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Leverage the power of python to process Big Data

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INTRODUCTION:

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

Python is in immense demand in the field of big data, why?

The combination of python and big data fits perfectly for data analytics. It is all because python has right tools for free like libraries and frameworks.

Python is Open Source:

Python is an open source programming language developed using community-based model. It is flexible to run on different environments like Windows and Linux. In addition, it is robust in nature, 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: 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 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:

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Easy Learning:

Python is a user friendly, easy to learn language because it has fewer lines of code. Python integrates simple syntax, code readability, scripting features, auto identification and association of datatypes.

Speed:

Python can accelerate code development because it is a high-level language. It enables prototyping which results in fast coding while maintaining transparency between code and its execution.

Compatibility with Hadoop:

As you have seen python and big data are compatible with each other, similarly Hadoop and python also work together with big data. Python has its own PyDoop package which helps in accessing HDFS API’s. It also helps in programming MapReduce which helps to solve complex problems with minimal effort.

Data Visualization:

Python has updated and improved its offerings in data visualization. With such massive amount of data been processed, the right way to shape the data is important for the company. Collecting huge stack of data and finding a trend in it makes analysts each to comprehend the data efficiently and eliminate problems.

Top 5 data visualization tools used are:

Ast Note

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