Data Workflows and Automation

Last updated on 2024-07-17 | Edit this page

Estimated time: 90 minutes

Overview

Questions

  • Can I automate operations in Python?
  • What are functions and why should I use them?

Objectives

  • Describe why for loops are used in Python.
  • Employ for loops to automate data analysis.
  • Write unique filenames in Python.
  • Build reusable code in Python.
  • Write functions using conditional statements (if, then, else).

So far, we’ve used Python and the pandas library to explore and manipulate individual datasets by hand, much like we would do in a spreadsheet. The beauty of using a programming language like Python, though, comes from the ability to automate data processing through the use of loops and functions.

For loops

Loops allow us to repeat a workflow (or series of actions) a given number of times or while some condition is true. We would use a loop to automatically process data that’s stored in multiple files (daily values with one file per year, for example). Loops lighten our work load by performing repeated tasks without our direct involvement and make it less likely that we’ll introduce errors by making mistakes while processing each file by hand.

Let’s write a simple for loop that simulates what a kid might see during a visit to the zoo:

PYTHON 

animals = ['lion', 'tiger', 'crocodile', 'vulture', 'hippo']
print(animals)

OUTPUT 

['lion', 'tiger', 'crocodile', 'vulture', 'hippo']

PYTHON 

for creature in animals:
    print(creature)

OUTPUT 

lion
tiger
crocodile
vulture
hippo

The line defining the loop must start with for and end with a colon, and the body of the loop must be indented.

In this example, creature is the loop variable that takes the value of the next entry in animals every time the loop goes around. We can call the loop variable anything we like. After the loop finishes, the loop variable will still exist and will have the value of the last entry in the collection:

PYTHON 

animals = ['lion', 'tiger', 'crocodile', 'vulture', 'hippo']
for creature in animals:
    pass

OUTPUT

PYTHON 

print('The loop variable is now: ' + creature)

OUTPUT 

The loop variable is now: hippo

We are not asking Python to print the value of the loop variable anymore, but the for loop still runs and the value of creature changes on each pass through the loop. The statement pass in the body of the loop means “do nothing”.

Challenge - Loops

  1. What happens if we don’t include the pass statement?

  2. Rewrite the loop so that the animals are separated by commas, not new lines (Hint: You can concatenate strings using a plus sign. For example, print(string1 + string2) outputs ‘string1string2’).

  1. PYTHON 

    animals = ['lion', 'tiger', 'crocodile', 'vulture', 'hippo']
for creature in animals:

    ERROR 

    IndentationError: expected an indented block

  2. Using the end argument to print:

    PYTHON 

    for creature in animals:
    print(creature + ',', end='')

    OUTPUT 

    lion,tiger,crocodile,vulture,hippo,

    This puts a comma on the end of the list, which is not ideal. To avoid this, we need to use an altogether different approach: string objects in Python have a join method, which can be used to concatenate items in a list with the string in between, e.g.

    PYTHON 

    ', '.join(animals)

    OUTPUT 

    'lion, tiger, crocodile, vulture, hippo'

Automating data processing using For Loops

The file we’ve been using so far, surveys.csv, contains 25 years of data and is very large. We would like to separate the data for each year into a separate file.

Let’s start by making a new directory inside the folder data to store all of these files using the module os:

PYTHON 

import os

os.mkdir('data/yearly_files')

The command os.mkdir is equivalent to mkdir in the shell. Just so we are sure, we can check that the new directory was created within the data folder:

PYTHON 

os.listdir('data')

OUTPUT 

['plots.csv',
 'portal_mammals.sqlite',
 'species.csv',
 'survey2001.csv',
 'survey2002.csv',
 'surveys.csv',
 'surveys2002_temp.csv',
 'yearly_files']

The command os.listdir is equivalent to ls in the shell.

In previous lessons, we saw how to use the library pandas to load the species data into memory as a DataFrame, how to select a subset of the data using some criteria, and how to write the DataFrame into a CSV file. Let’s write a script that performs those three steps in sequence for the year 2002:

PYTHON 

import pandas as pd

# Load the data into a DataFrame
surveys_df = pd.read_csv('data/surveys.csv')

# Select only data for the year 2002
surveys2002 = surveys_df[surveys_df.year == 2002]

# Write the new DataFrame to a CSV file
surveys2002.to_csv('data/yearly_files/surveys2002.csv')

To create yearly data files, we could repeat the last two commands over and over, once for each year of data. Repeating code is neither elegant nor practical, and is very likely to introduce errors into your code. We want to turn what we’ve just written into a loop that repeats the last two commands for every year in the dataset.

