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8. Pandas Series: Conditional Logic & Boolean Masking

Status

Done

All operations follow this fundamental pattern:

df['series'].operation(condition, [other_parameters])

Where:

df['series'] selects the column/Series
.operation() applies the conditional logic
condition is typically a boolean Series (True/False values)
Optional parameters modify default behavior

import pandas as pd
import seaborn as sns

# Load Titanic dataset
df = sns.load_dataset('titanic')

survived  pclass        sex     age  sibsp  parch     fare  embarked  class    who  adult_male deck  embark_town  alive   alone
0         0       3     male   22.0      1      0   7.2500        S  Third     man        True  NaN  Southampton      no  False
1         1       1   female   38.0      1      0  71.2833        C  First   woman       False    C    Cherbourg     yes  False
2         1       3   female   26.0      0      0   7.9250        S  Third   woman       False  NaN  Southampton     yes   True
3         1       1   female   35.0      1      0  53.1000        S  First   woman       False    C  Southampton     yes  False
4         0       3     male   35.0      0      0   8.0500        S  Third     man        True  NaN  Southampton      no   True

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1. Conditional Replacement Methods

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2. Membership Testing

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3. Boolean Evaluation

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Key Notes Table

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8. Pandas Series: Conditional Logic & Boolean Masking

1. Conditional Replacement Methods

2. Membership Testing

3. Boolean Evaluation

Key Notes Table

Best Practices