Data workflows and automation

Overview

Teaching: ?? min
Exercises: ?? min

Questions

• How do we automate repetitive tasks?

Objectives

• Review the basics of a python for loop.

• Review how to create a function.

• Learn how to loop through a directory of files, to automate processing.

• Learn the basics of 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:

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

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

for creature in animals:
   print(creature)

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:

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

print('The loop variable is now: ' + creature)
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 just means “do nothing”.

Challenge

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).

Automating data processing using For Loops

The file we’ve been using so far, articles.csv, contains 1 year of data. We would like to separate the data for each month 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:

import os

os.mkdir('data/monthly_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:

os.listdir('data')

['monthly_files',
 'doajarticlesample.zip',
 'doajarticlesample']

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 month of January:

import pandas as pd

# Load the data into a DataFrame
articles_df = pd.read_csv('articles.csv')

# Select only data for January
articles01 = articles_df[articles_df.Month == 1]

# Write the new DataFrame to a csv file
articles01.to_csv('data/monthly_files/articles01.csv')

To create monthly data files, we could repeat the last two commands over and over, once for each month 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 month in the dataset.

Let’s start by writing a loop that simply prints the names of the files we want to create - the dataset we are using is one year long and we’ll create a separate file for each month of the year. 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 months to loop over. We can get the months in our DataFrame with:

articles_df['Month']

but we want only unique months, which we can get using the unique function which we have already seen.

articles_df['Month'].unique()

array([11, 12,  8,  4, 10,  9,  7,  6,  5,  3,  2,  1])

Of course, we know that years normally have 12 months, so we could be alternatively use:

range(1,13)

Putting this into our for loop we get

for month in articles_df['Month'].unique():
   filename = 'data/monthly_files/articles{0:02d}.csv'.format(month)
   print(filename)

data/monthly_files/articles11.csv
data/monthly_files/articles12.csv
data/monthly_files/articles08.csv
data/monthly_files/articles04.csv
data/monthly_files/articles10.csv
data/monthly_files/articles09.csv
data/monthly_files/articles07.csv
data/monthly_files/articles06.csv
data/monthly_files/articles05.csv
data/monthly_files/articles03.csv
data/monthly_files/articles02.csv
data/monthly_files/articles01.csv

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

# Load the data into a DataFrame
for month in articles_df['Month'].unique():
    # Select data for the month
    articles_month = articles_df[articles_df.Month == month]

    # Write the new DataFrame to a csv file
    filename = 'data/monthly_files/articles{0:02d}.csv'.format(month)
    articles_month.to_csv(filename)

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

Writing Unique FileNames

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

filename = 'data/monthly_files/articles{0:02d}.csv'.format(month)

Let’s break down the parts of this name:

• The first part is simply some text that specifies the directory to store our data file in (data/monthly_files/) and the first part of the file name (articles): 'data/monthly_files/articles'
• The {0:02d} part gives us zero padding for our numbers. We specify the number we want to fill in using the .format() method. See Python str.format() syntax for more formatting options.

Notice that we use single quotes to add text strings. The variable is not surrounded by quotes. This code produces the string data/monthly_files/articles01.csv which contains the path to the new filename AND the file name itself.

Challenge

1. Some of months seem to have more articles than others. Modify the for loop so that the months with less than 100 articles are excluded.

2. Let’s say you want to look at data for each trimester of the year. How would you modify your loop in order to generate a data file for every 3 month period?

3. How would you modify your query so you can generate a list of articles for each publisher separately?

Building reusable and modular code with functions

Suppose that separating large data files into individual monthly files is a task that we frequently have to perform. We could write a for loop like the one above every time we needed to do it but that would be time consuming and error prone. A more elegant solution would be to create a reusable tool that performs this task with minimum input from the user. To do this, we are going to turn the code we’ve already written into a function.

Functions are reusable, self-contained pieces of code that are called with a single command. They can be designed to accept arguments as input and return values, but they don’t need to do either. Variables declared inside functions only exist while the function is running and if a variable within the function (a local variable) has the same name as a variable somewhere else in the code, the local variable hides but doesn’t overwrite the other.

Every method used in Python (for example, print) is a function, and the libraries we import (say, pandas) are a collection of functions. We will only use functions that are housed within the same code that uses them, but it’s also easy to write functions that can be used by different programs.

Functions are declared following this general structure:

def this_is_the_function_name(input_argument1, input_argument2):

    # The body of the function is indented
    # This function prints the two arguments to screen
    print('The function arguments are:', input_argument1, input_argument2, '(this is done inside the function!)')

    # And returns their product
    return input_argument1 * input_argument2

The function declaration starts with the word def, followed by the function name and any arguments in parenthesis, and ends in a colon. The body of the function is indented just like loops are. If the function returns something when it is called, it includes a return statement at the end.

This is how we call the function:

product_of_inputs = this_is_the_function_name(2,5)

The function arguments are: 2 5 (this is done inside the function!)

print('Their product is:', product_of_inputs, '(this is done outside the function!)')

