Libraries & Pandas

Last updated on 2024-05-17 | Edit this page

Estimated time: 30 minutes

Overview

Questions

  • How can I extend the capabilities of Python?
  • How can I use Python code that other people have written?
  • How can I read tabular data?

Objectives

  • Explain what Python libraries and modules are.
  • Write Python code that imports and uses modules from Python’s standard library.
  • Find and read documentation for standard libraries.
  • Import the pandas library.
  • Use pandas to load a CSV file as a data set.
  • Get some basic information about a pandas DataFrame.

Python libraries are powerful collections of tools.

A Python library is a collection of files (called modules) that contains functions that you can use in your programs. Some libraries (also referred to as packages) contain standard data values or language resources that you can reference in your code. So far, we have used the Python standard library, which is an extensive suite of built-in modules. You can find additional libraries from PyPI (the Python Package Index), though you’ll often find references to useful libraries as you’re reading tutorials or trying to solve specific programming problems. Some popular libraries for working with data in library fields are:

  • Pandas - tabular data analysis tool.
  • Pymarc - for working with bibliographic data encoded in MARC21.
  • Matplotlib - data visualization tools.
  • BeautifulSoup - for parsing HTML and XML documents.
  • Requests - for making HTTP requests (e.g., for web scraping, using APIs)
  • Scikit-learn - machine learning tools for predictive data analysis.
  • NumPy - numerical computing tools such as mathematical functions and random number generators.

You must import a library or module before using it.

Use import to load a library into a program’s memory. Then you can refer to things from the library as library_name.function. Let’s import and use the string library to generate a list of lowercase ASCII letters and to change the case of a text string:

PYTHON 

import string

print(f'The lower ascii letters are {string.ascii_lowercase}')
print(string.capwords('capitalise this sentence please.'))

OUTPUT 

The lower ascii letters are abcdefghijklmnopqrstuvwxyz
Capitalise This Sentence Please.

Dot notation

We introduced Python dot notation when we looked at methods like list_name.append(). We can use the same syntax when we call functions of a specific Python library, such as string.capwords(). In fact, this dot notation is common in Python, and can refer to relationships between different types of Python objects. Remember that it is always the case that the object to the right of the dot is a part of the larger object to the left. If we expressed capitals of countries using this syntax, for example, we would say, Brazil.São_Paulo() or Japan.Tokyo().

Use help to learn about the contents of a library module.

The help() function can tell us more about a module in a library, including more information about its functions and/or variables.

PYTHON 

help(string)

OUTPUT 

Help on module string:

NAME
    string - A collection of string constants.

MODULE REFERENCE
    https://docs.python.org/3.6/library/string
    
    The following documentation is automatically generated from the Python
    source files.  It may be incomplete, incorrect or include features that
    are considered implementation detail and may vary between Python
    implementations.  When in doubt, consult the module reference at the
    location listed above.

DESCRIPTION
    Public module variables:
    
    whitespace -- a string containing all ASCII whitespace
    ascii_lowercase -- a string containing all ASCII lowercase letters
    ascii_uppercase -- a string containing all ASCII uppercase letters
    ascii_letters -- a string containing all ASCII letters
    digits -- a string containing all ASCII decimal digits
    hexdigits -- a string containing all ASCII hexadecimal digits
    octdigits -- a string containing all ASCII octal digits
    punctuation -- a string containing all ASCII punctuation characters
    printable -- a string containing all ASCII characters considered printable

CLASSES
    builtins.object
        Formatter
        Template
⋮ ⋮ ⋮

Import specific items

You can use from ... import ... to load specific items from a library module to save space. This also helps you write briefer code since you can refer to them directly without using the library name as a prefix everytime.

PYTHON 

from string import ascii_letters

print(f'The ASCII letters are {ascii_letters}')

OUTPUT 

The ASCII letters are abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ

Module not found error

Before you can import a Python library, you sometimes will need to download and install it on your machine. Anaconda comes with many of the most popular Python libraries for scientific computing applications built-in, so if you installed Anaconda for this workshop, you’ll be able to import many common libraries directly. Some less common tools, like the PyMarc library, however, would need to be installed first.

PYTHON 

import pymarc

ERROR 

ModuleNotFoundError: No module named 'pymarc'

You can find out how to install the library by looking at the documentation. PyMarc, for example, recommends using a command line tool, pip, to install it. You can install with pip in a Jupyter notebook by starting the command with a percentage symbol, which allows you to run shell commands from Jupyter:

PYTHON 

%pip install pymarc
import pymarc

Use library aliases

You can use import ... as ... to give a library a short alias while importing it. This helps you refer to items more efficiently.

PYTHON 

import pandas as pd

Many popular libraries have common aliases. For example:

  • import pandas as pd
  • import numpy as np
  • import matplotlib as plt

Using these common aliases can make it easier to work with existing documentation and tutorials.

Pandas

Pandas is a widely-used Python library for statistics using tabular data. Essentially, it gives you access to 2-dimensional tables whose columns have names and can have different data types. We can start using pandas by reading a Comma Separated Values (CSV) data file with the function pd.read_csv(). The function .read_csv() expects as an argument the path to and name of the file to be read. This returns a dataframe that you can assign to a variable.

