{"id":166742,"date":"2023-02-15T11:27:13","date_gmt":"2023-02-15T10:27:13","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=166742"},"modified":"2025-06-04T14:08:28","modified_gmt":"2025-06-04T13:08:28","slug":"pandas-the-python-library","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/pandas-the-python-library","title":{"rendered":"Pandas : the Python library dedicated to Data Science"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"166742\" class=\"elementor elementor-166742\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-444399f e-flex e-con-boxed e-con\" data-id=\"444399f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a5a27c7 elementor-widget elementor-widget-text-editor\" data-id=\"a5a27c7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">Pandas is a library of the <\/span><b>Python programming language<\/b><span style=\"font-weight: 400;\">, entirely dedicated to Data Science. Find out what this tool is used for, and why it is essential for Data Scientists.<\/span><\/p><p><span style=\"font-weight: 400;\">Created in 1991, <\/span><strong><a href=\"https:\/\/datascientest.com\/en\/python-the-most-popular-programming-language\">Python<\/a><\/strong><span style=\"font-weight: 400;\"> is the most popular programming language for data analysis and Machine Learning. Several advantages explain its success with Data Scientists.<\/span><\/p><p><span style=\"font-weight: 400;\">First of all, it is a very easy-to-use language. Even a beginner can <\/span><b>quickly produce programs<\/b><span style=\"font-weight: 400;\"> thanks to its simple and intuitive syntax.<\/span><\/p><p><span style=\"font-weight: 400;\">This language federates a <\/span><b>vast community<\/b><span style=\"font-weight: 400;\">, having created many tools for Data Science. There are for example tools for Data Visualization such as Seaborn and Matplotlib, and software libraries such as NumPy. One of these libraries is Pandas, designed for data manipulation and analysis.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-84a60f9 elementor-widget elementor-widget-spacer\" data-id=\"84a60f9\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-column .elementor-spacer-inner{height:var(--spacer-size)}.e-con{--container-widget-width:100%}.e-con-inner>.elementor-widget-spacer,.e-con>.elementor-widget-spacer{width:var(--container-widget-width,var(--spacer-size));--align-self:var(--container-widget-align-self,initial);--flex-shrink:0}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container,.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer,.e-con>.elementor-widget-spacer>.elementor-widget-container,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer{height:100%}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner{height:var(--container-widget-height,var(--spacer-size))}<\/style>\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1668af2 e-flex e-con-boxed e-con\" data-id=\"1668af2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-31fde20 elementor-widget elementor-widget-heading\" data-id=\"31fde20\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-large\">What is Pandas?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-21b024c elementor-widget elementor-widget-text-editor\" data-id=\"21b024c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The Pandas <\/span><b>open-source software<\/b><span style=\"font-weight: 400;\"> library is specifically designed for data manipulation and analysis in Python. It is powerful, flexible, and easy to use.<\/span><\/p><p><span style=\"font-weight: 400;\">Thanks to Pandas, the Python language can finally be used to load, align, manipulate, or merge data. The performance is particularly impressive when the back-end source code is written in C or Python.<\/span><\/p><p><span style=\"font-weight: 400;\">The name &#8220;Pandas&#8221; is actually a contraction of the term &#8220;<\/span><b>Panel Data<\/b><span style=\"font-weight: 400;\">&#8221; for data sets that include observations over multiple periods. This library was created as a high-level tool for analysis in Python.<\/span><\/p><p><span style=\"font-weight: 400;\">The creators of Pandas plan to evolve this library to become <\/span><b>the<\/b> <b>most powerful and flexible <\/b><span style=\"font-weight: 400;\">open-source data analysis and manipulation tool in any programming language.<\/span><\/p><p><span style=\"font-weight: 400;\">In addition to data analysis, Pandas is widely used for <\/span><b>data wrangling<\/b><span style=\"font-weight: 400;\">. This term encompasses methods for transforming unstructured data to make it usable.