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Table of contents
1.
Introduction 
2.
What is Python Data Analytics?
3.
Steps of Data Analysis in Python
4.
Analyzing Numerical Data with NumPy
4.1.
Arrays in NumPy
4.2.
Python
4.2.1.
Create Array using numpy.zeros
4.3.
Python
4.3.1.
Operations on NumPy Arrays
4.4.
Python
4.4.1.
NumPy Array Indexing and Slicing
4.5.
Python
4.5.1.
NumPy Array Broadcasting
4.5.1.1.
Broadcasting Rules
5.
Analyzing Data Using Pandas
5.1.
Series
5.2.
Python
5.3.
DataFrame
5.4.
Pandas GroupBy
6.
Visualization with Matplotlib
6.1.
Pyplot
6.2.
Python
6.3.
Bar Chart
6.4.
Python
6.5.
Histogram
6.6.
Python
6.7.
Scatter Plot
6.8.
Python
7.
Frequently Asked Questions
7.1.
Why is Python preferred for data analytics?
7.2.
Can Python handle large datasets?
7.3.
Is Python suitable for complex data analysis?
7.4.
How does Python help in data visualization?
7.5.
What is the role of Pandas in Python data analytics?
8.
Conclusion
Last Updated: Mar 27, 2024
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Python Data Analytics

Author Pallavi singh
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Master Python: Predicting weather forecasts
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Ashwin Goyal
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Introduction 

Data analytics is the science of analyzing raw data to make conclusions about that information. Python, with its rich technology stack, has become a staple in the data analytics field due to its simplicity and powerful libraries. 

Python Data Analytics

In this article, we'll explore how Python is used in data analytics, diving into numerical data analysis with NumPy and data manipulation with Pandas, accompanied by practical code examples.

What is Python Data Analytics?

Python data analytics refers to the process of analyzing datasets to extract meaningful insights. Python, as a programming language, offers a plethora of libraries and tools that make this task not just possible but also efficient. It's the go-to for many data scientists and analysts due to its readability and straightforward syntax.

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Steps of Data Analysis in Python

Data analysis in Python can be broken down into several key steps:

  • Data Collection: Gathering the raw data from various sources.
     
  • Data Wrangling: Cleaning and preparing the data for analysis.
     
  • Exploratory Data Analysis: Understanding the data by summarizing its main characteristics often with visual methods.
     
  • Data Modeling: Creating models to predict or understand phenomena.
     
  • Data Interpretation: Making sense of the data and its analysis to make informed decisions.

Let's define these steps profoundly with examples using Python's libraries.

Also see, Python Operator Precedence

Analyzing Numerical Data with NumPy

NumPy is a fundamental package for scientific computing in Python. It provides support for arrays, matrices, and high-level mathematical functions to operate on these data structures.

Arrays in NumPy

An array is a central data structure of the NumPy library. It is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. NumPy arrays are faster and more compact than Python lists. An array consumes less memory and is convenient to use.
Create Array using numpy.empty

  • Python

Python

import numpy as np

# Create an uninitialized array of specified shape and dtype

empty_array = np.empty((3, 2), dtype=float)

print("Empty Array:")

print(empty_array)

This code snippet creates an empty array with random values depending on the state of the memory. Here, (3, 2) defines the shape of the array as 3 rows and 2 columns.

Output

output

Create Array using numpy.zeros

# Create an array filled with zeros

  • Python

Python

import numpy as np

zero_array = np.zeros((2, 3), dtype=int)

print("Zero Array:")

print(zero_array)

Output

output

The np.zeros function returns a new array of given shape and type, filled with zeros.

Operations on NumPy Arrays

Arithmetic Operations

NumPy provides a variety of mathematical operations that can be performed element-wise on arrays.

  • Python

Python

import numpy as np

a = np.array([1, 2, 3])

b = np.array([4, 5, 6])

# Element-wise addition

print("Addition:", a + b)


# Element-wise subtraction

print("Subtraction:", a - b)



# Element-wise multiplication

print("Multiplication:", a * b)


# Element-wise division

print("Division:", a / b)

Output

Output

Each of these operations is performed element-wise, meaning they are applied to each corresponding element of the arrays.

NumPy Array Indexing and Slicing

Indexing and slicing on arrays allow you to retrieve individual elements or specific sub-arrays.

  • Python

Python

import numpy as np

a = np.array([1, 2, 3])

b = np.array([4, 5, 6])


print("First element:", a[0])

# Slicing

print("First two elements:", a[:2])

Output

output

NumPy Array Broadcasting

Broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes.

