• 1 Fundamentals (Week 1)
  • 1.1 Orientation
  • 1.2 Jupyter commands
  • 1.3 Variables and Assignment
  • 1.4 Data Types and Type Conversion
  • 1.5 Built-in Functions and Help
  • 1.6 Libraries
  • 1.7 Lists
  • 1.8 For Loops
  • 1.9 Strings and methods
  • 1.10 Dictionaries
  • 1.11 (Optional) Other containers
• 2 Data manipulation with Pandas (Week 2)
  • 2.1 (Optional) Review collections
  • 2.2 A very brief introduction to NumPy
  • 2.3 A very brief introduction to Pandas
  • 2.4 (Optional) Where are we?
  • 2.5 Reading tabular data into data frames
  • 2.6 Data frames are objects that can tell you about their contents
  • 2.7 Subsetting Data
  • 2.8 Filtering (i.e. masking) data on contents
  • 2.9 Working with missing data
  • 2.10 Sorting and grouping
  • 2.11 Write output
  • 2.12 Working with multiple tables
  • 2.13 (Optional) Text processing in Pandas
  • 2.14 (Optional) Adding rows to DataFrames
  • 2.15 (Optional) Scientific Computing Libraries
  • 2.16 (Optional) Things we didn’t talk about
  • 2.17 (Optional) Pandas method chaining in the wild
  • 2.18 (Optional) Introspecting on the DataFrame object
  • 2.19 (Carpentries version) Group By: split-apply-combine
• 3 Building Programs (Week 3)
  • 3.1 Notebooks vs Python scripts
  • 3.2 (Optional) Python from the terminal
  • 3.3 Looping Over Data Sets
  • 3.4 Conditionals
  • 3.5 (Optional) Generic file handling
  • 3.6 (Optional) Text processing and data cleanup
  • 3.7 Writing Functions
  • 3.8 (Carpentries version) Conditionals
• 4 Visualization with Matplotlib and Seaborn (Week 4)
  • 4.1 Orientation
  • 4.2 Plotting with Matplotlib
  • 4.3 Visualization Strategy
  • 4.4 Fast visualization and theming with Seaborn
  • 4.5 (Optional) How Matplotlib works
• 5 Special Topics
  • 5.1 Environments
  • 5.2 Working with unstructured files
  • 5.3 Exception handling
  • 5.4 Performance and profiling
  • 5.5 Reducing memory usage
  • 5.6 Other optional topics
• 6 Endnotes
  • 6.1 Credits
  • 6.2 References
  • 6.3 Data Sources

Fundamentals (Week 1)

Orientation

What programming language should I use?

1. Use the language that your friends use (so you can ask them for help)
2. Use a language that has a community of practice for your desired use case (you can find documentation, bug reports, sample code, etc.)
3. Use a language that is “best” by some technical definition

Python is pretty good at lots of things

• “Glue” language intended to replace shell and Perl
• Concise, readable, good for rapid prototyping
• Access to linear algebra libraries in FORTRAN/C → user-friendly numeric computing
• General purpose, not just an academic language; we will spend more time on some of the general purpose aspects.

Literate programming and notebooks

• Blend code, documentation, and visualization
• Good for trying things, demos
• Bad for massive or long-running processes
• You can export notebooks as .py files when they outgrow the notebook format

Jupyter commands

How to start Jupyter Lab

1. Method 1

   1. Open Anaconda Navigator
   2. Run Jupyter Lab

2. Method 2 Open Terminal (MacOS/Linux) or Anaconda Prompt (Windows)

   cd Desktop/data
   jupyter lab
   

Navigation

• Navigate to where you want to be before creating new notebook
• Rename your notebook to something informative
• Use drag-and-drop interface to move .ipynb file to new location

Writing code

1. Execute cell with CTRL-Enter

   3 + 7
   

2. Execute cell and move to new cell with Shift-Enter

   # This is a comment
   print("hello")
   

3. Cells can be formatted as Code or Markdown

4. Many keyboard shortcuts are available; see https://gist.github.com/discdiver/9e00618756d120a8c9fa344ac1c375ac

5. (Optional) Jupyter Lab understands (some) terminal commands

   ls
   

6. (Optional) Jupyter Lab (IPython, actually) has “magic” commands that start with % (line) or %% (cell)

   # Print current items in memory
   %dirs
   
   # Get environment variables
   %env
   
   # Cell magic: Run bash in a subprocess
   %%bash
   
   # Cell magic: Time cell execution
   %%time
   

   • The magic command must be in the first line of the cell (no comments)
   • Some commands are not available on Windows (e.g. %%bash)

Variables and Assignment

Use variables to store values

Variables are names for values.

first_name = 'Derek'
age = 42

Rules for naming things

1. Can only contain letters, digits, and underscore
2. Cannot start with a digit
3. Are case sensitive: age, Age and AGE

Use print() to display values

print(first_name, 'is', age, 'years old')

• Functions are verbs
• Functions end in ()
• Functions take arguments (i.e. they do stuff with the values that you give them)
• print() useful for tracking progress, debugging

Jupyter Lab will always echo the last value in a cell

1. Python will evaluate and echo the last item

   first_name
   age
   

2. If you want to see multiple items, you should explicitly print them

   print(first_name)
   print(age)
   

(Optional) Variables must be created before they are used

# Prints an informative error message; more about this later
print(last_name)

Variables can be used in calculations

print(age)
age = age + 3
print(age)

Challenge: Variables only change value when something is assigned to them

Order of operations matters!

first = 1
second = 5 * first
first = 2

# What will this print?
print('first:', first)
print('second:', second)

Data Types and Type Conversion

Every value has a type

Most data is text and numbers, but there are many other types.

1. Integers: whole numbers (counting)
2. Floats: real numbers (math)
3. Strings: text
4. Files
5. Various collections (lists, sets, dictionaries, data frames, arrays)
6. More abstract stuff (e.g., database connection)

The type determine what operations you can perform with a given value

1. Example 1: Subtraction makes sense for some kinds of data but not others

   print(5 - 3)
   print('hello' - 'h')
   

2. Example 2: Some things have length and some don’t Note that we can put functions inside other functions!

   print(len('hello'))
   print(len(5))
   

Use the built-in function type() to find the type of a value

1. Variables point to values

   print(type(53), type(age))
   

2. There are many types

   print(type(3.12), type("hello"), type(True), type([]))
   

(Optional) Python is strongly-typed.

1. It will (mostly) refuse to convert things automatically. You can explicitly convert data to a different type.

2. Can’t do math with text

   1 + '2'
   

3. If you have string data, you can explicitly convert it to numeric data…

   print(1 + float('2'))
   print(1 + int('2'))
   

4. …and vice-versa

   text = str(3)
   
   print(text)
   print(type(text))
   

5. The exception is mathematical operations with integers and floats.

   int_sum = 3 + 4
   mixed_sum = 3 + 4.0
   
   print(type(int_sum))
   print(type(mixed_sum))
   

6. What’s going on under the hood?

   1. int, float, and str are types. More precisely, they are classes.
   2. int(), float(), and str() are functions that create new instances of their respective classes. The argument to the creation function (e.g., '2') is the raw material for creating the new instance.

7. This can work for more complex data types as well, e.g. Pandas data frames and Numpy arrays.

Challenge: Explain what each operator does

# Floor
print('5 // 3:', 5 // 3)

# Floating point
print('5 / 3:', 5 / 3)

# Modulus (remainder)
print('5 % 3:', 5 % 3)

Built-in Functions and Help

A function may take zero or more arguments

print('before')
print()
print('after')

Functions can have optional arguments

# By default, we round to the nearest integer
round(3.712)

# You can optionally specify the number of significant digits
round(3.712, 1)

Use the built-in function help() to get help for a function

1. View the documentation for round()

   help(round)
   

   • 1 mandatory argument
   • 1 optional argument with a default value: ndigits=None

2. You can proved arguments implicitly by order, or explicitly in any order

   # You can optionally specify the number of significant digits
   round(4.712823, ndigits=2)
   

3. Getting more help

   • Python.org tutorial
   • Standard library reference (we will discuss libraries in the next section)
   • References section of this document
   • Stack Overflow

4. Use comments to add documentation to your own programs

   # This line isn't executed by Python
   print("This cell has many comments")   # The rest of this line isn't executed either
   

Every function returns something

1. Collect the results of a function in a new variable. This is one of the ways we build complex programs.

   # You can optionally specify the number of significant digits
   rounded_num = round(4.712823, ndigits=2)
   print(rounded_num)
   

   result = len("hello")
   print(result)
   

2. (Optional) Some function only have “side effects”; they return None

   result = print("hello")
   print(result)
   # print(type(result))
   

(Optional) Functions will typically generalize in sensible ways

1. max() and min() do the intuitively correct thing with numerical and text data

   print(max(1, 2, 3))
   print(min('a', 'A', '0'))       # sort order is 0-9, A-Z, a-z
   

2. Mixed numbers and text aren’t meaningfully comparable

   max(1, 'a')
   

(Optional) Python produces informative error messages

1. Python reports a syntax error when it can’t understand the source of a program

   name = 'Bob
   age = = 54
   print("Hello world"
   

2. Python reports a runtime error when something goes wrong while a program is executing

Beginner Challenge: What happens when?

