Learn Python, NumPy, and Pandas from Scratch
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What you’ll learn
- Python Basics: A solid foundation in Python programming, including data types, loops, conditionals, functions
- Understanding Lists are different from arrays
- NumPy Fundamentals: Understanding the NumPy library to efficiently work with arrays, matrices, and perform mathematical operations.
- Pandas Essentials: Exploring the Pandas library in-depth, covering Series and DataFrames, data importing/exporting, data cleaning, filtering, sorting, grouping
Explore related topics
This course includes:
- 6.5 hours on-demand video
- Access on mobile and TV
- Full lifetime access
- Certificate of completion
Course content
10 sections • 97 lectures • 6h 26m total length
- Variables
- Arithmetic Operators in Python
- Relational Operators in Python
- Logical Operators in Python
- Shortcut Operators
- Bit-wise Operators in Python
- Type Conversion in Python
- Computing average of two given numbers in Python
- Computing area and circumference of circle in Python
- If Statement
- Example program on if statement
- If Else Statement
- Example program on if else statement
- Nested if Statement
- Example program on Nested if Statement
- Elif Statement
- Example on Elif Statement
- While Loop in Python
- For Loop in Python
- Program to compute sum of first 10 numbers
- Program to compute Sum of digits in a given number
- Program to display numbers using for loop
- Finding Factorial of a given number
- Using break and continue statements
- Introduction to Containers and Lists in Python
- Creating & Accessing Lists in Python
- Working with built-in functions in Lists
- Tuples in Python
- Dictionaries in Python
- Sets in Python
- Basics of Strings in Python
- Concatenation of string and a number
- How to access a string in python using While Loop
- Using For loop to work with Strings
- Understanding String Slicing
- Slicing String by leaving start/end position
- Program to Count all Letters, Digits & Special symbols
- Program to Count occurrences of character in String
- Program to Reverse a given String
- Program to remove Empty strings from given list of strings
- Functions in Python
- Example Program on Functions
- Example Program on Functions in Python
- Example Program on Functions
- Function to compute Cumulative Product of numbers in a List
- Function to Compute Duplicates in a List
- Modules in Python
- Computing GCD of two given numbers in Python
- Finding mean, median and mode on list of numbers in Python
- Online IDE for running Python Numpy programs
- Creating & Accessing elements in 1D Array
- Creating & Accessing elements in 2D Array
- Finding Dimension of the Array
- Using Negative Indexing to access elements in 1D array
- Using Negative Indexing to access elements in 2D array
- Slicing an Array
- Checking Datatype of an Array
- Copy Operation on an array
- Iterating 1D array
- Iterating 2D array
- Finding Shape of the Array
- Reshaping 1D Array to 2D Array
- Joining Two Arrays
- Splitting an Array
- Sorting an Array
- Searching for an Element in Array
- Filtering an Array
- Generate a Random Integer
- Generating a Random Array
- Question #1
- Solution to Question #1
- Question #2
- Solution to Question #2
- Question #3
- Solution to Question #3
- Question #4
- Solution to Question #4
- Question #5
- Solution to Question #5
- Question #6
- Solution to Question #6
- Introduction to Pandas
- Working with Series in Pandas
- Combining Numpy with Series
- Finding number of elements in a Series
- Computing mean, max and min in a series
- Sorting a Series
- Displaying unique values in a Series
- Summary of series statistics
- Creating Data Frame from series
- Creating Data Frame from List of Dictionaries
- Data Frame access using row-wise and column-wise
- Add, Rename and Delete Columns in a Data Frame
- Using Drop( ) for deleting rows and cols
- Boolean Indexing in Data Frames
- Concatenating Data Frames
- Bonus Lecture
- Python Programming
Description
Are you ready to take your data analysis and manipulation skills to the next level? Welcome to “Mastering Data Manipulation with Python: A Comprehensive Guide to NumPy and Pandas.” In this hands-on course, you’ll embark on a journey to become a proficient data wrangler and analyst using the powerful tools at your disposal.
NumPy is a basic level external library in Python used for complex mathematical operations. NumPy overcomes slower executions with the use of multi-dimensional array objects. It has built-in functions for manipulating arrays. We can convert different algorithms to can into functions for applying on arrays. NumPy has applications that are not only limited to itself. It is a very diverse library and has a wide range of applications in other sectors. Numpy can be put to use along with Data Science, Data Analysis and Machine Learning. It is also a base for other python libraries. These libraries use the functionalities in NumPy to increase their capabilities.
