Monday, April 17, 2017

Python Pandas Dataframe - Largest and Smallest Rows

Here is an example on how identify largest or smallest n rows.
Source Data : Census data for state of California.

Use Case : 
Read Population data for state of California from "censusdata.ire.org".
Get 3 Counties with highest population.
Get 3 Counties with lowest population.


Syntax : 


  DataFrame.nlargest()
  DataFrame.nsmallest()


Main Parameters :


  • Number of Items to retrieve
  • Columns to sort on
  • Rows to keep in case of duplicates ( keep=first or keep=last )


Sample Code


Read Source Data and select subset of columns.
County name and Population selected.

import pandas as pd
df = pd.read_csv("http://censusdata.ire.org/06/all_050_in_06.P1.csv")
df = df[["NAME","POP100"]]
df












Select Top 3 counties with highest population
df.nlargest(3,"POP100")












Select 3 counties with lowest population
df.nsmallest(3,"POP100")



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    1. In Python, using the Pandas library, you can efficiently find the largest and smallest rows in a DataFrame based on one or more columns.
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      Largest and Smallest Rows Based on a Single Column
      To find the rows with the largest and smallest values in a specific column, use the idxmax() and idxmin() methods, which return the index of the maximum and minimum values, respectively.
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      Largest and Smallest Rows Based on Multiple Columns
      If you want to find the largest and smallest rows based on multiple columns, you can use the loc method combined with idxmax() and idxmin() for multiple columns.
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