Course description

This course is designed for individuals with little or no experience with the Pandas library for Python. Pandas is a powerful and flexible open-source data analysis and manipulation tool that is widely used in data science and data analysis. This course will provide a comprehensive introduction to the library, starting with basic concepts and gradually building up to more advanced topics.

The course will begin by introducing the basics of Pandas, including its data structures (Series and DataFrames) and the various ways to import and export data. You will learn how to perform basic data cleaning and preprocessing tasks, including handling missing values, renaming columns, and filtering and sorting data. You will also learn how to use Pandas to perform basic statistical operations and data visualization.

As the course progresses, you will dive deeper into more advanced topics, such as merging and joining data, groupby operations, and advanced indexing techniques. You will also learn how to use Pandas to work with time series data, including how to handle and manipulate date and time data.

Throughout the course, you will work with real-world data sets, giving you hands-on experience with the tools and techniques covered. You will also complete a number of practical exercises and projects, allowing you to apply what you've learned to real-world problems.

By the end of this course, you will have a solid understanding of the Pandas library and be able to use it confidently to perform data analysis and manipulation tasks. Whether you're a beginner looking to start a career in data science or an experienced data analyst looking to improve your skills, this course is the perfect starting point.

Prerequisites: This course is designed for absolute beginners, and it will be helpful if you have basic knowledge of Python programming.

Course Outline:

1. Introduction to Pandas

2. Pandas Dataframes and Series

3. Indexes in Pandas

4. Conditional Filtering in Pandas

5. Update Rows and Columns in Pandas

6. Add/Remove Columns of Data

7. Master Data Sorting in Pandas

8. Clean & Save DataFrames

   
   

By the end of this course, you will be able to:

• Understand the basics of the Pandas library and its data structures

• Import and export data using Pandas

• Perform basic data cleaning and preprocessing tasks

• Use Pandas to perform basic statistical operations and data visualization

• Merge and join data using Pandas

• Use the groupby function in Pandas

• Apply advanced indexing techniques in Pandas

• Work with time series data using Pandas

• Apply your knowledge to real-world projects

What will i learn?

• 1. Understand and Use Core Pandas Data Structures (Series and DataFrames) Gain a strong understanding of Pandas DataFrames (tabular data) and Series (one-dimensional data), the fundamental data structures in Pandas. Learn how to create, index, slice, and access data in these structures, providing a foundation for all further analysis.
• 2. Load and Import Data Learn how to load data from various file formats (such as CSV, Excel, JSON, SQL databases, etc.) into Pandas for analysis. Understand how to efficiently handle data input/output and prepare data for processing and analysis.
• 3. Data Cleaning and Preprocessing Handle missing data: Learn how to detect and fill or remove missing values in datasets. Remove duplicates: Master techniques for identifying and removing duplicate entries in data. Data transformation: Learn how to transform and modify data (e.g., changing data types, creating new columns, renaming columns).
• 4. Perform Exploratory Data Analysis (EDA) Understand how to summarize and explore datasets using descriptive statistics, such as mean, median, mode, standard deviation, and quantiles. Learn to group data by categories and perform aggregation (e.g., sum, count, average). Visualize initial trends, outliers, and relationships in the data to inform further analysis.

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