<\/span><\/p><p><span style=\"font-weight: 400;\">In general, Pandas also excel at processing structured data in the form of tables, matrices, or time series. It is also compatible with other Python libraries.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-763fbab elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"763fbab\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"554\" height=\"369\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/02\/whatsnew_kde.png\" class=\"attachment-large size-large wp-image-196637\" alt=\"Distribution KDE et histogramme avec Pandas et Seaborn.\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/02\/whatsnew_kde.png 554w, https:\/\/datascientest.com\/en\/files\/2023\/02\/whatsnew_kde-300x200.png 300w\" sizes=\"(max-width: 554px) 100vw, 554px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Source : Pandas <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8cca668 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"8cca668\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\"> Learn to manipulate Pandas<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ab836ac elementor-widget elementor-widget-spacer\" data-id=\"ab836ac\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1c2f0d8 e-flex e-con-boxed e-con\" data-id=\"1c2f0d8\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1a1e69a elementor-widget elementor-widget-heading\" data-id=\"1a1e69a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">How does Pandas work?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86f7eb0 elementor-widget elementor-widget-text-editor\" data-id=\"86f7eb0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Pandas is based on &#8220;<\/span><b>DataFrames<\/b><span style=\"font-weight: 400;\">&#8220;: two-dimensional arrays of data, where each column contains the values of a variable and each row contains a set of values from each column. The data stored in a data frame can be numbers or characters.<\/span><\/p><p><a href=\"https:\/\/datascientest.com\/en\/data-scientist-salary\"><b>Data Scientists<\/b><\/a><span style=\"font-weight: 400;\"> and programmers are familiar with the R programming language for statistical computing and use DataFrames to store data in very simple grids for review. This is the reason why Panda is widely used for <\/span><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\"><b>Machine Learning<\/b><\/a><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">This tool allows you to import and export data in different formats like <\/span><b>CSV<\/b><span style=\"font-weight: 400;\"> or<\/span><b> JSON<\/b><span style=\"font-weight: 400;\">. On the other hand, Pandas also offers Data Cleaning features.<\/span><\/p><p><span style=\"font-weight: 400;\">This library is very useful for working with statistical data, tabular data like <\/span><a href=\"https:\/\/datascientest.com\/en\/sql-learn-all-about-the-programming-language-for-databases\"><b>SQL<\/b><\/a><span style=\"font-weight: 400;\"> or Excel tables, time series data, and arbitrary matrix data with row and column labels.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-663371e elementor-widget elementor-widget-spacer\" data-id=\"663371e\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-697a781 e-flex e-con-boxed e-con\" data-id=\"697a781\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-68a923a elementor-widget elementor-widget-heading\" data-id=\"68a923a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">What are the benefits of Pandas?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3453c86 elementor-widget elementor-widget-text-editor\" data-id=\"3453c86\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">For data scientists and developers, Pandas bring several advantages. This library makes it easy to compensate for missing data.<\/span><\/p><p><span style=\"font-weight: 400;\">It is a flexible tool, as columns can be <\/span><b>easily inserted or removed<\/b><span style=\"font-weight: 400;\"> within DataFrames. The alignment of data with labels can be automated.<\/span><\/p><p><span style=\"font-weight: 400;\">Another advantage is a <\/span><b>powerful data grouping tool<\/b><span style=\"font-weight: 400;\"> that allows split-apply-combine operations to be performed on datasets to aggregate or transform them.<\/span><\/p><p><span style=\"font-weight: 400;\">It is very easy to convert data indexed differently in other Python and <\/span><b>NumPy structures<\/b><span style=\"font-weight: 400;\"> into DataFrame objects. Similarly, data can be indexed or sorted using an intelligent label-based system.<\/span><\/p><p><span style=\"font-weight: 400;\"><strong>Datasets can be intuitively merged<\/strong>, and flexibly restructured. I\/O tools simplify the loading of data from CSV files, Excel files, databases, or the loading of data in HDF5 format.