Data workflows and automation

Last updated on 2024-02-13 | Edit this page

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)
['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:

PYTHON 

>>> 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 - 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’).

Automating data processing using For Loops

Suppose that we were working with a much larger set of books. It might be useful to split them out into smaller groups, e.g., by date of publication.

Let’s start by making a new directory inside our current working folder. Python has a built-in library called os for this sort of operating-system dependent behaviour.

PYTHON 

    import os

    os.mkdir('yearly_files')

We can check that the folder was created by listing the contents of the current directory:

PYTHON 

>>> os.listdir()
['authors.csv', 'yearly_files', 'places.csv', 'eebo.db', 'eebo.csv', '1635.csv', '1640.csv']

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 to write out records for the year 1636 as a single csv in the yearly_files directory

PYTHON 

import pandas as pd

# Load the data into a DataFrame
eebo_df = pd.read_csv('eebo.csv')

# Select only data for 1636
authors1636 = eebo_df[eebo_df.Date == "1636"]

# Write the new DataFrame to a csv file
authors1636.to_csv('yearly_files/authors1636.csv')

# Then check that that file now exists:
os.listdir("yearly_files")

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 simply 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 

>>> eebo_df['Date']

0      1625
1      1515
2      1528
3      1623
4      1640
5      1623
    ...    
141    1635
142    1614
143    1589
144    1636
145    1562
146    1533
147    1606
148    1618
Name: Date, Length: 149, dtype: int64

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

PYTHON 

>>> eebo_df['Date'].unique()
array([1625, 1515, 1528, 1623, 1640, 1624, 1607, 1558, 1599, 1622, 1613,
       1600, 1635, 1569, 1579, 1597, 1538, 1559, 1563, 1577, 1580, 1626,
       1631, 1565, 1632, 1571, 1554, 1615, 1549, 1567, 1605, 1636, 1591,
       1588, 1619, 1566, 1593, 1547, 1603, 1609, 1589, 1574, 1584, 1630,
       1621, 1610, 1542, 1534, 1519, 1550, 1540, 1557, 1606, 1545, 1537,
       1532, 1526, 1531, 1533, 1572, 1536, 1529, 1535, 1543, 1586, 1596,
       1552, 1608, 1611, 1616, 1581, 1639, 1570, 1564, 1568, 1602, 1618,
       1583, 1638, 1592, 1544, 1585, 1614, 1562])

Putting this into our for loop we get

PYTHON 

>>> for year in eebo_df['Date'].unique():
...    filename = 'yearly_files/authors{}.csv'.format(year)
...    print(filename)
...
yearly_files/authors1625.csv
yearly_files/authors1515.csv
yearly_files/authors1528.csv
yearly_files/authors1623.csv
yearly_files/authors1640.csv
yearly_files/authors1624.csv
yearly_files/authors1607.csv

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

PYTHON 

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

for year in eebo_df['Date'].unique():

    # Select data for the year
    publish_year = eebo_df[eebo_df.Date == year]

    # Write the new DataFrame to a csv file
    filename = 'yearly_files/authors{}.csv'.format(year)
    publish_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 Filenames

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

filename = 'yearly_files/authors{}.csv'.format(year)

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 yearly_files/ and the first part of the file name (authors): 'yearly_files/authors'
  • We want to dynamically insert the value of the year into the filename. We can do this by indicating a placeholder location inside the string with {}, and then specifying the value that will go there with .format(value)
  • Finally, we specify a file type with .csv. Since the . is inside the string, it doesn’t behave the same way as dot notation in Python commands.

Notice the difference between the filename - wrapped in quote marks - and the variable (year), which is not wrapped in quote marks. The result looks like 'yearly_files/authors1607.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. What happens if there is no data for a year?

  3. Instead of splitting out the data by years, a colleague wants to analyse each place separately. How would you write a unique csv file for each location?

Building reusable and modular code with functions

Suppose that separating large data files into individual yearly 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:

PYTHON 

def this_is_the_function_name(input_argument1, input_argument2):
    # The body of the function is indented
    """This is the docstring of the function. Wrapped in triple-quotes,
    it can span across multiple lines. This is what is shown if you ask
    for help about the function like this:
    >>> help(this_is_the_function_name)
    """
    
    # 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:

PYTHON 

>>> 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 - Functions

  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 yearly 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 year and saves that data to a file:

PYTHON 

def one_year_csv_writer(this_year, all_data):
    """
    Writes a csv file for data from a given year.
    
    Parameters
    ----------
    this_year: int
        year for which data is extracted
    all_data: pandas Dataframe
        DataFrame with multi-year data
    
    Returns
    -------
    None
    """

    # Select data for the year
    texts_year = all_data[all_data.Date == this_year]

    # Write the new DataFrame to a csv file
    filename = 'data/yearly_files/function_authors' + str(this_year) + '.csv'
    texts_year.to_csv(filename)

The text between the two sets of triple 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:

PYTHON 

one_year_csv_writer?

PYTHON 

one_year_csv_writer('1607',eebo_df)

We changed the root of the name of the csv file so we can distinguish it from the one we wrote before. Check the yearly_files directory for the file. Did it do what you expect?

What we really want to do, though, is create files for multiple years 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 years and repeatedly calling the function we just wrote, one_year_csv_writer:

PYTHON 

def yearly_data_csv_writer(start_year, end_year, all_data):
    """
    Writes separate csv files for each year of data.

    Parameters
    ----------
    start_year: int
        the first year of data we want
    end_year: int
        the last year of data we want
    all_data: pandas Dataframe
        DataFrame with multi-year data

    Returns
    -------
    None
    """

    # "end_year" is the last year of data we want to pull, so we loop to end_year+1
    for year in range(start_year, end_year+1):
        one_year_csv_writer(str(year), all_data)

Because people will naturally expect that the end year for the files is the last year with data, the for loop inside the function ends at end_year + 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 yearly files. Because we can specify the first and last year for which we want files, we can even use this function to create files for a subset of the years available. This is how we call this function:

PYTHON 

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

# Create csv files
yearly_data_csv_writer(1500, 1650, eebo_df)

BEWARE! If you are using IPython 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- More functions

  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 yearly_data_csv_writer to create a csv file for only one year? (Hint: think about the syntax for range)
  3. Make the functions return a list of the files they have 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 yearly_data_csv_writer so that the start_year and end_year 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).

PYTHON 

def yearly_data_arg_test(all_data, start_year=1000, end_year=2000):
    """
    Test function with default values. Doesn't actually do anything
    except return the arguments, to check what they end up as

    Parameters
    ----------
    all_data: pandas Dataframe
        DataFrame with multi-year data
    start_year: int, optional
        the first year of data we want
        Default 1000
    end_year: int, optional
        the last year of data we want

    Returns
    -------
    int
        start_year
    int: 
        end_year
    """

    return start_year, end_year


start,end = yearly_data_arg_test (eebo_df, 1600, 1660)
print('Both optional arguments:\t', start, end)

start,end = yearly_data_arg_test (eebo_df)
print('Default values:\t\t\t', start, end)
Both optional arguments:	1600 1660
Default values:			    1000 2000

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 1300 and end in 1700? We can modify the function so that it looks for the start and end years in the dataset if those dates are not provided:

PYTHON 

def yearly_data_arg_test(all_data, start_year=None, end_year=None):
    """
    Test function with default values. Doesn't actually do anything
    except return the arguments, to check what they end up as

    Parameters
    ----------
    all_data: pandas Dataframe
        DataFrame with multi-year data
    start_year: int, optional
        the first year of data we want.
        Defaults to earliest year in `all_data`
    end_year: int, optional
        the last year of data we want
        Defaults to latest year in `all_data`

    Returns
    -------
    int
        start_year
    int: 
        end_year
    """

    if start_year is None:
        start_year = min(all_data.Date)
    if end_year is None:
        end_year = max(all_data.Date)

    return start_year, end_year


start,end = yearly_data_arg_test (eebo_df, '1600', '1660')
print('Both optional arguments:\t', start, end)

start,end = yearly_data_arg_test (eebo_df)
print('Default values:\t\t\t', start, end)
Both optional arguments:	1600 1660
Default values:		        1500 1650

The default values of the start_year and end_year arguments in the function yearly_data_arg_test are now None. This is a build-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 - Variables

  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 yearly_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_year in the function call? Can you write the function call with only a value for end_year? (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!)

If Loops

The body of the test function now has two conditional loops (if loops) that check the values of start_year and end_year. If loops execute the body of the loop when some condition is met. They commonly look something like this:

PYTHON 

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!')

Which would return:

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 loops in the function yearly_data_arg_test check whether there is an object associated with the variable names start_year and end_year. If those variables are None, the if loops return the boolean True and execute whaever 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 loops 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_year and if end_year.

As we’ve written it so far, the function yearly_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_year, 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:

PYTHON 


start,end = yearly_data_arg_test (eebo_df)
print('Default values:\t\t\t', start, end)

start,end = yearly_data_arg_test (eebo_df, 1600, 1660)
print('No keywords:\t\t\t', start, end)

start,end = yearly_data_arg_test (eebo_df, start_year=1600, end_year=1660)
print('Both keywords, in order:\t', start, end)

start,end = yearly_data_arg_test (eebo_df, end_year=1660, start_year=1600)
print('Both keywords, flipped:\t\t', start, end)

start,end = yearly_data_arg_test (eebo_df, start_year=1600)
print('One keyword, default end:\t', start, end)

start,end = yearly_data_arg_test (eebo_df, end_year=1660)
print('One keyword, default start:\t', start, end)
Default values:			1515 1640
No keywords:			1600 1640
Both keywords, in order:	        1600 1660
Both keywords, flipped:		1600 1660
One keyword, default end:	        1600 1640
One keyword, default start:	        1515 1660

Challenge - Modifying functions

  1. Rewrite the one_year_csv_writer and yearly_data_csv_writer functions to have keyword arguments with default values

  2. Modify the functions so that they don’t create yearly files if there is no data for a given year 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.

PYTHON 

  if 'dir_name_here' in os.listdir('.'):
      print('Processed directory exists')
  else:
      os.mkdir('dir_name_here')
      print('Processed directory created')
  1. The code that you have written so far to loop through the years 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 year_list: