Python Data Analysis Quiz

NumPy

Pandas

Matplotlib

Scikit-learn

Supports only numerical data

Uses more memory for storing values

Allows vectorized operations, making computations faster

Only works with small datasets

df.shape

df.size

df.count()

df.rows()

df[['Name', 'Age']]

df['Name', 'Age']

df.loc('Name', 'Age')

df.get(['Name', 'Age'])

df.set_index()

df.reindex()

df.reset_index()

df.drop_index()

Removes all rows with at least one missing value

Replaces NaN values with 0

Fills missing values with column mean

Ignores missing values while processing

method='backfill'

method='forwardfill'

method='ffill'

Both B and C

df.drop_duplicates()

df.remove_duplicates()

df.clear_duplicates()

df.delete_duplicates()

df.apply()

df.map()

df.transform()

df.iterrows()

Changes column names

Replaces all occurrences of 'Male' with 'M' and 'Female' with 'F'

Deletes rows containing 'Male' and 'Female'

Adds a new column with abbreviations