chevron_left Time Series Cookbook

Adding new column containing the difference between two date columns Combining columns containing date and time Combining columns of years, months and days Converting a column of strings to datetime Converting dates to strings Converting datetime column to date and time columns Converting DatetimeIndex to Series of datetime Converting index to datetime Converting UNIX timestamp to datetime Creating a column of dates Creating a range of dates Extracting month and year from Datetime column Getting all weekdays between two dates Getting all weekends between two dates Getting day unit from date column Getting month unit from date column Getting name of months in date column Getting week numbers from a date column Getting day of week of date columns Getting year unit from date column Modifying dates Offsetting datetime Removing time unit from dates Setting date to beginning of month Sorting DataFrame by dates Using dates as the index of a DataFrame

thumb_up

thumb_down

chat_bubble_outline
0
 auto_stories new 
settings

Combining columns of years, months and days in Pandas

Programming

chevron_right

Python

chevron_right

Pandas

chevron_right

Cookbooks

chevron_right

DataFrame Cookbooks

chevron_right

Time Series Cookbook

schedule Jul 1, 2022

Last updated

local_offer Python●Pandas

Solution

To combine the Year, Month and Day columns to form another column:


        
        
            
                
                
                    df["Date"] = pd.to_datetime(df["Year"] + "/" + df["Month"] + "/" + df["Day"])
df
                
            
               Year   Month  Day  Date
0  2020   10     25   2020-10-25
1  2020   11     26   2020-11-26

Here, column Date is of type datetime.

Explanation

We first combine Year, Month and Day columns via simple string concatenation:


        
        
            
                
                
                    df["Year"] + "/" + df["Month"] + "/" + df["Day"]
                
            
            0    2020/10/25
1    2020/11/26
dtype: object

Here, the combined column (Series) is of type string. We can convert its data type to datetime using pd.to_datetime(~):


        
        
            
                
                
                    pd.to_datetime(df["Year"] + "/" + df["Month"] + "/" + df["Day"])
                
            
            0   2020-10-25
1   2020-11-26
dtype: datetime64[ns]