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

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Adding new column containing the difference between two date columns in Pandas DataFrame

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Time Series Cookbook

schedule Jul 1, 2022

Last updated

local_offer Python●Pandas

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Consider the following DataFrame:


        
        
            
                
                
                    dates1 = pd.date_range(start="2020/12/15", periods=3)
dates2 = pd.date_range(start="2020/12/20", periods=3, freq="2D")
df = pd.DataFrame({"A":dates1, "B":dates2})
df
                
            
               A           B
0  2020-12-15  2020-12-20
1  2020-12-16  2020-12-22
2  2020-12-17  2020-12-24

Pandas allows for date arithmetics using the standard binary operators (e.g. +, -):


        
        
            
                
                
                    df["C"] = df["B"] - df["A"]
df
                
            
               A           B           C
0  2020-12-15  2020-12-20  5 days
1  2020-12-16  2020-12-22  6 days
2  2020-12-17  2020-12-24  7 days

Here, column C is of type timedelta64[ns], which explains why the term "days" is appended. To remove the "days", use dt.days to extract the day unit as an int64:


        
        
            
                
                
                    df["C"] = (df["B"] - df["A"]).dt.days
df
                
            
               A           B           C
0  2020-12-15  2020-12-20  5
1  2020-12-16  2020-12-22  6
2  2020-12-17  2020-12-24  7

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Published by Isshin Inada

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