Ultimate Data Science Programming in Python: Master data science libraries with 300+ programs, 2 projects, and EDA GUI tools / Полное программирование в области науки о данных на Python: Освойте библиотеки обработки данных с более чем 300 программами, 2 проектами и графическими инструментами EDA
Год издания: 2025
Автор: Chandrakar Saurabh / Чандракар Саурабх
Издательство: BPB Publications
ISBN: 978-93-65895-667
Язык: Английский
Формат: PDF (Not True)/EPUB
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 775
Описание: In today’s data-driven world, the ability to extract meaningful insights from vast datasets is crucial for success in various fields. This ultimate book for mastering open-source libraries of data science in Python equips you with the essential tools and techniques to navigate the ever-evolving field of data analysis and visualization.
Discover how to use Python libraries like NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. This book also covers scientific computing with SciPy and integrates ChatGPT to boost your data science workflow. Designed for data scientists, analysts, and beginners, it offers a practical, hands-on approach to mastering data science fundamentals. With real-world applications and exercises, you will turn raw data into actionable insights, gaining a competitive edge. This book covers everything you need, including open-source libraries, Visual Explorer tools, and ChatGPT, making it a one-stop resource for Python-based data science.
Readers will gain confidence after going through this book and we assure you that all the minute details have been taken into consideration while delivering the content. After reading, learning, and practicing from this book, we are sure that all IT professionals, novices, or job seekers will be able to work on data science projects thus proving their mettle.
Key Features
● Master key Python libraries like NumPy, Pandas, and Seaborn for effective data analysis and visualization.
● Understand complex data science concepts through simple explanations and practical examples.
● Get hands-on experience with 300+ solved examples to solidify your Python data science skills.
What you will learn
● Learn to work with popular IDEs like VS Code and Jupyter Notebook for efficient Python development.
● Master open-source libraries such as NumPy, SciPy, Matplotlib, and Pandas through advanced, real-world examples.
● Utilize automated EDA tools like PyGWalker and AutoViz to simplify complex data analysis.
● Create sophisticated visualizations like heatmaps, FacetGrid, and box plots using Matplotlib and Seaborn.
● Efficiently handle missing data, outliers, and perform filtering, sorting, grouping, and aggregation using Pandas and Polars.
Who this book is for
This book is ideal for diploma, undergraduate, and postgraduate students from engineering and science fields to programming and software professionals. It is also perfect for data science, ML, and AI engineers looking to expand their expertise in cutting-edge technologies.
В современном мире, основанном на данных, способность извлекать значимую информацию из обширных наборов данных имеет решающее значение для успеха в различных областях. Эта книга, которая поможет вам освоить библиотеки данных с открытым исходным кодом на Python, познакомит вас с необходимыми инструментами и техниками для работы в постоянно развивающейся области анализа и визуализации данных.
Узнайте, как использовать библиотеки Python, такие как NumPy, Pandas и Matplotlib, для обработки, анализа и визуализации данных. В этой книге также рассматриваются научные вычисления с помощью SciPy и интеграция ChatGPT для улучшения вашего рабочего процесса в области обработки данных. Предназначенный для специалистов по обработке данных, аналитиков и новичков, он предлагает практический подход к освоению основ науки о данных. С помощью реальных приложений и упражнений вы превратите необработанные данные в полезную информацию, получив конкурентное преимущество. В этой книге есть все, что вам нужно, включая библиотеки с открытым исходным кодом, инструменты визуального обозревателя и ChatGPT, что делает ее универсальным ресурсом для работы с данными на основе Python.
Ознакомившись с этой книгой, читатели обретут уверенность в себе, и мы заверяем вас, что при подготовке материала были учтены все мельчайшие детали. Мы уверены, что после прочтения, изучения и применения на практике этой книги все ИТ-специалисты, новички или лица, ищущие работу, смогут работать над проектами в области науки о данных, доказав тем самым свой профессионализм.
Kлючевые функции
● Основные библиотеки Python, такие как NumPy, Pandas и Seaborn, для эффективного анализа и визуализации данных.
● Разберитесь в сложных концепциях науки о данных с помощью простых объяснений и практических примеров.
● Получите практический опыт работы с более чем 300 решенными примерами, чтобы укрепить свои навыки работы с данными на Python.
Что вы узнаете
● Научитесь работать с популярными IDE, такими как VS Code и Jupyter Notebook, для эффективной разработки на Python.
● Познакомьтесь с библиотеками с открытым исходным кодом, такими как NumPy, SciPy, Matplotlib и Pandas, на продвинутых реальных примерах.
● Используйте автоматизированные инструменты EDA, такие как PyGWalker и AutoViz, для упрощения сложного анализа данных.
