Python is a versatile programming language used widely for data analytics, manipulation, and visualization. The rich ecosystem of Python libraries offers several functions, allowing you to handle different data structures and perform mathematical computations. It also helps you to train machine learning models and create advanced visualizations. This article enlists the top 10 Python libraries for data analytics that professionals can use to fulfill their data-related objectives.
Why Python is Preferred for Data Analysis?
Python is the most preferred programming language used by data analysts for the following reasons:
Simplicity
Python has a simple syntax, unlike other programming languages that have complex syntax. As a result, it is a very user-friendly language, allowing you to execute queries more easily. You have to use indentation to separate blocks of code in Python, which makes them well-structured and organized. This increases readability and lowers the learning curve for you if you are a beginner in coding.
Community Support
Python has a vast global community of developers who actively interact and contribute to promoting and developing the programming language. There are also documentation, tutorials, and forums that help you learn the language quickly and resolve your queries if required.
Extensive Libraries
Python offers a diverse set of libraries for different computational operations. For data manipulation or cleaning, you can use NumPy and Pandas. The programming language supports the Scikit-learn library for machine learning, while you can use the Matplotlib and Seaborn libraries for visualizations. For scientific computing, Python offers SciPy, which is used extensively by the scientific community.
As a result, Python offers a comprehensive set of libraries to perform most computational tasks, making it a popular coding solution. In the section below, let’s look at some of these libraries in detail.
Top 10 Python Libraries for Data Analysis
Some essential Python libraries for data science are as follows:
1.NumPy
The Numerical Python or NumPy Python library is one of the most preferred Python libraries for data manipulation. It facilitates seamless operations on arrays and is used extensively in science and engineering domains. NumPy offers an extensive library of functions that can operate on multidimensional array data structures such as homogenous, N-dimensional ndarrays.
You can import it into your Python code using the following syntax:
import numpy as np
You can access any NumPy feature by adding a prefix (np.) before them. NumPy enables you to perform various operations on entire arrays simultaneously, making it faster than the loops. Arrays also consume less memory than Python lists, as they store similar types of elements together. This speeds up computation further, especially when you are handling large datasets.
NumPy facilitates using various mathematical functions for operations such as trigonometry, statistical analysis, linear algebra, and random number generation. Several Python libraries, such as Pandas, SciPy, or Scikit-learn, are based on NumPy. It is an important library of the Python ecosystem that helps you with data analysis, machine learning, and scientific computing.
2. Pandas
Pandas is an open-source Python library that allows you to perform data manipulation, analysis, and data science operations. It supports data structures such as series and data frames, simplifying working with single—or multidimensional tabular data structures. The syntax to import Pandas into your Python ecosystem is as follows:
import pandas as pd
You can use the prefix (pd.) to access any feature or function in Pandas. It offers features for data cleaning that enable you to handle missing values, filter rows, and transform data according to your requirements. You can also easily remove or add rows and columns in Pandas data frame for efficient data wrangling. To perform these tasks, you can use labels of rows and columns to access data rather than using integer-based indexing.
The Pandas library supports various data types, including CSV, Excel, SQL, JSON, and Parquet. It performs in-memory operations on large datasets, enabling fast data processing.
3. Matplotlib
Matplotlib is a library that helps you create static and interactive visualizations in Python. You can use it to create simple line plots, bar charts, histograms, and scatter plots. Matplotlib also facilitates the creation of advanced graphics such as 3D plots, contour plots, and custom visualizations. The syntax to import the Matplotlib library is as follows:
import matplotlib.pyplot as plt
You can use Matplotlib’s predefined visual styles or customize the colors, line styles, fonts, and axes according to your requirements. It can also be directly integrated with NumPy and Pandas to create visuals of series, data frames, and array data structures. Matplotlib is the basis of Seaborn, another Python library for visualization that aids in developing statistical plots.
You can save the graphical plots created in Matlplotlib and export them in different formats such as PNG, JPEG, GIF, or PDF.
4. Seaborn
Seaborn is one of the important Python libraries used for data analysis and visualizations. It is based on Matplotlib and is helpful for creating interactive statistical graphs. Seaborn streamlines the data visualization process by offering built-in themes, color palettes, and functions that simplify making statistical plots. It also provides dataset-oriented APIs that enable you to transition between different visual representations of the same variables. You can import this library using the following command:
import seaborn as sns
The plots in Seaborn are classified as relational, categorical, distribution, regression, and matrix plots. You can use any of these according to your requirements. The library is handy for creating visualizations of data frames and array data structures. You can use several customization options available to change figure style or color palette to create attractive visuals for your data reports.
5. Scikit-learn
Scikit Learn, also known as Sklearn, is a Python library known for its machine learning capabilities. It allows you to deploy different supervised and unsupervised machine learning algorithms, including classification, regression, clustering, gradient boosting, k-means, and DBSCAN. Scikit-learn also offers some sample datasets, such as iris_plant or diabetes, that you can use to experiment and understand how machine learning algorithms work.
