Welcome to the “Ultimate Beginners Guide to Pandas for Data Analysis” course, a comprehensive journey designed for beginners interested in exploring the Pandas library in the context of data analysis. This course has been carefully structured to provide a solid understanding of Pandas fundamentals and advanced techniques, empowering students to manipulate data with confidence and efficiency. Check out the modules and main topics below:
Section 1: Series
We start with Pandas installation and the creation of Series, the essential one-dimensional structure for storing data. Throughout the module, we explore fundamental concepts such as slicing, copying, accessing with iloc and loc, sorting, filtering, mathematical operations, and string manipulations. We also cover advanced topics, including numerical and categorical grouping, handling missing values, functions, and practical challenges.
Section 2: Dataframe
Continuing on, we delve into the creation and exploration of Dataframes, vital structures for analyzing more complex datasets. This module covers topics such as accessing with iloc and loc, manipulation of rows and columns, handling duplicate data and missing values, sorting, advanced filtering, creating and manipulating columns, aggregation, pivot tables, concatenation, joining, and import/export techniques. We include practical challenges to reinforce learning.
Section 3: Data Visualization
In the final module, we explore data visualization with Pandas. We cover the creation of line, bar, pie, scatter, and histogram plots, as well as formatting techniques and subplots. The module includes a practical challenge to apply the newly acquired skills in visualizing data.
Upon completing this course, participants will be equipped with the practical skills necessary to effectively use Pandas in data analysis. Get ready for an hands-on learning experience, empowering you to tackle real-world challenges in data manipulation and interpretation.
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Installation -
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Creating series
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Slicing
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Copy, conversion, and concatenation
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Accessing elements with iloc
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Accessing elements with loc
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Ordering
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Counting
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Filtering
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Mathematical operations
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String operations
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Numerical grouping
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Categorical grouping
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Missing values
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Functions
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HOMEWORK
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Homework solution
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Creating dataframes
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Exploring dataframes
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Accessing elements with iloc and loc
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Deleting rows and columns
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Duplicated rows
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Missing values
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Counting
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Ordering
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Filtering
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Rename and reorder columns
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Creating new columns
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Categorical features
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Aggregation
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Grouping
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Grouping with aggregation
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Aggregation with transform
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Pivot tables
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Concatenation and joining
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Date conversions
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Date indexes
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40
Importation and exportation
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41
HOMEWORK
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Homework solution
Lessons:
