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Pandas.to_datetime can be used to convert strings or integers into datetime objects. The format of the datetime object can be specified using the format argument, which takes in a string of the desired date-time format. This string should match the format of the input data, and it should contain directives which represent the components of the datetime, such as %Y for the year or %m for the month. Using this argument, pandas.to_datetime will convert the input into the specified datetime format.
You can use the pandas.to_datetime() function to convert a string column to a datetime column in a pandas DataFrame.
When using this function, you can use the format argument to specify the format that your date is in so that you avoid errors when converting it from string to datetime.
This function uses the following basic syntax:
df['datetime'] = pd.to_datetime(df['my_date_column'], format='%m%d%Y %H:%M:%S'])
Here are the most common directives that you can provide to the format argument:
- %m: Month as zero-padded number (01, 02, … 12)
- %d: Day of the month as zero-padded number (01, 02, … 31)
- %y: Year with century as number (2020, 2021, 2022, …)
- %H: Hour (24-hour clock) as zero-padded number (00, 01, … 23)
- %I: Hour (12-hour clock) as zero-padded number (01, 02, … 12)
- %p: Either AM or PM
- %M: Minute as zero-padded number (00, 01, … 59)
- %S: Second as zero-padded number (00, 01, … 59)
For a complete list of directives, refer to .
The following example shows how to use the format argument within the to_datetime() function in different scenarios.
Example: Specify Format in pandas.to_datetime
Suppose we have the following pandas DataFrame that contains information about total sales made on various dates at some retail store:
import pandas as pd #create DataFrame df = pd.DataFrame({'date': ['10012023 4:15:30', '10042023 7:16:04','10062023 9:25:00', '10142023 15:30:50', '10152023 18:15:00'], 'sales': [100, 140, 235, 120, 250]}) #view DataFrame print(df) date sales 0 10012023 4:15:30 100 1 10042023 7:16:04 140 2 10062023 9:25:00 235 3 10142023 15:30:50 120 4 10152023 18:15:00 250 #view data type of each column in DataFrame print(df.dtypes) date object sales int64 dtype: object
We can see that the date column is currently a string (i.e. object) column.
Suppose we attempt to use pandas.to_datetime() to convert this column to datetime:
#attempt to convert date column to datetime format
df['date'] = pd.to_datetime(df['date'])
ParserError: month must be in 1..12: 10012023 4:15:30 present at position 0 We receive an error because the pandas.to_datetime() function doesn’t recognize the date and time format that the date column is currently in.
We can also use the format argument to specify the format of the column:
#convert date column to datetime format
df['date'] = pd.to_datetime(df['date'], format='%m%d%Y %H:%M:%S')
#view DataFrame
print(df)
date sales
0 2023-10-01 04:15:30 100
1 2023-10-04 07:16:04 140
2 2023-10-06 09:25:00 235
3 2023-10-14 15:30:50 120
4 2023-10-15 18:15:00 250
#view updated type of each column
print(df.dtypes)
date datetime64[ns]
sales int64
dtype: object Note: You can find the complete documentation for the pandas to_datetime() function .