Pandas is an open-source Python library. It is widely used throughout the data science industry. It is a fast and very powerful Python tool to perform data analysis. Pandas provide commands to read, filter, inspect, manipulate, analyze, and plot data.
Pandas dataframe is a tabular structure. It is a two-dimensional data structure that consists data in the form of rows and columns. This library is used to group, aggregate, clean, and filter data.
Filtering data is a preliminary step for any data science and machine learning application. It allows us to create subsets from the original dataset by forming smaller dataframes.
Let’s take a look at a few different ways to filter and select rows in a Pandas dataframe based on multiple conditions.To start we’re going to create a simple dataframe in Python:
import pandas as pd
df=pd.DataFrame({
'name':['GT','CSK','LSG','RCB','MI','RR','KKR','PK','DC','SH'],
'Mat':[13,13,13,13,13,13,13,13,13,13],
'Won':[9,7,7,7,7,6,6,6,5,4],
'Lost':[4,5,5,6,6,7,7,7,8,9],
'Pts':[18,15,15,14,14,12,12,12,10,8],
'NRR':[0.835,0.381,0.304,0.180,-0.128,0.140,-0.256,-0.308,-0.572,-0.559]
})
Filter Pandas dataframe using relational conditions
This method is often used to select rows based on particular column values. Comparison operators are used to write these conditions.
Single Condition:
df[(df.NRR>0)]
Multiple Conditions:
We use the ampersand (&) operator and the pipe (|) operator, for “and” and “or” logical operators respectively.
Syntax: df [ (df[‘‘column name 1' ]==’column value’ ) & (df[‘‘column name 2' ]==’column value’ )]
df[(df['NRR']>0)| (df['Pts']>14)]
Add each condition you want to be included in the filtered result and concatenate them with the “&” and “|” operators.
Using Query Function
The query function takes a query parameter in the form of a condition. It evaluates whether the condition is True or False. In simple words, it is used to query the columns using a boolean expression. This function can be used for both single or multiple conditions. It returns a new dataframe.
df.query('NRR>0 and Pts>14')
If we have to update the changes in the original dataframe, use inplace=True as an argument in the query function.