CODEWITHSUNDEEP
pythonpandasdataframedatetimestrptime
Jewel_R
Jewel_R

Reputation: 126

Stacking multiple dataframes together for different timestamp format into one timestamp

I have multiple data frames each having data varying from 1 to 1440 minute (one day).Each dataframes are alike and same columns and same length. The time column values are in hhmm format. Lets say df_A has the data of 1st day, that is 2021-05-06 It looks like this.

>df_A
timestamp    col1   col2.....  col80
0
1
2
.
.
.
2359

And the next day's data is in df_B which is also the same. The date is 2021-05-07

>df_B
timestamp    col1   col2.....  col80
0
1
2
.
.
.
2359

How could I stack these together one under another and create one dataframe while identifying each rows with a column having values in format like YYYYMMDD HH:mm. Which somewhat will look like this:

>df
timestamp      col1    col2.....  col80
20210506 0000
20210506 0001
.
.
20210506 2359
20210507 0000
.
.
20210507 2359

How could I achieve this while dealing with multiple data frames at ones?

Upvotes: 0

Views: 182

Answers (1)

Epsi95
Epsi95

Reputation: 9047

df_A = pd.DataFrame(range(0, 10), columns=['timestamp'])
df_B = pd.DataFrame(range(0, 10), columns=['timestamp'])


df_A['date'] = pd.to_datetime('2021-05-06 ' + 
                              df_A['timestamp'].astype(str).str.zfill(4), format='%Y-%m-%d %H%M')

df_B['date'] = pd.to_datetime('2021-05-07 ' + 
                              df_A['timestamp'].astype(str).str.zfill(4), format='%Y-%m-%d %H%M')

df_final = pd.concat([df_A, df_B])

df_final

    timestamp   date
0   0   2021-05-06 00:00:00
1   1   2021-05-06 00:01:00
2   2   2021-05-06 00:02:00
3   3   2021-05-06 00:03:00
4   4   2021-05-06 00:04:00
5   5   2021-05-06 00:05:00
6   6   2021-05-06 00:06:00
7   7   2021-05-06 00:07:00
8   8   2021-05-06 00:08:00
9   9   2021-05-06 00:09:00
0   0   2021-05-07 00:00:00
1   1   2021-05-07 00:01:00
2   2   2021-05-07 00:02:00
3   3   2021-05-07 00:03:00
4   4   2021-05-07 00:04:00
5   5   2021-05-07 00:05:00
6   6   2021-05-07 00:06:00
7   7   2021-05-07 00:07:00
8   8   2021-05-07 00:08:00
9   9   2021-05-07 00:09:00

Upvotes: 1

Related Questions