Title: Selecting Pandas DataFrame Rows Based On Conditions
Slug: pandas_selecting_rows_on_conditions
Summary: Selecting Pandas DataFrame Rows Based On Conditions
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon

Preliminaries



In [38]:

    
# Import modules
import pandas as pd
import numpy as np



In [39]:

    
# Create a dataframe
raw_data = {'first_name': ['Jason', 'Molly', np.nan, np.nan, np.nan], 
        'nationality': ['USA', 'USA', 'France', 'UK', 'UK'], 
        'age': [42, 52, 36, 24, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'nationality', 'age'])
df









    Out[39]:






  
    
      
      first_name
      nationality
      age
    
  
  
    
      0
      Jason
      USA
      42
    
    
      1
      Molly
      USA
      52
    
    
      2
      NaN
      France
      36
    
    
      3
      NaN
      UK
      24
    
    
      4
      NaN
      UK
      70

Method 1: Using Boolean Variables



In [40]:

    
# Create variable with TRUE if nationality is USA
american = df['nationality'] == "USA"

# Create variable with TRUE if age is greater than 50
elderly = df['age'] > 50

# Select all casess where nationality is USA and age is greater than 50
df[american & elderly]









    Out[40]:






  
    
      
      first_name
      nationality
      age
    
  
  
    
      1
      Molly
      USA
      52

Method 2: Using variable attributes



In [41]:

    
# Select all cases where the first name is not missing and nationality is USA 
df[df['first_name'].notnull() & (df['nationality'] == "USA")]









    Out[41]:






  
    
      
      first_name
      nationality
      age
    
  
  
    
      0
      Jason
      USA
      42
    
    
      1
      Molly
      USA
      52