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.
Pandas Pop() method is common in most of the data structures but pop() method is a little bit different from the rest. In a stack, pop doesn’t require any parameters, it pops the last element every time. But the pandas pop method can take input of a column from a data frame and pop that directly.
Syntax: DataFrame.pop(item)
Parameters:
item: Column name to be popped in string
Return type: Popped column in form of Pandas Series
To download the CSV used in code, click here.
Example #1:
In this example, a column have been popped and returned by the function. The new data frame is then compared with the old one.
Python3
import pandas as pd# importing pandas packagedata = pd.read_csv("nba.csv")# making data frame from csv filepopped_col = data.pop("Team")# storing data in new vardata# display |
Output:
In the output images, the data frames are compared before and after using .pop(). As shown in second image, Team column has been popped out.
Dataframe before using .pop()
Dataframe after using .pop()
Example #2: Popping and pushing in other data frame
In this example, a copy of data frame is made and the popped column is inserted at the end of the other data frame.
Python3
import pandas as pd# importing pandas packagedata = pd.read_csv("nba.csv")# making data frame from csv filenew = data.copy()# creating independent copy of data framepopped_col = data.pop("Name")# storing data in new varnew["New Col"]= popped_col# creating new col and passing popped colnew# display |
Output:
As shown in the output image, the new data frame is having the New col at the end which is nothing but the Name column which was popped out earlier.
