{"id":22631,"date":"2018-07-25T04:10:17","date_gmt":"2018-07-25T04:10:17","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=22631"},"modified":"2026-04-28T12:58:58","modified_gmt":"2026-04-28T07:28:58","slug":"python-data-science-environment-setup","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/python-data-science-environment-setup\/","title":{"rendered":"Python Data Science Environment Setup"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:1830,&quot;href&quot;:&quot;https:\\\/\\\/www.anaconda.com\\\/download&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251208183307\\\/https:\\\/\\\/www.anaconda.com\\\/download&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-10 03:49:53&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2025-12-15 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11:28:07&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-04 21:03:39&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-10 14:56:12&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-16 16:30:34&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-20 17:58:01&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-07 07:28:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-10 21:18:12&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-16 18:24:58&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-22 06:24:08&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-11 11:42:51&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-20 13:00:15&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-25 04:49:12&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-04-28 05:40:28&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-04 14:02:48&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-05-10 04:45:56&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-18 15:21:10&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-31 12:36:17&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-11 02:48:11&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-11 02:48:11&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p><span style=\"font-weight: 400\">Today, in this<strong><a href=\"https:\/\/data-flair.training\/blogs\/python-tutorial-for-beginners\/\" target=\"_blank\" rel=\"noopener\"> Python<\/a><\/strong> Data Science tutorial, we will see the Data Science Environment Setup for Python. Moreover, we will tell you about all that you need to install for the Data Science Environment Setup, such as Python, Anaconda, and Miniconda. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Along with this, we will see how to set a virtual environment for Data Science Environment Setup and also import Data Science Packages. Today, we will guide you to set up your machine so you can begin your journey with data science.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Before you begin, we suggest you read up on<a href=\"https:\/\/data-flair.training\/blogs\/data-science-tutorial\/\" target=\"_blank\" rel=\"noopener\"> <strong>Python Data Science Introduction<\/strong><\/a> to make things flow more easily when you come back.<\/span><\/p>\n<p>So, let&#8217;s start the Python Data Science Environment Setup.<\/p>\n<h3>Install Python<\/h3>\n<p><span style=\"font-weight: 400\">Before anything else, you should get Python on your machine. You can refer to the <\/span><strong><a href=\"https:\/\/data-flair.training\/blogs\/install-python-windows\/\" target=\"_blank\" rel=\"noopener\">Step-by-Step Guide to Install Python on Windows<\/a><\/strong><span style=\"font-weight: 400\"> for this.<\/span><\/p>\n<p><span style=\"font-weight: 400\">While 2.7 is widely adopted, 3.x will take over the future and has already started to leave its mark. Apart from that, some software and features aren\u2019t backward-compatible. So take your pick.<\/span><\/p>\n<h3><strong>Getting Anaconda for Data Science Environment Setup<\/strong><\/h3>\n<div id=\"attachment_22656\" style=\"width: 457px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/anaconda.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22656\" class=\"wp-image-22656 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/anaconda.png\" alt=\"Python Data Science Environment Setup\" width=\"447\" height=\"223\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/anaconda.png 447w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/anaconda-150x75.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/anaconda-300x150.png 300w\" sizes=\"auto, (max-width: 447px) 100vw, 447px\" \/><\/a><p id=\"caption-attachment-22656\" class=\"wp-caption-text\">Data Science Environment Setup &#8211; Install Anaconda<\/p><\/div>\n<p><span style=\"font-weight: 400\">Anaconda is a Python distribution for data science and <strong><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-tutorial\/\" target=\"_blank\" rel=\"noopener\">machine learning<\/a><\/strong>. It is free and <strong>open-source<\/strong>\u00a0and makes managing and deploying packages simple.<\/span><\/p>\n<p><span style=\"font-weight: 400\">It has more than 1000 data science packages and the Conda package. Other tools it comes with are core Python and IPython, among others.<\/span><\/p>\n<p><strong>Uses of Anaconda:<\/strong><\/p>\n<ul>\n<li><strong>Package management:<\/strong> If you need any special tool, Anaconda finds it and installs it by making sure that all tools work together without crashing.<\/li>\n<li><strong>Machine learning\/ AI development:<\/strong> By using libraries like TensorFlow and PyTorch are used to build and destroy models.<\/li>\n<li><strong>Scientific research:<\/strong> Since anaconda keeps everything organised, scientists use it to run complex experiments.<\/li>\n<\/ul>\n<h4><strong>a. Anaconda Navigator<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Anaconda ships with a virtual environment manager- the Anaconda Navigator. This is a desktop <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-gui-programming\/\">GUI <\/a><\/strong>that lets you launch applications and manage packages, environments, and channels for conda. This lets you bypass the command-line commands. <\/span><\/p>\n<p><span style=\"font-weight: 400\">The Navigator searches for a package on the Anaconda Cloud, or in a local repository for Anaconda, and installs, runs, and updates them. It has the following applications-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Glueviz<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Jupyter Notebook<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">JupyterLab<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Orange 3 App<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">VSCode<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">RStudio<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Rodeo<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Spyder<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">QTConsole<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Anaconda will give you two package managers- pip and conda. When some packages aren\u2019t available with conda, you can use pip to install them. Note that using pip to install packages also available to conda may cause an installation error.<\/span><\/p>\n<h4><strong>b. Installing Anaconda<\/strong><\/h4>\n<p><strong>To download an Anaconda distribution, you can use the official download page:<\/strong><br \/>\n<strong><a href=\"https:\/\/www.anaconda.com\/download\/\" target=\"_blank\" rel=\"noopener\">https:\/\/www.anaconda.com\/download\/<\/a><\/strong><\/p>\n<p><span style=\"font-weight: 400\">Here, you can select your platform and then choose the installer. For this, you can choose which version you want and whether 32-bit or 64-bit.<\/span><\/p>\n<p><span style=\"font-weight: 400\">To install a package with conda, you can use the following <strong>command<\/strong>&#8211;<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">conda install scipy<\/pre>\n<h3><strong>Install Miniconda<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Miniconda is a minimal installer for conda, a small, bootstrap version of Anaconda. It is free and ships with conda, Python, and packages like pip and zlib. This lets you install more than 720 packages from conda. Since Miniconda is a lighter version of Anaconda, it lets you download faster.<\/span><\/p>\n<p><span style=\"font-weight: 400\">To install Miniconda, you can get to the following page-<\/span><br \/>\n<strong><a href=\"https:\/\/conda.io\/miniconda.html\" target=\"_blank\" rel=\"noopener\">https:\/\/conda.io\/miniconda.html<\/a><\/strong><br \/>\n<span style=\"font-weight: 400\">Here, choose your platform and then pick a 32-bit or a 64-bit installer according to the needs of your machine.<\/span><\/p>\n<h3><strong>Setting up a Virtual Environment<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Since here, we talk about setting up a data science <\/span><i><span style=\"font-weight: 400\">environment<\/span><\/i><span style=\"font-weight: 400\"> with Python, let\u2019s find out what a virtual environment is. Virtual environments are another important concept. These are isolated spaces where you can install specific packages without affecting the rest of your system. This is useful when working on multiple projects that need different versions of the same library. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Having a solid setup gives you a smooth experience while learning and building data science projects. It reduces friction and allows you to focus on solving the problem at hand<\/span><\/p>\n<p><span style=\"font-weight: 400\">You should check out this blog on <\/span><a href=\"https:\/\/data-flair.training\/blogs\/python-virtual-environment-package\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\"><strong>How to Create a<\/strong> <strong>Python Virtual Environment and Install Packages<\/strong><\/span><\/a><strong>.<\/strong><\/p>\n<p><span style=\"font-weight: 400\">For now, let\u2019s see how we can create one with Anaconda. Use the following command in your Anaconda prompt-<\/span><\/p>\n<div id=\"attachment_22657\" style=\"width: 684px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/prompt.