DataFrame 中存在 Pandas select 行相关列值

Question

假设您有这样一个数据框：

>>> df = pd.DataFrame({
        'epoch_minute': [i for i in reversed(range(25090627,25635267))],
        'count': [random.randint(11, 35) for _ in range(25090627,25635267)]})
>>> df.head()
   epoch_minute  count
0      25635266     12
1      25635265     20
2      25635264     33
3      25635263     11
4      25635262     35

和一些相关的纪元分钟增量，如下所示：

day = 1440
week = 10080
month = 302400

如何完成此代码块的等效项：

for i,r in df.iterrows():
    if r['epoch_minute'] - day in df['epoch_minute'].values and \
            r['epoch_minute'] - week in df['epoch_minute'].values and \
            r['epoch_minute'] - month in df['epoch_minute'].values:
        # do stuff

使用此语法：

valid_rows = df.loc[(df['epoch_minute'] == df['epoch_minute'] - day) &
                    (df['epoch_minute'] == df['epoch_minute'] - week) &
                    (df['epoch_minute'] == df['epoch_minute'] - month]

我明白为什么 loc select 不起作用，但我只是问是否有更优雅的方法 select 有效行而不用遍历数据框的行数。

Answer 1

为 bitwise AND 添加括号和 &，为检查成员资格添加 isin：

valid_rows = df[(df['epoch_minute'].isin(df['epoch_minute'] - day)) &
                (df['epoch_minute'].isin(df['epoch_minute'] - week)) &
                (df['epoch_minute'].isin(df['epoch_minute'] - month))]

valid_rows = df[((df['epoch_minute'] - day).isin(df['epoch_minute'])) &
                ((df['epoch_minute']- week).isin(df['epoch_minute'] )) &
                ((df['epoch_minute'] - month).isin(df['epoch_minute']))]

DataFrame 中存在 Pandas select 行相关列值

Pandas select rows where relative column values exist in DataFrame

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pandas-loc