Convert the Column Type from String to Datetime Format in Pandas DataFrameWhen we work with data in Pandas DataFrame of Python, it is pretty usual to encounter time series data. Panday is a strong tool that can handle time-series data in Python, and we might need to convert the string into Datetime format in the given dataset. In this tutorial, we will learn how to convert the DataFrame column of string into datetime format, “dd/mm/yy”. The user cannot execute any time-series based operations on the dates if they are not in the required format. To deal with this, we need to convert the dates into the required date-time format. Different Approaches for Converting Datatype Format in Python:In this section, we will discuss different approaches we can use for changing the datatype of Pandas DataFrame column from string to datetime: Approach 1: Using pandas.to_datetime() FunctionIn this approach, we will use “pandas.to_datetime()” function for converting the datatype in Pandas DataFrame column. Example: Output: The data is: Date Event Cost 0 12/05/2021 Music- Dance 15400 1 11/21/2018 Poetry- Songs 7000 2 01/12/2020 Theatre- Drama 25000 Here, in the output, we can see that the Datatype of the “Date” column in the DataFrame is “object”, which means it is a string. Now, we will convert the Datatype into datetime format by using the “pnd.to_datetime()” function: Output: The data is:
Date Event Cost
0 12/05/2021 Music- Dance 15400
1 11/21/2018 Poetry- Songs 7000
2 01/12/2020 Theatre- Drama 25000
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 3 non-null datetime64[ns]
1 Event 3 non-null object
2 Cost 3 non-null int64
dtypes: datetime64[ns](1), int64(1), object(1)
memory usage: 200.0+ bytes
Now, we can see that the format of the “Data” column in the DataFrame has been changed to the datetime format. Approach 2: Using DataFrame.astype() Function.In this approach, we will use “DataFrame.astype()” function for converting the datatype in Pandas DataFrame column. Example: Output: The data is:
Date Event Cost
0 12/05/2021 Music- Dance 15400
1 11/21/2018 Poetry- Songs 7000
2 01/12/2020 Theatre- Drama 25000
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 3 non-null object
1 Event 3 non-null object
2 Cost 3 non-null int64
dtypes: int64(1), object(2)
memory usage: 200.0+ bytes
Here, in the output, we can see that the Datatype of the “Date” column in the DataFrame is “object”, which means it is a string. Now, we will convert the datatype into datetime format by using the “Data_Frame.astype()” function: Output: The data is:
Date Event Cost
0 12/05/2021 Music- Dance 15400
1 11/21/2018 Poetry- Songs 7000
2 01/12/2020 Theatre- Drama 25000
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 3 non-null datetime64[ns]
1 Event 3 non-null object
2 Cost 3 non-null int64
dtypes: datetime64[ns](1), int64(1), object(1)
memory usage: 200.0+ bytes
Now, we can see that the format of the “Data” column in the DataFrame has been changed to the datetime format by using data_frame[‘Date’].astype(‘datetime64[ns]’. Approach 3:Suppose we have a date in “yymmdd” format in the DataFrame column, and we have to convert it from a string to a datetime format. Example: Output: The data is:
Date Patient Number
0 210302 67000
1 210901 62000
2 210706 61900
3 210402 59000
4 210802 74000
5 210804 54050
6 210109 57650
7 210509 67300
8 210209 76600
Date object
Patient Number int64
dtype: object
Here, in the output we can see that the Datatype of the “Date” column in the DataFrame is “object”, that means, it is string. Now, we will convert the datatype into datetime format by using “data_frame[‘Date’] = pnd.to_datetime(data_frame[‘Date’], format = ‘%y%m%d’)” function. Output: The data is:
Date Patient Number
0 210302 67000
1 210901 62000
2 210706 61900
3 210402 59000
4 210802 74000
5 210804 54050
6 210109 57650
7 210509 67300
8 210209 76600
Date datetime64[ns]
Patient Number int64
dtype: object
In the above code, we have changed the datatype of the column “Date” from “object” to “datetime64[ns]” by using “pnd.to_datetime(data_frame[‘Date’], format = ‘%y%m%d’)” function. Approach 4:We can convert multiple columns from “string” to “datetime” format, which means “YYYYMMDD” format, by using the “pandas.to_datetime()” function. Output: The data is: Treatment_starting_Date Patients Number Treatment_ending_Date 0 20210612 54000 20210812 1 20210814 65000 20210614 2 20210316 71500 20210316 3 20210519 45000 20210119 4 20210221 98000 20210221 5 20210124 23000 20210724 6 20210929 12000 20210924 Treatment_starting_Date object Patients Number int64 Treatment_ending_Date object dtype: object Here, in the output, we can see that the Datatype of the “Date” column in the DataFrame is “object”, which means it is a string. Now, we will convert the datatype “Date” column into datetime format by using “pnd.to_datetime(data_frame[”], format = ‘%y%m%d’)” function. Output: The data is: Treatment_starting_Date Patients Number Treatment_ending_Date 0 20210612 54000 20210812 1 20210814 65000 20210614 2 20210316 71500 20210316 3 20210519 45000 20210119 4 20210221 98000 20210221 5 20210124 23000 20210724 6 20210929 12000 20210924 Treatment_starting_Date datetime64[ns] Patients Number int64 Treatment_ending_Date datetime64[ns] dtype: object In the above output, we can see the datatype of “Treatment_starting_Date” and “Treatment_ending_Date” has been changed to datetime format by using the “pnd.to_datetime()” function. ConclusionIn this tutorial, we learned different methods of converting the column type of Pandas DataFrame from string to datetime in Python. |
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