Modifies the original DataFrame

Creates a new DataFrame

Drops the filled values

Reverts the changes made by the method

df.iloc[df['gender'] == 'F' and df['age'] >= 30]

df.loc[df['gender'] == 'F' and df['age'] >= 30]

df.loc[(df['gender'] == 'F') & (df['age'] >= 30), ['name', 'age']]

df.iloc[(df['gender'] == 'F') & (df['age'] >= 30), [0, 1]]

 df.fillna(value=df.mean())

df.fillna_mean()

 df.fillna(method='mean')

df.fillna_average()

Z=(x-μ)/σ

Z=(x-σ)/μ

Z=(x+μ)/σ

Z=(x+σ)/μ

To introduce variability

To make the data messier

To ensure uniformity and accuracy

To increase redundancy

To make data more diverse

To transform data into a standard scale

To introduce missing values

To increase redundancy

df.fillna(method='linear')

df.fillna_linear()

df.interpolate()

df.interpolate_linear()

Data Mining

Data Exploration

Data Cleaning

Feature Modeling

Making predictions

Identifying patterns

Spotting outliers

Understanding data structure

Time series data

Categorical data

Numerical data

Text data