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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