1. Python’s Numerical Computing Library: NumPy
NumPy is a Python library for numerical computing that provides support for arrays and matrices, as well as a wide range of mathematical functions to operate on them.
NumPy arrays are similar to lists in Python, but they are much more efficient for numerical calculations, especially when working with large amounts of data. NumPy arrays can be multi-dimensional, which means they can represent matrices and tensors.
Here are some key features of NumPy:
Array creation: NumPy provides a wide range of functions to create arrays, including ones(), zeros(), linspace(), arange(), and random.
Array manipulation: NumPy provides many functions to manipulate arrays, such as reshape(), transpose(), concatenate(), and split().
Mathematical functions: NumPy provides a wide range of mathematical functions to operate on arrays, such as sin(), cos(), exp(), and log().
Linear algebra: NumPy provides functions for linear algebra operations, such as dot(), det(), eig(), and svd().
Broadcasting: NumPy allows for broadcasting, which is a way to perform arithmetic operations between arrays of different shapes.
Performance: NumPy is designed for efficiency and can perform numerical computations much faster than the equivalent code written in Python.
Overall, NumPy is a powerful library that is widely used for scientific computing, data analysis, and machine learning in Python.
2.Python data visualization library: Seaborn
Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for creating informative and visually appealing statistical graphics. It is built on top of the matplotlib library and is closely integrated with pandas data structures, making it easy to visualize datasets that have been cleaned and organized with pandas.
Seaborn includes a wide variety of plot types, including scatterplots, line plots, bar plots, histograms, box plots, violin plots, heat maps, and more. It also includes specialized plots for visualizing distributions, relationships, and regression models.
Seaborn's default styles and color palettes are designed to be visually attractive and easy to read. It also provides extensive customization options for fine-tuning the appearance of plots, as well as options for integrating statistical calculations and annotations into the visualizations.
Seaborn is widely used in data science, machine learning, and scientific research communities to create high-quality visualizations that help explore, understand, and communicate data insights.
3.Matplotlib visualization library:
Matplotlib is a popular data visualization library for Python. It provides a wide range of tools for creating various types of plots, charts, and graphs to represent data in a visually appealing manner.
Matplotlib is highly customizable and offers a variety of styles and colors to choose from, making it a flexible tool for creating publication-quality visualizations. It can be used for creating simple line charts, scatter plots, histograms, bar charts, and more complex visualizations such as heat maps, 3D plots, and animations.
Matplotlib can be used interactively within a Python environment like Jupyter Notebooks, or as a standalone application to create and save high-quality images in various formats such as PNG, PDF, and SVG.
Matplotlib is an open-source project and is actively maintained by a large community of developers. It can be installed via pip or conda and is compatible with major operating systems like Windows, Linux, and macOS.
4.Pandas Data Analysis:
Pandas is a popular open-source Python library used for data manipulation and analysis. It provides various data structures and functions for working with structured data, such as tables or spreadsheets, which are represented as Pandas DataFrames.
Some of the key features of Pandas include:
Data manipulation: Pandas provides a wide range of functions for filtering, sorting, aggregating, merging, and reshaping data.
Data visualization: Pandas has built-in support for data visualization through integration with Matplotlib and Seaborn.
Data cleaning: Pandas provides functions for handling missing values, duplicates, and inconsistent data.
Data input/output: Pandas can read and write data in various formats, including CSV, Excel, SQL databases, and JSON.
Overall, Pandas is a powerful tool for data analysis and can be used for a wide range of applications, including scientific computing, finance, and business analytics.
DataFrame Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns.
Creating a series
Pandas Series will be created by loading the datasets from existing storage, storage can be SQL Database, CSV file, an Excel file. Pandas Series can be created from the lists, dictionary, and from a scalar value etc.
5. Python Scipy library:
Scipy is an open-source scientific computing library for Python. It is built on top of Numpy, another popular Python library for numerical computing, and provides a wide range of algorithms and tools for scientific and technical computing, including:
Linear algebra: Scipy provides functions for solving linear systems, finding eigenvalues and eigenvectors, and performing matrix decompositions.
Optimization: Scipy provides several optimization routines for finding the minimum or maximum of a function, both unconstrained and constrained.
Integration: Scipy provides several integration routines for evaluating definite integrals and solving ordinary differential equations.
Interpolation: Scipy provides functions for interpolating data, including linear, spline, and polynomial interpolation.
Signal processing: Scipy provides functions for filtering, spectral analysis, and signal generation.
Statistics: Scipy provides a wide range of statistical functions, including probability distributions, hypothesis tests, and statistical models.
Scipy is widely used in scientific and engineering applications, as well as in data science and machine learning. It is available under a BSD license and can be installed using pip or conda.