Select columns based on dates - Pandas

[Martin Di Paola]

You may want to reshape the dataset to a tidy format: Pandas works 
better with that format.

Let's assume the following dataset (this is what I understood from your 
message):

In [34]: df = pd.DataFrame({
...:     'Country': ['us', 'uk', 'it'],
...:     '01/01/2019': [10, 20, 30],
...:     '02/01/2019': [12, 22, 32],
...:     '03/01/2019': [14, 24, 34],
...:     })

In [35]: df
Out[35]:
   Country  01/01/2019  02/01/2019  03/01/2019
0      us          10          12          14
1      uk          20          22          24
2      it          30          32          34

Then, reshape it to a tidy format. Notice how each row now represents 
a single measure.

In [43]: pd.melt(df, id_vars=['Country'], var_name='Date', 
value_name='Cases')
Out[43]:
   Country        Date  Cases
0      us  01/01/2019     10
1      uk  01/01/2019     20
2      it  01/01/2019     30
3      us  02/01/2019     12
4      uk  02/01/2019     22
5      it  02/01/2019     32
6      us  03/01/2019     14
7      uk  03/01/2019     24
8      it  03/01/2019     34

I used strings to represent the dates but it is much handy work
with real date objects.

In [44]: df2 = _
In [45]: df2['Date'] = pd.to_datetime(df2['Date'])

Now we can filter by date:

In [50]: df2[df2['Date'] < '2019-03-01']
Out[50]:
   Country       Date  Cases
0      us 2019-01-01     10
1      uk 2019-01-01     20
2      it 2019-01-01     30
3      us 2019-02-01     12
4      uk 2019-02-01     22
5      it 2019-02-01     32

With that you could create three dataframes, one per month.

Thanks,
Martin.


On Thu, Sep 02, 2021 at 12:28:31PM -0700, Richard Medina wrote:
>Hello, forum,
>I have a data frame with covid-19 cases per month from 2019 - 2021 like a header like this:
>
>Country, 01/01/2019, 2/01/2019, 01/02/2019, 3/01/2019, ... 01/01/2021, 2/01/2021, 01/02/2021, 3/01/2021
>
>I want to filter my data frame for columns of a specific month range of march to September of 2019, 2020, and 2021 only (three data frames).
>
>Any ideas?
>Thank you
>
>
>-- 
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