rule that you formulated in the previous challenge.\n",
"```python\n",
" if not '':\n",
" print('empty string is not true')\n",
" if not 'word':\n",
" print('word is not true')\n",
" if not not True:\n",
" print('not not True is true')\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 19. Close Enough\n",
"\n",
"Write some conditions that print True if the variable a is within 10% of the variable b and False otherwise. Compare your implementation with your partner’s: do you get the same answer for all possible pairs of numbers?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 20. In-Place Operators\n",
"\n",
"Python (and most other languages in the C family) provides in-place operators that work like this:\n",
"```python\n",
"x = 1 # original value\n",
"x += 1 # add one to x, assigning result back to x\n",
"x *= 3 # multiply x by 3\n",
"print(x)\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 21. Counting Vowels\n",
"\n",
"Write a loop that counts the number of vowels in a character string.\n",
"Test it on a few individual words and full sentences.\n",
"Once you are done, compare your solution to your neighbor’s. Did you make the same decisions about how to handle the letter ‘y’ (which some people think is a vowel, and some do not)?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/ProgPy_Analyzing_Patient_Data.ipynb 0000664 0000000 0000000 00000045146 13565313113 0026574 0 ustar 00root root 0000000 0000000 {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyzing Patient Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are studying inflammation in patients who have been given a new treatment for arthritis, and need to analyze the first dozen data sets of their daily inflammation. The data sets are stored in comma-separated values (CSV) format: each row holds information for a single patient, and the columns represent successive days"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want to:\n",
"\n",
" * load that data into memory,\n",
" * calculate the average inflammation per day across all patients, and\n",
" * plot the result.\n",
"\n",
"To do all that, we’ll have to learn a little bit about programming."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python as a calculator\n",
"\n",
"Any Python interpreter can be used as a calculator:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here you have an overview of math operators:\n",
"\n",
"| Operators | Operations | Examples |\n",
"| :--------:| :---------------- | ---------- |\n",
"| + | Addition | 2 + 3 = 5 |\n",
"| - | Substraction | 3 - 2 = 1 |\n",
"| * | Multiplication | 2 * 3 = 6 |\n",
"| / | Division | 3 / 2 = 1.5|\n",
"| // | Integer division | 7 // 3 = 2 |\n",
"| % | remainder division| 7 % 2 = 1 |\n",
"| ** | Exponent | 2**3 = 8 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This only outputs a value. To use values in any manner, we have to access them in memory by using a variable.\n",
"\n",
"In Python, variable names:\n",
"\n",
"* can include letters, digits, and underscores\n",
"* cannot start with a digit\n",
"* are case sensitive.\n",
"\n",
"\n",
"Let's assign a value 55 to variable named ``weight_kg``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use ``print`` to output the value of a variable. Is a built-in function:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can do some arithmetics with variables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What the ...?? why is not simply giving 121.0???
\n",
"\n",
"Well it looks 121 enought to me.... Ok let's continue. \n",
"\n",
"We can change the value of a variable by re-assigning it:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Variables as sticky notes with the name put in a particular value.
