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Install openpyxl Library to Read and Write Excel Files

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Packt

Advanced Data Analysis and Visualization with Pandas

Course 3 of 3 in the Data Analysis with Pandas and Python Specialization

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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.

Skills You'll Learn

Pandas Visualization, Advanced Pandas, Pandas Excel Integration, Python Data Analysis, Python Matplotlib

From the lesson

Input and Output

In this module, we will explore input and output operations in Pandas, starting with an overview of essential data exchange techniques. You will learn how to export DataFrames to CSV files, a common format for data sharing. We will guide you through installing the openpyxl library to enable reading and writing Excel files in Pandas. Additionally, you'll master importing data from Excel files into Pandas and exporting DataFrames to Excel for effective data reporting and sharing.

Intro to the Input and Output Module3:58

Export DataFrame to CSV File5:27

Install openpyxl Library to Read and Write Excel Files2:28

Import Excel File into pandas10:38

Export Excel File from pandas9:47

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