Overview
Teaching: 0 min
Exercises: 0 minQuestions
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.
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”.
What happens if we don’t include the pass statement?
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’).
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:
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:
>>> os.listdir('data')
['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:
import pandas as pd
# Load the data into a DataFrame
surveys_df = pd.read_csv('https://ndownloader.figshare.com/files/2292172')
# Select only data for 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 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:
>>> surveys_df['year']
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 function
which we have already seen.
>>> surveys_df['year'].unique()
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
>>> for year in surveys_df['year'].unique():
... filename='data/yearly_files/surveys' + str(year) + '.csv'
... print(filename)
...
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:
# 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.
Notice that the code above created a unique filename for each year.
filename = 'data/yearly_files/surveys' + str(year) + '.csv'
Let’s break down the parts of this name:
'data/yearly_files/surveys'year by
using the plus + sign and the variable we want to add to the file name: +
str(year)+ '.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.
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.
What happens if there is no data for a year in the sequence (for example,
imagine we had used 1976 as the start year in range)?
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?
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?
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:
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!)
product_of_inputs =)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:
def one_year_csv_writer(this_year, all_data):
"""
Writes a csv file for data from a given year.
this_year --- year for which data is extracted
all_data --- DataFrame with multi-year data
"""
# Select data for the year
surveys_year = all_data[all_data.year == this_year]
# Write the new DataFrame to a csv file
filename = 'data/yearly_files/function_surveys' + str(this_year) + '.csv'
surveys_year.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:
one_year_csv_writer?
one_year_csv_writer(2002,surveys_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:
def yearly_data_csv_writer(start_year, end_year, all_data):
"""
Writes separate csv files for each year of data.
start_year --- the first year of data we want
end_year --- the last year of data we want
all_data --- DataFrame with multi-year data
"""
# "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(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:
# Load the data into a DataFrame
surveys_df = pd.read_csv('data/surveys.csv')
# Create csv files
yearly_data_csv_writer(1977, 2002, surveys_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.
yearly_data_csv_writer to create a csv file
for only one year? (Hint: think about the syntax for range)os library to list the contents of
directories.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).
def yearly_data_arg_test(all_data, start_year = 1977, end_year = 2002):
"""
Modified from yearly_data_csv_writer to test default argument values!
start_year --- the first year of data we want --- default: 1977
end_year --- the last year of data we want --- default: 2002
all_data --- DataFrame with multi-year data
"""
return start_year, end_year
start,end = yearly_data_arg_test (surveys_df, 1988, 1993)
print('Both optional arguments:\t', start, end)
start,end = yearly_data_arg_test (surveys_df)
print('Default values:\t\t\t', start, end)
Both optional arguments: 1988 1993
Default values: 1977 2002
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 1977 and end in 2002? We can modify the function so that it looks for the start and end years in the dataset if those dates are not provided:
def yearly_data_arg_test(all_data, start_year = None, end_year = None):
"""
Modified from yearly_data_csv_writer to test default argument values!
start_year --- the first year of data we want --- default: None - check all_data
end_year --- the last year of data we want --- default: None - check all_data
all_data --- DataFrame with multi-year data
"""
if not start_year:
start_year = min(all_data.year)
if not end_year:
end_year = max(all_data.year)
return start_year, end_year
start,end = yearly_data_arg_test (surveys_df, 1988, 1993)
print('Both optional arguments:\t', start, end)
start,end = yearly_data_arg_test (surveys_df)
print('Default values:\t\t\t', start, end)
Both optional arguments: 1988 1993
Default values: 1977 2002
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-it 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.
What type of object corresponds to a variable declared as None? (Hint:
create a variable set to None and use the function type())
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.
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!)
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:
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 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:
def yearly_data_arg_test(all_data, start_year = None, end_year = None):
"""
Modified from yearly_data_csv_writer to test default argument values!
start_year --- the first year of data we want --- default: None - check all_data
end_year --- the last year of data we want --- default: None - check all_data
all_data --- DataFrame with multi-year data
"""
if not start_year:
start_year = min(all_data.year)
if not end_year:
end_year = max(all_data.year)
return start_year, end_year
start,end = yearly_data_arg_test (surveys_df)
print('Default values:\t\t\t', start, end)
start,end = yearly_data_arg_test (surveys_df, 1988, 1993)
print('No keywords:\t\t\t', start, end)
start,end = yearly_data_arg_test (surveys_df, start_year = 1988, end_year = 1993)
print('Both keywords, in order:\t', start, end)
start,end = yearly_data_arg_test (surveys_df, end_year = 1993, start_year = 1988)
print('Both keywords, flipped:\t\t', start, end)
start,end = yearly_data_arg_test (surveys_df, start_year = 1988)
print('One keyword, default end:\t', start, end)
start,end = yearly_data_arg_test (surveys_df, end_year = 1993)
print('One keyword, default start:\t', start, end)
Default values: 1977 2002
No keywords: 1988 1993
Both keywords, in order: 1988 1993
Both keywords, flipped: 1988 1993
One keyword, default end: 1988 2002
One keyword, default start: 1977 1993
Rewrite the one_year_csv_writer and yearly_data_csv_writer functions to
have keyword arguments with default values
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!)
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 'dir_name_here' in os.listdir('.'):
print('Processed directory exists')
else:
os.mkdir('dir_name_here')
print('Processed directory created')
HINT: you can create a loop with a list as follows: for years in year_list:
Key Points