Data Wrangling in Python – How to Select pandas DataFrame Rows Based On Conditions

Selecting pandas DataFrame Rows Based On Conditions

Preliminaries


/* Import modules */
import pandas as pd
import numpy as np
/* 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
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

/* 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 cases where nationality is USA and age is greater than 50 */
df[american & elderly]
first_name nationality age
1 Molly USA 52

Method 2: Using variable attributes

/* Select all cases where the first name is not missing and nationality is USA  */
df[df['first_name'].notnull() & (df['nationality'] == "USA")]
first_name nationality age
0 Jason USA 42
1 Molly USA 52

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