visualization using Numpy and Matplotlib

 Today we are going to discuss on data visualization using Numpy and Matplotlib. This are one of the best python libraries for data scientists. Numpy is widely known for its working with arrays making it suitable for dealing with numerical data. Matplotlib on the other hand is widely known for its capabilities of creating visually attractive plots from the data provided, this means that if you want to create bar, histograms, line graphs, scatter points, 3D plots, animated plots etc then matplotlib might be your best friend. 

The best part is that to achieve your goal, you only use few lines of code. This means that debugging will be easy and will save you a lot of time plus you'll be Happier coding.

Numpy and Matplotlib are powerful combination for data visualization in python. Numpy provides the foundation for efficient data manipulation and storage.

Before we continue, let's take a look at the functionalities of this libraries.

1. Numpy

1. Creates and handles multidimensional arrays essential for storing and manipulation of data.

2. Offers array operations like element- wise calculations, filtering and transformations.

3. Enables efficient data analysis through functions like statistical operations and linear algebra.

2. Matplotlib

1. Generates various plots like line, scatter, bar, histograms etc

2. Provides customization options for aesthetics, labels, titles, and legends.

3. Can be integrated with Numpy, for seamless data visualization.

Examples

 
   import numpy as np

   import matplotlib.pyplot as plt

   Data_X = np.array([1,2,3,4,5])

   Data_Y = np.array([70,45,67,56,23])

   plt.bar(Data_X,Data_Y)

   plt.show()

//We first import both Numpy and Matplotlib in the first 2 lines.

//Data_X is the data to be shown on the X axis and Data_Y is the data to be displayed on the y-axis

//We use plt.bar() to tell matplotlib that we want a bar graph of the specific data.

//We use plt.show() to draw the plot.

//You should be able to get an output if you run the above code.

This is just a sample of the 2 libraries in use, you can always go beyond this and use real world data to create complex data visualizations.

Advantages of using Numpy and Matplotlib

1. Efficiency - Numpy's optimized arrays enable faster data plotting and manipulation compared to python lists. This translates to faster data rendering even if you have a large dataset.

2. Seamless integration - matplotlib is designed to work seamlessly with numpy arrays. You can directly use Numpy arrays to create plots without any complex data conversion.

3. Flexibility - matplotlib offers vast array of plot types and customization options.

Disadvantages of using Numpy and Matplotlib

1. Visualization complexity - matplotlib's fine grained control over plot element can lead to complex code.

2. Learning curve - while both libraries are powerful, they have a learning curve. Numpy's array-based operations might require some adaptations and Matplotlib visualization can be overwhelming for beginners.

3. Aesthetics - matplotlib's default visualization may appear less polished compared to some higher level libraries like seaborn which is built on top of matplotlib and offers more aesthetically pleasing themes out of the box.