300+ Pandas Interview Questions for Data Science

300+ Pandas Interview Questions for Data Science udemy course free download

Crack Data Science & Analytics Interviews Using Pandas - 300+ MCQs with In-Depth Explanations

This course is a comprehensive collection of MCQ-based interview questions focused entirely on Pandas, one of the most powerful and widely-used Python libraries for data analysis and manipulation. If you’re preparing for interviews in data science, analytics, machine learning, or any data-driven domain, mastering Pandas is a must — and this course helps you do exactly that.

Complete Pandas Study Guide

I. Pandas Fundamentals (Difficulty: Easy to Medium)

1. Introduction to Pandas (~20 MCQs)

What is Pandas?

• Definition, purpose, and relationship with NumPy

• Key features: fast, flexible, expressive, built for data analysis

Why use Pandas?

• Handling structured (tabular) data

• Data cleaning, transformation, analysis

Installation and Import Conventions

• import pandas as pd

2. Pandas Data Structures (~30 MCQs)

Series

• Definition: One-dimensional labeled array

• Creation from lists, NumPy arrays, dictionaries, scalar values

• Attributes: index, values, dtype, name

• Basic operations: indexing, slicing, arithmetic operations

DataFrame

• Definition: Two-dimensional labeled data structure with columns of potentially different types (tabular data)

• Creation from dictionaries of Series/lists, list of dictionaries, NumPy arrays, CSV/Excel files

• Attributes: index, columns, shape, dtypes, info(), describe()

• Basic operations:

  • Accessing rows and columns (df['col'], df[['col1', 'col2']])

  • Adding/deleting columns

  • Renaming columns (rename())

3. Data Loading and Saving (~25 MCQs)

Reading Data

• read_csv(): Common parameters (filepath, separator, header, index_col, names, dtype, parse_dates, na_values, encoding)

• read_excel(), read_sql(), read_json()

Writing Data

• to_csv(): Common parameters (filepath, index, header, mode)

• to_excel(), to_sql(), to_json()

4. Basic Data Inspection and Manipulation (~35 MCQs)

Viewing Data

• head(), tail(), sample()

Information

• info(), describe(), dtypes, shape, size, ndim

Indexing and Selection (Basic)

• Column selection: df['col_name'], df.col_name

• Row selection: df[start:end] (slice by integer position)

Sorting

• sort_values() (by column(s), ascending, inplace)

• sort_index()

Handling Duplicates

• duplicated(), drop_duplicates() (subset, keep, inplace)

Unique Values and Counts

• unique(), nunique(), value_counts()

II. Intermediate Pandas Operations (Difficulty: Medium)

1. Advanced Indexing and Selection (~40 MCQs)

loc vs. iloc

• loc: Label-based indexing (rows by label, columns by label)

• iloc: Integer-location based indexing (rows by integer position, columns by integer position)

• Detailed examples with single labels, lists of labels/integers, slices, and boolean arrays

Boolean Indexing/Masking

• Filtering rows based on conditions

at and iat

• For fast scalar access by label (at) or integer position (iat)

Setting/Resetting Index

• set_index(), reset_index() (drop parameter)

MultiIndex (Hierarchical Indexing)

• Creation: pd.MultiIndex.from_arrays(), set_index() with multiple columns

• Selection with MultiIndex: loc for partial indexing, xs()

2. Missing Data Handling (~30 MCQs)

Identifying Missing Data

• isnull(), isna(), notnull()

Dropping Missing Data

• dropna() (axis, how, thresh, subset, inplace)

Filling Missing Data

• fillna() (value, method: 'ffill', 'bfill', 'mean', 'median', 'mode', axis, inplace)

Interpolation

• interpolate() (method, limit_direction)

Practical Considerations

• Choosing appropriate methods for different scenarios

3. Grouping and Aggregation (groupby()) (~45 MCQs)

Concept

• Split-Apply-Combine strategy

Basic Grouping

• df.groupby('column')

Aggregation Functions

• mean(), sum(), count(), min(), max(), size(), first(), last(), nth()