<\/span><\/p><p><span style=\"font-weight: 400;\">Time series features complete the picture, including date range generation, frequency conversion, or moving statistical windows.<\/span><\/p><p><span style=\"font-weight: 400;\">These numerous strong points make Pandas a <\/span><b>must-have<\/b><span style=\"font-weight: 400;\"> library for Data Science in Python. It is a very useful tool for Data Scientists.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-495e1b3 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"495e1b3\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"579\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/02\/campaign-creators-pypeCEaJeZY-unsplash-1024x741.jpg\" class=\"attachment-large size-large wp-image-196638\" alt=\"Analyse de donn\u00e9es marketing sur Google Analytics avec tableau de bord interactif.\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/02\/campaign-creators-pypeCEaJeZY-unsplash-1024x741.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/02\/campaign-creators-pypeCEaJeZY-unsplash-300x217.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/02\/campaign-creators-pypeCEaJeZY-unsplash-768x556.jpg 768w, https:\/\/datascientest.com\/en\/files\/2023\/02\/campaign-creators-pypeCEaJeZY-unsplash-1536x1112.jpg 1536w, https:\/\/datascientest.com\/en\/files\/2023\/02\/campaign-creators-pypeCEaJeZY-unsplash-2048x1482.jpg 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ab39cbe elementor-widget elementor-widget-spacer\" data-id=\"ab39cbe\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ce84c59 e-flex e-con-boxed e-con\" data-id=\"ce84c59\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-566f739 elementor-widget elementor-widget-heading\" data-id=\"566f739\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">How do Data Scientists use Pandas?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-130d951 elementor-widget elementor-widget-text-editor\" data-id=\"130d951\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Some programming languages are traditionally used in scientific environments or by corporate research and development teams. However, these languages often <\/span><b>create problems<\/b><span style=\"font-weight: 400;\"> for Data Scientists.<\/span><\/p><p><span style=\"font-weight: 400;\">Python overcomes most of these limitations. It is an ideal language for the different stages of data science: cleaning, transformation, analysis, modeling, visualization, and reporting.<\/span><\/p><p><span style=\"font-weight: 400;\">Its interface is pleasant, the documentation is complete, and the use is relatively intuitive. The popularity of Pandas is also linked to its age. It is the<\/span><b> first library of its kind<\/b><span style=\"font-weight: 400;\"> to have been created or at least one of the first.<\/span><\/p><p><span style=\"font-weight: 400;\">Moreover, it is an open-source tool and many people have contributed to the project. This is what has made it so successful.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6fb8405 elementor-widget elementor-widget-spacer\" data-id=\"6fb8405\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9a486d1 e-flex e-con-boxed e-con\" data-id=\"9a486d1\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4191f7a elementor-widget elementor-widget-heading\" data-id=\"4191f7a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">Pandas, NumPy, and scikit-learn: 3 Python libraries for Data Science<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-288b75e elementor-widget elementor-widget-text-editor\" data-id=\"288b75e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Besides Pandas, there are other Python software libraries dedicated to Data Science. <\/span><b>NumPy<\/b><span style=\"font-weight: 400;\"> is a mathematical library allowing you to implement linear algebra and standard calculations in a very efficient way.<\/span><\/p><p><span style=\"font-weight: 400;\">Note that <\/span><b>Pandas is based on NumPy<\/b><span style=\"font-weight: 400;\">. Many data structures and features of Pandas come from NumPy. These two libraries are closely related to each other and are often used together.