02-Conditional-Filtering

Driptanil DattaSoftware Developer

Conditional Filtering

import numpy as np
import pandas as pd

df = pd.read_csv('tips.csv')

df.head()

HTML

total_bill
tip
sex
smoker
day

# df['total_bill'] > 30

bool_series = df['total_bill'] > 30

df[bool_series]

HTML

total_bill
tip
sex
smoker
day

df[df['total_bill']>30]

HTML

total_bill
tip
sex
smoker
day

df[df['sex'] == 'Male']

HTML

total_bill
tip
sex
smoker
day

Recall the steps:

Get the conditions
Wrap each condition in parenthesis
Use the | or & operator, depending if you want an
- OR | (either condition is True)
- AND & (both conditions must be True)
You can also use the ~ operator as a NOT operation

df[(df['total_bill'] > 30) & (df['sex']=='Male')]

HTML

total_bill
tip
sex
smoker
day

df[(df['total_bill'] > 30) & ~(df['sex']=='Male')]

HTML

total_bill
tip
sex
smoker
day

df[(df['total_bill'] > 30) & (df['sex']!='Male')]

HTML

total_bill
tip
sex
smoker
day

# The Weekend
df[(df['day'] =='Sun') | (df['day']=='Sat')]

HTML

total_bill
tip
sex
smoker
day

We can use .isin() operator to filter by a list of options.

options = ['Sat','Sun']
df['day'].isin(options)

RESULT

0       True
1       True
2       True
3       True
4       True

df[df['day'].isin(['Sat','Sun'])]

HTML

total_bill
tip
sex
smoker
day