Python如何从ex转换日期值

Question

我正在读取一个带有CDATE列的csv文件。立柱结构为：

|CDATE     |
|08/28/2018|
|08/28/2018|
|08/29/2018|
|08/30/2018|
|09/02/2018|
|09/04/2018|
...
|04/10/2019|

如您所见，此列中既有重复的日期，也有缺失的日期，我希望找到缺失的日期并将它们添加到我的数据框中。你知道吗

我的代码是：

import matplotlib.pyplot as plt
warnings.filterwarnings("ignore")
plt.style.use('fivethirtyeight')
import pandas as pd

df = pd.read_csv("XXX.csv")
dateCol = df['CDATE'].values.tolist()
dates = pd.to_datetime(dateCol, format='%m/%d/%Y')
startDate = dates.min()
endDate = dates.max()
df = df.sort_values('CDATE')
df_plastic = df['PLASTIC'].unique()
dateRange = pd.date_range(startDate, endDate)
df_date = df['CDATE'].unique()

for cursorDate in dateRange:
   if (cursorDate in df_date) is False:
     print('Data is missing date {} from range {}'.format(cursorDate, df_date))

但结果是：

Data is missing date 2019-02-21 00:00:00 from ['01/01/2019' '01/02/2019' '01/03/2019' '01/04/2019' '01/05/2019'
 '01/07/2019' '01/08/2019' '01/09/2019' '01/10/2019' '01/11/2019'
 '01/12/2019' '01/14/2019' '01/15/2019' '01/16/2019' '01/17/2019'
 '01/18/2019' '01/19/2019' '01/21/2019' '01/22/2019' '01/23/2019'
 '01/24/2019' '01/25/2019' '01/26/2019' '01/28/2019' '01/29/2019'
 '01/30/2019' '01/31/2019' '02/01/2019' '02/02/2019' '02/04/2019'
 '02/05/2019' '02/06/2019' '02/07/2019' '02/08/2019' '02/09/2019'
 '02/11/2019' '02/12/2019' '02/13/2019' '02/14/2019' '02/15/2019'
 '02/16/2019' '02/19/2019' '02/20/2019' '02/21/2019' '02/22/2019'
 '02/23/2019' '02/25/2019' '02/26/2019' '02/27/2019' '02/28/2019'
 '03/01/2019' '03/02/2019' '03/03/2019' '03/04/2019' '03/05/2019'
 '03/06/2019' '03/07/2019' '03/08/2019' '03/09/2019' '03/11/2019'
 '03/12/2019' '03/13/2019' '03/14/2019' '03/15/2019' '03/16/2019'
 '03/18/2019' '03/19/2019' '03/20/2019' '03/21/2019' '03/22/2019'
 '03/23/2019' '03/25/2019' '03/26/2019' '03/27/2019' '03/28/2019'
 '03/29/2019' '03/30/2019' '04/01/2019' '04/02/2019' '04/03/2019'
 '04/04/2019' '04/05/2019' '04/06/2019' '04/08/2019' '04/09/2019'
 '04/10/2019' '05/29/2018' '05/30/2018' '05/31/2018' '06/01/2018'
 '06/02/2018' '06/04/2018' '06/05/2018' '06/06/2018' '06/07/2018'
 '06/08/2018' '06/09/2018' '06/11/2018' '06/12/2018' '06/13/2018'
 '06/14/2018' '06/15/2018' '06/16/2018' '06/18/2018' '06/19/2018'
 '06/20/2018' '06/21/2018' '06/22/2018' '06/23/2018' '06/25/2018'
 '06/26/2018' '06/27/2018' '06/28/2018' '06/29/2018' '06/30/2018'
 '07/03/2018' '07/04/2018' '07/05/2018' '07/06/2018' '07/07/2018'
 '07/09/2018' '07/10/2018' '07/11/2018' '07/12/2018' '07/13/2018'
 '07/14/2018' '07/16/2018' '07/17/2018' '07/18/2018' '07/19/2018'
 '07/20/2018' '07/21/2018' '07/23/2018' '07/24/2018' '07/25/2018'
 '07/26/2018' '07/27/2018' '07/28/2018' '07/30/2018' '07/31/2018'
 '08/01/2018' '08/02/2018' '08/03/2018' '08/04/2018' '08/07/2018'
 '08/08/2018' '08/09/2018' '08/10/2018' '08/11/2018' '08/13/2018'
 '08/14/2018' '08/15/2018' '08/16/2018' '08/17/2018' '08/18/2018'
 '08/20/2018' '08/21/2018' '08/22/2018' '08/23/2018' '08/24/2018'
 '08/25/2018' '08/27/2018' '08/28/2018' '08/29/2018' '08/30/2018'
 '08/31/2018' '09/01/2018' '09/04/2018' '09/05/2018' '09/06/2018'
 '09/07/2018' '09/08/2018' '09/10/2018' '09/11/2018' '09/12/2018'
 '09/13/2018' '09/14/2018' '09/15/2018' '09/17/2018' '09/18/2018'
 '09/19/2018' '09/20/2018' '09/21/2018' '09/22/2018' '09/24/2018'
 '09/25/2018' '09/26/2018' '09/27/2018' '09/28/2018' '09/29/2018'
 '10/01/2018' '10/02/2018' '10/03/2018' '10/04/2018' '10/05/2018'
 '10/06/2018' '10/09/2018' '10/10/2018' '10/11/2018' '10/12/2018'
 '10/13/2018' '10/15/2018' '10/16/2018' '10/17/2018' '10/18/2018'
 '10/19/2018' '10/20/2018' '10/22/2018' '10/23/2018' '10/24/2018'
 '10/25/2018' '10/26/2018' '10/29/2018' '10/30/2018' '10/31/2018'
 '11/01/2018' '11/02/2018' '11/03/2018' '11/05/2018' '11/06/2018'
 '11/07/2018' '11/08/2018' '11/09/2018' '11/10/2018' '11/13/2018'
 '11/14/2018' '11/15/2018' '11/16/2018' '11/18/2018' '11/19/2018'
 '11/20/2018' '11/21/2018' '11/22/2018' '11/23/2018' '11/24/2018'
 '11/26/2018' '11/27/2018' '11/28/2018' '11/29/2018' '11/30/2018'
 '12/01/2018' '12/03/2018' '12/04/2018' '12/05/2018' '12/06/2018'
 '12/07/2018' '12/08/2018' '12/09/2018' '12/10/2018' '12/11/2018'
 '12/12/2018' '12/13/2018' '12/14/2018' '12/15/2018' '12/17/2018'
 '12/18/2018' '12/19/2018' '12/20/2018' '12/21/2018' '12/22/2018'
 '12/24/2018' '12/25/2018' '12/27/2018' '12/28/2018' '12/29/2018'
 '12/31/2018']

不知何故，cursorDate的数据类型被更改为Timestamp，使得值比较无法工作。你知道吗

它是如何转换日期时间格式的？你知道吗

Answer 1

基于我上面的评论。将循环前的最后一行更改为：

df_date = df['CDATE'].apply(pd.to_datetime).unique()