How to Change the Datetime Format in Pandas

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

Change dd-mm-yyyy date format of dataframe date column to yyyy-mm-dd

Use '%Y-%m-%d'

Ex:

import pandas as pd

df = pd.DataFrame({"Date": ["26-12-2007", "27-12-2007", "28-12-2007"]})
df["Date"] = pd.to_datetime(df["Date"]).dt.strftime('%Y-%m-%d')
print(df)

Output:

         Date
0  2007-12-26
1  2007-12-27
2  2007-12-28

Change datetime format to hours only in Pandas

If you only want to take the hours from the date strings, you can use .dt.strftime() after the pd.to_datetime() call, as follows:

ds['waktu'] = pd.to_datetime(ds['tanggal'], format='%a %b %d %H:%M:%S %z %Y').dt.strftime('%H:%M:%S')

Note that your format string for pd.to_datetime() is not correct and need to replace %A by %a.

+0000 is the time zone, which you can parse with %z in the format string.

Demo

ds = pd.DataFrame({'tanggal': ['Fri Apr 23 12:38:07 +0000 2021', 'Thu Apr 22 11:28:17 +0000 2021']})

ds['waktu'] = pd.to_datetime(ds['tanggal'], format='%a %b %d %H:%M:%S %z %Y').dt.strftime('%H:%M:%S')

print(ds)


                          tanggal     waktu
0  Fri Apr 23 12:38:07 +0000 2021  12:38:07
1  Thu Apr 22 11:28:17 +0000 2021  11:28:17

Pandas - Datetime format change to '%m/%d/%Y'

The reason you have to use errors="ignore" is because not all the dates you are parsing are in the correct format. If you use errors="coerce" like @phi has mentioned then any dates that cannot be converted will be set to NaT. The columns datatype will still be converted to datatime64 and you can then format as you like and deal with the NaT as you want.

Example

A dataframe with one item in Date not written as Year/Month/Day (25th Month is wrong):

>>> df = pd.DataFrame({'ID': [91060, 91061, 91062, 91063], 'Date': ['2017/11/10', '2022/05/01', '2022/04/01', '2055/25/25']})
>>> df
      ID        Date
0  91060  2017/11/10
1  91061  2022/05/01
2  91062  2022/04/01
3  91063  2055/25/25

>>> df.dtypes
ID       int64
Date    object
dtype: object

Using errors="ignore":

>>> df['Date'] = pd.to_datetime(df['Date'], errors='ignore')
>>> df
      ID        Date
0  91060  2017/11/10
1  91061  2022/05/01
2  91062  2022/04/01
3  91063  2055/25/25

>>> df.dtypes
ID       int64
Date    object
dtype: object

Column Date is still an object because not all the values have been converted. Running df['Date'] = df['Date'].dt.strftime("%m/%d/%Y") will result in the AttributeError

Using errors="coerce":

>>> df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
>>> df
      ID       Date
0  91060 2017-11-10
1  91061 2022-05-01
2  91062 2022-04-01
3  91063        NaT

>>> df.dtypes
ID               int64
Date    datetime64[ns]
dtype: object

Invalid dates are set to NaT and the column is now of type datatime64 and you can now format it:

>>> df['Date'] = df['Date'].dt.strftime("%m/%d/%Y")
>>> df
      ID        Date
0  91060  11/10/2017
1  91061  05/01/2022
2  91062  04/01/2022
3  91063         NaN

Note: When formatting datatime64, it is converted back to type object so NaT's are changed to NaN. The issue you are having is a case of some dirty data not in the correct format.

How to change datetime format while converting df to to_json using pandas

There is an Open PR for this issue which is still Open No way with to_json to write only date out of datetime.

May be one thing which you could try:

sql_data.to_json(orient='records', date_format='iso', date_unit='s')

Change datetime format in python dataframe

The key parameter for you to pass when using pd.to_datetime is dayfirst=True. Then, use .dt.strftime('%m/%d/%Y') to change to the desired format. I have also given you an example of how to rename a column and read/write to .csv. Again, I understand that you are on mobile, but next time, I would show more effort.

import pandas as pd
# df=pd.read_csv('filename.csv')
# I have manually created a dataframe below, but the above is how you read in a file.
df=pd.DataFrame({'Local time' : ['11.02.2015 00:00:00.000 GMT+0200',
                                '12.02.2015 00:00:00.000 GMT+0200',
                                '15.03.2015 00:00:00.000 GMT+0200']})
#Converting string to datetime and changing to desired format
df['Local time'] = pd.to_datetime(df['Local time'], 
                                  dayfirst=True).dt.strftime('%m/%d/%Y')
#Example to rename columns
df.rename(columns={'Local time' : 'Date'}, inplace=True)
df.to_csv('filename.csv', index=False)
df

how to change to datetime format in python

You could apply a custom function on your Date column to parse the date values.

import pandas as pd
import io
from datetime import datetime

temp_data=u"""Date
8/1/2021
8-5-2021
8-6-2021:08:00:00 PM
"""

data = pd.read_csv(io.StringIO(temp_data), sep=";", parse_dates=False)

def to_date(string_date):
    formats=["%d/%m/%Y","%d-%m-%Y","%d-%m-%Y:%I:%M:%S %p"]
    parsed_date=None
    for format in formats:
        try:
            parsed_date=datetime.strptime('8-6-2021:08:00:00 PM', "%d-%m-%Y:%I:%M:%S %p").date()
            return parsed_date
        except ValueError:
            pass
    raise RuntimeError(f"Unable to parse date {string_date} with available formats={formats}")

You can add new formats in the to_date function to parse any new date format.

data['Date']=data['Date'].apply(lambda row: to_date(row))

>>> data
        Date
0  2021-06-08
1  2021-06-08
2  2021-06-08

How to change datetime format with Pandas

You can use pd.to_datetime function:

In [1028]: df['date'] = df['date'].apply(pd.to_datetime)

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