● Создавайте сложные визуализации, такие как тепловые карты, FacetGrid и прямоугольные графики, используя Matplotlib и Seaborn.
● Эффективно обрабатывать недостающие данные, выбросы и выполнять фильтрацию, сортировку, группировку и агрегирование с помощью Pandas и Polars.
Для кого предназначена эта книга
Эта книга идеально подходит для дипломированных специалистов, студентов и аспирантов инженерных и научных специальностей, а также для специалистов в области программирования. Она также идеально подходит для инженеров в области обработки данных, ML и искусственного интеллекта, желающих расширить свой опыт в области передовых технологий.
Примеры страниц (скриншоты)
Оглавление
1. Environmental Setup for Using Data Science Libraries in Python
Introduction
Structure
Objectives
Introduction to Python
Setup installation in Windows for Jupyter Notebook
Insights of Jupyter Notebook
Demo program using Jupyter Notebook
Demo program using VSCode
Introduction to data science libraries in Python
Conclusion
Points to remember
Questions
2. Exploring Numpy Library for Data Science in Python
Introduction
Structure
Objectives
Commonalities shared by lists and numpy arrays
Contrasts separating lists from numpy arrays
The process of creating numpy arrays
Creating a 1-D array using a list
Creating a 1-D array using a tuple
Creating a 2-D array using nested lists
Creating an array with a specific dtype
Creating an array of object type
Creating a 1-D array with arange() function
Array creation using linspace() function
Utilizing the zeros() function in numpy
Utilizing the ones() function in numpy
Utilizing the full() function in numpy
Using eye() function
Utilizing diag() function
Utilizing empty() function
Contrast between zeros and empty function
Using random module for ndarray creation
randint()
shuffle()
Elements access of ndarray
Iterate elements of ndarray
Arithmetic operators in numpy
Broadcasting concept in numpy
Conclusion
Points to remember
Questions
References
3. Exploring Array Manipulations in Numpy
Introduction
Structure
Objectives
Array manipulation functions/variables in numpy
reshape()
resize()
flatten()
flat variable
ravel()
transpose()
Multiple arrays joining into a single array
concatenate()
stack()
vstack()
hstack()
dstack()
Splitting of arrays in numpy
split()
vsplit()
hsplit()
dsplit()
array_split()
Sorting of ndarray in numpy
Elements searching of ndarray in numpy
where()
condition based selection
Elements insert into ndarray
insert()
append()
Elements delete from ndarray in numpy
delete()
Usage of dot function for matrix multiplication
Usage of linalg module in numpy
Conclusion
Points to remember
Questions
4. Exploring Scipy Library for Data Science in Python
Introduction
Structure
Objectives
Differences between numpy and scipy
Exploring scipy constants
Metric
Force
Binary
Angle
Mass
Time
Pressure
Length
Volume
Speed
Temperature
Energy
Power
Area
Optimizers in scipy
Sparse data and sparse matrix in scipy
Block sparse row matrix
Coordinate list matrix
Compressed sparse column matrix
Compressed sparse row matrix
Diagonal sparse matrix
Dictionary of keys sparse matrix
Row-based linked list sparse matrix
Graphs in scipy
Adjacency matrix
connected_components method
Dijkstra method
Floyd_Warshall method
Spatial data in scipy
Triangulation function
Integrate in scipy
Single integration
Multiple integration
Interpolation in scipy
1D interpolation
Spline interpolation
splrep function
splev function
Interpolation with radial basis function
Conclusion
Points to remember
Questions
5. Line Plot exploration with Matplotlib Library
Introduction
Structure
Objectives
Python data visualization tools
Line plot
Line plot creation by passing 2 ndarrays
Adding title, xlabel and ylabel to the line plot
Advanced line plot
Linestyle property
Color property
Default color
Quick settings for color, marker and linestyle
mlc form
clm form
When unspecified then default color is blue
Alpha property
Linewidth and markersize property
Markerfacecolor property
Customizing the figure size
Plotting multiple lines in a same plot
Line plot creation by passing a single ndarray
Addition of grid lines to a plot
Major and minor grid lines display
Legends display to a plot
Using xlim and ylim functions to a plot
Conclusion
Points to remember
Questions
6. Charting Data with Various Visuals Using Matplotlib
Introduction
Structure
Objectives
Barplot
Vertical bar chart and simple bar chart
Color change of each bar
Width change of each bar
Bottom change of each bar
Left alignment of each bar
Right alignment of each bar
Enhancing Bar visualization with labels
Horizontal bar chart
Stacked bar chart
Other bar charts
Pie chart
Histogram
Scatter plot
Subplot in matplotlib
Conclusion
Points to remember
Questions
7. Exploring Pandas Series for Data Science in Python
