Python 按列值筛选时间序列数据帧
这个问题是这个问题的补充: 我希望Python 按列值筛选时间序列数据帧,python,pandas,time-series,filtering,Python,Pandas,Time Series,Filtering,这个问题是这个问题的补充: 我希望日期之后的所有数据(时间)从第一个值开始大于零。(适用于每个id) 输入数据示例: 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 想要的输出数
日期
之后的所有数据(时间)从第一个值开始
大于零。(适用于每个id
)
输入数据示例:
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
想要的输出数据示例:
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
首先按另一列筛选,然后创建,筛选更大的值,如0
,最后通过以下方式添加删除的值:
替换列的另一个想法是:
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
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