Pandas Convert Multiple Columns To DateTime Type

Let’s see different ways to convert multiple columns from string, integer, and object to DataTime (date & time) type using pandas.to_datetime(), DataFrame.apply() & astype() functions. Usually, we get Data & time from the sources in different formats and in different data types, by using these functions you can convert them to a data time type datetime64[ns] of pandas.

In this article, I will explain how to change multiple columns to DateTime type datetime64[ns] with examples.

1. Quick Examples of Convert Multiple Columns To DateTime

If you are in a hurry, below are some quick examples of how to convert multiple columns to DateTime in pandas DataFrame.

# Below are quick example

# Use DataFrame.apply() to convert multiple columns to datetime
df[['Inserted','Updated']] = df[['Inserted','Updated']].apply(pd.to_datetime)

# Convert pandas multiple columns to Datetime
df[['Inserted','Updated']] = df[['Inserted','Updated']].apply(pd.to_datetime, errors='coerce')

# Convert multiple column to datetime using astype() method
df[['Inserted','Updated']] = df[['Inserted','Updated']].astype(datetime64[ns])

Now, let’s create a DataFrame with a few rows and columns, execute these examples and validate results. Our DataFrame contains column names Courses, Fee, Duration, Discount, Inserted and Updated.

import pandas as pd
from datetime import datetime, timedelta
from pandas import DataFrame
df = DataFrame.from_dict(
      {'Courses':["Spark","Hadoop","pandas"],
     'Fee' :[20000,25000,30000],
     'Duration':['30days','40days','35days'],
     'Discount':[1000,2500,1500],
     'Inserted': ["10/02/2021, 10:39:24","09/12/2021, 08:09:24","01/22/2021, 10:29:14"],
     'Updated':  ["11/12/2021, 11:39:24","10/22/2021, 10:39:34","05/12/2021, 04:49:04"]},
    orient='index', 
    columns=['A', 'B', 'C']).T
print(df)

Yields below output.

  Courses    Fee Duration Discount              Inserted               Updated
A   Spark  20000   30days     1000  10/02/2021, 10:39:24  11/12/2021, 11:39:24
B  Hadoop  25000   40days     2500  09/12/2021, 08:09:24  10/22/2021, 10:39:34
C  pandas  30000   35days     1500  01/22/2021, 10:29:14  05/12/2021, 04:49:04

2. Use DataFrame.apply() to Convert String Multiple Columns to DateTime

You can use the DataFrame.apply() and pd.to_datetime() function to convert multiple columns to DataTime. apply() function applies function to each and every row and column of the DataFrame.

# Use DataFrame.apply() to convert multiple columns to datetime
df[['Inserted','Updated']] = df[['Inserted','Updated']].apply(pd.to_datetime)
print(df)

Yields below output.

  Courses    Fee Duration Discount            Inserted             Updated
A   Spark  20000   30days     1000 2021-10-02 10:39:24 2021-11-12 11:39:24
B  Hadoop  25000   40days     2500 2021-09-12 08:09:24 2021-10-22 10:39:34
C  pandas  30000   35days     1500 2021-01-22 10:29:14 2021-05-12 04:49:04

Now let’s convert Inserted and Updated columns to DateTime type.

# Convert pandas multiple columns to Datetime
df[['Inserted','Updated']] = df[['Inserted','Updated']].apply(pd.to_datetime, errors='coerce')
print(df)

Yields same output as above.

3. Convert Multiple Column to DateTime Using astype() Method

Use series.astype() method to convert the multiple columns to date & time type. First, select all the columns you wanted to convert and use astype() function with the type you wanted to convert as a param. astype() is also used to convert data types (String to int e.t.c) in pandas DataFrame

# Convert multiple column to datetime using astype() method
df[['Inserted','Updated']] = df[['Inserted','Updated']].astype(datetime64[ns])
print(df)

Yields same output as above.

4. Complete Example – Convert Multiple Columns To DateTime

import pandas as pd
from datetime import datetime, timedelta
from pandas import DataFrame
df = DataFrame.from_dict(
      {'Courses':["Spark","Hadoop","pandas"],
     'Fee' :[20000,25000,30000],
     'Duration':['30days','40days','35days'],
     'Discount':[1000,2500,1500],
     'Inserted': ["10/02/2021, 10:39:24","09/12/2021, 08:09:24","01/22/2021, 10:29:14"],
     'Updated':  ["11/12/2021, 11:39:24","10/22/2021, 10:39:34","05/12/2021, 04:49:04"]},
    orient='index', 
    columns=['A', 'B', 'C']).T
print(df)

# Use DataFrame.apply() to convert multiple columns to datetime
df[['Inserted','Updated']] = df[['Inserted','Updated']].apply(pd.to_datetime)
print(df)

# Convert pandas multiple columns to Datetime
df[['Inserted','Updated']] = df[['Inserted','Updated']].apply(pd.to_datetime, errors='coerce')
print(df)

# Convert multiple column to datetime using astype() method
df[['Inserted','Updated']] = df[['Inserted','Updated']].astype(str)
print(df)

Conclusion

In this article, you have learned how to convert multiple columns of pandas DataFrame to DateTime using pandas.to_datetime(), astype() and DataFrame.apply() function. Using these you can convert data from string, object, and int columns to DateTime format.

Happy Learning !!

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References

• https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html