I have the column in DataFrame where data is:
1 2017-07-14T04:00:00.3760000Z
2 2013-10-22T23:09:46.8630000Z
3 2015-01-14T16:07:05.0000000Z
4 2011-09-13T13:53:36.0000000Z
...
332 2018-03-25T07:00:01.0000000Z
333 2019-04-08T15:21:02.0000000Z
334 2017-09-17T11:10:12.5100000Z
335 2017-12-22T07:31:16.0000000Z
336 2020-05-05T13:01:20.8670000Z
I need to delete the 'Z' and 'T' letters and transform them into a format like:
2018-05-23 17:26:19 and so on...
I used lambda in my code like this:
import pandas as pd
df = pd.read_csv('All_Markets.csv')
print(df.dtypes)
df['data_trade_start'] = [lambda x: x[:19].replace('T', ' ') for x in df['data_trade_start']]
print(df['data_trade_start'])
And I got this strange format:
1 <function <listcomp>.<lambda> at 0x00000126690...
2 <function <listcomp>.<lambda> at 0x00000126690...
3 <function <listcomp>.<lambda> at 0x00000126690...
4 <function <listcomp>.<lambda> at 0x00000126690...
...
332 <function <listcomp>.<lambda> at 0x00000126690...
333 <function <listcomp>.<lambda> at 0x00000126690...
334 <function <listcomp>.<lambda> at 0x00000126690...
335 <function <listcomp>.<lambda> at 0x00000126690...
336 <function <listcomp>.<lambda> at 0x00000126690...
How can I change it into I wrote upper?
You can try
df['data_trade_start'] = [x[:19].replace('T', ' ') for x in df['data_trade_start']]
# or
df['col'] = df['col'].replace(['T', 'Z'], [' ', ''], regex=True)
or convert it to datetime with pd.to_datetime
df['data_trade_start'] = pd.to_datetime(df['data_trade_start'])