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64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import pandas as pd
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import pytest
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from pandas import DataFrame
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from cleaning import (
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SCORE_COLS,
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drop_empty_appellation,
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mean_score,
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fill_missing_scores,
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encode_appellation,
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)
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@pytest.fixture
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def df_raw() -> DataFrame:
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return pd.DataFrame({
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"Appellation": ["Pauillac", "Pauillac ", "Margaux", None, "Pomerol", "Pomerol"],
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"Robert": ["95", None, "bad", 90, None, None],
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"Robinson": [None, "93", 18, None, None, None],
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"Suckling": [96, None, None, None, 91, None],
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"Prix": ["10.0", "11.0", "20.0", "30.0", "40.0", "50.0"],
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})
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def test_drop_empty_appellation(df_raw: DataFrame):
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out = drop_empty_appellation(df_raw)
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assert out["Appellation"].isna().sum() == 0
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assert len(out) == 5
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def test_mean_score_zero_when_no_scores(df_raw: DataFrame):
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out = drop_empty_appellation(df_raw)
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m = mean_score(out, "Robert")
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assert list(m.columns) == ["Appellation", "mean_Robert"]
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# Pomerol n'a aucune note Robert => moyenne doit être 0
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pomerol_mean = m.loc[m["Appellation"].str.strip() == "Pomerol", "mean_Robert"].iloc[0]
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assert pomerol_mean == 0
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def test_fill_missing_scores(df_raw: DataFrame):
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out = drop_empty_appellation(df_raw)
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filled = fill_missing_scores(out)
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# plus de NaN dans les colonnes de scores
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for col in SCORE_COLS:
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assert filled[col].isna().sum() == 0
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assert filled.loc[1, "Robert"] == 95.0
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# pas de colonnes temporaires mean_*
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for col in SCORE_COLS:
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assert f"mean_{col}" not in filled.columns
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def test_encode_appellation(df_raw: DataFrame):
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out = drop_empty_appellation(df_raw)
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filled = fill_missing_scores(out)
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encoded = encode_appellation(filled)
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# la colonne texte disparaît
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assert "Appellation" not in encoded.columns
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assert "Pauillac" in encoded.columns
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assert encoded.loc[0, "Pauillac"] == 1 |