Introduction of Python

Python is an effective general-purpose, high-level, interpreted programming language. It was developed by Guido van Rossum on December 3, 1989, and is most frequently used for system programming, artificial intelligence, and scientific computing.Python is an open source software project, with more than 2500 contributors.

Python Tutorial : https://www.w3schools.com/python/

Python Video Tutorial : https://youtu.be/gfDE2a7MKjA

Some Python libraries give users the necessary functionality when crunching data. In this Blog, we are talking about four main libraries which are used in Data Analysis.

Library for Data Analysis

Numpy : For matrix operations, data structures, and linear algebra, use the Python module Numpy. In addition to matrix multiplication, addition, and subtraction, it also offers inverse matrix operations and determinant calculation. Additionally, Numpy provides a variety of strong data structures, such as arrays, lists, dictionaries, and matrices of lists. Linear systems and other linear algebraic issues can be effectively solved using Numpy.

Here are some Important function in NumPy for Data Analysis in Day to Day life

1. min and max: used to find the minimum and maximum value of a NumPy array
2. mean: used to find the mean value of the NumPy array
3. std: used to find the standard deviation of the NumPy array
4. median: used to find the median of a NumPy array
5. percentile: used to find the percentile in a NumPy array
6. linspace: used to get evenly spaced numbers over a specified interval
7. shape: used to get the shape of an array
8. reshape: used to reshape an array
9. copyto: copies the values of one array to another array
10. transpose: used to reverse the axes of an array
11. stack: used to join the sequence of an array along a new axis
12. vstack: used to join the sequence of an array along a new axis vertically
13. hstack: used to join the sequence of an array along a new axis horizontally
14. sort: used to get a sorted array

NumPy Tutorial : https://www.w3schools.com/python/numpy/default.asp

Pandas : The most popular application for the high-level interpreted language Python is data analysis. It is very adaptable and has an easy-to-understand syntax. For data analysis in Python, a variety of libraries are available, including pandas. Pandas is a Python-based data analysis library that is intended to make data analysis simple and quick. It offers a data structure with rows and columns that makes working with data simple. Additionally, pandas has other functions, like as data sorting, data filtering, and data charting, that facilitate and speed up data processing.

Here are some Important function in Pandas for Data Analysis in Day to Day life

• 1. read_csv()
• 2. head()
• 3. describe()
• 4. memory_usage()
• 5. astype()
• 6. loc[:]
• 7. to_datetime()
• 8. value_counts()
• 9. drop_duplicates()
• 10. groupby()
• 11. merge()
• 12. sort_values()
• 13. fillna()

Pandas Tutorial : https://www.w3schools.com/python/pandas/default.asp

Matplotlib : Python’s Matplotlib package is used to create 2D visuals. With support for Windows, Mac, and Linux, it is a well-liked option for data visualisation and scientific computing. The robust library Matplotlib has a lot of capabilities. Making data plots, graphical representations of mathematical models, and visualizing scientific data are some of the most popular applications for Matplotlib.

Types of Matplotlib Plots for Data Visualization in Data Science and Analytics :

• Scatter Plot
• Histograms
• Stacked Histogram
• Multiple Histogram
• Stacked Step Histogram
• Line Charts
• Strip Plot
• Swarm Plot
• Violin Plot
• Joint Plot
• Pair Plots
• Heat Maps
• Bar Chart
• Multiple Bar graph
• Stacked Bar Graph
• Pie Chart
• Stem Plots
• Box Plots

Matplotlib Tutorial : https://www.w3schools.com/python/matplotlib_pyplot.asp

Seaborn : Seaborn is a Python library for data visualization. It provides a flexible and easy-to-use interface for creating charts and graphs. Seaborn is built on the Python Statsmodels library and can work with data in various formats, including NumPy arrays, pandas DataFrames, and matplotlib figures.

What can Seaborn do?

Seaborn can produce high-quality charts and graphs that can help you visualize your data. Some of the features that Seaborn offers A rich variety of chart types, including line charts, bar charts, scatter plots, and more Configurable axes and titles Support for a variety of data formats, including NumPy arrays, pandas DataFrames, and matplotlib figures

Here are some Important function in Seaborn for Data Analysis in Day to Day life

Types of Seaborn Plots for Data Visualization in Data Science and Analytics :

• Scatter plot
• Histogram
• Bar plot
• Box plot
• Violin plot
• Facet grid
• Pair plot
• Heatmap

Seaborn Tutorial : https://seaborn.pydata.org/tutorial.html

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