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The Magic of Data Visualization using Matplotlib



      The Magic of Data Visualization Using Matplotlib


Matplotlib is a multiplatform data visualization library built on Numpy arrays and designed to work with broader Scipy Stack. Matplotlib was developed by John Hunter in 2003 with version 0.1. This project is supported by Space Telescopic institute for complete development and extension for better capabilities.

Matplotlib library enhances the plotting and visualization technique in python. As using the matplotlib we can create various plots, histogram, maps, chart and many more plotting.


Visualization of Data


    Important features of Matplotlib

  •  It play and operates well with many operating systems and graphics back-ends.
  •  Matplotlib have strength of running cross platform graphics engine smoothly and reliable to different types of graphics system.
  •  There are various API’s and wrappers make this library to useful to dive into Matplotlib’s syntax to adjust the final plot output.
  • Customization and configuration of plot styles, configurations and style-sheets in Matplotlib is very easy and efficient.
    

The Magic of Data Visualization Using different styles and plots in Matplotlib Tool

 Plotting the colorful plots, histogram, Bar-chart, heatmap to visualize the data more concisely and accurately using this library helps statisticians, data analysts and data scientist to no more about their data and find the tendency and key insights of their data to make it more accurate for analysis. Let see some examples where we use matplotlib to plot the data.

1) Plotting of Line plot:

a) Importing the matplotlib library and here %matplotlib inline lead to static images of your plot embedded in the network.

    

b) Plot the line plot with different 

So we also used Numpy for plotting of data here. It is a python library for all numerical computation and mathematical operation (e.g. adding vector, matrix multiplication). 

                                 

 As seen in the plot using few lines of code we plot a colorful lineplot using the representation (*) star representation of line for cos and (--) dashed line for sin. 

One of the nice feature of Matplotlib is the ability to save figures in a wide variety of formats. Using the savefig() command for save your figure. (Figure save in current working directory) We also use the save figure to visualize it again. 

                                    

                                  

2) Plotting subplot of  Line plot:

Matplotlib was basically written as  Python alternative for MATLAB. To plot responsive, colorful plot with various customization.

3) Adjusting the Line Colors and Styles:

Adjusting the line size, colors and styles is very easy using matplotlib also helps to distinguish between multiple attributes or values of a data-sets.


 

The magic of data visualization becomes more interactive when plot data have various colors and styles using matplotlib library.

   

                                               

4) Simple Scatter plots with plt.plot

Using the plt.plot we can easily plot scatter plot which are used to represent the variety of data point in plane. The Scatter plot are also very help in plotting clusters and mixed-valued data.


The output is interactive scatter plot of given marker.

                            



The primary difference of plt.scatter from plt.plot is that it can be used to create scatter plots where the properties of each point(individual) can be individually controlled or mapped to data. Properties includes(size, face color, edge color...)

5) Random Scatter plot with points of many colors and sizes  

 Creating a random scatter plot gives an amazing looks with various color transitions which shows the interactivity of plotting plots using matplotlib library.

  


The colorful random scatter plot generated using the matplotlib library:

                                         

So after visualizing various plots you see the magic of Matplotlib library and we can plot varieties of figures and plot using the efficient and customized a popular python library for data science.
 
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