\n",
"\n",
"\n",
"We can create new variables from others:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What happens to ``weight_lb`` if you change ``weight_kg``?:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"Command %whos
shows memory stored variables defined along the notebook:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"What does the following program print out?\n",
"\n",
"\n",
"first, second = 'Grace', 'Hopper'\n",
"third, fourth = second, first\n",
"print(third, fourth)\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using libraries (modules)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we will load a library. Let's import Numpy. Numpy is important for fancy number manipulations:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Libraries provide additional functionality to the basic Python package. Importing too many libraries can sometimes complicate and slow down your programs - so we only import what we need for each program.\n",
"\n",
"Now we read the first file where patient data are stored. \n",
"\n",
"We will use functions which belong to a certain library and perform \"something\" for us. In this case we want to load a data file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The expression ``numpy.loadtxt(...)`` is a function call that asks Python to run the function loadtxt which belongs to the numpy library. This dotted notation is used everywhere in Python: the thing that appears before the dot contains the thing that appears after."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"``numpy.loadtxt`` has two parameters: the name of the file we want to read, and the delimiter that separates values on a line. These both need to be character strings (or strings for short), so we put them in quotes."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We did not assign the output of the function to any variable, so the notebook just dumped the ouptut to an ``Out[]`` cell. But, just the same way we asigned values to a variable, we can assing the array of values to an variable using the same syntax:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"No output here, as we just assigned, as we did before with ``weight_kg``. To print out the variable value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check what type of \"thing\" ``data`` variable refers to:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we create a variable, we also create information about it, as the attribute shape! That's very important!!\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we have 60 rows and 40 columns in ``data`` variable."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to get a single number from the array we provide an index in square brackets, just as we do in maths"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Selecting intervals of data by using ranges : . Caution here!:ranges seem closed but they are [) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is how index is assigned by python:\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is no need to include upper and lower bounds:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"what does ``data[3:3, 4:4]`` produce? \n",
"What about ``data[3:3, :]``?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Some array math\n",
"Basic arithmetics: The operation is performed in every element of the array"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Array to Array operations: The operation is made on each corresponding element of the arrays:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"More complex operations:\n",
"
The following is a method. Method are actions that can be made on the variable, Python knows how to!. The actions available depend on the variable type:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"Reminder: method vs attribute\n",
"
\n",
"\n",
"\n",
"\n",
"- attributes are descriptions of variable properties\n",
"
- methods are actions that Python knows to perform on the variable\n",
"
\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Several important methods for numpy variables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes is useful to make some partial statistics. For instance max inflammation for first patient of all days: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Oh wait... maybe I am creating to many variables... let's check:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes we can avoid the use of some variables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What if we need max inflammation for all patients at once?. Operations along all columns or rows at once:
\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then the calculation for the average inflammation over all patients per day is:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A quick check that the array is ok:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or if preferred, the average inflammation over all days per patient:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data visualization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ploting is great for insight! Matplotlib is the facto standar. Some basics:\n",
"
\n",
"Just pick the pyplot library of Matplotlib:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"IPython Magic!\n",
"
\n",
"\n",
"\n",
"The % indicates an IPython magic function - a funciton that is valid only within the notebook environment.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot data array (the average inflammation per day):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Average inflammation over time. Linear plot:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This plot does not correspond with the expected. The model predices a smooth sharper rise and a slower after fall. Let's have a look to the other statistics:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"None of them seems very likely to fit the model. Something could be wrong with the data\n",
"
\n",
"
\n",
"We can further improve the visualization by grouping plots in a multiplot:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional Exercises \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Slicing strings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is the value of element[:4]? What about element[4:]? Or element[:]?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is element[-1]? What is element[-2]?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given those answers, explain what element[1:-1] does."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
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scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/ProgPy_Functions.ipynb 0000664 0000000 0000000 00001740236 13565313113 0024176 0 ustar 00root root 0000000 0000000 {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions\n",
"\n",
"What if?\n",
"- We have thousand of datasets and do not want to make a plot for every single one\n",
"
- We want to reuse the code in other different datasets or parts of the program\n",
"
- We are collaborating with other people working in the same code\n",
"It is a bad idea:\n",
"
- Commenting in and out lines in the plotting code\n",
"
- Copy and paste chuncks of code here and there\n",
"
\n",
"
\n",
"That makes the code illegible, redundant and prone to errors. And impossible to share it with others.\n",
"
\n",
"\n",
