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シリーズ型.map()とシリーズ型.replace()の違い

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カラム「fruits」の値を以下の規則性に従って変更する

『grape -> 0』『orange -> 1』『banana -> 2』『melon -> 3』

fruits_dict = {
'grape': 0,
'orange': 1,
'banana': 2,
'melon': 3
}

mapでもreplaceでもどちらでもOK

:o:train_df['fruits'] = train_df['fruits'].replace(fruits_dict)
:o:train_df['fruits'] = train_df['fruits'].map(fruits_dict)

print(train_df['fruits'].value_counts())

0 500

1 200

2 300

3 400

Name: weather, dtype: int64

例外 『grape -> 0』のみにする

fruits_dict = {
'grape': 0
}
:white_circle:train_df['fruits'] = train_df['fruits'].replace(fruits_dict)
print(train_df['fruits'].value_counts())

0 500

orange 200

banana 300

melon 400

Name: weather, dtype: int64


fruits_dict = {
'grape': 0
}
:white_circle:train_df['fruits'] = train_df['fruits'].replace(fruits_dict)
print(train_df['fruits'].value_counts())

0.0 500

となる。これは、grape以外の数値がNANとなるからである。
辞書オブジェクトのキーに含まれない値はNaNとなることに注意する必要がある。

→map()では置換されない値がNaNとなってしまう。replace()では元の値のまま。

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