Python is a widely-used programming language used on different platforms. It has gained huge popularity among 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. Below are some of the most prominent advantages of Python. And if you want to leverage the power of Python to process big data, you can hire Python developer that helps your company to achieve these benefits.
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:
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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
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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'))
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SciPy: Used for scientific and technical computing including various modules for optimization.
Example: Saving a MATLAB file
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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
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Scikit-learn: Data processing package with built-in operations like clustering, regression, preprocessing, etc.
Example: Splitting the dataset into train and test data
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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)
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#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:
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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:
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 code can accelerate development, as it is a high-level language. It enables prototyping, resulting in faster coding while maintaining transparency between the code and its execution.
Compatibility with Hadoop
As you can see, Python and Big Data are compatible with each other, so Hadoop and Python work with Big Data. Python has its PyDoop package, which helps in accessing HDFS API. 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 a massive amount of data being processed, the right way to shape the data is important for the company. Collecting a huge stack of data and finding a trend in it makes analysts each comprehend the data efficiently and eliminate problems.
Top 5 data visualization tools used are:
- Fusion Charts Suite XT
- QlikView
- Tableau
- Sisense
- Plot.ly