"### The Solution: Package and reuse code as functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let’s start by defining a function fahr_to_celsius that converts temperatures from Fahrenheit to Kelvin:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"- Definition of the function contains the name and the parameters needed by it to do its job
\n",
"- Body is the part that is compiled and has the instructions to make the task done
\n",
"- Return statement returns the result of the compilation of the function's body
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calling the function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just call the name "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Composing functions \n",
"\n",
"We make now a function to convert Kelvin to Celsius:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What about Farh to Celsius? One of the main proposes of functions is reusability. So let's reuse one of them to build some \"neat\" code:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Exercise\n",
"\n",
"#### The old switcheroo\n",
"Which of the following would be printed if you were to run this code? Why did you pick this answer?\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"1) 7 3\n",
"2) 3 7\n",
"3) 3 3\n",
"4) 7 7\n",
"\n",
"a = 3\n",
"b = 7\n",
"\n",
"def swap(a, b):\n",
" temp = a\n",
" a = b\n",
" b = temp\n",
"\n",
"swap(a, b)\n",
"\n",
"print(a, b)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tidying up"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now make to functions to wrap the code and make the analysis of our inflammation data easier to read and reuse:\n",
"
\n",
"First we create a function to plot the data by feeding a filename"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#First function: Makes avg,max,min plots of a file. Note: No return or void function\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Second we build a function that detects anomalies in the data. Also needs a filename as parameter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Second function: Find anomalies in a file. Again void function\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we use them (Do not forget to import numpy and matplotlib.pyplot because they are needed by the code)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"#### Rescaling an array\n",
"\n",
"Write a function rescale that takes an array as input and returns a corresponding array of values scaled to lie in the range 0.0 to 1.0. (Hint: If $L$ and $H$ are the lowest and highest values in the original array, then the replacement for a value $v$ should be $(v-L) / (H-L)$.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When using functions we need to test they are working as expected. By following the example we will see how it is"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To test the function we used controlled data instead real one:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks great. Let's center the real data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can make further tests to see if results are correct:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also test that the standard deviation has not changed showing the difference between the two:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Documentation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Is important to document each function to remember ourselves and let others know what the function does"
]
},
{
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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"![image_crawling.PNG](attachment:image_crawling.PNG)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#You can use this kind of comment but there is a better way\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"attachments": {
"good_comments.PNG": {
"image/png": 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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"![good_comments.PNG](attachment:good_comments.PNG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining Defaults "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes we like our parameters to be optional and if in case they are not specified then default to a chosen value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can ommit the optional parameter, in that case it takes the given default:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's add another optional parameter to comment the result:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imagine you want only to comment it:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Arguments are matched from left to right:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How we can omit the default desired value then?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that:\n",
"\n",
"- data must be provided because it do not have any defaults.\n",
"
- Given no name to the parameter makes the assumption that it is the next non-default parameter ordered from left to right.\n",
"
- It can be avoided it specifying the name of the optional parameter. \n",
"
\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"#### Mixing Default and Non-Default Parameters\n",
"Given the following code:"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"def numbers(one, two=2, three, four=4):\n",
" n = str(one) + str(two) + str(three) + str(four)\n",
" return n\n",
"\n",
"print(numbers(1, three=3))\n",
"what do you expect will be printed? What is actually printed? What rule do you think Python is following?\n",
"\n",
"1) 1234\n",
"2) one2three4\n",
"3) 1239\n",
"4) SyntaxError"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### All required parameters must be placed before any default arguments. Simply because they are mandatory, whereas default arguments are not. Syntactically, it would be impossible for the interpreter to decide which values match which arguments if mixed modes were allowed. A SyntaxError is raised if the arguments are not given in the correct order:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given that, what does the following piece of code display when run?"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"def func(a, b=3, c=6):\n",
" print('a: ', a, 'b: ', b, 'c:', c)\n",
"\n",
"func(-1, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Readable functions\n",
"\n",
"Functions are supposed to pack code to reuse and for readability to yourself or others... Check these two functions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"It its important to keep code readable!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Combining strings\n",
"“Adding” two strings produces their concatenation: 'a' + 'b' is 'ab'. Write a function called fence that takes two parameters called original and wrapper and returns a new string that has the wrapper character at the beginning and end of the original. A call to your function should look like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Selecting characters from strings\n",
"If the variable s refers to a string, then s[0] is the string’s first character and s[-1] is its last. Write a function called outer that returns a string made up of just the first and last characters of its input. A call to your function should look like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing and documenting your function\n",
