我有個問題。我有兩個日期欄位fromDate和toDate. toDate還包含時間戳，例如2021-03-22T18:59:59Z。問題是我想以天為單位計算這兩個值之間的差異。toDate - fromDate= 天數差異。但是，當我這樣做時，我收到以下錯誤[OUT] TypeError: unsupported operand type(s) for -: 'Timestamp' and 'datetime.date'。我已經轉換了toDate沒有時間戳的欄位。值得一提的是，這兩個欄位可以包含空值。
如何計算兩個日期欄位之間的天數差異？
資料框
id toDate fromDate
0 1 2021-03-22T18:59:59Z 2021-02-22
1 2 None 2021-03-18
2 3 2021-04-22T18:59:59Z 2021-03-22
3 4 2021-02-15T18:59:59Z 2021-02-10
4 5 2021-09-15T18:59:59Z 2021-09-07
5 6 2020-01-12T18:59:59Z None
6 7 2022-02-22T18:59:59Z 2022-01-18
代碼
import pandas as pd
d = {'id': [1, 2, 3, 4, 5, 6, 7],
'toDate': ['2021-03-22T18:59:59Z', None, '2021-04-22T18:59:59Z',
'2021-02-15T18:59:59Z', '2021-09-15T18:59:59Z', '2020-01-12T18:59:59Z', '2022-02-22T18:59:59Z'],
'fromDate': ['2021-02-22', '2021-03-18', '2021-03-22',
'2021-02-10', '2021-09-07', None, '2022-01-18']
}
df = pd.DataFrame(data=d)
display(df)
df['toDate'] = pd.to_datetime(df['toDate'], errors='coerce').dt.date
df['fromDate'] = pd.to_datetime(df['fromDate'], errors='coerce')
display(df)
#df['days'] = df['fromDate'].subtract(df['toDate'])
df['days'] = (df['fromDate'] - df['toDate']).dt.days
[OUT] TypeError: unsupported operand type(s) for -: 'Timestamp' and 'datetime.date'
我想要的是
id toDate fromDate days
0 1 2021-03-22 2021-02-22 30
1 2 NaT 2021-03-18 NaT
2 3 2021-04-22 2021-03-22 30
3 4 2021-02-15 2021-02-10 5
4 5 2021-09-15 2021-09-07 8
5 6 2020-01-12 NaT NaT
6 7 2022-02-22 2022-01-18 34
uj5u.com熱心網友回復：
對于減去也需要toDate列中的日期時間，因此要00:00:00使用設定時間Series.dt.normalize：
df['toDate'] = pd.to_datetime(df['toDate'], errors='coerce').dt.normalize()
或者Series.dt.floor：
df['toDate'] = pd.to_datetime(df['toDate'], errors='coerce').dt.floor('D')
另一個想法是將兩列都轉換為日期，在一些較舊的熊貓版本中應該失敗：
df['toDate'] = pd.to_datetime(df['toDate'], errors='coerce').dt.date
df['fromDate'] = pd.to_datetime(df['fromDate'], errors='coerce').dt.date
df['days'] = (df['toDate'] - df['fromDate']).dt.days
print (df)
id toDate fromDate days
0 1 2021-03-22 2021-02-22 28.0
1 2 NaT 2021-03-18 NaN
2 3 2021-04-22 2021-03-22 31.0
3 4 2021-02-15 2021-02-10 5.0
4 5 2021-09-15 2021-09-07 8.0
5 6 2020-01-12 NaT NaN
6 7 2022-02-22 2022-01-18 35.0
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