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Pandas Tutorial

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Pandas is an open-source library that is built on top of NumPy library. It is a Python package that offers various data structures and operations for manipulating numerical data and time series. It is mainly popular for importing and analyzing data much easier. Pandas is fast and it has high-performance & productivity for users.

Course Content

Pandas Tutorial

Working with Pandas DataFrame
A DataFrame is the core data structure in Pandas, providing a two-dimensional table for storing data. It is similar to a spreadsheet or SQL table and consists of rows and columns, where each column can be of a different data type (e.g., integer, float, string).DataFrames are extremely versatile and essential for data manipulation and analysis tasks. You can create, manipulate, and analyze data stored in DataFrames with a variety of operations that are both powerful and easy to use.

Working with Date and Time in Pandas
Handling date and time data is an essential part of data analysis. Pandas provides robust functionality for working with time series data, allowing you to parse, manipulate, and perform various operations on date and time information. This chapter will guide you through the essential tools and methods provided by Pandas to handle date and time data effectively.Pandas offers the datetime module along with its own Timestamp and DatetimeIndex classes to work with time data. It also provides a set of methods for parsing, converting, and manipulating time-related data.

Working with Text Data in Pandas
Text data, also known as string data, is one of the most common data types encountered in data analysis. In Pandas, strings are represented as object dtype, which is typically used for text or mixed types. Pandas provides a variety of functions to efficiently handle and manipulate text data, whether you're cleaning, transforming, or extracting meaningful information.This chapter will guide you through the various tools and techniques that Pandas provides for working with text data. We’ll explore string manipulation methods, cleaning operations, and more advanced use cases like extracting specific patterns using regular expressions.

Data Visualization with Pandas

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