Explain in simple terms the order of operations in the following program: when does the addition happen, when does the subtraction happen, when is each function called, etc. Extra credit: What is the final value of radiance?

radiance = 1.0
radiance = max(2.1, 2.0 + min(radiance, 1.1 * radiance - 0.5))

Libraries

Most of the power of a programming language is in its libraries

https://docs.python.org/3/library/index.html

A program must import a library module before using it

import math

print(math.pi)
print(math.cos(math.pi))

• Refer to things from the module as module-name.thing-name
• Python uses “.” to mean “part of” or “belongs to”.

Use help() to learn about the contents of a library module

help(math)                      # user friendly

dir(math)                       # brief reminder, not user friendly

(Optional) Import shortcuts

1. Import specific items from a library module. You want to be careful with this. It’s safer to keep the namespace.

   from math import cos, pi
   
   cos(pi)
   

2. Create an alias for a library module when importing it

   import math as m
   
   print(m.cos(m.pi))
   

Python has opinions about how to write your programs

import this

Lists

Lists are the central data structure in Python; we will explain many things by making analogies to lists.

A list stores many values in a single structure

fruits = ["apple", "banana", "cherry", "date", "elderberry", "fig"]
print(fruits)
print(len(fruits))

Lists are indexed by position, counting from 0

# First item
print(fruits[0])

# Fifth item
print(fruits[4])

You can get a subset of the list by slicing it

1. You slice a list from the start position up to, but not including, the stop position

   print(fruits[0:3])
   print(fruits[2:5])
   

2. You can omit the start position if you’re starting at the beginning…

   # Two ways to get the first 5 items
   print(fruits[0:5])
   print(fruits[:5])
   

3. …and you must omit the end position if you’re going to the end (otherwise it’s up to, but not including, the end!). This is useful if you don’t know how long the list is:

   # Everything but the first 3 items
   print(fruits[3:])
   

4. You can add an optional step interval (every 2nd item, every 3rd item, etc.)

   # First 5 items, every other item
   print(fruits[0:5:2])
   
   # Every third item
   print(fruits[::3])
   

(Optional) Why are lists indexed from 0?

cf. https://stackoverflow.com/a/11364711

1. Slice endpoints are compliments In both cases, the number you see represents what you want to do.

   # Get the first two items
   print(fruits[:2])
   
   # Get everything except the first two items
   print(fruits[2:])
   

2. For non-negative indices, the length of a slice is the difference of the indices

   len(fruits[1:3]) == 2
   

Challenge: Some other properties of indexes

Try these statements. What are they doing? Can you explain the differences in their behavior?

fruits[-1]
fruits[20]
fruits[-3:]

Solution

1. You can count backwards from the end with negative integers
2. Indexing beyond the end of the collection is an error

Lists are mutable

1. You can replace a value at a specific index location

   fruits[0] = "apricot"
   print(fruits)
   

2. Add an item to list with append(). This is a method of the list (more on this later!).

   fruits.append("grape")
   print(fruits)
   

3. Add the items from one list to another with extend()

   more_fruits = ["honeydew", "imbe", "jackfruit"]
   
   # Add all of the elements of more_fruits to fruits
   fruits.extend(more_fruits)
   print(fruits)
   

Many functions take collections as arguments

# Assessing the overall productivity of our wide receivers
receiving_yards = [450, 370, 870, 150]
mean_yards = sum(receiving_yards)/len(receiving_yards)
print(mean_yards)

(Optional) Removing items from a list

1. Use del to remove an item at an index location

   print(more_fruits)
   del more_fruits[1]
   print(more_fruits)
   

2. Use pop() to remove the last item and assign it to a variable. This is useful for destructive iteration.

   f = fruits.pop()
   
   print('Last fruit in list:', f)
   print(fruits)
   

Lists can contain anything

1. You can put anything in a list

   ages = ['Derek', 42, 'Bill', 24, 'Susan', 37]
   

2. (Optional) You could use this to manage complex data, but you shouldn’t

   # Get first pair
   print(ages[0:2])
   
   # Get all the names
   print(ages[::2])
   
   # Get all the ages
   print(ages[1::2])
   

3. You can put lists inside other lists

   ages.append(more_fruits)
   
   # List in our list
   print(ages)
   
   # The last item is a list
   print(ages[-1])
   
   # Get an item from that list
   print(ages[-1][0])
   

(Optional) Challenge: Reversing a list

Create a new list that contains all of the items from fruits in the reverse order.

Solution

rev_fruits = fruits[len(fruits)-1::-1]
print(rev_fruits)

For Loops

Usually you don’t need to find list items by index. What you actually want to do is go through each item in the list and use it for something.

A for loop executes commands once for each value in a collection

“For each thing in this group, do these operations”

for fruit in fruits:
    print(fruit)

• A for loop is made up of a collection, a loop variable, and a body
• The collection, fruits, is what the loop is being run on.
• The loop variable, fruit, is what changes for each iteration of the loop (i.e. the “current thing”)
• The body, print(fruit), specifies what to do for each value in the collection.

Whitespace is syntactically meaningful in Python!

for fruit in fruits:
print(fruit)

Loop variables can be called anything

for bob in fruits:
    print(bob)

The body of a loop can contain many statements

primes = [2, 3, 5]
for p in primes:
    squared = p ** 2
    cubed = p ** 3
    print(p, squared, cubed)

Create a new collection from an existing collection

We will learn how to vectorize this when we get to Numpy and Pandas

prime_exponents = []
for p in primes:
   prime_exponents.append(p**2)

print(prime_exponents)

Challenge: Accumulation

Get the total length of all the words in the fruits list.

Solution 1

total = 0
for f in fruits:
    total = total + len(f)

print(total)

Solution 2

lengths = []
for f in fruits:
    lengths.append(len(f))

print(sum(lengths))

Solution 3

sum(len(f) for f in fruits)

(Optional) Helpful tools for iteration

1. Use range() to iterate over a sequence of numbers

   for number in range(0, 3):
       print(number)
   

   • range() produces numbers on demand (a “generator” function)
   • useful for tracking progress

2. Use enumerate() to iterate over a sequence of items and their positions

   for number, fruit in enumerate(fruits):
       print(number, ":", fruit)
   

3. Use functional programming idioms

   • Comprehensions: generator, list, dictionary
   • itertools library

4. Test to see if an object is iterable

   # Lists, dictionaries, and strings are iterable
   hasattr(location, "__iter__")
   
   #Integers are not iterable
   hasattr(5, "__iter__")
   

Strings and methods

Strings are (kind of) like lists

1. Strings are indexed like lists

   # Use an index to get a single character from a string
   fruit = "gooseberry"
   print(fruit[0])
   print(fruit[0:3])
   

2. Strings have length

   len(fruit)
   

But! Strings are immutable

1. Can’t change a string in place

   fruit[0] = 'G'
   

2. Solution: String methods create a new string

   fruit_title = fruit.capitalize()
   print(fruit_title)
   

Use the built-in string methods to clean up data

bad_str1 = "  Hello world!   "
bad_str2 = "|...goodbye cruel world|"

good_str1 = bad_str1.strip()
good_str2 = bad_str2.strip("|")

print(good_str1, "\n", good_str2)

(Optional) Methods are functions that belong to objects

1. An object packages data together with functions that operate on that data. This is a very common organizational strategy in Python.

   sentence = "Hello world!"
   
