Interview Questions and Answers

Pandas is an open-source data manipulation and analysis library for Python.

import pandas as pd

pd.DataFrame(data)

Example:

df = pd.DataFrame({'column1': [1, 2, 3], 'column2': ['a', 'b', 'c']})

df[['column1', 'column2']]

Use the apply function. df.apply(my_function)

Use the rename function. df.rename(columns={'old_name': 'new_name'})

A Series is a one-dimensional labeled array, and a DataFrame is a two-dimensional table.

Use the values attribute. df.values

Use the reset_index function. df.reset_index()

Use the sort_values function. df.sort_values(by='column')

to_csv is used to write a DataFrame to a CSV file.

Example:

df.to_csv('output.csv', index=False)

Use the isin function. df['column'].isin([value])

read_csv is used to read data from a CSV file into a DataFrame.

Example:

df = pd.read_csv('file.csv')

describe generates descriptive statistics of a DataFrame, excluding NaN values.

Example:

df.describe()

Use the drop function. df.drop(['column1', 'column2'], axis=1)

Use the drop_duplicates() function. df.drop_duplicates()

to_datetime() is used to convert the argument to datetime.

Example:

df['date_column'] = pd.to_datetime(df['date_column'])

Use the astype() function. df['column'] = df['column'].astype('new_dtype')

nlargest() returns the first n largest elements from a DataFrame or Series.

Example:

df.nlargest(5, 'column')

Use the pd.DataFrame() constructor. df = pd.DataFrame({'column1': series1, 'column2': series2})

to_excel() is used to write a DataFrame to an Excel file.

Example:

df.to_excel('output.xlsx', index=False)

Use the corr() function. df.corr()

A DataFrame is a 2-dimensional labeled data structure with columns that can be of different types. It is similar to a spreadsheet or SQL table.

loc is label-based indexing, and iloc is integer-based indexing.

groupby is used to split the data into groups based on some criteria and then apply a function to each group independently.

Example:

df.groupby('column1').mean()

df.dropna() or df.fillna(value)

merge is used to combine two DataFrames based on a common column or index.

Example:

pd.merge(df1, df2, on='common_column')

melt is used to transform wide-format data to long-format data.

Example:

pd.melt(df, id_vars=['id_column'], value_vars=['value_column'])

Broadcasting is the ability of NumPy and Pandas to perform operations on arrays or DataFrames of different shapes.

concat is used to concatenate DataFrames along a particular axis.

Example:

pd.concat([df1, df2], axis=1)

nunique returns the number of unique elements in a Series or DataFrame.

Example:

df['column'].nunique()

cut is used to segment and sort data values into bins.

Example:

pd.cut(df['column'], bins=[0, 25, 50, 75, 100])

Method chaining is a way of applying multiple operations on a DataFrame in a single line of code.

Example:

df.dropna().mean()

iterrows() is used to iterate over DataFrame rows as (index, Series) pairs.

Example:

for index, row in df.iterrows():
    print(index, row['column'])

get_dummies() is used to convert categorical variable(s) into dummy/indicator variables.

Example:

pd.get_dummies(df['column'])

Series.value_counts() returns the counts of unique values in a Series, while DataFrame['column'].value_counts() returns counts for a specific column.

pd.to_numeric() is used to convert argument to a numeric type.

Example:

df['column'] = pd.to_numeric(df['column'], errors='coerce')

pd.cut() is used to segment and sort data values into bins. The `bins` parameter defines the bin edges.

Example:

pd.cut(df['column'], bins=[0, 25, 50, 75, 100])

Use the on parameter with a list of column names. pd.merge(df1, df2, on=['column1', 'column2'])

Use the pivot() function to reshape the DataFrame based on column values.

Example:

df.pivot(index='index_column', columns='column_to_pivot', values='value_column')

crosstab() computes a simple cross-tabulation of two (or more) factors.

Example:

pd.crosstab(df['factor1'], df['factor2'])

Use the applymap() function. df.applymap(my_function)

Method chaining is a way of applying multiple operations on a DataFrame in a single line of code.

Example:

df.dropna().mean()

pivot_table is used to create a spreadsheet-style pivot table as a DataFrame.

Example:

pd.pivot_table(df, values='value', index='index_column', columns='column_to_pivot')

MultiIndex is used to represent hierarchical index levels in a DataFrame.

transform() is used to perform group-specific computations and return a DataFrame with the same shape as the input.

Example:

df['normalized_column'] = df.groupby('group_column')['value_column'].transform(lambda x: (x - x.mean()) / x.std())

pipe() is used to apply a function to a DataFrame using method chaining.

Example:

df.pipe(my_function).dropna()

stack() is used to pivot the columns of a DataFrame to the rows. unstack() does the reverse operation.

Example:

df.stack()