Overview

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This course begins by setting up Anaconda and Jupyter Lab for Python and Pandas, providing foundational Python knowledge before diving into Pandas for data analysis. You'll learn Series and DataFrame structures for effective data management and manipulation. Key topics include - Handling dates - Performing file input/output operations crucial for real-world data tasks - Advanced data visualization using Matplotlib. - Advanced Pandas features and settings are explored to enhance data manipulation capabilities. By the course's end, you'll master data analysis techniques, adept at handling complex datasets, conducting detailed analysis, and presenting insights visually, preparing you for advanced roles in data analytics and manipulation. Ideal for data analysts, aspiring data scientists, and professionals aiming to deepen their skills in data manipulation and analysis using Pandas, this course bridges basic Python knowledge to advanced data handling and visualization techniques.

Syllabus

Course 1: Foundations of Data Analysis with Pandas and Python
- Offered by Packt. Embark on a comprehensive journey into data analysis with Python and Pandas. Learn to set up Anaconda and Jupyter Lab on ... Enroll for free.

Course 2: Intermediate Data Analysis Techniques with Pandas
- Offered by Packt. This Pandas course focuses on mastering DataFrame functionalities, starting with in-depth comparisons between Series and ... Enroll for free.

Course 3: Advanced Data Analysis and Visualization with Pandas
- Offered by Packt. This advanced Pandas course delves deep into date-time manipulation, covering Timestamps, DatetimeIndex objects, and ... Enroll for free.

Courses

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5 hours 21 minutes
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This advanced Pandas course delves deep into date-time manipulation, covering Timestamps, DatetimeIndex objects, and pd.date_range for effective time series handling. - You'll master techniques like using the dt attribute and DateOffset objects for arithmetic operations and timedeltas. - Learn essential input-output operations, including exporting DataFrames to CSV and Excel files using openpyxl, and seamless file imports. - Enhance data presentation with Matplotlib for basic visualizations, customizing aesthetics with templates, and creating bar and pie charts. Ideal for data analysts, scientists, and Python enthusiasts with intermediate to advanced Pandas skills, this course enriches data workflows and visualization capabilities.
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9 hours 24 minutes
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Embark on a comprehensive journey into data analysis with Python and Pandas. Learn to set up Anaconda and Jupyter Lab on macOS and Windows, navigate Jupyter Lab's interface, and execute code cells. - You'll start by mastering essential Python programming concepts, including data types, operators, variables, functions, and classes. - Then, dive into Pandas to create and manipulate Series and DataFrames. The course covers data importing from sources like CSV, Excel, and SQL databases, along with techniques for sorting, filtering, and data extraction. - Advanced analysis methods, including group-by operations, merging, joining datasets, and pivot tables, are also explored to equip you with the skills for efficient and sophisticated data analysis. Ideal for aspiring data analysts and scientists, no prior programming knowledge is necessary with the included Python crash course.
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12 hours 53 minutes
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This Pandas course focuses on mastering DataFrame functionalities, starting with in-depth comparisons between Series and DataFrame methods. You'll learn essential skills such as selecting columns, adding data, and utilizing methods like value_counts and fillna for effective data cleaning. Advanced topics include filtering data, optimizing memory usage, handling missing values, and managing MultiIndex and text data. By exploring techniques for merging and concatenating DataFrames, you'll gain proficiency in handling complex data analysis tasks. This course is tailored for data analysts, scientists, and professionals seeking to enhance their Pandas skills for practical applications and real-world data challenges.