   # Call the swapcase method on the my_string object
   print(sentence.swapcase())
   

2. You can chain methods into processing pipelines

   print(sentence.isupper())          # Check whether all letters are uppercase
   print(sentence.upper())            # Capitalize all the letters
   

   # The output of upper() is as string; you can use more string methods on it
   sentence.upper().isupper()
   

3. You can view an object’s attributes (i.e. methods and fields) using help() or dir(). Some attributes are “private”; you’re not supposed to use these directly.

   # More verbose help
   help(str)
   

   # The short, short version
   dir(my_string)
   

(Optional) Challenge: Putting it all together

You want to iterate through the fruits list in a random order. For each randomly-selected fruit, capitalize the fruit and print it.

1. Which standard library module could help you? https://docs.python.org/3/library/
2. Which function would you select from that module? Are there alternatives?
3. Try to write a program that uses the function.

Solution 1 (shuffle)

import random

random.shuffle(fruits)

for f in fruits:
    print(f.title())

Solution 2 (sample)

random_fruits = random.sample(fruits, len(fruits))

for f in random_fruits:
    print(f.title())

(Optional) Beginner Challenge: From Strings to Lists and Back

1. Given this Python code…

   print('string to list:', list('tin'))
   print('list to string:', ''.join(['g', 'o', 'l', 'd']))
   

2. What does list('some string') do?

3. What does '-'.join(['x', 'y', 'z']) generate?

Dictionaries

Dictionaries are sets of key/value pairs. Instead of being indexed by position, they are indexed by key.

ages = {'Derek': 42,
        'Bill': 24,
        'Susan': 37}

ages["Derek"]

Update dictionaries by assigning a key/value pair

1. Update a pre-existing key with a new value

   ages["Derek"] = 44
   
   print(ages)
   

2. Add a new key/value pair

   ages["Beth"] = 19
   print(ages)
   

Check whether the dictionary contains an item

1. Does a key already exist?

   "Derek" in ages
   

2. (Optional) Does a value already exist (you generally don’t want to do this; keys are unique but values are not)?

   24 in ages.values()
   

(Optional) Delete an item using del or pop()

print("Original dictionary", ages)
del ages["Derek"]
print("1st deletion", ages)

susan_age = ages.pop("Susan")
print("2nd deletion", ages)
print("Returned value", susan_age)

Dictionaries are the natural way to store tree-structured data

As with lists, you can put anything in a dictionary.

location = {'latitude': [37.28306, 'N'],
            'longitude': [-120.50778, 'W']}

print(location['longitude'][0])

Dictionary iteration

1. Iterate over key: value pairs

   for key, val in ages.items():
       print(key, ":", val)
   

2. (Optional) You can iterate over keys and values separately

   # Iterate over keys; you can also explicitly call .keys()
   for key in ages:
       print(key)
   
   # Iterate over values
   for val in ages.values():
       print(val)
   

3. (Optional) Iteration can be useful for unpacking complex dictionaries

   for key, val in location.items():
       print(key, 'is', val[0], val[1])
   

Challenge: Generate a dictionary

1. You have the following key/value pairs:

   'Derek' 42
   'Bill' 24
   'Susan' 37
   

2. Create a dictionary that contains all of them. You may find the following useful:

   help(dict)
   help(zip)
   

Solution 1 of many

names = ["Derek", "Bill", "Susan"]
ages = [42, 24, 37]

ages_dict = dict(zip(names, ages))

(Optional) Advanced Challenge: Convert a list to a dictionary

How can you convert our list of names and ages into a dictionary? Hint: You will need to populate the dictionary with a list of keys and a list of values.

# Starting data
ages = ['Derek', 42, 'Bill', 24, 'Susan', 37]

# Get dictionary help
help({})

Solution

ages_dict = dict(zip(ages[::2], ages[1::2]))

(Optional) Other containers

1. Tuples
2. Sets

Data manipulation with Pandas (Week 2)

(Optional) Review collections

Lists and dictionaries

1. Reference item by index/key
2. Insert item by index/key
3. Indices/keys must be unique

Strings

1. Similar to lists: Reference item by index, have length
2. Immutable, so need to use string methods
3. '/'.join() is a very useful method

A very brief introduction to NumPy

Introductory documentation: https://numpy.org/doc/stable/user/quickstart.html

1. NumPy is the linear algebra library for Python

   import numpy as np
   
   # Create an array of random numbers
   m_rand = np.random.rand(3, 4)
   print(m_rand)
   

2. Arrays are indexed like lists

   print(m_rand[0,0])
   

3. Arrays have attributes

   print(m_rand.shape)
   print(m_rand.size)
   print(m_rand.ndim)
   

4. Arrays are fast but inflexible - the entire array must be of a single type.

Linear algebra with NumPy

Don’t use for loops with DataFrames or Numpy matrices. There is almost always a faster vectorized function that does what you want.

x = np.arange(9)
y = np.arange(9)

print(x)
print(y)

1. Operations are element-wise by default

   print(x * y)
   

2. Matrix-wise operations (e.g. dot product) use NumPy functions

   # Use a special operator if it exists
   print(x @ y)
   
   # Otherwise, use a numpy function
   print(np.dot(x, y))
   

3. You can rearrange the same array into different configurations

   # Use method chaining to link actions together
   x1 = x.reshape(3,3)
   x2 = x.reshape(9,1)
   
   print(x1)
   print(x2)
   

4. (Optional) Matlab gotcha: 1-D arrays have no transpose

   print(x)
   print(x.T)
   print(x.reshape(-1,1))
   

Challenge: Matrix operations

1. Create a 3x3 matrix containing the numbers 0-8. Hint: Consult the NumPy Quickstart documentation here: https://numpy.org/doc/stable/user/quickstart.html
2. Multiply the matrix by itself (element-wise).
3. Multiply the matrix by its transpose.
4. Divide the matrix by itself. What happens?

Solutions

# Use method chaining to link actions together
x = np.arange(9).reshape(3,3)

print(x * x)
print(x * x.T)
print(x / x)

A very brief introduction to Pandas

1. Pandas is a library for working with spreadsheet-like data (“DataFrames”)
2. A DataFrame is a collection (dict) of Series columns
3. Each Series is a 1-dimensional NumPy array with optional row labels (dict-like, similar to R vectors)
4. Therefore, each series inherits many of the abilities (linear algebra) and limitations (single data type) of NumPy

(Optional) Where are we?

Python provides functions for working with the file system.

import os

# print current directory
print("Current working directory:", os.getcwd())
# print all of the files and directories
print("Working directory contents:", os.listdir())

These provide a rich Python alternative to shell functions

# Get 1 level of subdirectories
print("Just print the sub-directories:", sorted(next(os.walk('.'))[1]))

# Move down one directory
os.chdir("data")
print(os.getcwd())

# Move up one directory
os.chdir("..")
print(os.getcwd())

Reading tabular data into data frames

Import tabular data using the Pandas library

import pandas as pd

data = pd.read_csv('data/gapminder_gdp_oceania.csv')
print(data)

# Jupyter Lab will give you nice formatting if you echo
data

• File and directory names are strings
• You can use relative or absolute file paths

Use index_col to use a column’s values as row indices

Rows are indexed by number by default (0, 1, 2,….). For convenience, we want to index by country:

data = pd.read_csv('data/gapminder_gdp_oceania.csv', index_col='country')
print(data)

• By default, rows are indexed by position, like lists.
• Setting the index_col parameter lets us index rows by label, like dictionaries. For this to work, the index column needs to have unique values for every row.
• You can verify the contents of the CSV by double-clicking on the file in Jupyter Lab

Pandas help files are dense; you should prefer the online documentation

1. Main documentation link: https://pandas.pydata.org/docs/user_guide/index.html
2. Pandas can read many different data formats: https://pandas.pydata.org/docs/user_guide/io.html

Data frames are objects that can tell you about their contents

Data frames have methods (i.e. functions) that perform operations using the data frame’s contents as input

1. Use .info() to find out more about a data frame

   data.info()
   

2. Use .describe() to get summary statistics about data

   data.describe()
   

3. (Optional) Look at the first few rows

   data.head(1)
   

Data frames have fields (i.e. variables) that hold additional information

A “field” is a variable that belongs to an object.

