Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Series.sub() is used to subtract series or list like objects with same length from the caller series.
Syntax: Series.sub(other, level=None, fill_value=None, axis=0)
other: other series or list type to be subtracted from caller series
fill_value: Value to be replaced by NaN in series/list before subtracting
level: integer value of level in case of multi index
Return type: Caller series with subtracted values
To download the data set used in following example, click here.
In the following examples, the data frame used contains data of some NBA players. The image of data frame before any operations is attached below.
Example #1: Subtracting List
In this example, the top 5 rows are stored in new variable using .head() method. After that a list of same length is created and subtracted from the salary column using .sub() method
As shown in the output image, it can be compared that the Subtracted values column is having the subtracted values of Salary column – list.
Example #2: Adding series to series with null values
In this example, the Age column is subtracted from the Salary column. Since the salary column contains null values too, by default it returns NaN no matter what is subtracted. In this example, 20 is passed to replace null values with 20.
As shown in the output image, the Subtracted value column has subtracted age column from 20 in case of Null values.
- Python | pandas.map()
- Python | Pandas Series.all()
- Python | Pandas DatetimeIndex.day
- Python | Pandas Dataframe.pop()
- Python | Pandas DatetimeIndex.second
- Python | Pandas Series.sum()
- Python | Pandas DataFrame.loc
- Python | Pandas Index.where
- Python | Pandas Series.abs()
- Python | Pandas Series.get()
- Python | Pandas Timestamp.second
- Python | Pandas Series.ptp()
- Python | Pandas Series.pop()
- Python | Pandas Series.max()
- Python | Pandas Series.min()
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.