This course introduce with all majority of concept of NumPy – numerical python library.
You will learn following topics :
1) Creating Arrays using Numpy in Python
2) Accessing Arrays using Numpy in Python
3) Finding Dimension of the Array using Numpy in Python
4) Negative Indexing on Arrays using Numpy in Python
5) Slicing an Array using Numpy in Python
6) Checking Datatype of an Array using Numpy in Python
7) Copying an Array using Numpy in Python
8) Iterating through arrays using Numpy in Python
9) Shape of Arrays using Numpy in Python
10) Reshaping Arrays using Numpy in Python
11) Joining Arrays using Numpy in Python
12) Splitting Array using Numpy in Python
13) Sorting an Array using Numpy in Python
14) Searching in Array using Numpy in Python
15) Filtering an Array using Numpy in Python
16) Generating a Random Array using Numpy in Python
The Numpy arrays are homogenous sets of elements. The most important feature of NumPy arrays is they are homogenous in nature. This differentiates them from python arrays. It maintains uniformity for mathematical operations that would not be possible with heterogeneous elements. Another benefit of using NumPy arrays is there are a large number of functions that are applicable to these arrays. These functions could not be performed when applied to python arrays due to their heterogeneous nature.
Course Highlights:
- Build a Strong Foundation: Whether you’re a beginner or looking to solidify your understanding, this course is designed to guide you from the basics to advanced data manipulation techniques.
- Master NumPy: Learn how to efficiently work with arrays, matrices, and perform mathematical operations using the NumPy library. Discover how to handle data of various dimensions effortlessly.
- Harness the Power of Pandas: Dive deep into Pandas, the go-to library for data manipulation in Python. Explore data structures like Series and DataFrames, and learn how to filter, reshape, and aggregate data effectively.
- Real-world Projects: Apply your newfound skills to real-world scenarios. Analyze and manipulate datasets, clean messy data, and extract valuable insights that drive informed decision-making.
- Optimize Your Workflow: Streamline your data analysis process by mastering techniques for data cleaning, transformation, and visualization, all while writing efficient and readable code.
- Unlock Data Insights: Learn how to manipulate, transform, and visualize data to uncover patterns and trends that tell a compelling data-driven story.
- Comprehensive Guidance: Benefit from step-by-step explanations, practical examples, and quizzes that reinforce your learning and ensure you grasp each concept.
- Lifetime Access: Gain unlimited access to course materials, allowing you to revisit and reinforce your skills whenever you need to.
Whether you’re a business analyst, data scientist, student, or anyone intrigued by the power of data, this course equips you with the tools to tackle data challenges with confidence. Join us now and unlock the potential of Python, NumPy, and Pandas to master the art of data manipulation.
Enroll today and take your data analysis skills to new heights!
Remember to personalize the course description based on the specific content, benefits, and approach of your course. Highlighting the practical skills learners will gain and the real-world applications of Python, NumPy, and Pandas will attract potential students.
Happy learning
Surendra Varma Pericherla
Who this course is for:
- Beginners in Data Analysis: Individuals who are new to data analysis and want to build a strong foundation in Python programming, data manipulation, and analysis techniques.
- Data Enthusiasts: Anyone interested in working with data, regardless of their professional background, such as business professionals, marketers, researchers, and students.
- Aspiring Data Scientists: Individuals aiming to become data scientists or analysts, looking to acquire essential skills in data manipulation, cleaning, and visualization.
- Business Analysts: Professionals involved in business analysis, market research, or decision-making who want to enhance their ability to extract insights from raw data.
- Students and Researchers: Students studying various disciplines, including sciences, social sciences, economics, and more, who need to work with data for their studies or research.
- Professionals Upgrading Skills: Professionals in programming, IT, or related fields looking to expand their skill set to include data manipulation using Python, NumPy, and Pandas.
- Entrepreneurs: Individuals who run businesses and want to leverage data to make informed decisions, identify trends, and gain a competitive edge.
- Self-Learners: Those who enjoy self-paced learning and are eager to develop practical skills in data manipulation to enhance their career prospects.
Note: The course is free for a limited time and limited to 500 users. Enroll as soon as possible. If it is no longer free, it means you are late, and it has already exceeded its limits.
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