1. The .index field stores the row Index (list of row labels)

   print(data.index)
   

2. The .columns field stores the column Index (list of column labels)

   print(data.columns)
   

3. The .shape variable stores the matrix shape

   print(data.shape)
   

4. Use DataFrame.T to transpose a DataFrame. This doesn’t copy or modify the data, it just changes the caller’s view of it.

   print(data.T)
   print(data.T.shape)
   

(Optional) Pandas introduces some new types

# DataFrame type
type(data)
type(data.T)

# Series type
type(data['gdpPercap_1952'])

# Index type
type(data.columns)

• You can convert data between NumPy arrays, Series, and DataFrames
• You can read data into any of the data structures from files or from standard Python containers

(Optional) Beginner Challenge

1. Read the data in gapminder_gdp_americas.csv into a variable called americas and display its summary statistics.
2. After reading the data for the Americas, use help(americas.head) and help(americas.tail) to find out what DataFrame.head and DataFrame.tail do.
   1. How can you display the first three rows of this data?
   2. How can you display the last three columns of this data? (Hint: You may need to change your view of the data).
3. As well as the read_csv function for reading data from a file, Pandas provides a to_csv function to write DataFrames to files. Applying what you’ve learned about reading from files, write one of your DataFrames to a file called processed.csv. You can use help to get information on how to use to_csv.

Solution

americas = pd.read_csv('data/gapminder_gdp_americas.csv', index_col='country')
americas.describe()
americas.head(3)
americas.T.tail(3)
americas.to_csv('processed.csv')

Subsetting Data

Treat the data frame as a matrix and select values by position

Use DataFrame.iloc[..., ...] to select values by their (entry) position. The i in iloc stands for “index”.

#import pandas as pd
data = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')

data.iloc[0,0]

Treat the data frame as a table and select values by label

This is most common way to get data

1. Use DataFrame.loc[..., ...] to select values by their label

   # This returns a value
   data.loc["Albania", "gdpPercap_1952"]
   

Shorten the column names using vectorized string methods

1. Standard Python has string methods

   big_hello = "hello".title()
   print(big_hello)
   
   help("hello".title)
   print(dir("hello"))
   

2. Pandas data frames are complex objects

   print(data.columns)
   print(dir(data.columns.str))
   

3. Use built-in methods to transform the entire data frame

   # The columns index can update all of its values in a single operation
   data.columns = data.columns.str.strip("gdpPercap_")
   print(data.columns)
   

Use list slicing notation to get subsets of the data frame

1. Select multiple columns or rows using .loc and a named slice. This generalizes the concept of a slice to include labeled indexes.

   # This returns a DataFrame
   data.loc['Italy':'Poland', '1962':'1972']
   

2. Use : on its own to mean all columns or all rows. This is Python’s usual slicing notation, which allows you to treat data frames as multi-dimensional lists.

   # This returns a DataFrame
   data.loc['Italy':'Poland', :]
   

3. (Optional) If you want specific rows or columns, pass in a list

   data.loc[['Italy','Poland'], :]
   

4. (Optional) .iloc follows list index conventions (“up to, but not including)”, but .loc does the intuitive right thing (“A through B”)

   index_subset = data.iloc[0:2, 0:2]
   label_subset = data.loc["Albania":"Belgium", "1952":"1962"]
   
   print(index_subset)
   print(label_subset)
   

5. Result of slicing can be used in further operations

   subset = data.loc['Italy':'Poland', '1962':'1972']
   
   print(subset.describe())
   print(subset.max())
   

6. (Optional) Insert new values using .at (for label indexing) or .iat (for numerical indexing)

   subset.at["Italy", "1962"] = 2000
   print(subset)
   

Challenge: Collection types

1. Calculate subset.max() and assign the result to a variable. What kind of thing is it? What are its properties?
2. What is the maximum value of the new variable? Can you determine this without creating an intermediate variable?

Solution

1. Pandas always drills down to the most parsimonious representation. On one hand, this is convenient; on the other, it violates the Pythonic expectation for strong types.

   Shape of data selection	Pandas return type
   2D	DataFrame
   1D	Series
   0D	single value

2. Use method chaining

   print(subset.max().max())
   
   # Alternatively
   print(subset.max(axis=None))
   

(Optional) Filter on label properties

1. .filter() always returns the same type as the original item, whereas .loc and .iloc might return a data frame or a series.

   italy = data.filter(items=["Italy"], axis="index")
   print(italy)
   print(type(italy))
   

2. .filter() is a general-purpose, flexible method

   help(data.filter)
   data.filter(like="200", axis="columns")
   data.filter(like="200", axis="columns").filter(items=["Italy"], axis="index")
   

Filtering (i.e. masking) data on contents

Use comparisons to select data based on value

1. Show which data frame elements match a criterion.

   # Which GDPs are greater than 10,000?
   subset > 10000
   

2. Use the criterion match to filter the data frame’s contents. This uses index notation:

   df = subset[subset > 10000]
   print(df)
   

   1. subset > 10000 returns a data frame of True/False values
   2. subset[subset > 10000] filters its contents based on that True/False data frame. All True values are returned, element-wise.
   3. This section is more properly called “Masking Data,” because it involves operations for overlaying a data frame’s values without changing the data frame’s shape. We don’t drop anything from the data frame, we just replace it with NaN.

3. (Optional) Use .where() method to find elements that match the criterion:

   df = subset.where(subset > 10000)
   print(df)
   

You can filter using any method that returns a data frame

For example, get the GDP for all countries greater than the median.

# Get the overall median
subset.median()          # Returns Series
subset.median(axis=None) # Returns single valuey

# Which data points are above the median
subset > subset.median(axis=None)

# Return the masked data set
subset[subset > subset.median(axis=None)]

Use method chaining to create final output without creating intermediate variables

# The .rank() method turns numerical scores into ranks
data.rank()

# Get mean rank over time and sort the output
mean_rank = data.rank().mean(axis=1).sort_values()
print(mean_rank)

Working with missing data

By default, most numerical operations ignore missing data

Examples include min, max, mean, std, etc.

1. Missing values ignored by default

   print("Column means")
   print(df.mean())
   
   print("Row means")
   print(df.mean(axis=1))
   

2. Force inclusions with the skipna argument

   print("Column means")
   print(df.mean(skipna=False))
   
   print("Row means")
   print(df.mean(axis=1, skipna=False))
   

Check for missing values

1. Show which items are missing. “NA” includes NaN and None. It doesn’t include empty strings or numpy.inf.

   # Show which items are NA
   df.isna()
   

2. Count missing values

   # Missing by row
   print(df.isna().sum())
   
   # Missing by column
   print(df.isna().sum(axis=1))
   
   # Aggregate sum
   df.isna().sum().sum()
   

3. Are any values missing?

   df.isna().any(axis=None)
   

4. (Optional) Are all of the values missing?

   df.isna().all(axis=None)
   

Replace missing values

1. Replace with a fixed value

   df_fixed = df.fillna(99)
   print(df_fixed)
   

2. Replace values that don’t meet a criterion with an alternate value

   subset_fixed = subset.where(subset > 10000, 99)
   print(subset_fixed)
   

3. (Optional) Impute missing values. Read the docs, this may or may not be sufficient for your needs.

   df_imputed = df.interpolate()
   

Drop missing values

Drop all rows with missing values

df_drop = df.dropna()

Hard Challenge: The perils of missing data

1. Create an array of random numbers matching the data data frame

   random_filter = np.random.rand(30, 12) * data.max(axis=None)
   

2. Create a new data frame that filters out all numbers lower than the random numbers

3. Interpolate new values for the missing values in the new data frame. How accurate do you think they are?

Solution

new_data = data[data > random_filter]

# Data is not missing randomly
print(new_data)

new_data.interpolate()
new_data.interpolate().mean(axis=None)

(Optional) Challenge: Filter and trim with a boolean vector

A DataFrame is a dictionary of Series columns. With this in mind, experiment with the following code and try to explain what each line is doing. What operation is it performing, and what is being returned?

Feel free to use print(), help(), type(), etc as you investigate.

df["1962"]
df["1962"].notna()
df[df["1962"].notna()]

Solution

1. Line 1 returns the column as a Series vector
2. Line 2 returns a boolean Series vector (True/False)
3. Line 3 performs boolean indexing on the DataFrame using the Series vector. It only returns the rows that are True (i.e. it performs true filtering).

