Introduction
Python is an interpreted, high-level, general-purpose programming language created by Guido van Rossum released in 1991. It has a very simple syntax making it one ideal for beginners as a first language.
Lets understand the terms interpreted, high-level, general-purpose:
- Interpreted: Code is executed line by line by a program called an interpreter. When you write Python code, Python reads your instructions one line at a time and runs them immediately.
- High-level: Python is designed to be easy for humans to read and write by using simple English like words.
- General-purpose: It can be used to build different types of applications and is not limited to only one task. It is used for:
- Web development (Backend)
- Data analysis
- Artificial Intelligence (AI)
- Automation
- Game development
- Cybersecurity
Data Analysis is the process of inspecting, cleaning, transforming and modeling data to discover useful insights that can be used for decision making.
Why is Python used for Analytics?
Python is widely used in data analytics because:
- It is easy to learn, Python uses simple and clean English like syntax making it beginner-friendly, therefore one is able to focus on analyzing data rather than struggling with code.
- It can handle large data sets efficiently, most companies have huge amounts of data and Python can process and analyze these large datasets efficiently which makes it the go to language for these companies.
- It has powerful libraries with pre-built functions and tools for data analysis, for example, Pandas, NumPy, Matplotlib, etc.
- It supports automation, Python can automate repetitive analytics tasks that would normally take hours to complete manually.
- It is flexible, it integrates with other technologies, for example, databases, Excel, APIs, Business intelligence tools etc.
Python libraries used in data analytics
Pandas
Pandas is used for data manipulation, analysis and cleaning. It provides fast and flexible tools to work with structured or tabular data. It also provides convenient indexing functionality to enable you to reshape, slice and dice, perform aggregations, and select subsets of data.
Pandas blends the array-computing ideas of NumPy with the kinds of data manipulation capabilities found in spreadsheets and relational databases.
NumPy
NumPy, which stands for Numerical Python, is a Python library used for working with arrays. It provides data structures, algorithms and library needed for most scientific applications involving numerical data in Python. It has:
- Efficient multidimensional array object ndarray
- Functions for performing mathematical operations between arrays
- The necessary tools for reading and writing array-based datasets
- Linear algebra operations
Matplotlib
Matplotlib is the most popular Python library for creating static, animated and interactive visualizations. It is built on top of NumPy
SciPy
SciPy is a powerful open-source Python library for scientific computing, engineering and mathematics built upon NumPy. It contains such tools as:
- scipy.integrate: This is designed for numerical integration and solving ordinary differential equations (ODEs). I
- scipy.linalg: It provides useful tools to perform various linear algebra operations such as solving equations and computing matrix decompositions
- scipy.signal: It provides tools for signal processing, including filter design, spectral analysis and convolution
Scikit-learn
Scikit-learn is a general-purpose machine learning toolkit for python programmers. It includes submodules for such models as:
- Classification: SVM, nearest neighbors, random forest, logistic regression.
- Regression: Lasso, ridge regression. -Clustering: k-means, spectral clustering
Jupyter
Jupyter is an open source web application used for interactive computing. It is primarily used for data cleaning, transformation, numerical simulation, data analysis, data visualization and machine learning. It also allows you to write content in Markdown and HTML hence providing you a means to create rich documents with code and text.
How Python is used to clean, analyze, and visualize data
Python, because of its vast libraries for working with data, is very popular for data analytics. It helps analysts perform three major tasks:
- Data Cleaning
- Data analysis
- Data Visualization Let's deep further:
1. Data Cleaning
Real world data is often messy. It can have missing values, duplicates and inconsistent formats. Hence there is need to:
- Handle missing values
- Remove duplicates
- Fix data types
- Standardize formats
- Remove errors
Example
import requests
import pandas
products_url = 'https://dummyjson.com/products'
# Request data
products_data = requests.get(products_url)
json_products = products_data.json()
# Get the products content
products = json_products['products']
# Convert to DataFrame
products_df = pandas.DataFrame(products)
# Remove Duplicates
products_df.drop_duplicates()
# Convert data types
products_df['date'] = pandas.to_datetime(products_df['date'])
2. Data Analysis
After cleaning the data, python helps you to explore patterns, trends and relationships. Some of the common tasks are:
- Descriptive statistics
- Filtering and querying
- Correlation analysis
- Grouping and aggregation
Example
# Summary statistics
print(products_df.describe())
# Grouping data
sales_by_product = products_df.groupby('product_name')['sales'].sum()
# Filtering
high_sales = products_df[products_df['sales'] > 10000]
3. Data Visualization
Data Visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps to make data sets understandable by identifying trends, patterns and outliers. Python provides various libraries for visualizing data. These include:
- Matplotlib
- Seaborn
Example
import matplotlib.pyplot as plt
import seaborn as sns
# Line plot
plt.plot(products_df['date'], products_df['sales'])
plt.title("Annual Sales")
plt.show()
# Bar plot
sns.barplot(x='product_name', y='sales', data=products_df)
plt.show()
Real-world examples of Python in data analytics
1. Business analytics: It is used to process and analyze large volumes of sales and revenue data efficiently.
2. Finance: Python is used to identify fraudulent transactions in real time, for example, in banks
3. Healthcare It is used to analyze patient records, monitor disease trends, process medical data, predict health risks and support faster and accurate diagnoses.
4. Customer Behavior Analysis: Businesses analyze point-of-sale data to determine the best months for sales, which products are frequently bought together and the most effective times to display advertisements.
5. Manufacturing Industries: Used by industries in predictive maintenance to reduce downtimes caused by equipment failures.
Examples of Companies that use Python
- Google: Machine learning
- Meta: AI and ML pipelines
- Spotify: Content recommendation systems
- Uber: Real-time data analysis for ride matching and pricing algorithms.
- Pinterest: Content recommendation and image processing
Why beginners should learn Python
Since its first appearance in 1991, Python has become one of the most popular interpreted programming languages. It is considered a good programming language for beginners because:
- It has a clean and readable syntax almost like English hence beginners can focus on understanding concepts instead of struggling with syntax.
- It can be used in multiple areas like web development, data analysis, machine learning and automation.
- It has a huge ecosystem of libraries that do the heavy work for you, e.g. pandas, NumPy, Matplotlib etc.
- It has a massive global community hence one can ask for help whenever they are stuck.
- It is one of the most in demand skills in the job market.
Conclusion
Python has developed a large and active data analysis community. It is one of the most important languages for data science and machine learning hence the need to learn if you are getting into the data science or analysis space.
Some of the places to get started learning Python are:
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