Set Index - ( Pandas Indexing )
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Syntax
DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)
Example
import pandas as pd
data = {'name': ['John', 'Jane', 'Fred', 'Samantha'],
'age': [25, 30, 35, 40],
'country': ['USA', 'UK', 'Canada', 'Australia']}
df = pd.DataFrame(data)
df.set_index('name', inplace=True)
print(df)
Output
age country
name
John 25 USA
Jane 30 UK
Fred 35 Canada
Samantha 40 Australia
Explanation
In Pandas, an index is a way to uniquely identify each row in a DataFrame or a Series. The set_index()
function is used to set one or more columns as the index of a DataFrame. It takes the column(s) name as input and returns a new DataFrame with the specified column(s) as the index. The original DataFrame is not modified unless the inplace=True
argument is used.
In the above example, the DataFrame df
is indexed by the name
column using the set_index()
function. The inplace=True
argument is used to modify the original DataFrame.
Use
Setting an index is important for performing various operations on DataFrames, such as filtering, merging, and grouping. A properly defined index can improve the performance of data selection and manipulation operations.
Important Points
- An index is a way to uniquely identify each row in a DataFrame or a Series in Pandas.
- The
set_index()
function is used to set one or more columns as the index of a DataFrame. - It takes the column(s) name as input and returns a new DataFrame with the specified column(s) as the index.
- The original DataFrame is not modified unless the
inplace=True
argument is used. - A properly defined index can improve the performance of data selection and manipulation operations.
Summary
In conclusion, setting an index is an important step in data analysis using Pandas. The set_index()
function is used to set one or more columns as the index of a DataFrame. A properly defined index can improve the performance of data selection and manipulation operations.