Praktiska exempel

Introduktion till testning i Python

Alexander Levin

Data Scientist

Data och pipeline

Data: löner inom datavetenskap.

Varje rad innehåller information om en datavetenskapsanställd med lön, titel och andra attribut.

ds salaries table

Pipeline: beräkna medellönen:

Läs in data
Filtrera på anställningstyp
Beräkna medellönen
Spara resultaten

Pipelinens kod

import pandas as pd

# Fixture to get the data
@pytest.fixture
def read_df():
    return pd.read_csv('ds_salaries.csv')
# Function to filter the data
def filter_df(df):
    return df[df['employment_type'] == 'FT']
# Function to get the mean
def get_mean(df):   
    return df['salary_in_usd'].mean()

Integrationstester

Testfall:

Läsa in data
Skriva till fil

Kod:

def test_read_df(read_df):
    # Check the type of the dataframe
    assert isinstance(read_df, pd.DataFrame)
    # Check that df contains rows
    assert read_df.shape[0] > 0

Integrationstester

Exempel på att kontrollera att Python kan skapa filer.

def test_write():
    # Opening a file in writing mode
    with open('temp.txt', 'w') as wfile:
        # Writing the text to the file
        wfile.write('Testing stuff is awesome')
    # Checking the file exists
    assert os.path.exists('temp.txt')
    # Don't forget to clean after yourself
    os.remove('temp.txt')

Enhetstester

Testfall:

Filtrerad datamängd innehåller bara anställningstypen 'FT'
Funktionen get_mean() returnerar ett tal

Kod:

def test_units(read_df):
    filtered = filter_df(read_df)
    assert filtered['employment_type'].unique() == ['FT']
    assert isinstance(get_mean(filtered), float)

Särdragstester

Testfall:

Medelvärdet är större än noll
Medelvärdet överstiger inte den högsta lönen i datamängden

Kod:

def test_feature(read_df):
    # Filtering the data
    filtered = filter_df(read_df)
    # Test case: mean is greater than zero
    assert get_mean(filtered) > 0
    # Test case: mean is not bigger than the maximum
    assert get_mean(filtered) <= read_df['salary_in_usd'].max()

Prestandatester

Testfall:

Pipelinens körtid från start till slut

Kod:

def test_performance(benchmark, read_df):
    # Benchmark decorator
    @benchmark
    # Function to measure
    def get_result():
        filtered = filter_df(read_df)
        return get_mean(filtered)

Fullständig testsvit

import pytest

## Integration Tests
def test_read_df(read_df):
      # Check the type of the dataframe
    assert isinstance(read_df, pd.DataFrame)
    # Check that df contains rows
    assert read_df.shape[0] > 0
def test_write():
    with open('temp.txt', 'w') as wfile:
        wfile.write('12345')
    assert os.path.exists('temp.txt')
    os.remove('temp.txt')

## Unit Tests
def test_units(read_df):
    filtered = filter_df(read_df)
    assert filtered['employment_type'].unique() == ['FT']
    assert isinstance(get_mean(filtered), float)

## Feature Tests
def test_feature(read_df):
    # Filtering the data
    filtered = filter_df(read_df)
    # Test case: mean is greater than zero
    assert get_mean(filtered) > 0
    # Test case: mean is not bigger than the maximum
    assert get_mean(filtered) <= read_df['salary_in_usd'].max()

## Performance Tests
def test_performance(benchmark, read_df):
    # Benchmark decorator
    @benchmark
    # Function to measure
    def pipeline():
        filtered = filter_df(read_df)
        return get_mean(filtered)

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Introduktion till testning i Python