Before learning machine learning algorithms or data science programs, we need to first understand the libraries of python, which are used to create the data science and machine learning programs.
The following external open-source python library files are used to create data science and machine learning programs.
In this article, You will walk through the list of python libraries used for machine learning
We can use all the library files in the program or a combination of some library files in the program. All libraries have their own features to solve machine learning and data science problems.
Best Python Libraries For Machine Learning
Let’s understand the machine learning python libraries in detail. To understand more about any library, just go to the mentioned website link of respective library files.
NumPy was created in 2005 by Travis Oliphant. NumPy is a Python external package, which stands for ‘Numerical Python’ and it works with arrays. NumPy is used for efficient operation on regular data, which are stored in arrays or we can say NumPy is used in the manipulation of numerical data. Due to the NumPy library, python becomes equivalent to MATLAB, Yorick, and IDL.
NumPy provides different numerical operations for processing arrays like a log, LCM, etc. It provides Fourier transform and routines for shape manipulation, logical operations on arrays, and operations related to linear algebra and random number generation. By using NumPy, we can create multidimensional array objects like vectors, matrices, etc.
Why do we need to learn NumPy?
In python language; Tuple and List arrays are available and we can do all array-related works by using them.
So, why do we learn a new array based on data types?
- Mainly, in data science and machine learning, we work on multi-dimensional arrays, and in tuples and lists; we can create a one-dimensional array only. So, to resolve data science and machine learning array-based problems, we use NumPy.
- NumPy is faster than other python libraries and we can do mathematical calculations easily by using it.
- NumPy is written in C and C++ language.
- By using it, we can easily do the shaping, sorting, indexing, etc. array-based operations.
- NumPy is 50 times faster than List and it is improved to work with the latest CPU architectures.
Installing and Importing NumPy
- We can install NumPy in python by writing the command “pip install NumPy” in the system command prompt.
- NumPy is coming with a pandas library so when we install pandas, then automatically, NumPy is installed in python.
- We can import NumPy libraries in our program by using the below syntax. Import NumPy as np
In data science and machine learning, a pandas library is a very important and most used library because pandas are used for implementing the first few steps of data analysis like loading data, organizing data, cleaning messy data sets, exploring data, manipulating data, modeling data and analyzing data.
By using pandas, we can easily analyze big and complex data, and based on statistical theories, we can make conclusions. The process of pandas is to clean disorganized data sets and makes them readable and important. The name “Pandas” comes from “Panel Data”, and “Python Data Analysis”. It was created by Wes McKinney in 2008 and written in Cython, C, and python.
Pandas is a fast, flexible, and easy to use data analysis and manipulation tool compared to other tools. Pandas mainly worked on data tables. It has many easy functions for data analysis. Python with pandas use in a variety of academic and commercial domains like finance, economics, statistics, advertising, web analytics, etc.
The Key features of Pandas used for data processing and analysis
- Fast and efficient creation of Data Frame with default and modified indexing.
- Load data in any format
- Data alignment and integrated handling of missing data.
- Reshaping and pivoting of data sets.
- Label-based slicing, indexing, and sub-setting of large data sets.
- Apply CRUD operations on a data frame
- Group by data for aggregation and transformations.
- Merging and joining of data.
- Time Series functionality.
Installing and Importing Pandas
- We can install Pandas in python by writing the command “pip install pandas” in the system command prompt.
- We can import pandas libraries in our program by using the below syntax. import pandas as pd.
SciPy was created by NumPy’s creator Travis Oliphant and written in python and C language.
SciPy is a scientific library of python, which is used in mathematics, scientific computing, engineering, and technical computing. It uses NumPy underneath and stands for scientific python. NumPy provides many functions related to linear algebra, Fourier transforms, and random number generation but they are not equivalent to SciPy functions.
SciPy supports functions like gradient optimization, integration, and differentiation, etc. or we can say that all the general numerical computing is done via SciPy.
SciPy provides more utility functions for optimization, stats, and signal processing, which are frequently used in Data Science. SciPy is organized into sub-packages, which cover different scientific computing domains.
Installing and Importing SciPy
- We can install SciPy in python by writing the command “pip install SciPy” in the system command prompt.
- We can import the SciPy library into our program by using the below syntax. import sciPy
SymPy is popular for the scientific python ecosystem. It was developed by Ondrej CertiK and Maurer in 2007. SymPy is just like symbolic mathematics and used as an interactive mode and a programmatic application. It is a full-featured computer algebra system (CAS). It is written in Python. It depends on mpmat, which is a Python library for arbitrary floating-point arithmetic.