Their product is: 10 (this is done outside the function!)

Challenge

1. Change the values of the arguments in the function and check its output
2. Try calling the function by giving it the wrong number of arguments (not 2) or not assigning the function call to a variable (no product_of_inputs =)
3. Declare a variable inside the function and test to see where it exists (Hint: can you print it from outside the function?)
4. Explore what happens when a variable both inside and outside the function have the same name. What happens to the global variable when you change the value of the local variable?

We can now turn our code for saving monthly data files into a function. There are many different “chunks” of this code that we can turn into functions, and we can even create functions that call other functions inside them. Let’s first write a function that separates data for just one month and saves that data to a file:

def monthly_csv_writer(this_month, all_data):
    """
    Writes a csv file for data from a given month.

    this_month --- month for which data is extracted
    all_data   --- DataFrame with full-year data
    """

    # Select data for the month
    articles_month = all_data[all_data.Month == this_month]

    # Write the new DataFrame to a csv file
    filename = 'data/monthly_files/articles{0:02d}.csv'.format(month)
    articles_month.to_csv(filename)

The text between the two sets of triple double quotes is called a docstring and contains the documentation for the function. It does nothing when the function is running and is therefore not necessary, but it is good practice to include docstrings as a reminder of what the code does. Docstrings in functions also become part of their ‘official’ documentation:

monthly_csv_writer?

monthly_csv_writer(2, articles_df)

We changed the root of the name of the csv file (so it is function_articles instead of article) so we can distinguish it from the one we wrote before. Check the monthly_files directory for the file. Did it do what you expect?

What we really want to do, though, is create files for multiple months without having to request them one by one. Let’s write another function that replaces the entire For loop by simply looping through a sequence of months and repeatedly calling the function we just wrote, monthly_csv_writer:

def monthly_data_csv_writer(start_month, end_month, all_data):
    """
    Writes separate csv files for each month of data.

    start_month --- the first month of data we want
    end_month --- the last month of data we want
    all_data --- DataFrame with full-year data
    """

    # "end_month" is the last month of data we want to pull, so we loop to end_month+1
    for month in range(start_month, end_month+1):
        monthly_csv_writer(month, all_data)

Because people will naturally expect that the end month for the files is the last month with data, the for loop inside the function ends at end_month + 1. By writing the entire loop into a function, we’ve made a reusable tool for whenever we need to break a large data file into monthly files. Because we can specify the first and last month for which we want files, we can even use this function to create files for a subset of the months available. This is how we call this function:

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

# Create csv files
monthly_data_csv_writer(4, 6, articles_df)

BEWARE! If you are using IPython or Jupyter Notebooks and you modify a function, you MUST re-run that cell in order for the changed function to be available to the rest of the code. Nothing will visibly happen when you do this, though, because simply defining a function without calling it doesn’t produce an output. Any cells that use the now-changed functions will also have to be re-run for their output to change.

Challenge:

1. Add two arguments to the functions we wrote that take the path of the directory where the files will be written and the root of the file name. Create a new set of files with a different name in a different directory.
2. How could you use the function monthly_data_csv_writer to create a csv file for only one month? (Hint: think about the syntax for range)
3. Make the functions return a list of the names of the files which have been written. There are many ways you can do this (and you should try them all!): either of the functions can print to screen, either can use a return statement to give back numbers or strings to their function call, or you can use some combination of the two. You could also try using the os library to list the contents of directories.
4. Explore what happens when variables are declared inside each of the functions versus in the main (non-indented) body of your code. What is the scope of the variables (where are they visible)? What happens when they have the same name but are given different values?

The functions we wrote demand that we give them a value for every argument. Ideally, we would like these functions to be as flexible and independent as possible. Let’s modify the function monthly_data_csv_writer so that the start_month and end_month default to the full range of the data if they are not supplied by the user. Arguments can be given default values with an equal sign in the function declaration. Any arguments in the function without default values (here, all_data) is a required argument and MUST come before the argument with default values (which are optional in the function call).

def monthly_data_arg_test(all_data, start_month=1, end_month=12):
    """
    Modified from monthly_data_csv_writer to test default argument values!

    start_month --- the first month of data we want --- default: 1
    end_month --- the last month of data we want --- default: 12
    all_data --- DataFrame with full-year data
    """

    return start_month, end_month


start,end = monthly_data_arg_test (articles_df, 4, 6)
print('Both optional arguments:\t', start, end)

start,end = monthly_data_arg_test (articles_df)
print('Default values:\t\t\t', start, end)

Both optional arguments:	4 6
Default values:			1 12

The “\t” in the print statements are tabs, used to make the text align and be easier to read.