Sorting and grouping

Motivating example: Calculate the wealth Z-score for each country

# Calculate z scores for all elements
# z = (data - data.mean(axis=None))/data.std()
# As of July 2024, pandas dataframe.std(axis=None) doesn't work. We are dropping down to
# Numpy to use the .std() method on the underlying values array.
z = (data - data.mean(axis=None))/data.values.std(ddof=1)

# Get the mean z score for each country (i.e. across all columns)
mean_z = z.mean(axis=1)

# Group countries into "wealthy" (z > 0) and "not wealthy" (z <= 0)
z_bool = mean_z > 0

print(mean_z)
print(z_bool)

Append new columns to the data frame containing our summary statistics

Data frames are dictionaries of Series:

data["mean_z"] = mean_z
data["wealthy"] = z_bool

Sort and group by new columns

data.sort_values(by="mean_z")

# Get descriptive statistics for the group
data.groupby("wealthy").mean()
data.groupby("wealthy").describe()

Write output

Capture the results of your filter in a new file, rather than overwriting your original data.

# Save to a new CSV, preserving your original data
data.to_csv('gapminder_gdp_europe_normed.csv')

# If you don't want to preserve row names:
#data.to_csv('gapminder_gdp_europe_normed.csv', index=False)

Working with multiple tables

Concatenating data frames

surveys = pd.read_csv('data/surveys.csv', index_col="record_id")
print(surveys.shape)

df1 = surveys.head(10)
df2 = surveys.tail(10)

df3 = pd.concat([df1, df2])
print(df3.shape)

(Optional) Joining data frames (in an SQL-like manner)

1. Import species data

   species = pd.read_csv('data/species.csv', index_col="species_id")
   print(species.shape)
   

2. Join tables on common column. The “left” join is a strategy for augmenting the first table (surveys) with information from the second table (species).

   df_join = surveys.merge(species, on="species_id", how="left")
   print(df_join.head())
   print(df_join.shape)
   

3. The resulting table loses its index because surveys.record_id is not being used in the join. To keep record_id as the index for the final table, we need to retain it as an explicit column.

   # Don't set record_id as index during initial import
   surveys = pd.read_csv('data/surveys.csv')
   df_join = surveys.merge(species, on="species_id", how="left").set_index("record_id")
   
   df_join.index
   

4. Get the subset of species that match a criterion, and join on that subset. The “inner” join only includes rows where both tables match on the key column; it’s a strategy for filtering the first table by the second table.

   # Get the taxa column, masking the rows based on which values match "Bird"
   birds = species[species["taxa"] == "Bird"]
   df_birds = surveys.join(birds, on="species_id").set_index("record_id")
   
   print(df_birds.head())
   print(df_birds.shape)
   

(Optional) Text processing in Pandas

cf. https://pandas.pydata.org/docs/user_guide/text.html

1. Import tabular data that contains strings

   species = pd.read_csv('data/species.csv', index_col='species_id')
   
   # You can explicitly set all of the columns to type string
   # species = pd.read_csv('data/species.csv', index_col='species_id', dtype='string')
   
   # ...or specify the type of individual columns
   # species = pd.read_csv('data/species.csv', index_col='species_id',
   #                       dtype = {"genus": "string",
   #                                "species": "string",
   #                                "taxa": "string"})
   
   print(species.head())
   print(species.info())
   print(species.describe())
   

2. A Pandas Series has string methods that operate on the entire Series at once

   # Two ways of getting an individual column
   print(type(species.genus))
   print(type(species["genus"]))
   
   # Inspect the available string methods
   print(dir(species["genus"].str))
   

3. Use string methods for filtering

   # Which species are in the taxa "Bird"?
   print(species["taxa"].str.startswith("Bird"))
   
   # Filter the dataset to only look at Birds
   print(species[species["taxa"].str.startswith("Bird")])
   

4. Use string methods to transform and combine data

   binomial_name = species["genus"].str.cat(species["species"].str.title(), " ")
   species["binomial"] = binomial_name
   
   print(species.head())
   

(Optional) Adding rows to DataFrames

A row is a view onto the nth item of each of the column Series. Appending rows is a performance bottleneck because it requires a separate append operation for each Series. You should concatenate data frames instead.

1. Create a single row as a data frame and concatenate it.

   row = pd.DataFrame({"1962": 5000, "1967": 5000, "1972": 5000}, index=["Latveria"])
   pd.concat([subset, row])
   

2. If you have individual rows as Series, pd.concat() will produce a data frame.

   # Get each row as a Series
   italy = data.loc["Italy", :]
   poland = data.loc["Poland", :]
   
   # Omitting axis argument (or axis=0) concatenates the 2 series end-to-end
   # axis=1 creates a 2D data frame
   # Transpose recovers original orientation
   # Column labels come from Series index
   # Row labels come from Series name
   pd.concat([italy, poland], axis=1).T
   

(Optional) Scientific Computing Libraries

Libraries

1. SciPy projects
   1. Numpy: Linear algebra
   2. Pandas
   3. Scipy.stats: Probability distributions and basic tests
2. Statsmodels: Statistical models and formulae built on Scipy.stats
3. Scikit-Learn: Machine learning tools built on NumPy
4. Tensorflow/PyTorch: Deep learning and other voodoo

The basics of Scikit-Learn

Scikit-Learn documentation: https://scikit-learn.org/stable/

1. Motivating example: Ordinary least squares regression

   from sklearn import linear_model
   
   # Create some random data
   x_train = np.random.rand(20)
   y = np.random.rand(20)
   
   # Fit a linear model
   reg = linear_model.LinearRegression()
   reg.fit(x_train.reshape(-1,1), y)
   print("Regression slope:", reg.coef_)
   

2. Estimate model fit

   from sklearn.metrics import r2_score
   
   # Test model fit with new data
   x_test = np.random.rand(20)
   y_prediction = reg.predict(x_test.reshape(-1,1))
   
   # Get model stats
   mse = mean_squared_error(y, y_prediction)
   r2 = r2_score(y, y_prediction)
   
   print("R squared:", "{:.3f}".format(r2))
   

3. (Optional) Inspect our prediction

   import matplotlib.pyplot as plt
   
   fig, ax = plt.subplots()
   ax.scatter(x_train, y, color="black")
   ax.plot(x_test, y_prediction, color="blue")
   
   # `fig` in Jupyter Lab
   fig.show()
   

4. (Optional) Compare with Statsmodels

   # Load modules and data
   import statsmodels.api as sm
   
   # Fit and summarize OLS model (center data to get accurate model fit
   mod = sm.OLS(y - y.mean(), x_train - x_train.mean())
   res = mod.fit()
   
   print(res.summary())
   

(Optional) Statsmodels regression example with applied data

1. Import data

   data = pd.read_csv('surveys.csv')
   
   # Check for NaN
   print("Valid weights:", data['weight'].count())
   print("NaN weights:", data['weight'].isna().sum())
   print("Valid lengths:", data['hindfoot_length'].count())
   print("NaN lengths:", data['hindfoot_length'].isna().sum())
   

2. Fit OLS regression model

   from statsmodels.formula.api import ols
   
   model = ols("weight ~ hindfoot_length", data, missing='drop').fit()
   print(model.summary())
   

3. Generic parameters for all models

   import statsmodels
   
   help(statsmodels.base.model.Model)
   

(Optional) Things we didn’t talk about

1. pipe
2. map/applymap/apply (in general you should prefer vectorized functions)

(Optional) Pandas method chaining in the wild

https://gist.githubusercontent.com/adiamaan92/d8ebee8937d271452def2a7314993b2f/raw/ce9fbb5013d94accf0779a25e182c4be77678bd0/wine_mc_example.py

wine.rename(columns={"color_intensity": "ci"})
.assign(color_filter=lambda x: np.where((x.hue > 1) & (x.ci > 7), 1, 0))
.query("alcohol > 14 and color_filter == 1")
.sort_values("alcohol", ascending=False)
.reset_index(drop=True)
.loc[:, ["alcohol", "ci", "hue"]]

(Optional) Introspecting on the DataFrame object

1. DataFrames have a huge number of fields and methods, so dir() is not very useful

   print(dir(data))
   

2. Create a new list that filters out internal attributes

   df_joinpublic = [item for item in dir(data) if not item.startswith('_')]
   print(df_public)
   

3. (Optional) Pretty-print the new list

   importort pprint
   
   pp = pprint.PrettyPrinter(width=100, compact=True, indent=2)
   pp.pprint(df_public)
   

4. Objects have fields (i.e. data/variables) and methods (i.e. functions/procedures). The difference between a method and a function is that methods are attached to objects, whereas functions are free-floating (“first-class citizens”). Methods and functions are “callable”:

   # GeneratorExitenerate a list of public methods and a list of public fields. We do this
   # by testing each attribute to determine whether it is "callable".
   # NB: Because Python allows you to override any attribute at runtime,
   # testing with `callable` is not always reliable.
   
