Leverage the Power of Python to Process Big Data

Python is a widely-used programming language used on different platforms. It has gained huge popularity among hire python developers and analysts. Also, it has been ranked as the number one programming language because of its user-friendly syntax which makes it easy to learn.

Why is Python so in demand in the field of big data?

The combination of Python and Big Data fits perfectly for data analytics. It is because Python has the right tools for free, such as libraries and frameworks.

Python is Open Source

Python is an open-source programming language developed using a community-based model. It is flexible to run on different environments like Windows and Linux. In addition, it is robust, i.e. it is transportable to different platforms.

Powerful Libraries:

Here is a list of some common libraries used while handling big data:

• Pandas: used for data analyzing and manipulation.

  Example:

  dict = {"Fruits": ["Apple", "Banana", "Mango", "Litchi", "Guava"], "Location": ["Brasilia", "Moscow", "New Delhi", "Beijing", "Pretoria"], "Liked by": [“B”, “S”, “A”, “G”, “P”]} #creating a dictionary import pandas as pd #importing the pandas library with the alias ‘pd’ brics = pd.DataFrame(dict) #putting the data in a pandas frame print(brics) #printing the frame data Output: -------------------------------------------------- Fruits Location Liked by 0 Apple Brasilia B 1 Banana Moscow S 2 Mango New Delhi A 3 Litchi Beijing G 4 Guava Pretoria P

• NumPy: The library used for manipulating large multi-dimensional arrays for arbitrary data

  Example: import numpy as np #importing numpy library arr = np.array( [[ 1, 2, 3],[4,2,5]]) #creating numpy array print("Array is of type: ", type(arr)) #to display the type of array print("No. of dimensions: ", arr.ndim) #to display the array dimensions print("Shape of array: ", arr.shape) #to display the array shape- number of rows and columns print("Size of array: ", arr.size) #to display the number of elements in the array print("Array stores elements of type: ", arr.dtype) #to display the array data type Output: ---------------------------------------------- ('Array is of type: ', <type 'numpy.ndarray'> ) ('No. of dimensions: ', 2) ('Shape of array: ', (2, 3)) ('Size of array: ', 6) ('Array stores elements of type: ', dtype('int64'))

• SciPy: Used for scientific and technical computing including various modules for optimization.

  Example: Saving a MATLAB file

  import scipy.io as sio import numpy as np vect=np.arange(10) #creates a vector with equally spaced 10 values sio.savemat(‘array.mat’,{‘vect’:vect}) #saving the MATLAB file

• Scikit-learn: Data processing package with built-in operations like clustering, regression, preprocessing, etc.

  Example: Splitting the dataset into train and test data

  from sklearn.model_selection import train_test_split X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.20)

  #splitting the 2 datasets X and y into training and testing data with 20% of the elements being in the test set

• Matplotlib: It’s a library that helps in the 2D plotting of data. Matplotlib enables generating to create bar charts, histograms, error charts, power spectra, scatter plots, and more.

  Example:

  import matplotlib.pyplot as plt #importing matplotlib library import numpy as np #importing numpy library x = np.array((1,2,3,4)) #creating a numpy array with contents (1,2,3,4) y = np.array((2,4,6,8)) plt.plot(x,y) #plotting the 2 arrays in a line graph plt.show() #displaying the line graph

  Output: