Description

Welcome to the data manipulation in Python course. Our goal in this course is to provide you with all the tools and skills necessary to master Python, NumPy, and Pandas for data science. No previous skills or expertise are required. Only a drive to succeed!

Data science is quickly becoming one of the most promising careers in the twenty-first century. It's automated, program-driven, and analytical. As a result, it's no surprise that the demand for data scientists has been increasing in the job market over the last few years. This course begins with a quick refresher on Python fundamentals; however, if you're already familiar with Python, you can skip to the next chapter. The next three sections dive deep into data science, starting with the essential Python libraries for data science, progressing toward fundamental NumPy properties, mathematics, and its applications in data science. Once you've gained insights into data science, you'll learn about Python Pandas DataFrames and series, followed bydata cleaning. Next, you'll learn how to use Python to visualize data and leverage Python for data analysis on some sample datasets. Finally, you'll cover time series in Python and learn how to work with and convert datasets to time series. By the end of this course, you'll be able to execute data manipulation for data science and analytics in Python with ease.

What You Will Learn

A quick refresher to Python fundamentals
Learn to use Pandas for data analysis
Learn to work with numerical data in Python
Learn statistics and math with Python
Learn how to code in Jupyter Notebook
Learn how to install packages in Python

Audience

This course is open to students of all skill levels. Prior programming or statistical knowledge is not required to get started.

Approach

This course emphasizes step-by-step learning. We continue to build on what we have learned so far in each following lecture. Our goal is to provide you with all of the necessary tools and skills that you'll need to master Python, NumPy, and Pandas.

Key Features

Discover the basics of Python programming * Work with essential Python libraries for data science * Learn how to use Python to clean, visualize, and analyze data

Github Repo

https://github.com/PacktPublishing/Data-Manipulation-in-Python---Master-Python-NumPy-and-Pandas

About the Author

Meta Brains

Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for finance, coding, and Excel. They bring together both professional and educational experiences to create world-class training programs accessible to everyone. Currently, they're focused on the next great revolution in computing: The Metaverse. Their ultimate objective is to train the next generation of talent so that we can code and build the metaverse together!

Course Outline

1. Python Quick Refresher (Optional)

In this section, we will take a quick refresher to Python fundamentals for beginners. This is completely optional.

1. Welcome to the course!

In this video, we will cover a quick introduction to the course learning objective.

2. Introduction to Python

In this video, we will take a quick Introduction to Python.

3. Setting up Python

In this video, you will learn how to set up Python.

4. What is Jupyter?

In this video, we will understand what is Jupyter?

5. Anaconda Installation: Windows, Mac, and Ubuntu

In this video, you will learn how to install Anaconda on Windows, Mac, and Ubuntu systems.

6. How to Implement Python in Jupyter?

In this video, you will learn how to implement Python in Jupyter?

7. Managing Directories in Jupyter Notebook

In this video, you will learn how to manage directories in Jupyter Notebook.

8. Input/Output

In this video, you will learn about Input/Output.

9. Working with Different Datatypes

In this video, we will start working with different datatypes.

10. Variables

In this video, you will learn about Variables.

11. Arithmetic Operators

In this video, you will learn about arithmetic operators.

12. Comparison Operators

In this video, you will learn about comparison operators.

13. Logical Operators

In this video, you will learn about logical operators

14. Conditional Statements

In this video, you will learn about conditional statements.

15. Loops

In this video, you will learn about Loops.

16. Sequences: Lists

In this video, you will learn about lists in sequences.

17. Sequences: Dictionaries

In this video, you will learn about dictionaries in sequences.

18. Sequences: Tuples

In this video, you will learn about tuples in sequences.

19. Functions: Built-in Functions

In this video, you will learn about the built-in functions.

20. Functions: User-Defined Functions

In this video, you will learn about the user-defined functions.

2. Essential Python Libraries for Data Science

In this section, we will cover the essential Python libraries for data science.

1. Installing Libraries

In this video, you will learn how to install libraries.

2. Importing Libraries

In this video, you will learn how to import libraries.

3. Pandas Library for Data Science

In this video, you will learn about Pandas library for data science.

4. NumPy Library for Data Science

In this video, you will learn about NumPy library for data science.

5. Pandas versus NumPy

In this video, you will learn about Pandas versus NumPy.

6. Matplotlib Library for Data Science

In this video, you will learn about Matplotlib library for data science.

7. Seaborn Library for Data Science

In this video, you will learn about Seaborn library for data science.

3. Fundamental NumPy Properties

In this section, we will walk through the fundamental NumPy properties.

1. Introduction to NumPy Arrays

In this video, we will take a quick introduction to NumPy arrays.

2. Creating NumPy Arrays

In this video, you will learn how to create a NumPy arrays.

3. Indexing NumPy Arrays

In this video, you will learn how to index NumPy arrays.

4. Array Shape

In this video, you will learn about Array shape.

5. Iterating Over NumPy Arrays

In this video, you will learn how to iterate over NumPy arrays.

4. Mathematics for Data Science

In this section, we will start with mathematics and understand how to use it for data science.

1. Basic NumPy Arrays: zeros()

In this video, you will learn about zeros().

2. Basic NumPy Arrays: ones()

In this video, you will learn about ones().

3. Basic NumPy Arrays: full()

In this video, you will learn about full().

4. Adding a Scalar

In this video, you will learn how to add a scalar.

5. Subtracting a Scalar

In this video, you will learn how to subtract a scalar.

6. Multiplying by a Scalar

In this video, you will learn how to multiply by a scalar.

7. Dividing by a Scalar

In this video, you will learn how to divide by a scalar.

8. Raise to a Power

In this video, you will learn about raise to a power.

9. Transpose

In this video, you will learn about Transpose.

10. Element-Wise Addition

In this video, you will learn how to perform element-wise addition.

11. Element-Wise Subtraction

In this video, you will learn how to perform element-wise subtraction.

12. Element-Wise Multiplication

In this video, you will learn how to perform element-wise multiplication.

13. Element-Wise Division

In this video, you will learn how to perform element-wise division.

14. Matrix Multiplication

In this video, you will learn about matrix multiplication.

15. Statistics

In this video, you will learn about statistics.

5. Python Pandas DataFrames and Series

In this section, you will learn about the Python Pandas DataFrames and series.

1. What is a Python Pandas DataFrame?

In this video, we will understand what is a Python Pandas DataFrame?

2. What is a Python Pandas Series?

In this video, we will understand what Python Pandas series is.

3. DataFrame versus Series

In this video, you will learn about DataFrame versus series.

4. Creating a DataFrame Using Lists

In this video, you will learn how to create a DataFrame using lists.

5. Creating a DataFrame Using a Dictionary

In this video, you will learn how to create a DataFrame using a dictionary.

6. Loading CSV Data into Python

In this video, you will learn how to load CSV data into Python.

7. Changing the Index Column

In this video, you will learn how to change the index column.

8. Inplace

In this video, you will learn about Inplace.

9. Examining the DataFrame: Head and Tail

In this video, you will learn how to examine the head and tail of a DataFrame.

10. Statistical Summary of the DataFrame

In this video, you will learn about statistical summary of the DataFrame.

11. Slicing Rows Using Bracket Operators

In this video, you will learn how to slice rows using bracket operators.

12. Indexing Columns Using Bracket Operators

In this video, you will learn how to index columns using bracket operators.

13. Boolean List

In this video, you will learn about Boolean list.

14. Filtering Rows

In this video, you will learn how to filter rows.

15. Filtering rows using '&' and '|' Operators

In this video, you will learn how to filter rows using '&' and '|' operators.

16. Filtering Data Using loc()

In this video, you will learn how to filter data using loc().

17. Filtering Data Using iloc()

In this video, you will learn how to filter data using iloc().

18. Adding and Deleting Rows and Columns

In this video, you will learn how to add and delete rows and columns.

19. Sorting Values

In this video, you will learn how to sort values.

20. Exporting and Saving Pandas DataFrames

In this video, you will learn how to export and save Pandas DataFrames.

21. Concatenating DataFrames

In this video, you will learn how to concatenate DataFrames.

22. Groupby()

In this video, you will learn about groupby().

6. Data Cleaning

In this section, we will move to data cleaning.

1. Introduction to Data Cleaning

In this video, we will take a quick Introduction to data cleaning.

2. Quality of Data

In this video, you will learn about quality of data.

3. Examples of Anomalies

In this video, we will explore some examples of anomalies.

4. Median-based Anomaly Detection

In this video, you will learn about median-based anomaly detection.

5. Mean-Based Anomaly Detection

In this video, you will learn about mean-based anomaly detection.

6. Z-Score-Based Anomaly Detection

In this video, you will learn about Z-score-based anomaly detection.

7. Interquartile Range for Anomaly Detection

In this video, you will learn about interquartile range for anomaly detection.

8. Dealing with Missing Values

In this video, you will learn how to deal with missing values.

9. Regular Expressions

In this video, you will learn about regular expressions.

10. Feature Scaling

In this video, you will learn about feature scaling

7. Data Visualization using Python

In this section, we will take the step and learn how to do data visualization using Python.

1. Introduction

In this video, we will take a quick Introduction.

2. Setting Up Matplotlib

In this video, you will learn how to set up Matplotlib.

3. Plotting Line Plots using Matplotlib

In this video, you will learn how to plot line plots using Matplotlib.

4. Title, Labels, and Legend

In this video, you will learn about title, labels, and legend.

5. Plotting Histograms

In this video, you will learn how to plot histograms.

6. Plotting Bar Charts

In this video, you will learn how to plot bar charts.

7. Plotting Pie Charts

In this video, you will learn how to plot pie charts.

8. Plotting Scatter Plots

In this video, you will learn how to plot scatter plots.

9. Plotting Log Plots

In this video, you will learn how to plot log plots.

10. Plotting Polar Plots

In this video, you will learn how to plot polar plots.

11. Handling Dates

In this video, you will learn how to handle dates

12. Creating Multiple Subplots in One Figure

In this video, you will learn how to create multiple subplots in one figure.

8. Exploratory Data Analysis

In this section, we will be doing data analysis with some sample datasets and get data in action.

1. Introduction

In this video, we will take a quick Introduction.

2. What is Exploratory Data Analysis?

In this video, we will understand what is exploratory data analysis?

3. Univariate Analysis

In this video, you will learn about univariate analysis.

4. Univariate Analysis: Continuous Data

In this video, you will learn about continuous data.

5. Univariate Analysis: Categorical Data

In this video, you will learn about categorical data.

6. Bivariate Analysis: Continuous and Continuous

In this video, we will understand continuous and continuous in bivariate analysis.

7. Bivariate Analysis: Categorical and Categorical

In this video, we will understand categorical and categorical in bivariate analysis.

8. Bivariate Analysis: Continuous and Categorical

In this video, we will understand continuous and categorical in bivariate analysis.

9. Detecting Outliers

In this video, you will learn how to detect outliers.

10. Categorical Variable Transformation

In this video, you will learn about categorical variable transformation.

9. Time Series in Python

In this section, we will concentrate on Time series in Python.

1. Introduction to Time Series

In this video, we will take a quick Introduction to Time series.

2. Getting Stock Data Using yfinance

In this video, you will learn how to get stock data using yfinance data.

3. Converting a Dataset into Time Series

In this video, you will learn how to convert a dataset into Time series.

4. Working with Time Series

In this video, we will start working with Time series.

5. Time Series Data Visualization with Python

In this video, you will learn about Time series data visualization with Python.

Course Images

Data Manipulation in Python - Master Python, NumPy, and Pandas

By Packt

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