   # List of methods (callable attributes)
   df_methods = [item for item in dir(data) if not item.startswith('_')
                 and callable(getattr(data, item))]
   # List of fields (non-callable attributes)
   df_attr = [item for item in dir(data) if not item.startswith('_')
              and not callable(getattr(data, item))]
   
   pp.pprint(df_methods)
   pp.pprint(df_attr)
   

(Carpentries version) Group By: split-apply-combine

1. Split data according to criterion, do numeric transformations, then recombine.

   # Get all GDPs greater than the mean
   mask_higher = data > data.mean()
   
   # Count the number of time periods in which each country exceeds the mean
   higher_count = mask_higher.aggregate('sum', axis=1)
   
   # Create a normalized wealth-over-time score
   wealth_score = higher_count / len(data.columns)
   wealth_score
   

2. A DataFrame is a spreadsheet, but it is also a dictionary of columns.

   data['gdpPercap_1962']
   

3. Add column to data frame

   # Warningealth Score is a series
   type(wealth_score)
   
   data['normalized_wealth'] = wealth_score
   

Building Programs (Week 3)

Notebooks vs Python scripts

Differences between .ipynb and .py

1. Export notebook to .py file
2. Move .py file into data directory
3. Compare files in TextEdit/Notepad

Workflow differences between notebooks and scripts

Broadly, a trade-off between managing big code bases and making it easy to experiment. See: https://github.com/elliewix/Ways-Of-Installing-Python/blob/master/ways-of-installing.md#why-do-you-need-a-specific-tool

1. Interactive testing and debugging
2. Graphics integration
3. Version control
4. Remote scripts

(Optional) Python from the terminal

1. Python is an interactive interpreter (REPL)

   python
   

2. Python is a command line program

   # hello.py
   print("Hello!")
   

   python hello.py
   

3. (Optional) Python programs can accept command line arguments as inputs

   1. List of command line inputs: sys.argv (https://docs.python.org/3/library/sys.html#sys.argv)
   2. Utility for working with arguments: argparse (https://docs.python.org/3/library/argparse.html)

Looping Over Data Sets

File paths as an example of increasing abstraction in program development

1. File paths as literal strings
2. File paths as string patterns
3. File paths as abstract Path objects

Use a for loop to process files given a list of their names

import pandas as pd

file_list = ['data/gapminder_gdp_africa.csv', 'data/gapminder_gdp_asia.csv']
for filename in file_list:
    data = pd.read_csv(filename, index_col='country')
    print(filename)
    print(data.head(1))

Use glob.glob to find sets of files whose names match a pattern

1. Get a list of all the CSV files

   import glob
   glob.glob('data/*.csv')
   

2. In Unix, the term “globbing” means “matching a set of files with a pattern”. It uses shell expansion rules, not regular expressions, so there’s an upper limit to how flexible it can be. The most common patterns are:

   • `*` meaning “match zero or more characters”
   • `?` meaning “match exactly one character”

3. (Optional) Get a list of all CSV or TSV files

   glob.glob('data/*.?sv')
   

4. Get a list of all the Gapminder CSV files

   glob.glob('data/gapminder_gdp_*.csv')
   

5. (Optional) Exclude the “all” CSV file

   glob.glob('data/gapminder_[!all]*.csv')
   

Use glob and a for loop to process batches of files

data_frames = []
for filename in glob.glob('data/gapminder_gdp_*.csv'):
    print(filename)
    data = pd.read_csv(filename)
    data_frames.append(data)

all_data = pd.concat(data_frames)
print(all_data.shape)

Conditionals

Evaluating the truth of a statement

1. Does a file end in "all.csv"?

   for filename in glob.glob('data/gapminder*.csv'):
      print("Current file:", filename)
      print(filename.endswith("all.csv")
   

2. Value of a variable

   mass = 3
   
   print(mass == 3)
   print(mass > 5)
   print(mass < 4)
   

3. Membership in a collection

   primes = [2, 3, 5]
   
   print(2 in primes)
   print(7 in primes)
   

4. Missing values

   # Recreate data frame with missing data if necessary
   data = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')
   subset = data.loc['Italy':'Poland', '1962':'1972']
   df = subset[subset > 10000]
   
   # Which values are missing?
   print(df.isna())
   
   # Are any values missing?
   print(df.isna().any(axis=None))
   

5. (Optional) Truth of a collection Note that any() and all() evaluate each item using .__bool__() or .__len()__, which tells you whether an item is “truthy” or “falsey” (i.e. interpreted as being true or false).

   my_list = [2.75, "green", 0]
   
   print(any(my_list))
   print(all(my_list))
   

6. (Optional) Understanding “truthy” and “falsey” values in Python (cf. https://stackoverflow.com/a/53198991) Every value in Python, regardless of type, is interpreted as being True except for the following values (which are interpreted as False). “Truthy” values satisfy if or while statements; “Falsey” values do not.

   1. Constants defined to be false: None and False.
   2. Zero of any numeric type: 0, 0.0, 0j, Decimal(0), Fraction(0, 1)
   3. Empty sequences and collections: '', (), [], {}, set(), range(0)

Use if statements to control whether or not a block of code is executed

1. An if statement (more properly called a conditional statement) controls whether some block of code is executed or not.

   mass = 3.5
   if mass > 3.0:
       print(mass, 'is large')
   

   mass = 2.0
   if mass > 3.0:
       print (mass, 'is large')
   

2. Structure is similar to a for statement:

   • First line opens with if and ends with a colon
   • Body containing one or more statements is indented (usually by 4 spaces)

3. Use conditionals to decide which files to process

   data_frames = []
   for filename in glob.glob('data/gapminder*.csv'):
       print("Current file:", filename)
        if not filename.endswith("all.csv"):
           print("Passes test:", filename)
           data = pd.read_csv(filename)
           data_frames.append(data)
   
   all_data = pd.concat(data_frames)
   print(all_data.shape)
   

Use else to execute a block of code when an if condition is not true

1. else can be used following an if. This allows us to specify an alternative to execute when the if branch isn’t taken.

   if m > 3.0:
       print(m, 'is large')
   else:
       print(m, 'is small')
   

2. This lets you explicitly handle the base case

   data_frames = []
   for filename in glob.glob('data/gapminder*.csv'):
       if filename.endswith("all.csv"):
           print("I don't want any of that")
       else:
           print("Passes test:", filename)
           data = pd.read_csv(filename)
           data_frames.append(data)
   
   all_data = pd.concat(data_frames)
   print(all_data.shape)
   

Challenge: Process small files

Iterate through all of the CSV files in the data directory. Print the name and length (number of lines) of any file that is less than 30 lines long.

Solution

Note that the data frame will report the number of data rows, which doesn’t include the column headers (the actual file has a leading row with the header names).

for filename in glob.glob('data/*.csv'):
    data = pd.read_csv(filename)
    if len(data) < 30:
        print(filename, len(data))

Challenge: Find the European data

Iterate through all of the CSV files in the data directory. Print the file name that includes “europe”, then print the column names for the file.

Solution

for filename in glob.glob('data/*.csv'):
    if "europe" in filename.lower():
        print(filename)
        data = pd.read_csv(filename)
        print(data.columns)

Use elif to specify additional tests

May want to provide several alternative choices, each with its own test; use elif (short for “else if”) and a condition to specify these.

if m > 9.0:
    print(m, 'is huge')
elif m > 3.0:
    print(m, 'is large')
else:
    print(m, 'is small')

• Always associated with an if.
• Must come before the else (which is the “catch all”).

