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 examples

# 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.

# Create DataFrame
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.

# 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.

# 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