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22657\" class=\"wp-image-22657 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/prompt.png\" alt=\"Data Science Environment Setup\" width=\"674\" height=\"341\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/prompt.png 674w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/prompt-150x76.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/prompt-300x152.png 300w\" sizes=\"auto, (max-width: 674px) 100vw, 674px\" \/><\/a><p id=\"caption-attachment-22657\" class=\"wp-caption-text\">Data Science Environment Setup &#8211; Setting up a Virtual Environment<\/p><\/div>\n<p><span style=\"font-weight: 400\">This should give you an idea of what the Anaconda prompt looks like. Now, to activate this environment, you can type-<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">conda activate demo<\/pre>\n<p><span style=\"font-weight: 400\">This lets you start using it. Now to deactivate it, try-<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">conda deactivate<\/pre>\n<p><span style=\"font-weight: 400\">The following command tells you all the environments that exist; the asterisk (*) marks the current-<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">conda info -e<\/pre>\n<h3><strong>Important Python Data Science Packages<\/strong><\/h3>\n<div id=\"attachment_22678\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Data-Science-Packages-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22678\" class=\"wp-image-22678 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Data-Science-Packages-01.jpg\" alt=\"Data Science Environment Setup\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Data-Science-Packages-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Data-Science-Packages-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Data-Science-Packages-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Data-Science-Packages-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Data-Science-Packages-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-22678\" class=\"wp-caption-text\">Important Python Data Science Packages<\/p><\/div>\n<p><span style=\"font-weight: 400\">Working with data science, out of more than 1000 packages available, you will need a few that will let you implement the basic functionalities. Let\u2019s take a quick look at some of those <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-packages\/\" target=\"_blank\" rel=\"noopener\">packages<\/a><\/strong>.<\/span><\/p>\n<h4><strong>a. NumPy in Python<\/strong><\/h4>\n<div id=\"attachment_22658\" style=\"width: 390px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/numpy-1.jpeg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22658\" class=\"wp-image-22658 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/numpy-1.jpeg\" alt=\"Data Science Environment Setup\" width=\"380\" height=\"133\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/numpy-1.jpeg 380w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/numpy-1-150x53.jpeg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/numpy-1-300x105.jpeg 300w\" sizes=\"auto, (max-width: 380px) 100vw, 380px\" \/><\/a><p id=\"caption-attachment-22658\" class=\"wp-caption-text\">Python Data Science Packages &#8211; NumPy<\/p><\/div>\n<p><span style=\"font-weight: 400\">As discussed many times earlier, <a href=\"https:\/\/data-flair.training\/blogs\/python-numpy-tutorial\/\" target=\"_blank\" rel=\"noopener\"><strong>NumPy<\/strong> <\/a>lets you deal with large, multi-dimensional arrays and matrices. To act on these, it also gives us various high-level mathematical functions.<\/span><\/p>\n<h4><strong>b. SciPy in Python<\/strong><\/h4>\n<div id=\"attachment_22659\" style=\"width: 210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/scipy.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22659\" class=\"wp-image-22659 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/scipy.png\" alt=\"Data Science Environment Setup\" width=\"200\" height=\"187\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/scipy.png 200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/scipy-150x140.png 150w\" sizes=\"auto, (max-width: 200px) 100vw, 200px\" \/><\/a><p id=\"caption-attachment-22659\" class=\"wp-caption-text\">Python Data Science Packages &#8211; SciPy<\/p><\/div>\n<p><span style=\"font-weight: 400\"><a href=\"https:\/\/data-flair.training\/blogs\/scipy-tutorial\/\" target=\"_blank\" rel=\"noopener\"><strong>Scipy<\/strong><\/a> is a <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-library\/\" target=\"_blank\" rel=\"noopener\">Python library<\/a><\/strong> for scientific and technical computing, and is free and open-source. Modules from SciPy include those for-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Optimization<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Linear algebra<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Integration<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Interpolation<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Special functions<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">FFT<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Signal and Image processing<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ODE solvers<\/span><\/li>\n<\/ul>\n<h4><strong>c. Matplotlib in Python<\/strong><\/h4>\n<div id=\"attachment_22660\" style=\"width: 1132px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/matplotlib.