按列值過濾時間序列 pandas dataframe

Question

這個問題是這個問題的附加問題： filter multi-indexed grouped pandas dataframe

我希望從大於零的第一個value開始的date之后的所有數據（按時間）。 （適用於每個id ）

示例輸入數據：

id  timestamp   date        value
1   2001-01-01  2001-05-01  1
1   2001-10-01  2001-05-01  0
1   2001-10-02  2001-05-01  1
1   2001-10-03  2001-05-01  0
1   2001-10-04  2001-05-01  1

想要的 Output 數據示例：

id  timestamp   date        value
1   2001-10-02  2001-05-01  1
1   2001-10-03  2001-05-01  0
1   2001-10-04  2001-05-01  1

Answer 1

首先按Series.gt過濾另一列，然后創建GroupBy.cumsum ，過濾大於0並最后添加刪除的值DataFrame.reindex ：

df['timestamp'] = pd.to_datetime(df['timestamp'])
df['date'] = pd.to_datetime(df['date'])
df = df.sort_values(['id','timestamp'])

m = df['timestamp'].gt(df['date'])
m1 = df[m].groupby('id')['value'].cumsum().gt(0).reindex(df.index, fill_value=False)
df = df[m1]
print (df)
   id  timestamp       date  value
2   1 2001-10-02 2001-05-01      1
3   1 2001-10-03 2001-05-01      0
4   1 2001-10-04 2001-05-01      1

用Series.where替換列的另一個想法：

df['timestamp'] = pd.to_datetime(df['timestamp'])
df['date'] = pd.to_datetime(df['date'])
df = df.sort_values(['id','timestamp'])

m = df['timestamp'].gt(df['date'])
m1 = df.assign(value = df['value'].where(m, 0)).groupby('id')['value'].cumsum().gt(0)
df = df[m1]
print (df)
   id  timestamp       date  value
2   1 2001-10-02 2001-05-01      1
3   1 2001-10-03 2001-05-01      0
4   1 2001-10-04 2001-05-01      1