Broadcasting Rules
  • If arrays do not have the same rank, prepend the shape of the lower rank array with 1s until both shapes have the same length.
     
  • The two arrays are said to be compatible in a dimension if they have the same size in the dimension or if one of the arrays has size 1 in that dimension.
     
  • The arrays can be broadcast together if they are compatible in all dimensions.
     
  • After broadcasting, each array behaves as if it had shape equal to the element-wise maximum of shapes of the two input arrays.
     
  • In any dimension where one array had size 1 and the other array had a size greater than 1, the first array behaves as if it were copied along that dimension.

Analyzing Data Using Pandas

Pandas is a library providing high-performance, easy-to-use data structures, and data analysis tools for Python. The two primary data structures of pandas are Series (1-dimensional) and DataFrame (2-dimensional).

Series

A Series is a one-dimensional labeled array capable of holding any data type.

  • Python

Python

import pandas as pd

Import numpy as np

# Creating a Series

s = pd.Series([1, 3, 5, np.nan, 6, 8])

print(s)

Output 

output

DataFrame

A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types.

Creating DataFrame from CSV

# Reading data from CSV file into DataFrame

df = pd.read_csv('data.csv')
print(df.head())

Filtering DataFrame

The filter() function is used to select columns or rows.

# Filtering columns using their names

filtered_df = df.filter(['Column1', 'Column2'])
print(filtered_df.head())


Sorting DataFrame

DataFrames can be sorted by the values of one or more columns.

# Sorting by a column

sorted_df = df.sort_values(by='Column1', ascending=True)
print(sorted_df.head())

Pandas GroupBy

GroupBy allows you to group rows together and call aggregate functions.

# Grouping and then applying the sum() function to the resulting groups.

grouped_df = df.groupby('Column1').sum()
print(grouped_df)


Concatenating DataFrame

Concatenation is performed with the concat() function by combining Series or DataFrames while keeping the index intact.

# Concatenating two DataFrames

result = pd.concat([df1, df2])
print(result)


Merging DataFrame

Merge DataFrame or named Series objects with a database-style join.

# Merging two DataFrames

merged_df = pd.merge(df1, df2, on='key')
print(merged_df)


Joining DataFrame

Join columns with other DataFrame either on index or on a key column.

# Joining two DataFrames

joined_df = df1.join(df2)
print(joined_df)

Visualization with Matplotlib

Matplotlib is a plotting library for Python which gives you control over every aspect of a figure. It has functions for plotting a variety of graphs such as line, bar, scatter, histogram, etc.

Pyplot

Pyplot provides a MATLAB-like interface for making plots.

  • Python

Python

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [1, 4, 9, 16])

plt.axis([0, 6, 0, 20])

plt.show()

Output

Pyplot

Bar Chart

  • Python

Python

import matplotlib.pyplot as plt

plt.bar(['A', 'B', 'C'], [3, 4, 5])

plt.show()

Output

Bar Chart

Histogram

  • Python

Python

import matplotlib.pyplot as plt

import numpy as np

data = np.random.randn(1000)

plt.hist(data, bins=30)

plt.show()

Output

Histogram

Scatter Plot

  • Python

Python

import matplotlib.pyplot as plt

import numpy as np

x = np.random.rand(50)

y = np.random.rand(50)

plt.scatter(x, y)

plt.show()

Output

Scatter Plot

Frequently Asked Questions

Why is Python preferred for data analytics?

 Python is preferred for its simplicity, readability, and the rich ecosystem of data analysis libraries available.

Can Python handle large datasets?

Yes, Python can handle large datasets, especially with libraries like Pandas and NumPy that are optimized for performance.

Is Python suitable for complex data analysis?

Absolutely, Python's libraries provide advanced functionalities for complex data analysis tasks.

How does Python help in data visualization?

Python has libraries like Matplotlib and Seaborn that offer a wide range of functions to create visually appealing and informative statistical graphics.

What is the role of Pandas in Python data analytics?

Pandas provide structured data operations and functions that are essential for data cleaning, transformation, manipulation, and analysis.

Conclusion

Python's simplicity and the vast array of libraries make it an excellent choice for data analytics. NumPy and Pandas simplify data manipulation and analysis, while Matplotlib provides powerful tools for data visualization. With Python, you can handle the entire data analysis pipeline, from cleaning and analyzing data to visualizing and presenting results.

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