<\/span><\/p><p><span style=\"font-weight: 400;\">On the other hand, <\/span><b>Scikit-learn<\/b><span style=\"font-weight: 400;\"> is the reference for most Machine Learning applications in Python. To create a predictive model, we generally use Pandas and NumPy to load, analyze and format the data to be used. This data is then used to feed the model from scikit-learn. This model is then used to make predictions. Thus, Pandas, Numpy, and Scikit-learn are three tools commonly used in <\/span><a href=\"https:\/\/datascientest.com\/en\/data-science-definition-issues-and-use-cases\"><b>Data Science<\/b><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20933e9 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"20933e9\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"576\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/02\/stephen-dawson-qwtCeJ5cLYs-unsplash-1024x737.jpg\" class=\"attachment-large size-large wp-image-196639\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/02\/stephen-dawson-qwtCeJ5cLYs-unsplash-1024x737.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/02\/stephen-dawson-qwtCeJ5cLYs-unsplash-300x216.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/02\/stephen-dawson-qwtCeJ5cLYs-unsplash-768x553.jpg 768w, https:\/\/datascientest.com\/en\/files\/2023\/02\/stephen-dawson-qwtCeJ5cLYs-unsplash-1536x1105.jpg 1536w, https:\/\/datascientest.com\/en\/files\/2023\/02\/stephen-dawson-qwtCeJ5cLYs-unsplash-2048x1474.jpg 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a8c121 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"7a8c121\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\"> Learn to manipulate Pandas<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-57aca6d elementor-widget elementor-widget-spacer\" data-id=\"57aca6d\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-bdceebf e-flex e-con-boxed e-con\" data-id=\"bdceebf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ce07b89 elementor-widget elementor-widget-heading\" data-id=\"ce07b89\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">Alternatives to Pandas<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e242d32 elementor-widget elementor-widget-text-editor\" data-id=\"e242d32\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">There is no real alternative to Pandas in Python. On the other hand, <\/span><a href=\"https:\/\/datascientest.com\/en\/python-or-r-which-to-choose\"><b>R language<\/b><\/a><span style=\"font-weight: 400;\"> users can turn to the &#8220;<\/span><b>Dplyr<\/b><span style=\"font-weight: 400;\">&#8221; library.<\/span><\/p><p><span style=\"font-weight: 400;\">The concept is similar to Pandas. This library is dedicated to data manipulation and allows for simplifying and accelerating some functionalities.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0a3ed84 elementor-widget elementor-widget-spacer\" data-id=\"0a3ed84\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ac76413 e-flex e-con-boxed e-con\" data-id=\"ac76413\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-761f7dd elementor-widget elementor-widget-heading\" data-id=\"761f7dd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">Which companies use Pandas?\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fffd3c elementor-widget elementor-widget-text-editor\" data-id=\"3fffd3c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Any company using Python for <\/span><b>data analysis<\/b><span style=\"font-weight: 400;\"> needs Pandas and its versatility. All companies handling tabular data will find this tool a valuable help.<\/span><\/p><p><span style=\"font-weight: 400;\">On the other hand, Pandas is <\/span><b>not necessarily adequate<\/b><span style=\"font-weight: 400;\"> for working with incompatible data formats such as images, audio files, or certain textual data. The structure of these data types is not suitable for use with Pandas. It is therefore important to consider the type of data to be processed before choosing a tool.<\/span><\/p><p><span style=\"font-weight: 400;\">This library is<\/span><b> widely used<\/b><span style=\"font-weight: 400;\"> among companies processing relational customer data and transaction data to analyze trends and model behavior.<\/span><\/p><p><span style=\"font-weight: 400;\">Similarly, many real estate companies use it to analyze large quantities of prices and characteristics to determine trends and create predictive models.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e83d9d4 elementor-widget elementor-widget-spacer\" data-id=\"e83d9d4\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-39308d4 e-flex e-con-boxed e-con\" data-id=\"39308d4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-36158d3 elementor-widget elementor-widget-heading\" data-id=\"36158d3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">How to learn to use Pandas?\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a8b4434 elementor-widget elementor-widget-text-editor\" data-id=\"a8b4434\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">After learning the basics of Python, it is very easy to learn how to use Pandas. Mastering these two tools allows you to work with <\/span><b>any type of data<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">The Pandas library is the easiest way to format a data set and analyze it to extract valuable information. For a data scientist, it is simply a must-have.