(Optional) Conditions are tested once, in order

Python steps through the branches of the conditional in order, testing each in turn. Order matters! The following is wrong:

grade = 85
if grade >= 70:
    print('grade is C')
elif grade >= 80:
    print('grade is B')
elif grade >= 90:
    print('grade is A')

Compound Relations Using and, or, and Parentheses

Often, you want some combination of things to be true. You can combine relations within a conditional using and and or.

mass = [1, 2, 3, 4, 5]
velocity = [5, 4, 3, 2, 5]

for m, v in zip(mass, velocity):
    if m <= 3 and v <= 3:
        print("Small and slow")
    elif m <= 3 and v > 3:
        print("Small and fast")
    elif m > 3 and v <= 3:
        print("Large and slow")
    else:
        print("Check data")

• Use () to group subsets of conditions
• Aside: For a more natural way of working with many lists, look at zip()

(Optional) Use the modulus to print occasional status messages

for count, filename in enumerate(glob.glob('data/gapminder_*.csv')):
    # Print every other filename
    if count % 2 == 0:
        print(count, filename)

(Optional) Use pathlib to write code that works across operating systems

1. Pathlib provides cross-platform path objects

   from pathlib import Path
   
   # Create Path objects
   raw_path = Path("data")
   processed_path = Path("data/processed")
   
   print("Relative path:", processed_path)
   print("Absolute path:", processed_path.absolute())
   

2. The file objects have methods that provide much better information about files and directories.

   #Note the careful testing at each level of the code.
   data_frames = []
   
   if raw_path.exists():
       for filename in raw_path.glob('gapminder_gdp_*.csv'):
           if filename.is_file():
               data = pd.read_csv(filename)
               print(filename)
               data_frames.append(data)
   
   all_data = pd.concat(data_frames)
   
   # Check for destination folder and create if it doesn't exist
   if not processed_path.exists():
       processed_path.mkdir()
   
   all_data.to_csv(processed_path.joinpath("combined_data.csv"))
   

(Optional) Generic file handling

Pandas understands specific file types, but what if you need to work with a generic file?

Open the file with a context manager

with open("data/bouldercreek_09_2013.txt", "r") as infile:
    lines = infile.readlines()

• The context manager closes the file when you’re done reading it
• "bouldercreek_09_2013.txt" is the name of the file
• infile is a variable that refers to the file on disk

A file is a collection of lines

.readlines() produces the file contents as a list of lines; each line is a string.

print(len(text))
print(type(text))

# View the first 10 lines
print(text[:10])

Strings contain formatting marks

Compare the following:

# This displays the nicely-formatted document
print(lines[0])

# This shows the true nature of the string; you can see newlines (/n),
# tabs (/t), and other hidden characters
lines[0]

(Optional) Text processing and data cleanup

Use string methods to determine which lines to keep

1. The file contains front matter that we can discard

   tabular_lines = []
   for line in lines:
       if not line.startswith("#"):
           tabular_lines.append(line)
   

2. Now the first line is tab-separated data. Note that the print statement prints the tabs instead of showing us the \t character.

   tabular_lines[0]
   

Open an output file for writing

outfile_name = "data/tabular_data.txt"

with open(outfile_name, "w") as outfile:
    outfile.writelines(tabular_lines)

Format output as a comma-delimited text file

1. Strip trailing whitespace

   stripped_line = tabular_lines[0].strip()
   stripped_line
   

2. Split each line into a list based using the tabs.

   split_line = stripped_line.split("\t")
   split_line
   

3. Use a special-purpose library to create a correctly-formatted CSV file

   import csv
   
   outfile_name = "data/csv_data.csv"
   with open(outfile_name, "w") as outfile:
       writer = csv.writer(outfile)
       for line in tabular_lines:
           csv_line = line.strip().split("\t")
           writer.writerow(csv_line)
   

4. You can initialize csv.reader and csv.writer with different “dialects” or with custom delimiters and quotechars; see https://docs.python.org/3/library/csv.html

(Optional) Avoid memory limitations by processing the input file one line at a time

infile_name = "data/bouldercreek_09_2013.txt"
outfile_name = "data/csv_data.csv"

with open(infile_name, "r") as infile, open(outfile_name, "w") as outfile:
    writer = csv.writer(outfile)
    for line in infile:
        if not line.startswith("#"):
            writer.writerow(line.strip().split("\t"))

(Optional) Notes

1. Pandas has utilities for reading fixed-width files: https://pandas.pydata.org/docs/reference/api/pandas.read_fwf.html

2. Saving datasets with new-style string formatting

   for i in datasets_list:
      do_something(f'{i}.png'
   

Writing Functions

Break programs down into functions to make them easier to understand

• Human beings can only keep a few items in working memory at a time.
• Understand larger/more complicated ideas by understanding and combining pieces
• Functions serve the same purpose in programs:
  1. Encapsulate complexity so that we can treat it as a single “thing”
  2. Removes complexity from remaining code, making it easier to test
  3. Enables re-use: Write one time, use many times

Define a function using def with a name, parameters, and a block of code

def print_greeting():
    print('Hello!')

• Begin the definition of a new function with def, followed by the name of the function.
• Must obey the same rules as variable names.
• Parameters in parentheses; empty parentheses if the function doesn’t take any inputs.
• Indent function body

Defining a function does not run it

print_greeting()

• Like assigning a value to a variable
• Must call the function to execute the code it contains.

Arguments in call are matched to parameters in definition

1. Positional arguments

   def print_date(year, month, day):
       joined = '/'.join([year, month, day])
       print(joined)
   
   print_date(1871, 3, 19)
   

2. (Optional) Keyword arguments

   print_date(month=3, day=19, year=1871)
   

Functions may return a result to their caller using return

1. Use return ... to give a value back to the caller. return ends the function’s execution and returns you to the code that originally called the function.

   def average(values):
       """Return average of values, or None if no values are supplied."""
   
       if len(values) == 0:
           return None
       else:
           return sum(values) / len(values)
   

   a = average([1, 3, 4])
   print(a)
   

2. You should explicitly handle common problems:

   print(average([]))
   

3. Notes:

   1. return can occur anywhere in the function, but functions are easier to understand if return occurs:
      1. At the start to handle special cases
      2. At the very end, with a final result
   2. Docstring provides function help. Use triple quotes if you need the docstring to span multiple lines.

(Optional) challenge: Encapsulate text processing in a function

Write a function that takes line as an input and returns the information required by writer.writerow().

Challenge (data normalization): Encapsulate Z score calculations in a function

Write a function that encapsulates the Z-score calculations from the Pandas workshop. Your function needs to do the following:

1. Read a CSV file into a data frame
2. Calculate the Z score for each item
3. Calculate the mean Z score for each country
4. Append the mean Z scores as a new column
5. Return the data frame

Solution

def norm_data(filename):
    """Add a Z score column to a data frame."""

    df = pd.read_csv(filename, index_col = "country")

    # If you need to drop the continent column
    if "continent" in df.columns:
        df.drop("continent", axis=1)

    # Calculate individual Z scores
    z = (data - data.mean(axis=None))/data.values.std(ddof=1)

    # Get the mean z score for each country
    mean_z = z.mean(axis=1)

    df["mean_z"] = mean_z

    return df

df = norm_data("data/gapminder_gdp_europe.csv")

# If you need to drop the contintent column
# mean_z, z_bool = norm_data(data.drop("continent", axis=1))

(Optional) Use the function to process all files

for filename in glob.glob('data/gapminder_*.csv'):
    # Print a status message
    print("Current file:", filename)

    # Read the data into a DataFrame and modify it
    data = pd.read_csv(filename, index_col = "country")
    mean_z, z_bool = norm_data(data)

    # Append to DataFrame
    data["mean_z"] = mean_z
    data["wealthy"] = z_bool

    # Generate an output file name
    parts = filename.split(".csv")
    newfile = ''.join([parts[0], "_normed.csv"])
    data.to_csv(newfile)

(Optional) A worked example: The Lorenz attractor

https://matplotlib.org/stable/gallery/mplot3d/lorenz_attractor.html

(Carpentries version) Conditionals

Use if statements to control whether or not a block of code is executed

An if statement (more properly called a conditional statement) controls whether some block of code is executed or not.

mass = 3.54
if mass > 3.0:
    print(mass, 'is large')

mass = 2.07
if mass > 3.0:
    print (mass, 'is large')

Structure is similar to a for statement:

• First line opens with if and ends with a colon
• Body containing one or more statements is indented (usually by 4 spaces)

Conditionals are often used inside loops

Not much point using a conditional when we know the value (as above), but useful when we have a collection to process.

masses = [3.54, 2.07, 9.22, 1.86, 1.71]
for m in masses:
    if m > 3.0:
        print(m, 'is large')

Use else to execute a block of code when an if condition is not true

else can be used following an if. This allows us to specify an alternative to execute when the if branch isn’t taken.

masses = [3.54, 2.07, 9.22, 1.86, 1.71]
for m in masses:
    if m > 3.0:
        print(m, 'is large')
    else:
        print(m, 'is small')

Use elif to specify additional tests

May want to provide several alternative choices, each with its own test; use elif (short for “else if”) and a condition to specify these.

masses = [3.54, 2.07, 9.22, 1.86, 1.71]
for m in masses:
    if m > 9.0:
        print(m, 'is HUGE')
    elif m > 3.0:
        print(m, 'is large')
    else:
        print(m, 'is small')

• Always associated with an if.
• Must come before the else (which is the “catch all”).

