Converting Date Column in Data Frame

Convert Pandas Column to DateTime

Use the to_datetime function, specifying a format to match your data.

raw_data['Mycol'] =  pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')

Pandas - Converting date column from dd/mm/yy hh:mm:ss to yyyy-mm-dd hh:mm:ss

If you know you will have a consistent format in your column, you can pass this to to_datetime:

df['sale_date'] = pd.to_datetime(df['sale_date'], format='%d/%m/%y %H:%M:%S')

If your formats aren't necessarily consistent but do have day before month in each case, it may be enough to use dayfirst=True though this is difficult to say without seeing the data:

df['sale_date'] = pd.to_datetime(df['sale_date'], dayfirst=True)

how to convert date format in a dataframe

You can use:

fmt = '%Y-%m-%dT%H:%M:%S'
df2 = (df
  .assign(debut_interval=pd.to_datetime(df['debut_interval'], dayfirst=True),
          fin_interval=lambda d: d['debut_interval'].add(pd.to_timedelta('23:59:59')).dt.strftime(fmt))
  .assign(debut_interval=lambda d: d['debut_interval'].dt.strftime(fmt)
          )
       )

Output:

   N_Ord       PSN       debut_interval         fin_interval
0      1  A4BA0D07  2022-01-01T00:00:00  2022-01-01T23:59:59
1      2  04BB0607  2022-01-01T00:00:00  2022-01-01T23:59:59

Change date format of dataframe date column

Use:

df['Date'] = pd.to_datetime(df['Date']).dt.strftime('%Y-%m-%d')

How to change the datetime format in Pandas

You can use dt.strftime if you need to convert datetime to other formats (but note that then dtype of column will be object (string)):

import pandas as pd

df = pd.DataFrame({'DOB': {0: '26/1/2016', 1: '26/1/2016'}})
print (df)
         DOB
0  26/1/2016 
1  26/1/2016

df['DOB'] = pd.to_datetime(df.DOB)
print (df)
         DOB
0 2016-01-26
1 2016-01-26

df['DOB1'] = df['DOB'].dt.strftime('%m/%d/%Y')
print (df)
         DOB        DOB1
0 2016-01-26  01/26/2016
1 2016-01-26  01/26/2016

How to convert a Date column with lubridate and keep it within the dataframe?

You are overwriting your entire data frame with the value for the newly created date column.

Instead do

dc.crime.complete$Date <- ymd(dc.crime.complete$Date)

This will overwrite your date column with the new values.

How to convert the date format in a dataframe using Python?

import pandas as pd
dates = ['05/01/2021','05/02/2021','05/03/2021','05/04/2021','05/05/2021']
values = [1,2,3,4,5]

df = pd.DataFrame({'dates': dates,
               'values': values})

df['to_datetime'] = pd.to_datetime(df['dates'])

# this is converted date
df['target_date'] = '0'+ df['to_datetime'].dt.day.astype(str) + '/' + \
'0'+ df['to_datetime'].dt.month.astype(str) + '/' + 
df['to_datetime'].dt.year.astype(str)

Dataframe column when converting to pandas datetime format results in NaT

Solved myself. The format option requires the current format of the dataframe 'Date' column not the expected 'Date' format. I have given the expected format as format= '%Y-%m-%d' and changed to the current format of the date and it works.

Wrong code:

df['Date'] = pd.to_datetime(df['Date'], errors='coerce', format= '%Y-%m-%d')

Right code:

df['Date'] = pd.to_datetime(df['Date'], errors='coerce', format= '%d-%m-%Y')

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