Data analytics is used to explore and analyze large datasets to boost data-driven decision-making and make better predictions. With the help of data analytics, users can collect, transfer, and clean the data to drive useful insights. Moreover, data analytics help in answering the questions, disproving theories, and testing hypotheses. Using various tools, such as Python, R, Power BI, and others, data scientists can do data analytics. Here, we have mentioned the details about data analytics in Python. But before proceeding to the details, let’s know why Python is used for data analytics.
Why do data scientists choose data analytics in Python?
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There are several programming languages for data analytics, but it has been seen that most statisticians, data scientists, and engineers prefer to use Python for data analytics. Below are some of the common reasons why data analytics in Python getting popular day by day:
- Python programming language is flexible and scalable (which means the ability of the system is higher to manage a developing work).
- Python considers to be easy to understand and learn programming languages, and it has an easy and simple syntax.
- It offers various libraries for data visualization and graphics to make plots.
- Python has unique libraries for data manipulation and numerical computation.
- If you are looking for broad community support that can easily provide an immediate solution to your queries, then Python is the best of this.
List of Python libraries available for data analytics
Because of the availability of various Python libraries, data scientists use Python to analyze data. Moreover, python libraries are easy to use that is why it gains popularity among data analysts. Here are some of the python libraries that use for data analytics:
- NumPy: It supports n-dimensional arrays and offers an excellent feature of numerical computation. Moreover, it uses for calculating the value of Fourier Transform and Linear Algebra.
- Matplotlib: This Python library uses by the analysts to plot various data points and create various interactive data visualizations.
- Scikit-Learn: The feature of this library enables the user to do classification, clustering models, and regression.
- Pandas: This python library offers the functioning of handling missing data, manipulating the data, and performing mathematical operations.
- SciPy: This is mostly used for scientific computations. This Python library includes linear algebra, interpolation, signal and image processing, optimization, integration, and special functions.
Example: How to do data analytics in python using NumPy library?
Here, we have provided an example of how the data analyst performs data manipulation and numerical analysis with the NumPy library.
First of all, create an individual NumPy array. | import NumPy as np# Generate a 1D arrayarr1 = np.array([ 4, 5 ])print(arr1)Output: [4 5] |
Access & manipulate components within the given array. | # To access the elements from the given arrayarr1[1]Output: 5# To change any element from the given arrayarr1[1] = 2 arr1Output: array([4, 2]) |
Create a 2D array and check its shape. | # Create the 2D arrayarr2 = np.array([ [4,5], [6,7] ])print(arr2)Output: [ [4 5 ] [6 7 ] ]# To check the arry’s shapeprint(“The shape is 2 rows and 2 columns:”, arr2.shape)Output: The shape is 2 rows and 2 columns: (2, 2) |
Access components of the 2D array with the help of index positions. | print(arr2[0][1])print(arr2[0, 1])print(arr2[0, -1])print(arr2[-1, 0])Output:5556 |
Create a type string array. | # Type string arrayarr3 = np.array( [ ‘China’, ‘India’, ‘Mexico’, ‘USA’ ])print(arr3)Output: [ ‘China’, ‘India’, ‘Mexico’, ‘USA’ ]arr3 [2]Output: ‘Mexico’ |
Use arange() & linspace() function to provide the even space within the particular interval. | # Evenly spaced array value that is distributed in the particular interval.arr = np.arrange(2, 20, 3)print(arr)Output: [ 2 5 8 11 14 17 20 ]# Evenly spaced array number that is distributed in the particular interval.arr = np.linespace(0, 10, 5)print(arr)Output: [ 0 2.5 5.0 7.5 10.] |
Create a random value array between 0 & 1 within the provided shape. | # Random valued array between 0 & 1 within the provided shapearr = np.random.rand (5)print(arr)print(‘\n’)arr = np.random.rand (2,3)print(arr)Output: [ 0.4 0.1 0.5 0.8 0.9] [ [ 0.72 0.35 0.34] [ 0.68 0.49 0.55] [ 0.94 0.31 0.44] ] |
Create an identity matrix with the help of identity() & eye() function. | # Create an identity matrixidentity_martrix = np.identity(2)print(identity_matrix)Output: [ [ 1 0] [ 0 1] ]identity_matrix = np.eye(2)print(identity_matrix)Output: [ [ 1 0] [ 0 1] ] |
Create a 3×3 array matrix using the random no in between 0 & 1. | arr = np.random.rand(3,3)print(arr)Output: [ [ 0.9 0.7 0.2] [ 0.8 0.1 0.4] [ 0.7 0.4 0.1] ] |
Sum the value of the array along the given column. | # sum the number along the given columnprint(np.sum(arr, axis=0))Output: [ 2.4 1.2 0.7 ] |
Sum the value of the array along the given row. | # Sum along with the rowprint(np.sum(arr, axis=1))Output:[ 1.8 1.3 1.2 ] |
Delete many elements of an array matrix. | # Deleting many elementsarr2 = np.array( [1,2,3,4,5,6,7,8,9,10] )print(arr2)print(‘\n’)arr3 = np.delete(arr2, [2,6])print(arr3)Output: [ 1 2 3 4 5 6 7 8 9 10 ] [1 2 4 5 6 8 9 10 ] |
Concatenate each element into 2 different arrays. | # Combining and spiting of an arrayarr1 = np.array ([ [ 1,2,3], [1,2,3] ]) arr2 = np.array ([ [ 5,6,7], [5,6,7] ])# Combining the array elements by columncat = np.concatenate((arr1, arr2), axis=0)print(cat)Output: [ [ 1 2 3] [ 1 2 3] [ 5 6 7] [ 5 6 7] ]# Combining the array elements by rowcat = np.concatenate((arr1, arr2), axis=1)print(cat)Output: [ [ 1 2 3 5 6 7] [ 1 2 3 5 6 7] ] |
Create constant values in an array. | # Constant value arrayprint(np.full((2,3), 5))Output: [ [ 5 5 5] [ 5 5 5] ] |
Append array elements with the help of append() function. | # Append elementsarr= np.array([1,2,3,4]) arr1= np.append(arr,5)arr1Output: array([1,2,3,4,5])arr2= np.append(arr, [6,7,8])print(arr2)Output: [ 1 2 3 4 6 7 8] |
Conclusion
Nowadays, data plays an important role in each sector, such as for analyzing the needs of the clients to grow the business and so on. Therefore, data is collected and generated in several formats to conclude the useful results. Moreover, several companies and organizations rely on data analytics by which they can check the opportunities and hidden insights to grow their businesses. Data analytics in Python is the major topic that each data analyst must know. Therefore, we have provided all the details about it. Users can use various Python libraries, such as NumPy, Matplotlib, and Pandas, to perform data analytics. Moreover, we have provided an example for Data analytics in Python using the NumPy library. This helps you to know how to use the Python library for analyzing the collected data. So keep practicing and become a good data analyst. and also if you need a python helper online, then contact us.