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22660\" class=\"wp-image-22660 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/matplotlib.png\" alt=\"Data Science Environment Setup\" width=\"1122\" height=\"247\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/matplotlib.png 1122w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/matplotlib-150x33.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/matplotlib-300x66.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/matplotlib-768x169.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/matplotlib-1024x225.png 1024w\" sizes=\"auto, (max-width: 1122px) 100vw, 1122px\" \/><\/a><p id=\"caption-attachment-22660\" class=\"wp-caption-text\">Python Data Science packages &#8211; Matplotlib<\/p><\/div>\n<p><span style=\"font-weight: 400\">We\u2019ve used<a href=\"https:\/\/data-flair.training\/blogs\/python-matplotlib-tutorial\/\" target=\"_blank\" rel=\"noopener\"> <strong>Matplotlib<\/strong><\/a> so far to plot many of the figures we needed to get started with visualization. Some of these were bubble charts and scatter plots. This is a plotting library with Python that extends NumPy. <\/span><\/p>\n<p><span style=\"font-weight: 400\">An object-oriented API, it lets you embed plots into applications. For this, it uses GUI toolkits like Tkinter, Qt, GTK+, and wxPython.<\/span><\/p>\n<h4><strong>d. Pandas in Python<\/strong><\/h4>\n<div id=\"attachment_22661\" style=\"width: 610px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/pandas-6.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22661\" class=\"wp-image-22661 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/pandas-6.png\" alt=\"Data Science Environment Setup\" width=\"600\" height=\"125\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/pandas-6.png 600w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/pandas-6-150x31.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/pandas-6-300x63.png 300w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><\/a><p id=\"caption-attachment-22661\" class=\"wp-caption-text\">Python Data Science Packages &#8211; Pandas<\/p><\/div>\n<p><span style=\"font-weight: 400\">We have taken an extensive <a href=\"https:\/\/data-flair.training\/blogs\/pandas-tutorial\/\" target=\"_blank\" rel=\"noopener\"><strong>Pandas Tutorial<\/strong><\/a>. Now, it\u2019s time for a quick recap. Pandas is a software library for Python that is supposed to serve for data manipulation and analysis. It is free and lets you manipulate numerical tables and time series using data structures and operations.<\/span><\/p>\n<h4><strong>e. scikit-learn in Python<\/strong><\/h4>\n<div id=\"attachment_22662\" style=\"width: 287px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/scikit-learn.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22662\" class=\"wp-image-22662 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/scikit-learn.png\" alt=\"Data Science Environment Setup\" width=\"277\" height=\"150\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/scikit-learn.png 277w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/scikit-learn-150x81.png 150w\" sizes=\"auto, (max-width: 277px) 100vw, 277px\" \/><\/a><p id=\"caption-attachment-22662\" class=\"wp-caption-text\">Python Data Science Packages &#8211; Scikit-learn<\/p><\/div>\n<p><span style=\"font-weight: 400\">scikit-learn is a software machine learning library for Python. It is free and offers different algorithms for classification, regression, and clustering-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Support Vector Machines<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Random forests<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Gradient boosting<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">K-means<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">DBSCAN<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">We usually use it alongside NumPy and SciPy.<\/span><\/p>\n<h4><strong>f. seaborn in Python<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Finally, seaborn is a visualization library for Python and is based on matplotlib. It lets us statistically perform data visualization with a high-level interface that results in attractive graphics.<\/span><br \/>\n<strong><a href=\"https:\/\/data-flair.training\/blogs\/python-regex-tutorial\/\" target=\"_blank\" rel=\"noopener\">Let&#8217;s revise the Python regular expression<\/a><\/strong><\/p>\n<h3><strong>How to Get Jupyter Notebook?