Let’s start by writing a loop that prints the names of the files we want to create - the dataset we are using covers 1977 through 2002, and we’ll create a separate file for each of those years. Listing the filenames is a good way to confirm that the loop is behaving as we expect.

We have seen that we can loop over a list of items, so we need a list of years to loop over. We can get the years in our DataFrame with:

PYTHON 

surveys_df['year']

OUTPUT 

0        1977
1        1977
2        1977
3        1977
         ...
35545    2002
35546    2002
35547    2002
35548    2002

but we want only unique years, which we can get using the unique method which we have already seen.

PYTHON 

surveys_df['year'].unique()

OUTPUT 

array([1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,
       1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,
       1999, 2000, 2001, 2002], dtype=int64)

Putting this into our for loop we get

PYTHON 

for year in surveys_df['year'].unique():
   filename='data/yearly_files/surveys' + str(year) + '.csv'
   print(filename)

OUTPUT 

data/yearly_files/surveys1977.csv
data/yearly_files/surveys1978.csv
data/yearly_files/surveys1979.csv
data/yearly_files/surveys1980.csv
data/yearly_files/surveys1981.csv
data/yearly_files/surveys1982.csv
data/yearly_files/surveys1983.csv
data/yearly_files/surveys1984.csv
data/yearly_files/surveys1985.csv
data/yearly_files/surveys1986.csv
data/yearly_files/surveys1987.csv
data/yearly_files/surveys1988.csv
data/yearly_files/surveys1989.csv
data/yearly_files/surveys1990.csv
data/yearly_files/surveys1991.csv
data/yearly_files/surveys1992.csv
data/yearly_files/surveys1993.csv
data/yearly_files/surveys1994.csv
data/yearly_files/surveys1995.csv
data/yearly_files/surveys1996.csv
data/yearly_files/surveys1997.csv
data/yearly_files/surveys1998.csv
data/yearly_files/surveys1999.csv
data/yearly_files/surveys2000.csv
data/yearly_files/surveys2001.csv
data/yearly_files/surveys2002.csv

We can now add the rest of the steps we need to create separate text files:

PYTHON 

# Load the data into a DataFrame
surveys_df = pd.read_csv('data/surveys.csv')

for year in surveys_df['year'].unique():

    # Select data for the year
    surveys_year = surveys_df[surveys_df.year == year]

    # Write the new DataFrame to a CSV file
    filename = 'data/yearly_files/surveys' + str(year) + '.csv'
    surveys_year.to_csv(filename)

Look inside the yearly_files directory and check a couple of the files you just created to confirm that everything worked as expected.

Writing Unique File Names

Notice that the code above created a unique filename for each year.

PYTHON 

filename = 'data/yearly_files/surveys' + str(year) + '.csv'

Let’s break down the parts of this name:

  • The first part is some text that specifies the directory to store our data file in (data/yearly_files/) and the first part of the file name (surveys): 'data/yearly_files/surveys'
  • We can concatenate this with the value of a variable, in this case year by using the plus + sign and the variable we want to add to the file name: + str(year)
  • Then we add the file extension as another text string: + '.csv'

Notice that we use single quotes to add text strings. The variable is not surrounded by quotes. This code produces the string data/yearly_files/surveys2002.csv which contains the path to the new filename AND the file name itself.

Challenge - Modifying loops

  1. Some of the surveys you saved are missing data (they have null values that show up as NaN - Not A Number - in the DataFrames and do not show up in the text files). Modify the for loop so that the entries with null values are not included in the yearly files.

  2. Let’s say you only want to look at data from a given multiple of years. How would you modify your loop in order to generate a data file for only every 5th year, starting from 1977?

  3. Instead of splitting out the data by years, a colleague wants to do analyses each species separately. How would you write a unique CSV file for each species?

  1. PYTHON 

    surveys_year = surveys_df[surveys_df.year == year].dropna()

  2. You could just make a list manually, however, why not check the first and last year making use of the code itself?

    PYTHON 

    n_year = 5  # better overview by making variable from it
first_year = surveys_df['year'].min()
last_year = surveys_df['year'].max()

for year in range(first_year, last_year, n_year):
    print(year)

    # Select data for the year
    surveys_year = surveys_df[surveys_df.year == year].dropna()

  3. python for species in surveys_df['species_id'].dropna().unique(): surveys_species = surveys_df[surveys_df.species_id == species] filename = 'episodes/data/species_files/surveys' + species + '.csv' surveys_species.to_csv(filename)