Which python is best for data science?
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Here is a list of the top 15 Python libraries used in Data Science and Machine Learning. Show
Python has 8.2 million active users, according to SlashData, with 69 percent of machine learning engineers and data scientists adopting the language. If you are an aspiring data scientist- always learning, exploring, and playing with data, then this blog post will help you get ready to begin your career in data science with Python. Python Language has a rich and healthy ecosystem with vast libraries for data analysis, data I/O, and data munging. The best way to make sure that you are all set to become a data scientist is to make yourself well-versed with the various Python libraries and tools that people use in the industry for doing data science. We asked our data science faculty to list 15 Python libraries for data science and machine learning that they think every data scientist must know how to use. Check them out below:
Build a Music Recommendation Algorithm using KKBox's DatasetDownloadable solution code | Explanatory videos | Tech Support Start Project Python Libraries for Data ScienceThis blog will cover some of the top Python libraries for machine learning and data science. Depending on their purposes, these libraries have been divided into data processing and model deployment, data mining and scraping, and data visualization. Table of Contents
Python Libraries for Data Processing and Model Deployment1) PandasAll of us can do data analysis using pen and paper on small data sets. We require specialized tools and techniques to analyze and derive meaningful information from massive datasets. Pandas Python is one of those libraries for data analysis that contains high-level data structures and tools to manipulate data in a simple way. Providing an effortless yet effective way to analyze data requires the ability to index, retrieve, split, join, restructure, and various other analyses on both multi and single-dimensional data. Key Features of PandasPandas data analysis library has some unique features that provide these capabilities- i) The Series and DataFrame Objects These two are high-performance array and table structures for representing the heterogeneous and homogeneous data sets in Pandas Python. ii) Restructuring of Data Sets Pandas python provides the flexibility for reshaping the data structures to be inserted in both rows and columns of tabular data. iii) Labelling To allow automatic data alignment and indexing, pandas provide labeling on series and tabular data. iv) Multiple Labels for a Data Item Heterogeneous indexing of data spread across multiple axes, which helps in creating more than one label on each data item. v) Grouping The functionality to perform split-apply-combine on series as well on tabular data. vi) Identify and Fix Missing Data Programmers can quickly identify and mix missing data floating and non-floating pointing numbers using pandas. vii) Powerful capabilities to load and save data from various formats such as JSON, CSV, HDF5, etc. viii) Conversion from NumPy and Python data structures to pandas objects. ix) Slicing and sub-setting of datasets, including merging and joining data sets with SQL- like constructs. Although pandas provide many statistical methods, it is not enough to do data science in Python. Pandas depend upon other python libraries for data science like NumPy, SciPy, Sci-Kit Learn, Matplotlib, ggvis in the Python ecosystem to conclude from large data sets. Thus, making it possible for Pandas applications to take advantage of the robust and extensive Python framework. Pros of using Pandas
Cons of using Pandas
Data Science Projects on Pandas for Practice
Get Closer To Your Dream of Becoming a Data Scientist with 150+ Solved End-to-End ML Projects 2) NumPyNumerical Python code name: - NumPy is a Python library for numerical calculations and scientific computations. NumPy provides numerous features which Python enthusiasts and programmers can use to work with high-performing arrays and matrices. NumPy arrays provide vectorization of mathematical operations, which gives it a performance boost over Python’s looping constructs. Pandas Series and DataFrame objects rely primarily on NumPy arrays for all the mathematical calculations like slicing elements and performing vector operations. Key Features of NumPyBelow are some of the features provided by NumPy-
Pros of using NumPy
Cons of using NumPy
Data Science Projects on NumPy for Practice
3) SciPyScientific Python code name, SciPy-It is an assortment of mathematical functions and algorithms built on Python’s extension NumPy. SciPy provides various high-level commands and classes for manipulating and visualizing data. SciPy is useful for data-processing and prototyping systems. Apart from this, SciPy provides other advantages for building scientific applications and many specialized, sophisticated applications backed by a robust and fast-growing Python community. Pros of using SciPy
Cons of using SciPy
Data Science Projects on SciPy for Practice
4) Sci-Kit LearnFor machine learning practitioners, Sci-Kit Learn is the savior. It has supervised and unsupervised machine learning algorithms for production applications. Sci-Kit Learn focuses on code quality, documentation, ease of use, and performance as this library provides learning algorithms. Sci-Kit Learn has a steep learning curve. Pros of using Sci-Kit Learn
Cons of using Sci-Kit Learn
Data Science Projects on Sci-kit Learn for Practice
5) PyCaretPyCaret is a fully accessible machine learning package for model deployment and data processing. It allows you to save time because it is a low-code library. It's a user-friendly machine learning library that will help you run end-to-end machine learning tests, whether you're trying to suggest missing values, analyzing categorical data, engineering features, tuning hyperparameters, or generating ensemble models. Key Features of PyCaret
Pros of using PyCaret
Cons of using PyCaret
6) TensorflowTensorFlow is a free end-to-end open-source platform for Machine Learning that includes a wide range of tools, libraries, and resources. The Google Brain team first released it on November 9, 2015. TensorFlow makes it simple to design and train Machine Learning models using high-level APIs like Keras. It also offers various abstraction levels, allowing you to select the best approach for your model. TensorFlow also enables you to deploy Machine Learning models in multiple environments, including the cloud, browser, and your device. If you want the complete experience, choose TensorFlow Extended (TFX); TensorFlow Lite if you're going to use TensorFlow on mobile devices; and TensorFlow.js if you're going to train and deploy models in JavaScript contexts. Key Features of TensorFlow
Pros of using TensorFlow
Cons of using TensorFlow
Data Science Project on Tensorflow for Practice
7) OpenCVLicensed under the BSD, OpenCV is a free machine learning and computer vision library. It offers a shared architecture for computer vision applications to streamline the implementation of computer vision in commercial products. Key Features of OpenCV
Pros of using OpenCV
Cons of using OpenCV
Data Science Project on OpenCV for Practice
Python Libraries for Data Mining and Data Scraping8) SQLAlchemySQLAlchemy is the database toolkit in Python that helps access data warehouses efficiently. It features the most widely implemented patterns for high-performance database access. SQLAlchemy ORM and SQLAlchemy Core are the two main components of SQLAlchemy. Covering Python database APIs and characteristics, SQLAlchemy core adds a level of abstraction. It also delivers SQL statements and schema to users. SQLAlchemy ORM is a self-contained object-relational mapper. SQLAlchemy allows developers to control their databases while also automating redundant activities. Key Features of SQLAlchemy
Pros of using SQLAlchemy
Cons of using SQLAlchemy
9) ScrapyIf you work with data scraping where the data is retrieved from the screen (display data), Scrapy is a valid Python package for you. Scrapy allows you to improve screen-scraping as well as web crawling. Data scientists use Scrapy for data mining and automated testing. Scrapy is an open-source tool for extracting data from web pages, which many IT professionals worldwide use. Scrapy is developed in Python and is cross-platform, running on Linux, Windows, BSD, and Mac OS X. Due to Scrapy’s great interactivity, many software professionals prefer Python for data analysis and scraping purposes. Key Features of Scrapy
Pros of using Scrapy
Cons of using Scrapy
10) BeautifulSoupBeautifulSoup is a Python data scraping and mining library that scrapes HTML and XML source data. It allows data scientists to develop a web crawler that crawls across websites. BeautifulSoup can retrieve data and structure it in the desired format. The scraped HTML data includes a lot of scrambled web data that users can't interpret. Its most recent version, BS4 (BeautifulSoup 4), arranges the jumbled web data into easy-to-understand XML structures, allowing for data analysis. BeautifulSoup identifies encodings automatically and smoothly interprets HTML documents, including those with special characters. We can search through a parsed document and find what we're looking for in it. Key Features of BeautifulSoup
Pros of using BeautifulSoup
Cons of using BeautifulSoup
Python Libraries for Data Visualization11) MatplotlibWe all have heard this quote “Necessity is the mother of all invention.” The same holds for matplotlib. This open-source project can handle different types of data generated from multiple sources in epilepsy. matplotlib is a 2D graphical Python library. However, it also supports 3D graphics, but this is very limited. With the increasing demand for Python in many folds in recent years, the growth of matplotlib has given tough competition to giants like MATLAB and Mathematica. Pros of using Matplotlib