SymPy has functions for calculus, polynomials, discrete math, statistics, geometry, combinatorics, matrices, physics, and plotting. It can format the results in various forms like MathML, LaTeX, etc.
Installing and Importing SymPy
- We can install SymPy in python by writing the command “pip install SymPy” in the system command prompt.
- We can import SymPy libraries in our program by using the below syntax. Import SymPy
Matplotlib is a low-level graph plotting library used to create 2D/3D graphs and plots. It is used with graphical tools like wxPython, Tkinter, and PyQt. To use Matplotlib with NumPy is to create an alternative to MATLAB. It has a module named pyplot, which is used for plotting graphs and provides functions to control line styles, size of the graph, font properties, formatting axes, etc.
We can create different kinds of graphs and plots like histograms, line charts, bar charts, power spectra, error charts, subplots, etc. by using Matplotlib.
Installing and Importing Pandas
- We can install Matplotlib in python by writing the command “pip install Matplotlib” in the system command prompt.
- We can import the Matplotlib library in our program by using the below syntax. From Matplotlib import pyplot as plt.
Seaborn is used for statistical data visualization and provides a high-level interface to draw attractive and useful statistical graphics. Seaborn extends Matplotlib and by using seaborn, we can easily do hard things with Matplotlib.
Seaborn works on data frames and arrays and helps us to explore and understand the data. Seaborn performs necessary semantic mapping and statistical aggregation to produce informative plots.
We can create a histogram, joint plot, pair plot, factor plots, violin plots, etc. by using seaborn.
Seaborn is mainly used in machine learning compared to data science.
Key Features of Seaborn
- Lots of themes available in seaborn to work with different graphics
- We can visualize both univariate and multivariate data in seaborn.
- Seaborn support for visualizing varieties of regression model data in ML.
- Seaborn allows easy plotting of statistical data for time-series analytics.
- All-in-one performance with Pandas, NumPy, and other Python libraries
Installing and Importing Seaborn
- We can install seaborn in python by writing the command “pip install seaborn” in the system command prompt.
- We can import the seaborn library in our program by using the below syntax. Import seaborn as sns
As per the Bokeh documentation, Bokeh is used for creating interactive visualizations for modern web browsers and it provides very interactive charts and plots.
We can easily integrate the bokeh plot with any website, which has been created in Django and flask framework. Bokeh can bind with python, R, Lua, and Julia language and produce JSON files, which works with BokehJs to present data to the web browsers. We can easily convert Bokeh results in a notebook, HTML and
The reasons as easy interactivity, intelligent suggestions on errors, exporting to HTML, easy integration with pandas, easy work with Jupyter notebook, and themes attract us to use Bokeh for plotting. By using Bokeh, we can make our visuals stand out compared to Matplotlib charts.
Key features of Bokeh
- By using the simple commands of Bokeh, we can easily and quickly build complex statistical plots.
- Bokeh can easily work with websites and transform visualizations, which are created in other plots like seaborn, Matplotlib, etc.
- Bokeh has flexibility for applying interaction, layouts, and different styling options to plots.
Installing and Importing Bokeh
- We can install Bokeh in python by writing the command “pip install bokeh” in the system command prompt.
- We can import the bokeh library in our program by using the below syntax. Import bokeh
These are the features, which attract us to learn Plotly.
- The plots created in Plotly are interactive
- Plotly exports plot for print or publication
- Plotly allows manipulating or embedded the plot on the web.
- Plotly stores charts as JSON files and allows them to open and read in different languages like R, Python, MATLAB, and Julia.
Plotly is a data visualization library. It plots different types of graphs and charts like scatter plots, line charts, box plots, pie charts, histograms, animated plots, etc. In the bokeh plot, we can do endless customization to make our plot more meaningful and understandable.
Mainly for machine learning classification plots and charts, we use Plotly libraries to make our data plot more understandable. Plotly makes interactive graphs online and allows us to save them offline as per our requirement.
Installing and Importing Plotly
- We can install Plotly in python by writing the command “pip install Plotly” in the system command prompt.
- We can import the Plotly library in our program by using the below syntax. Import Plotly
It was developed by David Cournapeau in 2007. Later, in 2010, Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, and Vincent Michel, from FIRCA (French Institute for Research in Computer Science and Automation), took this project to another level and made the first public release (v0.1 beta) on 1st Feb. 2010.
