计算相同 2 个日期的 pandas groupby 对象中 2 个日期的差异
Calculate difference of 2 dates in a pandas groupby object of the same 2 dates
我正在尝试创建一个新的 pandas.DataFrame
列,其中包含两个日期列之间的工作日数。我无法将日期列中的日期引用为函数调用中的参数(我收到 TypeError: Cannot convert input 错误)。但是,我能够将系列中的值压缩到列表中并使用 For 循环来引用参数。理想情况下,我更愿意从两个日期列创建一个 GroupBy 对象并计算差异。
创建数据框:
import pandas as pd
df = pd.DataFrame.from_dict({'Date1': ['2017-05-30 16:00:00',
'2017-05-30 16:00:00',
'2017-05-30 16:00:00'],
'Date2': ['2017-06-16 16:00:00',
'2017-07-21 16:00:00',
'2017-08-18 16:00:00'],
'Value1': [2.97, 3.3, 4.03],
'Value2': [96L, 14L, 2L]})
df['Date1'] = pd.to_datetime(df['Date1'])
df['Date2'] = pd.to_datetime(df['Date2'])
df.dtypes
验证数据帧:
Date1 datetime64[ns]
Date2 datetime64[ns]
Value1 float64
Value2 int64
dtype: object
定义函数:
def date_diff(startDate, endDate):
return float(len(pd.bdate_range(startDate, endDate)) - 1)
尝试从 date_diff 函数调用的结果列:
df['DateDiff'] = date_diff(df['Date1'], df['Date2'])
类型错误:
TypeError: Cannot convert input [0 2017-05-30 16:00:00
1 2017-05-30 16:00:00
2 2017-05-30 16:00:00
Name: Date1, dtype: datetime64[ns]] of type <class 'pandas.core.series.Series'> to Timestamp
引用包含日期的元组列表的 "For Loop" 有效:
date_List = list(zip(df['Date1'], df['Date2']))
for i in range(len(date_List)):
df.loc[(df['Date1'] == date_List[i][0]) & (df['Date2'] == date_List[i][1]), 'diff'] = date_diff(date_List[i][0], date_List[i][1])
Date1 Date2 Value1 Value2 diff
0 2017-05-30 16:00:00 2017-06-16 16:00:00 2.97 96 13.0
1 2017-05-30 16:00:00 2017-07-21 16:00:00 3.30 14 38.0
2 2017-05-30 16:00:00 2017-08-18 16:00:00 4.03 2 58.0
理想情况下,我想利用一个 GroupBy 对象(按 Date1 和 Date2):
grp = df.groupby(['Date1', 'Date2'])
期望的输出:
[((Timestamp('2017-05-30 16:00:00'), Timestamp('2017-06-16 16:00:00')),
Date1 Date2 Value1 Value2 diff
0 2017-05-30 16:00:00 2017-06-16 16:00:00 2.97 96 13.0),
((Timestamp('2017-05-30 16:00:00'), Timestamp('2017-07-21 16:00:00')),
Date1 Date2 Value1 Value2 diff
1 2017-05-30 16:00:00 2017-07-21 16:00:00 3.3 14 38.0),
((Timestamp('2017-05-30 16:00:00'), Timestamp('2017-08-18 16:00:00')),
Date1 Date2 Value1 Value2 diff
2 2017-05-30 16:00:00 2017-08-18 16:00:00 4.03 2 58.0)]
你需要一个类型转换为 datetime64[D]
来让 numpy 开心,比如:
代码:
import numpy as np
def date_diff(start_dates, end_dates):
return np.busday_count(
start_dates.values.astype('datetime64[D]'),
end_dates.values.astype('datetime64[D]'))
测试代码:
import pandas as pd
df = pd.DataFrame.from_dict({'Date1': ['2017-05-30 16:00:00',
'2017-05-30 16:00:00',
'2017-05-30 16:00:00'],
'Date2': ['2017-06-16 16:00:00',
'2017-07-21 16:00:00',
'2017-08-18 16:00:00'],
'Value1': [2.97, 3.3, 4.03],
'Value2': [96L, 14L, 2L]})
df['Date1'] = pd.to_datetime(df['Date1'])
df['Date2'] = pd.to_datetime(df['Date2'])
df['DateDiff'] = date_diff(df['Date1'], df['Date2'])
print(df)
结果:
Date1 Date2 Value1 Value2 DateDiff
0 2017-05-30 16:00:00 2017-06-16 16:00:00 2.97 96 13
1 2017-05-30 16:00:00 2017-07-21 16:00:00 3.30 14 38
2 2017-05-30 16:00:00 2017-08-18 16:00:00 4.03 2 58