Question

我有以下数据框。
日期是dtype='datetime64[ns, UTC]'

    announce_date  announce_time
0   2013-01-23     After Market Close
1   2013-04-23     Before Market Open
2   2013-07-20     After Market Close

如何创建新的偏移量列
如果announce_date是announce_time，则"After Market Close"乘以1。

    announce_date  announce_time          impact_date
0   2013-01-23     After Market Close     2013-01-24
1   2013-04-23     Before Market Open     2013-04-23
2   2013-07-20     After Market Close     2013-07-21

我可以做一个偏移日期列，但还没有弄清楚如何应用该条件。

import pandas as pd

df['impact_date'] = df['announce_date'] + pd.DateOffset(days=1)

Answer 1

使用pd.to_timedelta生成布尔掩码并从其生成偏移量。

# Convert "announce_date" to datetime if not already done.
# df['announce_date'] = pd.to_datetime(df['announce_date'], errors='coerce')
# Now calculate the offset.
offset = pd.to_timedelta(
    df['announce_time'].eq("After Market Close").astype(int), unit='D')    
df['impact_date'] = df['announce_date'] + offset

print(df)
  announce_date       announce_time impact_date
0    2013-01-23  After Market Close  2013-01-24
1    2013-04-23  Before Market Open  2013-04-23
2    2013-07-20  After Market Close  2013-07-21

Answer 2

您可以使用np.where

df ['impact_date'] = df ['announce_date'] + np.where（df ['announce_time'] ==“收市后”，pd.DateOffset（天= 1），pd.DateOffset（天= 0））

This answer有详细说明。

Answer 3

您可以像这样创建遍历announce_time的while循环...

count = 0
list1 = []
while count != len(df):
    if df.iloc[count,1] == 'After Market Close':
        list1.append(df.iloc[count,0] + pd.DateOffset(days=1))
        count += 1
    else:
        list1.append(df.iloc[count,0])
        count += 1

df['impact_date'] = list1

如何添加根据现有日期列和条件列计算得出的数据框日期列？

3 个答案: