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Pandas Tutorial
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What is Pandas?
Getting Started with Pandas
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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.
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Creating a DataFrame
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Inspecting a DataFrame
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Accessing and Indexing Data
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Modifying a DataFrame
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Handling Missing Data
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Sorting and Filtering Data
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Merging and Joining DataFrames
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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.
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Importing Required Libraries
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Creating Date and Time Objects
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Date and Time Operations
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Time Series Data
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Working with Time Zones
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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.
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Accessing and Modifying Text Columns
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Cleaning Text Data
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Extracting Information Using Regular Expressions
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Handling Missing Data in Text Columns
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Data Visualization with Pandas
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Setting Up the Environment
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Creating a Dataset
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Line Plot
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Bar Plot
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Scatter Plot
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Histogram
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Pie Chart
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Customizing Plots
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Multiple Lines in a Single Plot
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Saving a Plot
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Pandas Tutorial
    About Lesson

    Pie charts are useful for showing proportions.

     

    # Pie chart of Sales
df.set_index('Month')['Sales'].plot(kind='pie', title='Sales by Month', autopct='%1.1f%%', figsize=(6, 6))
plt.ylabel('')  # Remove y-axis label for aesthetics
plt.show()
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