# Pandas Series Assertions

In this you'll learn how to use most used Pandas Series assertion functions. Below are the functions that are covered.

assert_pd_series_variable_equals_variable(student_variable_name, expected_outcome_variable_name): Checks that the student's Series in student_variable_name matches the variable in expected_outcome_variable_name.
assert_pd_series_variable_equals_csv(student_variable_name, solution_csv_name, read_csv_kwargs=None): Checks that the student's Series in student_variable_name matches the Series contained in the solution CSV file named solution_csv_name.
assert_pd_series_variable_equals_pickle(student_variable_name, pickle_name, read_pickle_kwargs=None, series_testing_kwargs=None): Checks that the student's Series in student_variable_name matches the Series contained in the solution pickle file named pickle_name. This used when we have pivoted data or multi-column data.

Load the utils.py file to use the assertion functions.

exec(open('utils.py').read())

import pandas as pd

# Activities

Now, with activities examples, you'll learn how to use the assertion functions.

# 1. Create a student series

Use below data to create a series named student_data.

data = ['Albert', 'John', 'Peter', 'James', 'Robert']

student_data = ...

Solution:

data = ['Albert', 'John', 'Peter', 'James', 'Robert']
student_data = pd.Series(data)

As the expected output is small series, so we use assert_pd_series_variable_equals_variable() function to assert the solution with the student series.

Assertions:

expected_output = pd.Series(['Albert', 'John', 'Peter', 'James', 'Robert'])
assert_pd_series_variable_equals_variable('student_data', 'expected_output')

Here, we passed first student variable then expected variable.

# 2. Create a prime number series

Create a series named prime_numbers_series which contains the first 10,000 prime numbers.

prime_numbers_series = ...

Solution:

prime_numbers = []
num = 2
while len(prime_numbers) < 10000:
    for i in range(2, num):
        if num % i == 0:
            break
    else:
        prime_numbers.append(num)
    num += 1

prime_numbers_series = pd.Series(prime_numbers)

In previous example, the expected series is small, so we've used assert_pd_series_variable_equals_variable() function to assert the solution with the student series but in this example, the expected series is large, so we'll save the expected series to a csv file and then use assert_pd_series_variable_equals_csv() function to assert the solution with the csv file.

prime_numbers_series.to_csv('activity_solutions_files/activity_1.csv', index=False)

Assertions:

assert_pd_series_variable_equals_csv('field_goal_perc', 'activity_1.csv')

# 3. Create a student series with index and multi-column data

Create a series named student_data with below data and index.

data = {'Name': ['Albert', 'John', 'Peter', 'James', 'Robert'],
        'Age': [25, 30, 35, 40, 45],
        'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']}
index = ['A', 'B', 'C', 'D', 'E']

student_data = ...

Solution:

data = {'Name': ['Albert', 'John', 'Peter', 'James', 'Robert'],
        'Age': [25, 30, 35, 40, 45],
        'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']}
index = ['A', 'B', 'C', 'D', 'E']

student_data = pd.Series(data, index=index)

Here, we'll use pickle file to save the expected series and then use assert_pd_series_variable_equals_pickle() function to assert the solution with the pickle file.

student_data.to_pickle('activity_solutions_files/activity_3.pkl')

Assertions:

assert_pd_series_variable_equals_pickle('student_data', 'activity_3.pkl')