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    Overview of Pandas


    Introduction to Pandas

    If you've ever worked with tabular data in Python, you've probably heard of Pandas. It’s one of the most powerful and flexible data analysis libraries in the Python ecosystem. Whether you're exploring CSV files, cleaning datasets, or building dashboards, Pandas will likely be at the heart of your workflow.

    What is Pandas?

    Pandas is an open-source library designed for data manipulation and analysis. It provides two core data structures:

    • Series: A one-dimensional labeled array (similar to a list but with indexing).
    • DataFrame: A two-dimensional labeled data structure (like a table or spreadsheet).

    Installing Pandas

    If you don’t have Pandas installed yet, run this in your terminal or command prompt:

    pip install pandas

    If you're using Jupyter Notebook, you can run it directly inside a cell:

    !pip install pandas

    Importing Pandas

    The standard convention is to import it as pd:

    import pandas as pd

    Creating a Pandas Series

    A Series is like a single column of data. Let’s create one from a Python list:

    
    import pandas as pd
    
    data = [10, 20, 30, 40]
    s = pd.Series(data)
    print(s)

    Expected Output:

    
    0    10
    1    20
    2    30
    3    40
    dtype: int64

    Notice how Pandas automatically assigns an index to each item. This index helps with fast and powerful data access.

    Creating a DataFrame

    DataFrames are more powerful—think of them as full-blown tables. Here's a simple example:

    
    import pandas as pd
    
    data = {
        'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'Los Angeles', 'Chicago']
    }
    
    df = pd.DataFrame(data)
    print(df)

    Expected Output:

    
          Name  Age         City
    0    Alice   25     New York
    1      Bob   30  Los Angeles
    2  Charlie   35      Chicago

    Each column has a label, and each row has an index—just like a spreadsheet but fully programmable.

    Accessing Data

    You can access columns in a DataFrame using the column name:

    print(df['Name'])
    
    0     Alice
    1       Bob
    2   Charlie
    Name: Name, dtype: object

    Basic Verification and Checks

    Before you dive deeper into analysis, it’s wise to run a few sanity checks:

    Check for Missing Values

    print(df.isnull())

    View Data Types

    print(df.dtypes)

    Get Summary Statistics

    print(df.describe())

    Why Use Pandas?

    Pandas abstracts away many of the repetitive steps in data analysis—filtering, grouping, transforming, joining, and reshaping—into a clean and intuitive interface. If you're working with structured data, Pandas will help you:

    • Load data quickly from multiple formats (CSV, Excel, SQL, JSON)
    • Perform complex transformations in just a few lines
    • Clean and prepare data for machine learning or reporting

    Final Thoughts

    At its core, Pandas is about readability, structure, and power. This tutorial only scratches the surface, but it should give you enough confidence to start experimenting. In upcoming modules, we’ll dive deeper into filtering, grouping, merging, and visualizing with Pandas.

    What’s Next?

    • How to read and write CSV files with Pandas
    • Data filtering and conditional selections
    • Advanced operations like merging and pivoting


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