Scikit-learn (Sklearn) is mainly used in machine learning for modeling the data. It is an extended form of SciPy. It provides methods for learning algorithms and statistical modeling like classification, regression, clustering, etc.
Sklearn is written in python. It was built upon SciPy, Matplotlib, and Numpy.
It provides supervised and unsupervised learning algorithms via a consistent interface in Python.
It is distributed under many Linux distributions. It encourages academic and commercial uses.
Scikit-learn includes functionality for regression like linear and logistic regression, classification like K-Nearest Neighbors, model selection, preprocessing like min-max normalization, and clusterings like K-Means and K-Means++.
The following group of models presents in scikit-learn.
1. Supervised Learning Algorithms
It provides functions for all the supervised learning algorithms like Linear Regression, Support Vector Machine (SVM), Decision Tree, naïve bayes, discriminant analysis, etc.
2. Unsupervised Learning Algorithms
It provides functions for all the unsupervised learning algorithms from clustering, PCA, and factor analysis to unsupervised neural networks.
It is used for grouping unlabeled data like K-Means.
4. Manifold Learning
It is used to summarize and represent complex multi-dimensional data.
It is used to check the accuracy of supervised models on hidden data.
6. Dimensionality Reduction
It is used for reducing parameters in data, which can be used in the future for summarization, visualization, and feature selection like PCA (Principal Component Analysis).
7. Ensemble Methods
It is used for joining the predictions of multiple supervised models.
8. Feature Extraction
It is used to take the parameters from data and then defines them in image and text data.
9. Parameter Tuning
It is used for getting most of the data out of the supervised models.
10. Feature Selection
It is used to classify the meaningful parameters to create supervised models.
It is used to test datasets and to generate datasets with specific parameters for investigating model performance.
Installing and Importing Scikit-learn
- We can install Scikit-learn in python by writing the command “pip install scikit-learn” in the system command prompt.
- Before installing scikit-learn, we need to install pandas, NumPy, SciPy, and Matplotlib.
- We can import the scikit-learn library in our program by using the below syntax. Import sklearn
10. Beautiful Soup
The beautiful Soup is used to pull the data/text from HTML and XML documents. It is used for easy web scraping tasks. It is a web scraping package and as the name suggests, it parses the annoying data and helps to establish and format the untidy web data by fixing bad HTML and present it to us in easily traversable XML structures. It is named after a Lewis Carroll poem of the same name in “Alice’s Adventures in Wonderland”.
Installing and Importing Beautiful Soup
- We can install Beautiful Soup in python by writing the command “pip install beautifulsoup4” in the system command prompt.
- Before installing beautiful soup, we need to install requests and urllib2 library files.
- We can import beautiful soup libraries in our program by using the below syntax. From bs4 import Beautiful Soup
Scrapy is used for large-scale web scraping. By using it, we can easily extract data from websites and then process it as per the requirement and then store it in proper structure and format. We can fetch millions of data by using Scrapy. Scrapy uses spiders, which are self-contained crawlers. Scrapy is easy to build and scale large crawling projects by using reuse code.
Difference between Scrapy and BeautifulSoup
In data science, we use Scrapy and Beautiful Soup for data extracting from the web but due to some reasons, Scrapy is more popular than beautiful soup for complex data extracting.
It is an HTML and XML parser and used with requests, urllib2 library files to open URLs and save the result.
It is a complete package for extracting web pages means no need for any additional library. It processes the extracting data and saves it in files and databases.
It is used for simple scraping work. If we use it without multiprocessing, it is slower than Scrapy. It works like synchronous means we can go forward to the next work after completing the previous work.
It is used for complex scraping work. It can extract a group of URLs in a minute. The time taken for group extracting is depending on the group size. It uses Twister, which works non-blocking for concurrency means we can go forward to the next work before completing previous work.
It is easy to understand and takes less time to learn. It can do smaller tasks within a minute.
It provides lots of ways to extract the web page and lots of functions so it is not easy to understand and learn.
It is used where much more logic is not required.
It is used where more customization is required like data pipelines, managing cookies, proxies, etc.
Installing and Importing Scrapy
- We can install Scrapy in python by writing the command “pip install Scrapy” in the system command prompt.
- We use Scrapy in anaconda or Miniconda.
- We can import the Scrapy library in our program by using the below syntax. Import Scrapy
The high appetite for computer expertise would necessitate greater refinement of specialized roles throughout data science.
It will be fascinating to see how this domain unravels within the next couple of years.
As we have finally understood the libraries of Python, we are ready to dive into the exciting lucrative world of data science and machine learning.