如何获取一个月的最后一天的日期列表

Question

I find different solution for my problem, but i find the correct answer. SO now i have to ask:

I want to find the last day of a month in a list... Every time i get the following Error:

AttributeError: 'RangeIndex' object has no attribute 'month'

I started like this:

temp=pandas.read_csv(filename, delimiter=',')
dates=pandas.to_datetime(temp['Var1'])

and i get a list like this:

0      2017-01-01 06:00:00
1      2017-01-01 07:00:00
2      2017-01-01 08:00:00
...
Name: Var1, Length: 4053, dtype: datetime64[ns]

In the list different day are missed, for example some Month can have only 10 days.

I used the following code

dates[dates.groupby(dates.index.month).apply(lambda s: np.max(s.index))].tolist()

Answer 1

Try this:

import pandas as pd
df = pd.DataFrame({'a':['2017-01-01 06:00:00',
                        '2017-01-01 07:00:00',
                        '2017-02-02 08:00:00']})
df['a'] = pd.to_datetime(df['a'])
print(df['a'].groupby(df.a.dt.to_period("M")).apply(lambda x: x.values.max()))

Output:

a
2017-01   2017-01-01 07:00:00
2017-02   2017-02-02 08:00:00
Freq: M, Name: a, dtype: datetime64[ns]

Answer 2

Where other solutions only use the .month value, you might miss the differences across different years. Grouping across .year and .month can avoid this. I'm not sure if this is what you require

df = pd.DataFrame(dict(dates=pd.date_range(start=pd.datetime(2017, 10, 1), periods=20, freq='3W')))
Out[]:
        dates
0  2017-10-01
1  2017-10-22
2  2017-11-12
3  2017-12-03
4  2017-12-24
5  2018-01-14
6  2018-02-04
7  2018-02-25
8  2018-03-18
9  2018-04-08
10 2018-04-29
11 2018-05-20
12 2018-06-10
13 2018-07-01
14 2018-07-22
15 2018-08-12
16 2018-09-02
17 2018-09-23
18 2018-10-14
19 2018-11-04


df.groupby([df.dates.dt.year, df.dates.dt.month]).max()
Out[]:
                 dates
dates dates           
2017  10    2017-10-22
      11    2017-11-12
      12    2017-12-24
2018  1     2018-01-14
      2     2018-02-25
      3     2018-03-18
      4     2018-04-29
      5     2018-05-20
      6     2018-06-10
      7     2018-07-22
      8     2018-08-12
      9     2018-09-23
      10    2018-10-14
      11    2018-11-04

df.groupby([df.dates.dt.year, df.dates.dt.month]).max().values
Out[]:
array([['2017-10-22T00:00:00.000000000'],
       ['2017-11-12T00:00:00.000000000'],
       ['2017-12-24T00:00:00.000000000'],
       ['2018-01-14T00:00:00.000000000'],
       ['2018-02-25T00:00:00.000000000'],
       ['2018-03-18T00:00:00.000000000'],
       ['2018-04-29T00:00:00.000000000'],
       ['2018-05-20T00:00:00.000000000'],
       ['2018-06-10T00:00:00.000000000'],
       ['2018-07-22T00:00:00.000000000'],
       ['2018-08-12T00:00:00.000000000'],
       ['2018-09-23T00:00:00.000000000'],
       ['2018-10-14T00:00:00.000000000'],
       ['2018-11-04T00:00:00.000000000']], dtype='datetime64[ns]')