"Run the commands help(numpy.arange) and help(numpy.linspace) to see how to use these functions to generate regularly-spaced values, then use those values to test your rescale function. Once you’ve successfully tested your function, add a docstring that explains what it does."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding docstrings:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Variables inside and outside functions\n",
"What does the following piece of code display when run - and why?\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"f = 0\n",
"k = 0\n",
"\n",
"def f2k(f):\n",
" k = ((f-32)*(5.0/9.0)) + 273.15\n",
" return k\n",
"\n",
"f2k(8)\n",
"f2k(41)\n",
"f2k(32)\n",
"\n",
"print(k)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Readable code"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/ProgPy_Lists.ipynb 0000664 0000000 0000000 00000014320 13565313113 0023307 0 ustar 00root root 0000000 0000000 {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lists\n",
"They are used to store many values. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Difference with array?\n",
" - They are very similar but they do not belong to numpy, and do not have some of the attributes of numpy arrays, such as mean sd max min etc... Beside this they are pretty similar in concept\n",
"
- We create a list the same "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Selecting elements of a list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can loop over a list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List are also pretty similar to strings. There is an IMPORTANT difference between strings and lists: Elements in a list can be modified but not ones in a string:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### List of Lists or nested lists\n",
"Making a list whose elements are also lists."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example: Products in the shelves of a shop. We can print the results of the index operations in the images:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can print the first element of the list
x
:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Printing first element of x[0]
list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Modifying list contents\n",
"Many ways to change a string"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Caution here!: List are treated slightly conterintuitive:
\n",
"Pro-tip: Technically it is said that lists are copied \"by reference\" instead of \"by value\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"#### Turn string into a list\n",
"\n",
"Use a for-loop to convert the string “hello” into a list of letters:\n",
"\n",
"\n",
"[\"h\", \"e\", \"l\", \"l\", \"o\"]\n",
"
\n",
"\n",
"Hint: You can create an empty list like this:\n",
"\n",
"``my_list = []``"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can make a single copy of the original list using list built-in"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Reminder: When not to use \"list\" method?
\n",
"\n",
"\n",
"- Reference copy uses less resources \n",
"
- Is the default when relating lists through assignment\n",
"
- But whether you need to do a reference or a copy is your choice!!\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"#### Overloading\n",
"\n",
"`+` usually means addition, but when used on strings or lists, it means “concatenate”. Given that, what do you think the multiplication operator * does on lists? In particular, what will be the output of the following code?\n",
"\n",
"\n",
"counts = [2, 4, 6, 8, 10]\n",
"repeats = counts * 2\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/ProgPy_Loops.ipynb 0000664 0000000 0000000 00000011062 13565313113 0023305 0 ustar 00root root 0000000 0000000 {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Repeating Actions with Loops"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We found some suspicious features in our firs dataset \"inflammation-01.csv\". It is possible that other datasets are also affected by this?? We would like to check... Fast!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Repeat tasks:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have a lot of data sets and we have to tell the computer how to repeat things.\n",
"
\n",
"Let's first do something simpler: Show each character in a string!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bad approach: tedious and if the string is larger it misses some characters, and if it shorter:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### There is a better approach: Recursion\n",
"Recursion is made by for loops:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example 1. A better way:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The method supports strings of any lenght:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see it in action:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### General form of a for-loop:\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can choose any name we want for variables. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example 2. This loop repeatedly updates a variable that acts as counter. It is very usual to find this structure:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The for body is executed 5 times\n",
"\n",
"- 1st: vowel eqs a; lenght eqs 1\n",
"
- 2st: vowel eqs e; lenght eqs 2\n",
"
- etc.."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example 3. This loop overrides the value of a external variable:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Off-topic tip: Some loops frecuently used are wraped inside very useful methods, such as
len
which returns the length of the variable. The methods avaiable by default in Python without import a thing are called built-in methods"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From 1 to N"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python has a built-in function called range that creates a sequence of numbers. Using range, write a loop that uses range to print the first 3 odd natural numbers. Use the builtin help!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/ProgPy_Making_choices.ipynb 0000664 0000000 0000000 00000017517 13565313113 0025127 0 ustar 00root root 0000000 0000000 {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Making choices\n",
"\n",
"In last section we discovered something suspicios in our inflammation data by drawing some plots. How can we use Python\n",
"to automatically detect the anomalies we found and take an action for each of them?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By writing code that runs only certain conditions:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Conditionals\n",
"\n",
"A conditional is a piece of code that takes different actions depending on a condition:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imagine we want to determine if a certain number is lower or greater than 100. The flow of this conditional:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This scheme is achieved in Python by means of the if
/else
clause:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes there is no need to use a else, it depends on the logic of the problem:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes the flow of the decision is more complex. There are still multiple choices available \n",
"when the first condition is discarded.