Introduction
Structure
Objectives
Pandas series
Pandas series constructor
Creating pandas series by passing a list
Creating pandas series by passing a dictionary
Creating pandas series by passing a numpy array
Creating pandas series from scalar value
Creating pandas series using RangeIndex
Creating pandas series with dtype and name
Exploring pandas series using copy parameter
Accessing series values using head and tail methods
Accessing elements in the pandas series
Pandas series slicing
Values extraction using loc and iloc indexers
Masking using Booleans for condition-based selection
Pandas series filtering
Callable object usage in selecting elements
Usage of the apply method to Pandas series
Aggregating of pandas series
Series object transformation
Iterate elements of the series
Conclusion
Points to remember
Questions
8. Exploring Pandas Dataframe for Data Science in Python
Introduction
Structure
Objectives
Pandas dataframe
Pandas dataframe constructor
Pandas dataframe creation
Pandas methods and attributes for dataframe
Dropping dataframe rows with null values
Arithmetic operators for dataframes
New columns addition to dataframes
Usage of fillna() method to the dataframe
Sorting values of the dataframe
Sorting dataframe by multiple columns
Sorting dataframe based on index
Ranking series of values by using the rank() method
Filter data from dataframe
Inclusion check in dataframe
Usage of isnull and notnull method
Usage of duplicated method
Usage of drop_duplicates method in dataframe
Usage of unique and nunique methods
Conclusion
Points to remember
Questions
9. Advanced Dataframe Filtering Techniques
Introduction
Structure
Objectives
Approaches of setting customized columns as index
Rows selection by index label with loc[] indexer
Accessing rows of dataframe by using index position
Renaming index and column names in dataframe
Renaming of index labels
Renaming of column names
Rows and columns deletion from the Dataframe
Filter dataframe using query method
Without using the query method
in and not in operators using the query method
Handling spaces in column names with query
Output:
Usage of apply() method to pandas dataframe
Using nlargest() and nsmallest() in pandas dataframe
Filtering dataframe with where() method
Handling of text data in Pandas
Filtering dataframe rows with string methods
Modifying data in pandas dataframe
Example of data aggregation in Pandas DataFrame
Conclusion
Points to remember
Questions
10. Exploring Polars Library for Data Science in Python
Introduction
Structure
Objectives
Preference of polars over pandas
Differences between pandas and polars
Polars data types
Numeric group
Nested group
Temporal group
Others group
Data structures in polars
Series
Dataframe
Contexts in polars
Basic operations, select and filter concept
GroupBy concept
Importance of expressions in polars
Concept of lazy API in polars
Conclusion
Points to remember
Questions
11. Exploring Expressions in Polars
Introduction
Structure
Objectives
Concept of expressions in polars
Basic operators
Column selections
Functions
Casting
Strings
Aggregation
Missing data
Folds
List and arrays
Usage of numpy in polars
Operations comparison between pandas and polars
Conclusion
Points to remember
Questions
12. Exploring Seaborn Library for Data Science in Python
Introduction
Structure
Objectives
Some basic statistical terms to know
Quantitative variables
Qualitative variables
Built in seaborn datasets
Plot styling in Seaborn
Concept of color palette in Seaborn
Qualitative palettes
Sequential palettes
Diverging palettes
Heatmap plot in Seaborn
The box plot in seaborn
Conclusion
Points to remember
Questions
13. Crafting Seaborn Plots: KDE, Line, Violin, and Facets
Introduction
Structure
Objectives
KDE plot in seaborn
The violin plot in seaborn
Line plot in seaborn
The scatter plot in seaborn
Facet grid in seaborn
Conclusion
Points to remember
Questions
14. Integrating Data Science Libraries with ChatGPT Prompts
Introduction
Structure
Objectives
ChatGPT prompts for exploring data science libraries
Querying ChatGPT for Python code with data science libraries
Querying ChatGPT with Python error
Data science library cheat sheets using ChatGPT
Novice challenges querying Python code via ChatGPT
Conclusion
Points to remember
Questions
15. Exploring Automated EDA Libraries for Machine Learning
Introduction
Structure
Objectives
Usage of PyGWalker visual library with a dataset
Usage of dataprep library with a dataset
Usage of autoviz library with a dataset
Usage of pandas_visual_analysis library with a dataset
Conclusion
Points to remember
Questions
16. Case Studies Using Data Science Libraries
Introduction
Structure
Objectives
Case study on electrical fault classification
Case study on titanic dataset
Conclusion
Points to remember
Questions
Index