To install the Scikit-learn library, you must first install the NumPy and SciPy libraries. After installation, you can import specific modules or functions according to your requirements rather than importing the whole Scikit-learn library. You can use the following command to import Scikit-learn:
import sklearn
After extraction, you can preprocess your data effectively using splitting, feature scaling, and selection features of Scikit-learn. You can then use this data to train your machine learning models. The models can be evaluated with functions that can track certain metrics. This involves calculating accuracy, precision, and F1 scores for the classification model and mean squared error for regression models.
6. SciPy
SciPy is a scientific Python library that enables you to perform scientific and technical operations. It is built on NumPy and aids in performing complex data manipulation tasks. While NumPy provides functions that help you work on linear algebra, or Fourier transforms, SciPy contains comprehensive versions of these functions. The command to import SciPy in your Python notebook is as follows:
import scipy
SciPy offers the required functions in the form of several scientific computational subpackages. Some subpackages are cluster, constants, optimize, integrate, interpolate, and sparse. You can import these subpackages whenever you want to use them for any task.
7. TensorFlow
TensorFlow is an open-source Python library that simplifies conducting numerical computations for machine learning and deep learning. It was developed by the Google Brains team and has simplified the process of building machine learning models using data flow graphs. You can call TensorFlow in Python notebook using the following command:
import tensorflow as tf
TensorFlow facilitates data preprocessing, model building, and training. You can create a machine learning model using a computational graph or eager execution approach. In the computational method, data flow is represented as a graph. In a graph, mathematical operations are represented as nodes, while the graph edges represent data as tensors that flow between the operations. Tensors can be understood as multidimensional data arrays.
In eager execution, each operation is run and evaluated immediately. The models are trained on powerful computers or data centers using GPUs and then run on different devices, including desktops, mobiles, or cloud services.
TensorFlow consists of several additional features, such as TensorBoard, which allows you to monitor the model training process visually. The library supports Keras, a high-level API for accomplishing various tasks such as data processing, hyperparameter tuning, or deployment in machine learning workflows.
8. PyTorch
PyTorch is a machine learning library based on Python and Torch library. The Torch library is an open-source machine learning library written in Lua scripting language and is used to create deep neural networks. To use PyTorch in your Python ecosystem, you have to import the Torch library using the following command:
import torch
PyTorch was developed by Facebook’s AI research lab and is used especially for deep learning tasks like computer vision or natural language processing. It offers a Python package to compute tensors using strong GPU support. PyTorch also supports dynamic computational graphs that are created simultaneously with computational operation executions. These graphs represent the data flow between operations and facilitate reverse-automatic differentiation.
In machine learning, differentiation measures how a change in one variable can affect the model’s outcome. In automatic differentiation, you can find derivatives of complex functions using the chain rule of calculus. In this differentiation technique, the function is broken down into simpler components whose derivatives are known and then combined afterward to get the overall derivative. Forward and reverse are two modes of automated derivates, and PyTorch supports reverse-automatic differentiation.
9. Requests
Requests is an HTTP client library for Python that is used to send HTTP requests and handle responses. You can use it to interact with websites, APIs, or web services while using the Python ecosystem. Python provides an interface to send and retrieve data from such sources using HTTP methods such as GET, POST, PUT, or END.
You can use the below command to import the requests library in Python:
import requests
After installing and importing the requests library, you can make a simple HTTP request using the requests. get () function. This function takes a URL as an input argument and returns a response object.
The response object contains all the information returned by the server, which is a data source. It contains the status code, header, and response body, which you can access and then make the next request again. As a result, the Requests library enables you to perform web-related tasks effectively in Python.
10. Beautiful Soup
Beautiful Soup is a web-scraping Python library for extracting data from HTML and XML files. It is a simple library and supports various markup formats, making it a popular web scraping solution. While using Beautiful Soup, you can create parse trees for the documents from which you want to extract data. A parse tree is a hierarchical representation of the derivation process of an input program. You can use the code below to call the Beautiful Soup library:
from bs4 import BeautifulSoup
Beautiful Soup library enables you to navigate, search, and modify the tags, attributes, and texts in the parse trees using various methods and functions. You can use this library along with Requests to download web pages for faster parsing.
How to Choose a Python Library
You can keep the following things in mind while choosing Python libraries for data analytics:
Define Clear Objectives
You should clearly understand the purpose for which you want to use a Python library. You can use Python libraries for data analytics, manipulation, visualization, machine learning operations, or web scraping. You should list these functions to identify the specific library or set libraries that will help you achieve your goals.
Ease of Use
Evaluate the library’s learning curve, especially if you are working on tight deadlines. You should always try to prioritize ease of use and how well the library integrates with other tools or libraries. The library should also have detailed documentation and tutorials to help you better understand its functions.
Performance and Scalability
For high-performing libraries, you should compare the features of the one you intend to use with those of other libraries with similar functionality. As your organization grows, you have to deal with large amounts of data. The library should be scalable to accommodate this increase in data volume.
Community Support
Active community support is essential for troubleshooting and quick resolution of any queries. A responsive user base contributes regularly to updating the functionalities of any library. They also guide you if you are a new user to overcome challenges and become proficient in using the library.
Conclusion
The extensive set of libraries offered by Python makes it highly useful for data-driven operations. The 10 Python libraries for data analytics explained here have their unique capabilities and contribute to the efficient functionalities of Python. Leveraging these libraries can help you handle complex datasets and retrieve meaningful insights.