Cons of using Matplotlib
Data Science Projects using matplotlib for Practice
12) GgplotGgplot is a Python data visualization library based on the ggplot2 implementation for the R programming language, with a 3k+ star rating on Github. Ggplot can create data visualizations such as bar charts, pie charts, histograms, scatterplots, error charts, and more using a high-level API. It also allows you to merge various data visualization components or layers into a combined visualization. After specifying which variables should be mapped to some aspects in the plot, ggplot handles the rest, allowing you to focus on analyzing rather than designing representations. ggplot, on the other hand, does not allow you to generate highly customized graphics. Key Features of ggplot
Pros of using Ggplot
Cons of using Ggplot
13) PlotlyWith over 50 million users globally, Plotly is an open-source Python 3D data visualization framework. It's a web-based data visualization tool built on the Plotly JavaScript library (plotly.js). Plotly supports scatter plots, histograms, line charts, bar charts, box plots, multiple axes, sparklines, dendrograms, 3-D graphs, and other chart types. Plotly also includes contour plots, distinguishing it from other data visualization frameworks. Plotly may be used to create web-based data visualizations embedded in Jupyter notebooks or Dash web apps or exported as standalone HTML files. Key Features of Plotly
Pros of using Plotly
Cons of using Plotly
Data Science Projects Using Plotly to PracticeBuild a Collaborative Filtering Recommender System in Python 14) AltairAltair is a statistical data visualization tool written in Python. It is developed on Vega and Vega-Lite's declarative languages, which are used to create, save, and share interactive data visualization designs. Altair can create attractive data visualizations of plots with minimal coding, such as bar charts, pie charts, histograms, scatterplots, error charts, stemplots, and more. Dependencies for Altair include Python 3.6, NumPy, and Pandas, which are all installed automatically via the Altair installation procedures. To create data visualizations in Altair, you can use Jupyter Notebooks or JupyterLab. Key Features of Altair
Pros of using Altair
Cons of using Altair
15) AutovizIt is the most undervalued Python package for performing exploratory data analysis. This package visualizes any type of dataset, including huge ones. With a single line of code, you can create stunning visualizations. You just need to give your data file (txt, JSON, or CSV), which will be visualized automatically. Key Features of AutoViz
Pros of using AutoViz
Wrapping UpPython ecosystem is a vast ocean with so many libraries to be unleashed for data scientists, and these were just a few of them. Check out ProjectPro’s repository for end-to-end solved Data Science projects that leverage these Python libraries for data science and machine learning. FAQsIs Keras a machine learning or a deep learning library for Python?Keras is a Python-based deep learning API that runs on top of TensorFlow, a machine learning platform. How to install a machine learning library in Python?Step 1- Install pip, a Python package manager: sudo apt-get install python3-pip Step 2- Simply modify the ~/.bashrc file to make Python3 as default when running pip or python instructions from the command line. Step 3- The next step is to create a virtual environment. You can install all the python packages you'll need for Machine Learning there. Step 4- Install the necessary packages first: sudo pip install virtualenvvirtualenvwrapper Step 5- Add the following lines to the ~/.bashrc file, and save it: export WORKON_HOME=$HOME/.virtualenvs export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3 source /usr/local/bin/virtualenvwrapper.sh Step 6- Finally, you can construct your virtual environment as follows: mkvirtualenvve The following command allows you to enter the virtual environment: workonve Step 7- Make a sample.txt file with a list of all the packages you want to install, like: pandas numpy matplotlib bokeh Plotly Step 8- After that, simply run the following command: pip install -r requirements.txt
Which version of Python is best for data science?Numerical Python or NumPy, in short, is one of the best options available in Python for the computation of mathematical problems. You can utilize the concept of numpy arrays for simplifying the complex math that is involved in the field of data science.
What Python do I need for data science?Most Commonly used libraries for data science :. Numpy: Numpy is Python library that provides mathematical function to handle large dimension array. ... . Pandas: Pandas is one of the most popular Python library for data manipulation and analysis. ... . Matplotlib: Matplotlib is another useful Python library for Data Visualization.. |