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Introduction

  • Pandas is a powerful and open-source Python library used for data manipulation and analysis.

  • It is built on top of the NumPy library and is well-suited for working with tabular data such as spreadsheets or SQL tables.

  • Pandas provides two primary data structures: Series (one-dimensional) and DataFrame (two-dimensional).

  • The library is essential for data analysts, scientists, and engineers working with structured data in Python.

  • Pandas can handle data from various sources, including CSV, JSON, Excel, and SQL databases.

  • It offers functionalities for data cleaning, merging, joining, and visualization.

  • Pandas is often used in conjunction with other libraries like Matplotlib for plotting, SciPy for statistical analysis, and Scikit-learn for machine learning.

What is Pandas? [1]

  • Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with relational or labeled data easy and intuitive.

  • It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

  • The name 'Pandas' is derived from 'panel data' and 'Python Data Analysis'.

  • Pandas is built on top of the NumPy library, which means it leverages many of NumPy's structures.

  • It is widely used in data science for tasks such as data cleaning, transformation, and analysis.

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Installing Pandas [1]

  • Ensure Pandas is installed in your system using the pip command: pip install pandas.

  • For Anaconda users, you can install Pandas using: conda install pandas.

  • After installation, import the library in your Python script using: import pandas as pd.

  • Using an alias like 'pd' helps in writing less code when calling methods or properties.

  • Refer to detailed installation guides for different operating systems if needed.

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Pandas Series [1]

  • A Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, Python objects, etc.).

  • Series can be created from lists, dictionaries, scalar values, and more.

  • The axis labels are collectively called indexes, and they need not be unique but must be of a hashable type.

  • Series support both integer and label-based indexing, providing methods for operations involving the index.

  • Example of creating a Series: ser = pd.Series([1, 2, 3], index=['a', 'b', 'c']).

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Pandas DataFrame [1]

  • A Pandas DataFrame is a two-dimensional data structure with labeled axes (rows and columns).

  • DataFrames can be created from lists, dictionaries, a list of dictionaries, and more.

  • They are similar to SQL tables or Excel spreadsheets.

  • DataFrames support operations like merging, joining, and grouping data.

  • Example of creating a DataFrame: df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}).

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Basic Operations [1]

  • Pandas allows for data cleaning, merging, and joining.

  • It provides easy handling of missing data (represented as NaN) in both floating point and non-floating point data.

  • Columns can be inserted and deleted from DataFrame and higher-dimensional objects.

  • Pandas offers powerful group by functionality for performing split-apply-combine operations on data sets.

  • Data visualization is also supported through integration with libraries like Matplotlib.

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Advanced Techniques [2]

  • Pandas can be used for time series analysis, handling dates and times efficiently.

  • It supports resampling, shifting, and rolling time series data.

  • Pandas can handle categorical data, allowing for encoding and decoding categories.

  • It provides functionalities for text data manipulation, including applying regular expressions and text analysis.

  • Advanced data manipulation techniques include transforming, reshaping, and pivoting data.

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Related Videos

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<div class="-md-ext-youtube-widget"> { "title": "Learn Python Pandas: 1-Hour Pandas Course for Beginners", "link": "https://www.youtube.com/watch?v=CIQJtJ38-hI", "channel": { "name": ""}, "published_date": "Aug 23, 2022", "length": "" }</div>

<div class="-md-ext-youtube-widget"> { "title": "Pandas Python Tutorial for Beginners -Introduction - Full Course", "link": "https://www.youtube.com/watch?v=eAjZAnsg9ek", "channel": { "name": ""}, "published_date": "Aug 22, 2023", "length": "" }</div>