Aspettative sullo schema

Introduzione alla Data Quality con Great Expectations

Davina Moossazadeh

Data Scientist

Aspettative su forma e schema

Expectation - Un'asserzione verificabile sui dati

Aspettative sulle colonne
Aspettative su forma e schema
- schema - il progetto della struttura di un dataset

¹ https://docs.greatexpectations.io/docs/reference/learn/terms/expectation

Numero di righe

row_count_expectation = gx.expectations.ExpectTableRowCountToEqual(
    value=118000
)

validation_results = batch.validate(expect=row_count_expectation)

print(validation_results.success)

False

print(validation_results.result)

{'observed_value': 118066}

Intervallo numero righe

Usa ExpectTableRowCountToBeBetween per definire un intervallo di conteggio delle righe:

row_count_expectation = gx.expectations.ExpectTableRowCountToBeBetween(

    min_value=117000, max_value=119000
)

validation_results = batch.validate(expect=row_count_expectation)

print(validation_results.success)

True

print(validation_results.result)

{'observed_value': 118066}

Numero di colonne

col_count_expectation = gx.expectations.ExpectTableColumnCountToEqual(
    value=15
)
validation_results = batch.validate(expect=col_count_expectation)

print(validation_results.success)

False

print(validation_result.result)

{'observed_value': 18}

Intervallo numero colonne

Usa ExpectTableColumnCountToBeBetween per definire un intervallo di conteggio delle colonne:

col_count_expectation = gx.expectations.ExpectTableColumnCountToBeBetween(
    min_value=14, max_value=18
)
validation_results = batch.validate(expect=col_count_expectation)

print(validation_results.success)

True

print(validation_result.result)

{'observed_value': 18}

Set di nomi colonna

Usa ExpectTableColumnsToMatchSet per validare i nomi colonna rispetto a un set:

expected_cols = ['clouds_all', 'snow_1h', 'rain_1h', 'wind_speed', 'humidity', 
'pressure', 'temp', 'GHI', 'Energy delta[Wh]', 'Time', 'Time']

col_names_expectation = gx.expectations.ExpectTableColumnsToMatchSet(
    column_set=expected_cols
)

print(col_names_expectation.success, col_names_expectation.result)

True 
{'observed_value': ['Time', 'Energy delta[Wh]', 'GHI', 'temp', 'pressure',
'humidity', 'wind_speed', 'rain_1h', 'snow_1h', 'clouds_all'}

Nomi colonna singoli

Usa ExpectColumnToExist per verificare se una colonna specifica è presente nel dataset:

col_name_expectation = gx.expectations.ExpectColumnToExist(column="not_a_column")
validation_result = batch.validate(expect=col_name_expectation)
print(validation_result.success)

False

col_name_expectation = gx.expectations.ExpectColumnToExist(column="GHI")
validation_result = batch.validate(expect=col_name_expectation)
print(validation_result.success)

True

Cheat sheet

Aspettative sulla forma:

ExpectTableRowCountToEqual(value: int)
ExpectTableRowCountToBeBetween(
    min_value: int, max_value: int
)
ExpectTableColumnCountToEqual(
    value: int
)
ExpectTableColumnCountToBeBetween(
    min_value: int, max_value: int
)

Aspettative sui nomi colonna:

ExpectTableColumnsToMatchSet(
    column_set: set
)
ExpectColumnToExist(column: str)

Ayo berlatih!

Introduzione alla Data Quality con Great Expectations