Pandas & Numpy for Data Science – The Right Way 2024

Welcome to “Pandas & Numpy for Data Science“

NumPy: NumPy, short for Numerical Python, is a fundamental package for scientific computing in Python. NumPy provides powerful tools for numerical operations, making it essential for data scientists. In our course, you will quickly master NumPy, gaining expertise in data manipulation and analysis through meticulously prepared content. Each concept in NumPy is explained with suitable examples to ensure easy understanding and efficient learning. Whether it’s array creation, mathematical functions, or linear algebra, our course covers all foundational aspects of NumPy, making you a pro in no time. Experience seamless learning as each topic builds on the previous one, allowing you to grasp NumPy concepts logically and effortlessly. The expert guidance provided throughout the course ensures you can clear any doubts you have about NumPy effortlessly, enhancing your learning journey with NumPy.

Our course goes beyond textbooks and universities, taking you deeper into the world of data science with NumPy. By integrating practical examples, we ensure that you can apply NumPy in real-world scenarios effectively. Learn how to manipulate arrays, perform complex calculations, and utilize NumPy’s powerful broadcasting capabilities. The course is designed for maximum efficiency, allowing you to learn more about NumPy in less time. As you progress, you will find that each NumPy topic is interconnected, providing a cohesive learning experience. Dive into the world of NumPy and become proficient in data manipulation and analysis, leveraging NumPy as an essential tool for your data science projects. NumPy is the backbone of scientific computing in Python, and our course ensures you become adept at using NumPy for all your data needs. Mastering NumPy is crucial for any data scientist, and our course guarantees you will be proficient in NumPy by the end.

Numpy: 1. Numpy_Array_Creation

Creating a 1-D & 2-D array, attributes of an array, printing the values of an array, converting an array into a list with examples of 1-D and 2-D array.

Numpy: 2. Array_arange_reshape functions
Creating an Array using arange function, Reshaping an array, Constructing a 3D array, Creating a 2-D array with a single column.

Numpy: 3. Creating different types of Array

Creating a random 1-D, 2-D, 3-D & 4-D interger Array, Creating a random 1-D, 2-D non-integer Array, Functions to create arrays of different types, Data type Conversion of an Array.

Numpy: 4. Accessing Numpy Array Values

Accessing & Slicing a 1-D array values, Accessing & Slicing a 2-D array values, Creating an Array View and an Array Copy, difference between Array Copy and Array View.

Numpy: 5. Numpy Operations
Efficiency of using a Numpy Array over Python’s built-in data structures, Aggregate of an array, Elementwise Arithmatic Operation, Arithmatic Operation using Numpy functions, Shape mismatch of an Array, Logical Operation using Numpy functions, Comparison Operation using Numpy functions, Bitwise Operation using Numpy functions, Trigonometric Operation using functions, Statistical Operation using Numpy functions.

Numpy: 6. Fancy Indexing and Sorting Arrays
Conditionally selecting values from an Array, Pseudo Random State, Counting and Analyzing values in an Array, Sorting Arrays using sort and argsort, Sorting along rows or columns.

Numpy: 7. Array Product and Concatenation

Product of Arrays using matmul & dot function, Difference between dot and matmul, Concatenating Arrays of same dimension, Concatenating Arrays of mixed dimension, Flattening of Arrays, Transpose of an Array, Resizing an Array.

Numpy: 8. Broadcasting

Broadcasting explanation, Simple Broadcasting example, Broadcasting two arrays together follows these rules, Examples of incompatible Arrays.

Pandas: Pandas, a powerful data manipulation and analysis library, is essential for anyone looking to excel in data science. Our course is designed to help you master Pandas quickly, covering all foundational concepts to make you proficient in handling data with Pandas. From dataframes to series, our meticulously prepared content ensures you gain a deep understanding of Pandas through practical examples. Each concept is explained clearly, making it easy to grasp and apply Pandas in real-world scenarios. The seamless learning experience provided by our course ensures a logical flow of topics, building your knowledge of Pandas step by step. With expert guidance available throughout the course, you can clear any doubts about Pandas effortlessly, enhancing your learning process with Pandas.

Go beyond textbooks and universities with our comprehensive course on Pandas, diving deeper into the world of data manipulation and analysis. Learn how to handle missing data, merge and join datasets, and perform group operations using Pandas. The course is designed for maximum efficiency, allowing you to learn more about Pandas in less time. By integrating suitable examples, we ensure that you can apply Pandas effectively in your data science projects. Each topic in Pandas builds on the previous one, providing a cohesive and logical learning journey. Experience the power of Pandas and become proficient in data analysis, leveraging Pandas as an essential library to excel in your data science endeavors. Pandas is indispensable for data manipulation, and our course ensures you become adept at using Pandas for all your data analysis needs. By the end of our course, you will be a Pandas expert, ready to tackle any data challenge with Pandas.

Below is the description of the Course   

Pandas: 1. Introduction to Pandas

Pandas: 2. Pandas Series
Introduction to Pandas Series, Creating Series , Attributes of a Series, Accessing values within a Series, Deleting a value in a Series, Adding two Series.

Pandas: 3. DataFrame
Introduction to DataFrames, Creating new DataFrames, Attributes of a DataFrame, Selecting Column/Columns from a DataFrame, Creating a new column in a DataFrame, Dropping rows and columns, Inspecting data within a DataFrame, Selecting subset of rows and columns.

Pandas: 4. Handling missing data in DataFrame
Introduction to missing Data, None Datatype, Representing missing values in an Array and a DataFrame, Dropping Rows and Columns with NaN values, Filling missing values, Forward filling and backward filling Row wise and Column wise, Miscellaneous methods.

Pandas: 5. Conditional Selection and Reindexing of a DataFrame
Conditionally Selecting values, Multiple Conditional Selection, Resetting and Setting New Index.

Pandas: 6. Data Input and Data Output
Reading data from a CSV File, Writing DataFrame to a CSV File, Writing DataFrame to an Excel File, Reading data from an HTML File, Reading data from a SAS File.

Pandas: 7. Data Processing
Introduction to Data Processing, Reading first and last rows, Renaming Column names in a DataFrame, Deleting a Column, Dropping Rows and Columns simultaneously, Dropping a range of Rows and Columns, Applying Functions to the columns, Sorting or Ordering a DataFrame, Sorting by a single column, Sorting by multiple columns.

Pandas: 8. Grouping & Aggregation and Pivot Table
Introduction Grouping and aggregation, Grouping by single column, Grouping by multiple Columns, Pivot Tables.

Pandas: 9. Concatenating DataFrames and Inserting new rows
Concatenation, Combining DataFrames along the horizontal axis, Combining DataFrames along the vertical axis, Adding a new row into a DataFrame, Replacing a row at an index, Inserting a new row at an index.

Pandas: 10. Merging and Joining DataFrames
Merging two or more DataFrames, Inner Join, Left Join, Right Join, Outer Join, Merging on columns with different names, Joining DataFrames using Join function.

Pandas: 11. Logic Explanation for Merging and Joining of two DataFrames.

Pandas: 12. Cartesian Product between two DataFrames explanation.

Pandas: 13. Handling Duplicates in a DataFrame
Count of unique values in a Column, Determining duplicate rows in a DataFrame, Extracting duplicate rows, Dropping duplicate rows, considering certain Columns for dropping duplicates, Dropping all duplicate rows.

Pandas: 14. Handling Strings in a DataFrame
Converting Columns to string dtype, String manipulation using different String functions.

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