https://github.com/pabitra-33/assignment
https://github.com/pabitra-33/assignment
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/pabitra-33/assignment
- Owner: Pabitra-33
- Created: 2024-03-02T09:47:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-04T11:35:17.000Z (over 1 year ago)
- Last Synced: 2024-04-04T12:35:40.264Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 10.7 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# CUTM_Python_Assignment
Given an assignment by our teaching faculty to implement basic operation of pandas in python and analyze the pandas importance in the visualisaton techniques.
The Pandas library in Python is a powerful tool for data manipulation and analysis.
Here are some important points about Pandas used:
1. Data Structures: Pandas provides two main data structures: Series and DataFrame.
- Series: A one-dimensional array-like object containing an array of data and an associated array of labels called the index.
- DataFrame: A two-dimensional labeled data structure with columns of potentially different types. It's similar to a spreadsheet or SQL table.
2. Data Input/Output: Pandas supports various file formats for data input and output, including CSV, Excel, JSON, SQL databases, and more.
3. Data Cleaning and Preparation: Pandas provides functions and methods to clean, preprocess, and prepare data for analysis. This includes handling missing data, removing duplicates, reshaping data, and transforming data types.
4. Indexing and Selection: Pandas allows for intuitive indexing and selection of data from Series and DataFrame objects using labels, integers, boolean masks, or a combination of these methods.
5. GroupBy Operations**: Pandas supports powerful group-by functionality, allowing you to split data into groups based on some criteria and apply operations to each group independently.
6. Merging and Joining Data: Pandas provides tools for combining data from different sources through merging and joining operations, similar to SQL JOIN operations.
7. Time Series Analysis: Pandas has extensive support for working with time series data, including date/time indexing, resampling, time zone handling, and date shifting.
8. Visualization: Although Pandas itself doesn't provide visualization capabilities, it integrates well with libraries like Matplotlib and Seaborn for creating plots and visualizations from DataFrame data.
9. Performance: Pandas is optimized for performance, but it may not always be the most efficient choice for very large datasets. However, it integrates well with other libraries like NumPy and Dask for handling larger-than-memory datasets or distributed computing.
10. Community and Documentation: Pandas has a large and active community of users and contributors. The official Pandas documentation is comprehensive and provides detailed explanations and examples for all functions and methods.
Overall, Pandas is an essential library for data analysis and manipulation in Python, widely used in various fields such as data science, finance, research, and more.