\n",
"As an example imagine we want to know if a given number is positive negative or zero. We make use of the elif
clause:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note the \"==\". This symbol checks equality rather than \"=\" wich means assingment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercises\n",
"#### What Is Truth?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if '':\n",
" print('empty string is true')\n",
"if 'word':\n",
" print('word is true')\n",
"if []:\n",
" print('empty list is true')\n",
"if [1, 2, 3]:\n",
" print('non-empty list is true')\n",
"if 0:\n",
" print('zero is true')\n",
"if 1:\n",
" print('one is true')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### That’s Not Not What I Meant"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if not '':\n",
" print('empty string is not true')\n",
"if not 'word':\n",
" print('word is not true')\n",
"if not not True:\n",
" print('not not True is true')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Composed conditions\n",
"We can write complex conditions in the if clause by using concatenation of simple conditions by means of and
and or
.
\n",
"As in human language an and
condition is True if all its components are True:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Whereas an or
condition is True if any of it components is True:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"#### Close Enough\n",
"\n",
"Write some conditions that print True if the variable a is within 10% of the variable b and False otherwise. Compare your implementation with your partner’s: do you get the same answer for all possible pairs of numbers?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Checking our Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we are in conditions to check our suspicious data in the inflammation tests.
\n",
"First two files max inflammation seems to rise linearly. So let's write a code that check this behavior and show warning:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We saw a different issue in file 3. All minima where 0. So we write a code to check that:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can check now several files putting all toghether:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional Exercises\n",
"\n",
"### In-place operators"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Python (and most other languages in the C family) provides in-place operators that work like this:\n",
"\n",
"```x = 1 # original value\n",
"x += 1 # add one to x, assigning result back to x\n",
"x *= 3 # multiply x by 3\n",
"print(x)```\n",
"\n",
"\n",
"``6``\n",
"\n",
"Write some code that sums the positive and negative numbers in a list separately, using in-place operators. Do you think the result is more or less readable than writing the same without in-place operators?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Counting Vowels "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"* Write a loop that counts the number of vowels in a character string.\n",
"* Test it on a few individual words and full sentences.\n",
"* Once you are done, compare your solution to your neighbor’s. Did you make the same decisions about how to handle the letter ‘y’ (which some people think is a vowel, and some do not)?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/ProgPy_Multiple_files.ipynb 0000664 0000000 0000000 00000004424 13565313113 0025172 0 ustar 00root root 0000000 0000000 {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multiple files\n",
"\n",
"We almost have all the tools needed to process all files. We need an extra library that will make the task easier:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Glob has a function called glob
that finds files and directories whose names match a pattern:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Output is an unsorted list of filenames to loop over. We can sort it using sorted
built-in"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can bring together all loops, lists and loops over lists, and use it to process all files: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting Differences\n",
"\n",
"Plot the difference between the average of the first dataset and the average of the second dataset, i.e., the difference between the leftmost plot of the first two figures."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/Python_Images/ 0000775 0000000 0000000 00000000000 13565313113 0022414 5 ustar 00root root 0000000 0000000 scw-be25570a09191471676779c4d6586fc701419a97/SCW-2019/Python_Images/good_comments.PNG 0000664 0000000 0000000 00000532170 13565313113 0025627 0 ustar 00root root 0000000 0000000 PNG
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