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?
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