Converting Integers to Datetime in Pandas DataFrame
If you’re working with data in Python, you’ll eventually come across the need to convert datatypes, specifically integers to datetime. This can be accomplished easily with Pandas’ to_datetime() function.
In this article, we will explore how to convert integers to datetime in Pandas DataFrame.
Gathering Data
Before we create the DataFrame, we need to gather the data that we want to convert. This data should contain integers that represent datetime.
For example, if we have a dataset that contains timestamps in the format YYYYMMDD, we can convert the integers into datetime.
Creating the DataFrame
To create the DataFrame, we first need to import Pandas:
import pandas as pd
We can then create the DataFrame and add our data to it:
df = pd.DataFrame({‘date’: [20210101, 20210102, 20210103]})
This will create a DataFrame with three rows and one column named ‘date’. Each row contains an integer that represents a date as YYYYMMDD.
Converting the Integers
Now that we have our DataFrame, we can convert the integers to datetime using Pandas’ to_datetime() function:
df[‘date’] = pd.to_datetime(df[‘date’], format=’%Y%m%d’)
The format parameter specifies the format of the date in the integer. In this case, we use ‘%Y%m%d’ to represent YYYYMMDD.
Pandas will then convert the integers into datetime and update the DataFrame.
Converting Additional Formats
While converting integers in the format YYYYMMDD is common, there are other date formats that you may encounter. Here are some examples of how to convert additional formats:
Converting YYMMDD
If your data contains dates in the format YYMMDD, you can convert them using the following code:
df[‘date’] = pd.to_datetime(df[‘date’], format=’%y%m%d’)
This uses ‘%y%m%d’ to represent YYMMDD.
Converting Dates with Times
Sometimes your data will contain both dates and times. In this case, the format parameter needs to include both the date and time formats.
Here is an example:
df = pd.DataFrame({‘datetime’: [202101011200, 202101021300, 202101031400]})
This creates a DataFrame with three rows and one column named ‘datetime’. Each row contains an integer that represents a datetime as YYYYMMDDhhmm.
To convert this to datetime, we use the following code:
df[‘datetime’] = pd.to_datetime(df[‘datetime’], format=’%Y%m%d%H%M’)
This uses ‘%Y%m%d%H%M’ to represent YYYYMMDDhhmm.
Conclusion
In conclusion, converting integers to datetime in Pandas DataFrame is a useful skill to have when working with data in Python. By using Pandas’ to_datetime() function and the correct format parameter, you can easily convert integers to datetime for further analysis.
Remember to pay attention to the format of your date in the integers and adjust the format parameter accordingly. In this article, we learned how to convert integers to datetime in Pandas DataFrame.
We highlighted the importance of gathering data before creating a DataFrame and converting the integers. We also provided examples of converting other popular date formats like YYMMDD and dates with times.
In conclusion, the ability to convert integers to datetime in Pandas DataFrame is a valuable skill when working with data in Python. By using Pandas’ to_datetime() function and appropriate formatting, you can easily convert integers to datetime for more detailed analysis.
Remember to properly format your date in the integers and adjust the formatting parameter accordingly.