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 where() method is used to check a data frame for one or more condition and return the result accordingly. By default, The rows not satisfying the condition are filled with NaN value.
Syntax:
DataFrame.where(cond, other=nan, inplace=False, axis=None, level=None, errors=’raise’, try_cast=False, raise_on_error=None)
Parameters:
cond: One or more condition to check data frame for.
other: Replace rows which don’t satisfy the condition with user defined object, Default is NaN
inplace: Boolean value, Makes changes in data frame itself if True
axis: axis to check( row or columns)
For link to the CSV file used, Click here.
Example #1: Single Condition operation
In this example, rows having particular Team name will be shown and rest will be replaced by NaN using .where() method.
import pandas as pd
data = pd.read_csv("nba.csv")
data.sort_values("Team", inplace = True)
filter = data["Team"]=="Atlanta Hawks"
data.where(filter, inplace = True)
data
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Output:
As shown in the output image, every row which doesn’t have Team = Atlanta Hawks is replaced with NaN.
Example #2: Multi-condition Operations
Data is filtered on the basis of both Team and Age. Only the rows having Team name “Atlanta Hawks” and players having age above 24 will be displayed.
import pandas as pd
data = pd.read_csv("nba.csv")
data.sort_values("Team", inplace = True)
filter1 = data["Team"]=="Atlanta Hawks"
filter2 = data["Age"]>24
data.where(filter1 & filter2, inplace = True)
data
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Output:
As shown in the output image, Only the rows having Team name “Atlanta Hawks” and players having age above 24 are displayed.