Find your CSV files

From the file browser in the left sidebar you can select the data folder to view the contents of the folder. If you downloaded and uncompressed the dataset correctly, you should see a series of CSV files from 2011 to 2022. If you double-click on the first file, 2011_circ.csv, you will see a preview of the CSV file in a new tab in the main panel of JupyterLab.

Let’s load that file into a pandas DataFrame, and save it to a new variable called df.

PYTHON 

df = pd.read_csv('data/2011_circ.csv')
print(df)

OUTPUT 

                       branch                  address     city  zip code  \
    0             Albany Park     5150 N. Kimball Ave.  Chicago   60625.0   
    1                 Altgeld    13281 S. Corliss Ave.  Chicago   60827.0   
    2          Archer Heights      5055 S. Archer Ave.  Chicago   60632.0   
    3                  Austin        5615 W. Race Ave.  Chicago   60644.0   
    4           Austin-Irving  6100 W. Irving Park Rd.  Chicago   60634.0   
    ..                    ...                      ...      ...       ...   
    75           West Pullman         830 W. 119th St.  Chicago   60643.0   
    76              West Town     1625 W. Chicago Ave.  Chicago   60622.0   
    77  Whitney M. Young, Jr.         7901 S. King Dr.  Chicago   60619.0   
    78       Woodson Regional      9525 S. Halsted St.  Chicago   60628.0   
    79     Wrightwood-Ashburn      8530 S. Kedzie Ave.  Chicago   60652.0   

        january  february  march  april    may   june   july  august  september  \
    0      8427      7023   9702   9344   8865  11650  11778   11306      10466   
    1      1258       708    854    804    816    870    713     480        702   
    2      8104      6899   9329   9124   7472   8314   8116    9177       9033   
    3      1755      1316   1942   2200   2133   2359   2080    2405       2417   
    4     12593     11791  14807  14382  11754  14402  14605   15164      14306   
    ..      ...       ...    ...    ...    ...    ...    ...     ...        ...   
    75     3312      2713   3495   3550   3010   2968   3844    3811       3209   
    76     9030      7727  10450  10607  10139  10410  10601   11311      11084   
    77     2588      2033   3099   3087   3005   2911   3123    3644       3547   
    78    10564      8874  10948   9299   9025  10020  10366   10892      10901   
    79     3062      2780   3334   3279   3036   3801   4600    3953       3536   

        october  november  december     ytd  
    0     10997     10567      9934  120059  
    1       927       787       692    9611  
    2      9709      8809      7865  101951  
    3      2571      2233      2116   25527  
    4     15357     14069     12404  165634  
    ..      ...       ...       ...     ...  
    75     3923      3162      3147   40144  
    76    10657     10797      9275  122088  
    77     3848      3324      3190   37399  
    78    13272     11421      9474  125056  
    79     4093      3583      3200   42257  

    [80 rows x 17 columns]

File Not Found

Our lessons store their data files in a data sub-directory, which is why the path to the file is data/2011_circ.csv. If you forget to include data/, or if you include it but your copy of the file is somewhere else in relation to your Jupyter Notebook, you will get an error that ends with a line like this:

ERROR 

FileNotFoundError: [Errno 2] No such file or directory: 'data/2011_circ.csv'

df is a common variable name that you’ll encounter in pandas tutorials online, but in practice it’s often better to use more meaningful variable names. Since we have twelve different CSVs to work with, for example, we might want to add the year to the variable name to differentiate between the datasets.

Also, as seen above, the output when you print a dataframe in Jupyter isn’t very easy to read. We can use .head() to look at just the first few rows in our dataframe formatted in a more convenient way for our Notebook.

PYTHON 

df_2011 = pd.read_csv('data/2011_circ.csv')
df_2011.head()
branch address city zip code january february march april may june july august september october november december ytd
0 Albany Park 5150 N. Kimball Ave. Chicago 60625.0 8427 7023 9702 9344 8865 11650 11778 11306 10466 10997 10567 9934 120059
1 Altgeld 13281 S. Corliss Ave. Chicago 60827.0 1258 708 854 804 816 870 713 480 702 927 787 692 9611
2 Archer Heights 5055 S. Archer Ave. Chicago 60632.0 8104 6899 9329 9124 7472 8314 8116 9177 9033 9709 8809 7865 101951
3 Austin 5615 W. Race Ave. Chicago 60644.0 1755 1316 1942 2200 2133 2359 2080 2405 2417 2571 2233 2116 25527
4 Austin-Irving 6100 W. Irving Park Rd. Chicago 60634.0 12593 11791 14807 14382 11754 14402 14605 15164 14306 15357 14069 12404 165634

Use the DataFrame.info() method to find out more about a dataframe.

