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Module 1: Introduction to Pandas

• Week 1: Understanding Data Science and Pandas

  • What is Data Science?
  • Introduction to Pandas: History and Evolution
  • The Role of Pandas in Data Analysis

• Week 2: Setting Up the Environment

  • Installing Pandas via Anaconda and pip
  • Overview of Jupyter Notebooks
  • Importing Pandas and Common Libraries

    import pandas as pd

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Module 2: Basics of Pandas Series

• Week 3: Introduction to Pandas Series

  • What is a Pandas Series?
  • Creating a Series from a List

    s = pd.Series([1, 2, 3, 4])

  • Creating a Series from a Dictionary

    s = pd.Series({'a': 1, 'b': 2, 'c': 3})

• Week 4: Accessing and Modifying Series

  • Accessing Elements by Index
    • Positional Indexing
    • Label Indexing
  • Modifying Series Data
    • Changing Values and Index

    s[0] = 10

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Module 3: Series Attributes and Methods

• Week 5: Understanding Series Attributes

  • Key Attributes: index, values, dtype, name
  • Using len() to Get Series Length
  • Data Types in Series

• Week 6: Common Series Methods

  • Summary Statistics: .sum(), .mean(), .min(), .max()
  • Using .describe() for Quick Overview
  • String Methods: .str Accessor

    s = pd.Series(['apple', 'banana', 'cherry'])
    s.str.upper()

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Module 4: Advanced Series Operations

• Week 7: Handling Missing Data in Series

  • Identifying Missing Values: isnull(), notnull()
  • Filling Missing Values: fillna()
  • Dropping Missing Values: dropna()

• Week 8: Vectorized Operations and Functions

  • Applying Functions: apply(), map(), applymap()
  • Common Mathematical Operations
  • Using Numpy Functions with Series

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Module 5: Introduction to Pandas DataFrames

• Week 9: What is a DataFrame?

  • Understanding the Structure of a DataFrame
  • Creating DataFrames from Various Data Structures (Lists, Dictionaries, Numpy Arrays)

    df = pd.DataFrame({
        'A': [1, 2, 3],
        'B': ['a', 'b', 'c']
    })

• Week 10: DataFrame Attributes and Basic Operations

  • Key Attributes: shape, columns, index, dtypes
  • Basic Operations: .head(), .tail(), .info(), .describe()
  • Accessing and Modifying Columns

    df['C'] = [4, 5, 6]

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Module 6: Indexing and Selecting Data in DataFrames

• Week 11: Basic Indexing and Slicing

  • Selecting Columns and Rows
  • Label-Based Indexing with .loc[]
  • Position-Based Indexing with .iloc[]

• Week 12: Advanced DataFrame Selection

  • Conditional Selection and Filtering
  • Using Boolean Indexing
  • Chaining Conditions

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Module 7: Data Cleaning and Preparation

• Week 13: Handling Missing Data in DataFrames

  • Identifying Missing Data
  • Imputing and Filling Missing Values
  • Dropping Rows and Columns with Missing Data

• Week 14: Data Type Conversion and Transformation

  • Converting Data Types: astype()
  • DataFrame Transformation Methods
  • String Manipulations: Using .str Methods in DataFrames

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Module 8: Grouping, Aggregating, and Merging DataFrames

• Week 15: Grouping DataFrames

  • Introduction to groupby()
  • Aggregating Data: Mean, Sum, Count
  • Custom Aggregation Functions

• Week 16: Merging and Joining DataFrames

  • Different Types of Joins: Inner, Outer, Left, Right
  • Using merge() and join() Methods
  • Concatenating DataFrames: concat()

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Module 9: Time Series in DataFrames

• Week 17: Time Series Basics

  • Creating DateTime Index
  • Resampling Time Series Data
  • Handling Time Zones

• Week 18: Time Series Operations

  • Rolling and Expanding Windows
  • Time Series Visualization Techniques
  • Date Arithmetic

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Module 10: Data Visualization with Pandas

• Week 19: Introduction to Visualization

  • Basic Plotting with Pandas
  • Customizing Plots: Titles, Labels, Legends
  • Plot Types: Line, Bar, Histogram, Boxplot

• Week 20: Advanced Visualization Techniques

  • Creating Subplots and Multiple Plots
  • Integrating with Matplotlib for Custom Visuals
  • Saving and Exporting Plots

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Module 11: Best Practices and Optimization

• Week 21: Performance Optimization Techniques

  • Profiling DataFrames: Memory Usage
  • Using Vectorization for Speed
  • Best Practices for Handling Large Datasets

• Week 22: Writing Clean and Maintainable Code

  • Code Readability and Documentation
  • Error Handling Techniques
  • Developing Reusable Functions

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Supplementary Resources

• Recommended Books: Python for Data Analysis by Wes McKinney, Pandas Cookbook by Theodore Petrou
• Online Courses: DataCamp, Coursera, edX
• Practice Datasets: Kaggle, Open Data Sources

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Final Assessment

• Comprehensive Quiz on Series and DataFrame Concepts
• Hands-on Project: Data Analysis using Series and DataFrames
• Peer Review and Feedback Session

Special Thanks to the Contributors

• GitHub IDs: rubydamodar and jiwooverse, who are actively working on this project!