Python Pandas 中 DataFrame 中计算的月数?

Amount of months calculation in DataFrame in Python Pandas?

我有如下所示的 DataFrame:

df = pd.DataFrame({"ID" : ["1", "2", "3"],
                   "Date" : ["12/11/2020", "12/10/2020", "05/04/2020"]})

我需要计算从日期列到今天的月数。下面我上传我需要的结果:

尝试使用此代码将 'Date' 列中的时间减去,我也使用 np.ceil,因为它会四舍五入一个数字:

df['Date'] = pd.to_datetime(df['Date'])
df['Amount'] = ((pd.to_datetime('now') - df['Date']) / np.timedelta64(1, 'M')).apply(np.ceil)
print(df)

您可以修改 以减去标量 d:

df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)

d = pd.to_datetime('now')
df['Amount'] = 12 * (d.year - df['Date'].dt.year) + d.month - df['Date'].dt.month

print (df)
  ID       Date  Amount
0  1 2020-11-12       1
1  2 2020-10-12       2
2  3 2020-04-05       8
from datetime import datetime
import pandas as pd
import numpy as np
df = pd.DataFrame({"ID" : ["1", "2", "3"],
                   "Date" : ["12/11/2020", "12/10/2020", "05/04/2020"]})
df['Month_diff'] = round(((datetime.now() - pd.to_datetime(df.Date,infer_datetime_format=True,dayfirst=True))/np.timedelta64(1, 'M'))-0.5)

这将是一个单行代码,您可以在其中将列 Date 转换为 datetimeformat,然后执行操作。输出:

   ID   Date        Month_diff
0   1   12/11/2020  1.0
1   2   12/10/2020  2.0
2   3   05/04/2020  8.0