Applying Multiple Aggregations

• agg() with dictionary or list of functions

Custom Aggregation Functions

• Using apply() or lambda functions within agg()

Multi-column Grouping

Transformations

• transform() (e.g., normalizing within groups)

Filtering Groups

• filter() (e.g., selecting groups that meet a certain condition)

4. Combining DataFrames (~35 MCQs)

concat()

• Concatenating along rows (axis=0) and columns (axis=1)

• ignore_index, keys (for MultiIndex)

merge()

• SQL-style joins: inner, outer, left, right

• Parameters: on, left_on, right_on, left_index, right_index, suffixes

• Understanding merge logic and output for different how arguments

join()

• Merging on index by default

• Similar to merge but optimized for index-based joins

• Parameters: on, how, lsuffix, rsuffix

When to Use

• concat vs. merge/join decision criteria

III. Advanced Topics & Performance (Difficulty: Hard)

1. Reshaping and Pivoting Data (~20 MCQs)

pivot()

• Reshaping data based on index, columns, and values

• Limitations (requires unique index/column pairs)

pivot_table()

• More flexible than pivot()

• Parameters: index, columns, values, aggfunc, fill_value, margins

• Similar to Excel pivot tables

stack() and unstack()

• Converting DataFrame to Series (stack) and vice-versa (unstack) with MultiIndex

• Use cases for transforming data between "long" and "wide" formats

melt()

• Unpivoting DataFrames from wide to long format

2. Working with Text Data (String Methods) (~15 MCQs)

.str accessor

• String methods: lower(), upper(), strip(), contains(), startswith(), endswith(), replace(), split(), findall()

• Regular expressions with string methods

Vectorized String Operations

3. Time Series Functionality (~20 MCQs)

DatetimeIndex

• Creating and using datetime indices

pd to_datetime()

• Converting to datetime objects (errors, format parameters)

Time-based Indexing and Selection

• Slicing by date/time strings

• Partial string indexing

Resampling

• resample() (downsampling, upsampling)

• Aggregation methods with resample()

Time Deltas

• pd.Timedelta(), operations with time deltas

Shifting and Lagging

• shift()

Rolling Window Operations

• rolling() (mean, sum, std)

4. Applying Functions (apply, map, applymap) (~15 MCQs)

apply()

• Applying functions along an axis (rows or columns of DataFrame)

• Applying functions to a Series

map()

• Element-wise mapping for Series

• Using dictionaries or functions

applymap()

• Element-wise application for DataFrames (cell by cell)

• Note: For newer Pandas versions, applymap is deprecated in favor of map on DataFrames directly or using apply for row/column operations

Performance Considerations

• apply vs. vectorized operations

5. Performance Optimization (~10 MCQs)

Vectorization over Iteration

• Emphasizing why using Pandas' built-in vectorized operations is faster than explicit loops

Data Types

• Using appropriate dtypes (e.g., category for categorical data, smaller integer types) to reduce memory usage

Method Chaining

• Avoiding unnecessary intermediate DataFrame creation

copy() vs. view

• Understanding SettingWithCopyWarning and how to avoid it

df values and NumPy operations

• When to convert to NumPy for highly optimized numerical operations

Behind-the-scenes Optimizations

• Using numexpr and bottleneck

IV. Practical Scenarios & Best Practices (Difficulty: Medium to Hard)

1. Common Use Cases and Problem Solving (~15 MCQs)

Data Cleaning

• Identifying and fixing inconsistent data, typos

Feature Engineering

• Creating new columns from existing ones

Data Aggregation for Reporting

• Summarizing data for insights

Joining Multiple Datasets

Handling Messy Real-world Data

Practical Examples

• Calculating moving averages

• Customer churn analysis

• Retail analytics

2. Best Practices and Pitfalls (~5 MCQs)

Code Quality

• Readability and maintainability of Pandas code

Debugging

• Debugging Pandas code effectively

Memory Management

• Handling large datasets efficiently

Object Model Understanding

• Views vs. copies in Pandas