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    <title>Data Wrangling using Python Pandas</title>
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<h1>Data Wrangling using Python Pandas</h1>

<h2>Abstract</h2>
<p>
    This research paper explores the techniques and best practices for data wrangling using the Python Pandas library. Data wrangling, also known as data preprocessing, is a critical step in the data analysis pipeline. It involves cleaning, transforming, and organizing raw data into a suitable format for analysis. Python Pandas is a popular data manipulation library that provides powerful tools for data wrangling.
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<h2>Introduction</h2>
<p>
    Data wrangling is an essential process in data analysis and machine learning. It encompasses various tasks, such as data cleaning, data transformation, handling missing values, and aggregating data. Python Pandas is a versatile library that simplifies these tasks, making it a go-to choice for data scientists and analysts.
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<h2>1. Data Wrangling with Python Pandas</h2>

<h3>1.1 Installation</h3>
<p>
    Before we delve into data wrangling with Pandas, it's crucial to ensure that you have the library installed. You can use pip, the Python package manager, to install Pandas:

    <code>pip install pandas</code>
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<h3>1.2 Importing Pandas</h3>
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    To start working with Pandas, import it into your Python script or Jupyter Notebook:

    <code>import pandas as pd</code>
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<h3>1.3 Data Structures in Pandas</h3>
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    Pandas provides two primary data structures: Series and DataFrame. A Series is a one-dimensional labeled array, while a DataFrame is a two-dimensional table-like data structure. These structures are the building blocks for data manipulation in Pandas.
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<h3>1.4 Data Cleaning</h3>
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    Data cleaning involves handling missing values, removing duplicates, and correcting data types. Pandas offers methods like <code>dropna()</code>, <code>fillna()</code>, and <code>astype()</code> for these tasks.
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