But what if our dataset doesn’t start in 1 and end in 12? We can modify the function so that it looks for the start and end months in the dataset if those dates are not provided:

def monthly_data_arg_test(all_data, start_month=None, end_month=None):
    """
    Modified from monthly_data_csv_writer to test default argument values!

    start_month --- the first month of data we want --- default: None - check all_data
    end_month --- the last month of data we want --- default: None - check all_data
    all_data --- DataFrame with full-year data
    """

    if not start_month:
        start_month = min(all_data.Month)
    if not end_month:
        end_month = max(all_data.Month)

    return start_month, end_month


start,end = monthly_data_arg_test (articles_df, 4, 6)
print('Both optional arguments:\t', start, end)

start,end = monthly_data_arg_test (articles_df)
print('Default values:\t\t\t', start, end)

Both optional arguments:	4 6
Default values:			1 12

Challenge

We got the same result because our data set does start with 1 and end with 12. How would you slice the dataset, so we have a different result (for example, the last 6 months of the year).

The default values of the start_month and end_month arguments in the function monthly_data_arg_test are now None. This is a built in constant in Python that indicates the absence of a value - essentially, that the variable exists in the namespace of the function (the directory of variable names) but that it doesn’t correspond to any existing object.

Challenge

1. What type of object corresponds to a variable declared as None? (Hint: create a variable set to None and use the function type())

2. Compare the behavior of the function monthly_data_arg_test when the arguments have None as a default and when they do not have default values.

3. What happens if you only include a value for start_month in the function call? Can you write the function call with only a value for end_month? (Hint: think about how the function must be assigning values to each of the arguments - this is related to the need to put the arguments without default values before those with default values in the function definition!)

The body of the test function now has two conditional statements (if statements) that check the values of start_month and end_month. if statements execute the body of the statement when some condition is met. They commonly look something like this:

a = 5

if a < 0: # meets first condition?

    # if a IS less than zero
    print('a is a negative number')

elif a > 0: # did not meet first condition. meets second condition?

    # if a ISN'T less than zero and IS more than zero
    print('a is a positive number')

else: # met neither condition

    # if a ISN'T less than zero and ISN'T more than zero
    print('a must be zero!')

a is a positive number

Change the value of a to see how this function works. The statement elif means “else if”, and all of the conditional statements must end in a colon.

The if statements in the function monthly_data_arg_test check whether there is an object associated with the variable names start_month and end_month. If those variables are None, the if statements return the boolean True and execute whatever is in their body. On the other hand, if the variable names are associated with some value (they got a number in the function call), the if statements return False and do not execute. The opposite conditional statements, which would return True if the variables were associated with objects (if they had received value in the function call), would be if start_month and if end_month.

As we’ve written it so far, the function monthly_data_arg_test associates values in the function call with arguments in the function definition just based in their order. If the function gets only two values in the function call, the first one will be associated with all_data and the second with start_month, regardless of what we intended them to be. We can get around this problem by calling the function using keyword arguments, where each of the arguments in the function definition is associated with a keyword and the function call passes values to the function using these keywords:

def monthly_data_arg_test(all_data, start_month = None, end_month = None):
    """
    Modified from monthly_data_csv_writer to test default argument values!

    start_month --- the first month of data we want --- default: None - check all_data
    end_month --- the last month of data we want --- default: None - check all_data
    all_data --- DataFrame with full-year data
    """

    if not start_month:
        start_month = min(all_data.Month)
    if not end_month:
        end_month = max(all_data.Month)

    return start_month, end_month


start,end = monthly_data_arg_test (articles_df)
print('Default values:\t\t\t', start, end)

start,end = monthly_data_arg_test (articles_df, 4, 6)
print('No keywords:\t\t\t', start, end)

start,end = monthly_data_arg_test (articles_df, start_month = 4, end_month = 6)
print('Both keywords, in order:\t', start, end)

start,end = monthly_data_arg_test (articles_df, end_month = 6, start_month = 4)
print('Both keywords, flipped:\t\t', start, end)

start,end = monthly_data_arg_test (articles_df, start_month = 4)
print('One keyword, default end:\t', start, end)

start,end = monthly_data_arg_test (articles_df, end_month = 6)
print('One keyword, default start:\t', start, end)

Default values:			1 12
No keywords:			4 6
Both keywords, in order:	4 6
Both keywords, flipped:		4 6
One keyword, default end:	4 12
One keyword, default start:	6 12

Challenge

1. Rewrite the monthly_csv_writer and monthly_data_csv_writer functions to have keyword arguments with default values

2. Modify the functions so that they don’t create monthly files if there is no data for a given month and display an alert to the user (Hint: use conditional statements and if loops to do this. For an extra challenge, use try statements!)

3. The code below checks to see whether a directory exists and creates one if it doesn’t. Add some code to your function that writes out the CSV files, to check for a directory to write to.

	if 'dirNameHere' in os.listdir('.'):
	    print('Processed directory exists')
	else:
	    os.mkdir('dirNameHere')
	    print('Processed directory created')

1. The code that you have written so far to loop through the months is good, however it is not necessarily reproducible with different datasets. For instance, what happens to the code if we have additional years of data in our CSV files? Using the tools that you learned in the previous activities, make a list of all years represented in the data. Then create a loop to process your data, that begins at the earliest year and ends at the latest year using that list.

HINT: you can create a loop with a list as follows: for years in yearList:

Key Points

• Loops.

• Functions.