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How to split a date column into separate day , month ,year column in pandas

Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about How to split a date column into separate day , month ,year column in pandas in Python. So Here I am Explain to you all the possible Methods here.

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Table of Contents

How to split a date column into separate day , month ,year column in pandas?

  1. How to split a date column into separate day , month ,year column in pandas?

    The problem is that datetime_utc is in your index instead a column, so you have to access your index to be able to make your new columns:

  2. split a date column into separate day , month ,year column in pandas

    The problem is that datetime_utc is in your index instead a column, so you have to access your index to be able to make your new columns:

Method 1

The problem is that datetime_utc is in your index instead a column, so you have to access your index to be able to make your new columns:

df['day'] = df.index.day
df['month'] = df.index.month
df['year'] = df.index.year

print(df)
                 Dewptm  Fog   Humidity    Pressurem      Tempm     Wspdm  \
datetime_utc                                                                
1996-11-01    11.666667  0.0  52.916667 -2659.666667  22.333333  2.466667   
1996-11-02    10.458333  0.0  48.625000  1009.833333  22.916667  8.028571   
1996-11-03    12.041667  0.0  55.958333  1010.500000  21.791667  4.804545   
1996-11-04    10.222222  0.0  48.055556  1011.333333  22.722222  1.964706   

              Rainfall  day  month  year  
datetime_utc                              
1996-11-01           0    1     11  1996  
1996-11-02           0    2     11  1996  
1996-11-03           0    3     11  1996  
1996-11-04           0    4     11  1996  

If you want datetime_utc as a column you have to reset your index and then you can access the datetime methods with dt.monthdt.year and dt.day like following:

# Reset our index so datetime_utc becomes a column
df.reset_index(inplace=True)

# Create new columns
df['day'] = df['datetime_utc'].dt.day
df['month'] = df['datetime_utc'].dt.month
df['year'] = df['datetime_utc'].dt.year

print(df)
  datetime_utc     Dewptm  Fog   Humidity    Pressurem      Tempm     Wspdm  \
0   1996-11-01  11.666667  0.0  52.916667 -2659.666667  22.333333  2.466667   
1   1996-11-02  10.458333  0.0  48.625000  1009.833333  22.916667  8.028571   
2   1996-11-03  12.041667  0.0  55.958333  1010.500000  21.791667  4.804545   
3   1996-11-04  10.222222  0.0  48.055556  1011.333333  22.722222  1.964706   

   Rainfall  day  month  year  
0         0    1     11  1996  
1         0    2     11  1996  
2         0    3     11  1996  
3         0    4     11  1996  

Note if your index is not in datetime type yet, use the following before you try to extract year, month and day:

df.index = pd.to_datetime(df.index)

Summery

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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