Conditions are tested once, in order

Python steps through the branches of the conditional in order, testing each in turn. Order matters! The following is wrong:

grade = 85
if grade >= 70:
    print('grade is C')
elif grade >= 80:
    print('grade is B')
elif grade >= 90:
    print('grade is A')

Use conditionals in a loop to “evolve” the values of variables

velocity = 10.0
for i in range(5): # execute the loop 5 times
    print(i, ':', velocity)
    if velocity > 20.0:
        velocity = velocity - 5.0
    else:
        velocity = velocity + 10.0
print('final velocity:', velocity)

• This is how dynamical systems simulations work

Compound Relations Using and, or, and Parentheses (optional)

Often, you want some combination of things to be true. You can combine relations within a conditional using and and or. Continuing the example above, suppose you have:

mass     = [ 3.54,  2.07,  9.22,  1.86,  1.71]
velocity = [10.00, 20.00, 30.00, 25.00, 20.00]

i = 0
for i in range(5):
    if mass[i] > 5 and velocity[i] > 20:
        print("Fast heavy object.  Duck!")
    elif mass[i] > 2 and mass[i] <= 5 and velocity[i] <= 20:
        print("Normal traffic")
    elif mass[i] <= 2 and velocity[i] <= 20:
        print("Slow light object.  Ignore it")
    else:
        print("Whoa!  Something is up with the data.  Check it")

• Use () to group subsets of conditions
• Aside: For a more natural way of working with many lists, look at zip()

Visualization with Matplotlib and Seaborn (Week 4)

Orientation

Briefly revisit week 1

1. Python orientation
2. Jupyter orientation

A brief history of plotting in Matplotlib

1. Multiple interfaces
2. Local graphs and global settings
3. Matplotlib is the substrate for higher-level libraries
4. Drawing things is verbose in any language

Plotting with Matplotlib

The basic plot

import matplotlib.pyplot as plt
fig, ax = plt.subplots()

time = [0, 1, 2, 3]
position = [0, 100, 200, 300]

ax.plot(time, position)

Two kinds of plotting objects

type(fig)

print(type(fig))
print(type(ax))

• Figure objects handle display, printing, saving, etc.
• Axes objects contain graph information

(Optional) Three ways of showing a figure

1. Show figure inline (Jupyter Lab default)

   fig
   

2. Show figure in a separate window (command line default)

   fig.show()
   

3. Show figure in a separate window from Jupyter Lab. You may need to specify a different “backend” parameter for matplotlib.use() depending on your exact setup: https://matplotlib.org/stable/tutorials/introductory/usage.html#the-builtin-backends

   import matplotlib
   
   matplotlib.use('TkAgg')
   
   fig.show()
   

The lifecycle of a custom plot

1. Create mock data

   import numpy as np
   
   y = np.random.random(10) # outputs an array of 10 random numbers between 0 and 1
   x = np.arange(1980,1990,1) # generates an ordered array of numbers from 1980 to 1989
   
   # Check that x and y contain the same number of values
   assert len(x) == len(y)
   

2. Inspect our data

   print("x:", x)
   print("y:", y)
   

3. Create the basic plot

   # Convert y axis into a percentage
   y = y * 100
   
   # Draw plot
   fig, ax = plt.subplots()
   ax.plot(x, y)
   

4. Show available styles

   # What are the global styles?
   plt.style.available
   

   # Set a global figure style
   plt.style.use("dark_background")
   
   # The style is only applied to new figures, not pre-existing figures
   fig
   

   # Re-creating the figure applies the new style
   fig, ax = plt.subplots()
   ax.plot(x, y)
   

5. Customize the graph In principle, nearly every element on a Matplotlib figure is independently modifiable.

   # Set figure size
   fig, ax = plt.subplots(figsize=(8,6))
   
   # Set line attributes
   ax.plot(x, y, color='darkorange', linewidth=2, marker='o')
   
   # Add title and labels
   ax.set_title("Percent Change in Stock X", fontsize=22, fontweight='bold')
   ax.set_xlabel(" Years ", fontsize=20, fontweight='bold')
   ax.set_ylabel(" % change ", fontsize=20, fontweight='bold')
   
   # Adjust the tick labels
   ax.tick_params(axis='both', which='major', labelsize=18)
   
   # Add a grid
   ax.grid(True)
   

6. Save your figure

   fig.savefig("mygraph_dark.png", dpi=300)
   

Plotting multiple data sets

In this example, plot GDP over time for multiple countries.

1. Import data

   import pandas as pd
   data = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')
   

   # Inspect our data
   data.head(3)
   

2. Transform column headers into an ordinal scale

   1. (Optional) Original column names are object (i.e. string) data

      data.columns
      

   2. Strip off non-numeric portion of each column title

      years = data.columns.str.strip('gdpPercap_')
      years
      

   3. Convert years strings into integers and replace original data frame column headers

      data.columns = years.astype(int)
      

3. Extract rows from the DataFrame

   x_years = data.columns
   y_austria = data.loc['Austria']
   y_bulgaria = data.loc['Bulgaria']
   

4. Create the plot object

   # Change global background back to default
   plt.style.use("default")
   
   # Create GDP figure
   fig, ax = plt.subplots(figsize=(8,6))
   
   # Create GDP plot
   ax.plot(x_years, y_austria, label='Austria', color='darkgreen', linewidth=2, marker='x')
   ax.plot(x_years, y_bulgaria, label='Bulgaria', color='maroon', linewidth=2, marker='o')
   
   # Decorate the plot
   ax.legend(fontsize=16, loc='upper center') #automatically uses labels
   ax.set_title("GDP of Austria vs Bulgaria", fontsize=22, fontweight='bold')
   ax.set_xlabel("Years", fontsize=20, fontweight='bold')
   ax.set_ylabel("GDP", fontsize=20, fontweight='bold')
   

(Optional) Plot directly from Pandas

Don’t do this.

1. The basic plot syntax

   ax = data.loc['Austria'].plot()
   fig = ax.get_figure()
   fig
   

2. Decorate your Pandas plot

   ax = data.loc['Austria'].plot(figsize=(8,6), color='darkgreen', linewidth=2, marker='*')
   ax.set_title("GDP of Austria", fontsize=22, fontweight='bold')
   ax.set_xlabel("Years",fontsize=20, fontweight='bold' )
   ax.set_ylabel("GDP",fontsize=20, fontweight='bold' )
   
   fig = ax.get_figure()
   fig
   

3. Overlaying multiple plots on the same figure with Pandas. This is super unintuitive.

   # Create an Axes object with the Austria data
   ax = data.loc['Austria'].plot(figsize=(8,6), color='darkgreen', linewidth=2, marker='*')
   print("Austria graph", id(ax))
   
   # Overlay the Bulgaria data on the same Axes object
   ax = data.loc['Bulgaria'].plot(color='maroon', linewidth=2, marker='o')
   print("Bulgaria graph", id(ax))
   

4. The equivalent Matplotlib plot (optional)

   # extract the x and y values from dataframe
   x_years = data.columns
   y_gdp = data.loc['Austria']
   
   # Create the plot
   fig, ax = plt.subplots(figsize=(8,6))
   ax.plot(x_years, y_gdp, color='darkgreen', linewidth=2, marker='x')
   # etc.
   

Visualization Strategy

There are many kinds of plots

## Visualize the same data using a scatterplot
plt.style.use('ggplot')

# Create a scatter plot
fig, ax = plt.subplots(figsize=(8,6))
ax.scatter(y_austria, y_bulgaria, color='blue', linewidth=2, marker='o')

# Decorate the plot
ax.set_title("GDP of Austria vs Bulgaria", fontsize=22, fontweight='bold')
ax.set_xlabel("GDP of Austria",fontsize=20, fontweight='bold' )
ax.set_ylabel("GDP of Bulgaria",fontsize=20, fontweight='bold' )