PYTHON 

df_2011.info()

OUTPUT 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 80 entries, 0 to 79
Data columns (total 17 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   branch     80 non-null     object 
 1   address    80 non-null     object 
 2   city       80 non-null     object 
 3   zip code   80 non-null     float64
 4   january    80 non-null     int64  
 5   february   80 non-null     int64  
 6   march      80 non-null     int64  
 7   april      80 non-null     int64  
 8   may        80 non-null     int64  
 9   june       80 non-null     int64  
 10  july       80 non-null     int64  
 11  august     80 non-null     int64  
 12  september  80 non-null     int64  
 13  october    80 non-null     int64  
 14  november   80 non-null     int64  
 15  december   80 non-null     int64  
 16  ytd        80 non-null     int64  
dtypes: float64(1), int64(13), object(3)
memory usage: 10.8+ KB

The info() method tells us - we have a RangeIndex of 83, which means we have 83 rows. - there are 18 columns, with datatypes of - objects (3 columns) - 64-bit floating point number (1 column) - 64-bit integers (14 columns). - the dataframe uses 11.8 kilobytes of memory.

The DataFrame.columns variable stores info about the dataframe’s columns.

Note that this is data, not a method, so do not use () to try to call it. It helpfully gives us a list of all of the column names.

PYTHON 

print(df_2011.columns)

OUTPUT 

Index(['branch', 'address', 'city', 'zip code', 'january', 'february', 'march',
       'april', 'may', 'june', 'july', 'august', 'september', 'october',
       'november', 'december', 'ytd'],
      dtype='object')

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

DataFrame.describe() gets the summary statistics of only the columns that have numerical data. All other columns are ignored, unless you use the argument include='all'.

PYTHON 

df_2011.describe()
zip code january february march april may june july august september october november december ytd
count 80.000000 80.000000 80.000000 80.00000 80.000000 80.000000 80.000000 80.000000 80.000000 80.000000 80.000000 80.000000 80.00000 80.000000
mean 60632.675000 7216.175000 6247.162500 8367.36250 8209.225000 7551.725000 8581.125000 8708.887500 8918.550000 8289.975000 9033.437500 8431.112500 7622.73750 97177.475000
std 28.001254 10334.622299 8815.945718 11667.93342 11241.223544 10532.352671 10862.742953 10794.030461 11301.149192 10576.005552 10826.494853 10491.875418 9194.44616 125678.282307
min 60605.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 0.000000 0.000000 0.00000 9218.000000
25% 60617.000000 2388.500000 1979.250000 2708.50000 2864.250000 2678.500000 2953.750000 3344.750000 3310.500000 3196.750000 3747.000000 3168.000000 3049.75000 37119.250000
50% 60629.000000 5814.500000 5200.000000 6468.50000 6286.000000 5733.000000 6764.500000 6194.000000 6938.500000 6599.500000 7219.500000 6766.000000 5797.00000 73529.000000
75% 60643.000000 9021.000000 8000.000000 10737.00000 10794.250000 9406.250000 10852.750000 11168.000000 11291.750000 10520.000000 11347.500000 10767.000000 9775.00000 124195.750000
max 60827.000000 79210.000000 67574.000000 89122.00000 88527.000000 82581.000000 82100.000000 80219.000000 85193.000000 81400.000000 82236.000000 79702.000000 68856.00000 966720.000000

This gives us, for example, the count, minimum, maximum, and mean values from each numeric column. In the case of the zip code column, this isn’t helpful, but for the usage data for each month, it’s a quick way to scan the range of data over the course of the year.

Importing With Aliases

  1. Fill in the blanks so that the program below prints 0123456789.
  2. Rewrite the program so that it uses import without as.
  3. Which form do you find easier to read?

PYTHON 

import string as s
numbers = ____.digits
print(____)

PYTHON 

import string as s
numbers = s.digits
print(numbers)

can be written as

PYTHON 

import string
numbers = string.digits
print(numbers)

Since you just wrote the code and are familiar with it, you might actually find the first version easier to read. But when trying to read a huge piece of code written by someone else, or when getting back to your own huge piece of code after several months, non-abbreviated names are often easier, expect where there are clear abbreviation conventions.

Locating the Right Module

Given the variables year, month and day, how would you generate a date in the standard iso format:

PYTHON 

year = 1971
month = 8
day = 26
  1. Which standard library module could help you?
  2. Which function would you select from that module?
  3. Try to write a program that uses the function.

The datetime module seems like it could help you.

You could use date(year, month, date).isoformat() to convert your date:

PYTHON 

import datetime

iso_date = datetime.date(year, month, day).isoformat()
print(iso_date)

or more compactly:

PYTHON 

import datetime

print(datetime.date(year, month, day).isoformat())

According to Washington County Cooperative Library Services: “1971, August 26 – Ohio University’s Alden Library takes computer cataloging online for the first time, building a system where libraries could electronically share catalog records over a network instead of by mailing printed cards or re-entering records in each catalog. That catalog eventually became the core of OCLC WorldCat – a shared online catalog used by libraries in 107 countries and containing 517,963,343 records.”

Key Points

  • Most of the power of a programming language is in its libraries.
  • A program must import a library module in order to use it.
  • Use help to learn about the contents of a library module.
  • Import specific items from a library to shorten programs.
  • Create an alias for a library when importing it to shorten programs.