Implementing a test suite
Last updated on 2024-09-26 | Edit this page
Estimated time: 30 minutes
Overview
Questions
- How can we implement a test suite when someone adds new code to our project?
Objectives
After completing this episode, participants should be able to:
- Implement a test suite using
pytesttesting framework for automated testing
This extra episode provides additional exercises on writing tests and should be followed after the episode on code correctness and with the starter code from the end of that episode.
A member of our research team shares the following code with us to add to our existing codebase:
PYTHON
def summarise_categorical(df, varname):
"""
Tabulate the distribution of a categorical variable
Args:
df (pd.DataFrame): The input dataframe.
varname (str): The name of the variable
Returns:
pd.DataFrame: dataframe containing the count and percentage of
each unique value of varname_
Examples:
>>> df_example = pd.DataFrame({
'vehicle': ['Apollo 16', 'Apollo 17', 'Apollo 17'],
}, index=[0, 1, 2)
>>> summarise_categorical(df_example, "vehicle")
Tabulating distribution of categorical variable vehicle
vehicle count percentage
0 Apollo 16 1 33.0
1 Apollo 17 2 67.0
"""
print(f'Tabulating distribution of categorical variable {varname}')
# Prepare statistical summary
count_variable = df[[varname]].copy()
count_summary = count_variable.value_counts()
percentage_summary = round(count_summary / count_variable.size, 2) * 100
# Combine results into a summary data frame
df_summary = pd.concat([count_summary, percentage_summary], axis=1)
df_summary.columns = ['count', 'percentage']
df_summary.sort_index(inplace=True)
df_summary = df_summary.reset_index()
return df_summaryThis looks like a useful tool for creating summary statistics tables,
so let’s integrate this into our eva_data_analysis.pycode
and then write a minimal test suite to check that this code is behaving
as expected.
PYTHON
...
def main(input_file, output_file, graph_file):
print("--START--")
eva_data = read_json_to_dataframe(input_file)
write_dataframe_to_csv(eva_data, output_file)
eva_data = add_crew_size_column(eva_data)
table_crew_size = summarise_categorical(eva_data, "crew_size") # new line added
write_dataframe_to_csv(table_crew_size, "results/table_crew_size.csv")
plot_cumulative_time_in_space(eva_data, graph_file)
print("--END--")
To write tests for this function, we will need to be able to compare
dataframes. The pandas.testing module in the
pandas library provides functions and utilities for testing
pandas objects and includes a function
assert_frame_equal that we can use to compare two
dataframes.
Exercise 1 - typical inputs
First, check that the function behaves as expected with typical input values. Fill in the gaps in the skeleton test below:
PYTHON
import pandas.testing as pdt
def test_summarise_categorical_typical():
"""
Test that summarise_categorical correctly tabulates
distribution of values (counts, percentages) for a ground truth
example (typical values)
"""
test_input = pd.DataFrame({
'country': _________________________________________, # FIX-ME
}, index=[0, 1, 2, 3, 4])
expected_result = pd.DataFrame({
'country': ["Russia", "USA"],
'count': [2, 3],
'percentage': [40.0, 60.0],
}, index=[0, 1])
actual_result = ____________________________________________ # FIX-ME
pdt.__________________(actual_result, _______________) #FIX-MEPYTHON
import pandas.testing as pdt
def test_summarise_categorical():
"""
Test that summarise_categorical correctly tabulates
distribution of values (counts, percentages) for a simple ground truth
example
"""
test_input = pd.DataFrame({
'country': ['USA', 'USA', 'USA', "Russia", "Russia"],
}, index=[0, 1, 2, 3, 4])
expected_result = pd.DataFrame({
'country': ["Russia", "USA"],
'count': [2, 3],
'percentage': [40.0, 60.0],
}, index=[0, 1])
actual_result = summarise_categorical(test_input, "country")
pdt.assert_frame_equal(actual_result, expected_result)Exercise 2 - edge cases
Now let’s check that the function behaves as expected with edge
cases.
Does the code behave as expected when the column of interest contains
one or more missing values (pd.NA)? (write a new test).
Fill in the gaps in the skeleton test below:
PYTHON
import pandas.testing as pdt
def test_summarise_categorical_missvals():
"""
Test that summarise_categorical correctly tabulates
distribution of values (counts, percentages) for a ground truth
example (edge case where all column contains missing values)
"""
test_input = _______________
_______________
_______________ # FIX-ME
expected_result = _______________
_______________
_______________ # FIX-ME
actual_result = summarise_categorical(test_input, "country")
pdt.assert_frame_equal(actual_result, expected_result)PYTHON
import pandas.testing as pdt
def test_summarise_categorical_missvals():
"""
Test that summarise_categorical correctly tabulates
distribution of values (counts, percentages) for a ground truth
example (edge case where column contains missing values)
"""
test_input = pd.DataFrame({
'country': ['USA', 'USA', 'USA', "Russia", pd.NA],
}, index=[0, 1, 2, 3, 4])
expected_result = pd.DataFrame({
'country': ["Russia", "USA", np.nan], # np.nan because pd.NA is cast to np.nan
'count': [1, 3, 1],
'percentage': [20.0, 60.0, 20.0],
}, index=[0, 1, 2])
actual_result = summarise_categorical(test_input, "country")
pdt.assert_frame_equal(actual_result, expected_result)Exercise 3 - invalid inputs
Now write a test to check that the summarise_categorical
function raises an appropriate error when asked to tabulate a column
that does not exist in the data frame.
Hint: lookup pytest.raises in the pytest
documentation.
PYTHON
def test_summarise_categorical_invalid():
"""
Test that summarise_categorical raises an
error when a non-existent column is input
"""
test_input = pd.DataFrame({
'country': ['USA', 'USA', 'USA', "Russia", "Russia"],
}, index=[0, 1, 2, 3, 4])
with pytest.raises(KeyError):
summarise_categorical(test_input, "vehicle")