<\/strong><\/h3>\n<div id=\"attachment_22663\" style=\"width: 334px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22663\" class=\"wp-image-22663 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter.png\" alt=\"Data Science Environment Setup\" width=\"324\" height=\"298\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter.png 324w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter-150x138.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter-300x276.png 300w\" sizes=\"auto, (max-width: 324px) 100vw, 324px\" \/><\/a><p id=\"caption-attachment-22663\" class=\"wp-caption-text\">Data Science Environment Setup &#8211; getting Jupyter Notebook<\/p><\/div>\n<p><span style=\"font-weight: 400\">As we saw earlier, the Jupyter Notebook ships with Anaconda. To run it, you can get in your virtual environment and type the following-<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">jupyter notebook<\/pre>\n<p><span style=\"font-weight: 400\">You can also install it with pip-<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">python3 -m pip install --upgrade pip\r\npython3 -m pip install jupyter<\/pre>\n<p>The notebook looks something like this-<\/p>\n<div id=\"attachment_22664\" style=\"width: 775px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22664\" class=\"wp-image-22664 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter-1.png\" alt=\"Data Science Environment Setup\" width=\"765\" height=\"303\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter-1.png 765w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter-1-150x59.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter-1-300x119.png 300w\" sizes=\"auto, (max-width: 765px) 100vw, 765px\" \/><\/a><p id=\"caption-attachment-22664\" class=\"wp-caption-text\">Data Science Environment Setup &#8211; Jupyter Notebook<\/p><\/div>\n<p><span style=\"font-weight: 400\">You can find this at <\/span><strong>http:\/\/localhost:8888\/<\/strong><br \/>\n<span style=\"font-weight: 400\">Now to run Python here, you can create a new file. It looks like this-<\/span><\/p>\n<div id=\"attachment_22665\" style=\"width: 781px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter2.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22665\" class=\"wp-image-22665 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter2.png\" alt=\"Data Science Environment Setup\" width=\"771\" height=\"415\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter2.png 771w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter2-150x81.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter2-300x161.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/jupyter2-768x413.png 768w\" sizes=\"auto, (max-width: 771px) 100vw, 771px\" \/><\/a><p id=\"caption-attachment-22665\" class=\"wp-caption-text\">Data Science Environment Setup &#8211; Jupyter Notebook<\/p><\/div>\n<p><span style=\"font-weight: 400\">You can quit using the logout button at the top-right corner.<\/span><br \/>\n<strong><a href=\"https:\/\/data-flair.training\/blogs\/python-array-module\/\" target=\"_blank\" rel=\"noopener\">Let&#8217;s revise the Python Array Module<\/a><\/strong><br \/>\nSo, this was all in the Data Science Environment Setup with Python. Hope you like our explanation.<\/p>\n<h3><strong>Conclusion<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Hence, in this Python Data Science Environment Setup tutorial, we discussed all that is needed to install a data science environment. Moreover, we look at Python packages such as Numpy, Scipy, and matplotlib.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">With this, we conclude our Data Science environment setup tutorial on how to set your machine up for data science. Still, if any query regarding Python Data Science Environment setup, feel free to drop your questions in the comments below.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Today, in this Python Data Science tutorial, we will see the Data Science Environment Setup for Python. Moreover, we will tell you about all that you need to install for the Data Science Environment&#46;&#46;&#46;<\/p>\n","protected":false},"author":7,"featured_media":22654,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19,46],"tags":[5100,6349,6628,6750,6790,8731,10458],"class_list":["post-22631","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science","category-python","tag-getting-the-jupyter-notebook","tag-how-to-use-conda","tag-important-data-science-packages","tag-install-anaconda","tag-install-python-for-data-science","tag-miniconda-create-environment","tag-python-data-science-packages"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Python Data Science Environment Setup - DataFlair<\/title>\n<meta name=\"description\" content=\"Let&#039;s see how to set a virtual environment for Data Science Environment Setup and also import Data Science Packages.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/python-data-science-environment-setup\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python Data Science Environment Setup - 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