<\/span><\/p><p><span style=\"font-weight: 400;\">Learning to use Pandas offer many opportunities, as this <\/span><b>skill is sought after<\/b><span style=\"font-weight: 400;\"> by companies. Companies in all sectors are using Data Science more and more, and therefore need to surround themselves with experts who know how to use the appropriate tools.<\/span><\/p><p><span style=\"font-weight: 400;\">It is very easy to master the most <\/span><b>basic operations<\/b><span style=\"font-weight: 400;\"> with Pandas. However, knowing how to use the more advanced features can be <\/span><b>complex and time-consuming<\/b><span style=\"font-weight: 400;\">. This is the case for aggregate calculations, DataFrames merging, or time series processing.<\/span><\/p><p><span style=\"font-weight: 400;\">To learn how to use Pandas, you can start by consulting the official documentation. This is a good way to discover the basics and understand how it works.<\/span><\/p><p><span style=\"font-weight: 400;\">There are also <\/span><b>code repositories <\/b><span style=\"font-weight: 400;\">containing online challenges for Pandas. These &#8220;repos&#8221; can allow you to test your skills over time and as you progress.<\/span><\/p><p><span style=\"font-weight: 400;\">Websites like<\/span><b> Kaggle<\/b><span style=\"font-weight: 400;\"> allow you to discover datasets and view how others have used Pandas to analyze them. This provides a better understanding of how this library is used to work with real-world data.<\/span><\/p><p><span style=\"font-weight: 400;\">Starting your project with Pandas is a <\/span><b>great way to progress<\/b><span style=\"font-weight: 400;\">. Simply find a data set, and try to analyze it with Pandas. By choosing data that interests you, this work will seem more concrete and you will learn faster. Correct your mistakes little by little, so you can <\/span><b>learn from them and improve<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">To learn how to use Pandas and all its subtleties, you can choose the <\/span><b>DataScientest trainings<\/b><span style=\"font-weight: 400;\">. This Python library is part of our <\/span><strong><a href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">Data Scientist<\/a><\/strong><span style=\"font-weight: 400;\">, <\/span><strong><a href=\"https:\/\/datascientest.com\/en\/data-analyst-course\">Data Analyst<\/a><\/strong><span style=\"font-weight: 400;\">, and <\/span><strong><a href=\"https:\/\/datascientest.com\/en\/data-management-course\">Data Management courses<\/a><\/strong><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Our different courses allow you to acquire all the skills required to work in the field of Data Science. At the end of the course, you will be ready to work and will receive a diploma certified by the <\/span><b>Dauphine-PSL University<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">All our courses can be taken as BootCamp or Continuing Education. The courses are taken online, at your own pace, on a <\/span><b>Cloud platform coached by professionals<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0e89585 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"0e89585\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover DataScientest training courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eb4e197 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"eb4e197\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"600\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/02\/mimi-thian-ZKBzlifgkgw-unsplash-1024x768.jpg\" class=\"attachment-large size-large wp-image-196640\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/02\/mimi-thian-ZKBzlifgkgw-unsplash-1024x768.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/02\/mimi-thian-ZKBzlifgkgw-unsplash-300x225.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/02\/mimi-thian-ZKBzlifgkgw-unsplash-768x576.jpg 768w, https:\/\/datascientest.com\/en\/files\/2023\/02\/mimi-thian-ZKBzlifgkgw-unsplash-1536x1152.jpg 1536w, https:\/\/datascientest.com\/en\/files\/2023\/02\/mimi-thian-ZKBzlifgkgw-unsplash-2048x1536.jpg 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3ade11e e-flex e-con-boxed e-con\" data-id=\"3ade11e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-774c7ac elementor-widget elementor-widget-html\" data-id=\"774c7ac\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What is Pandas?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"The Pandas open-source software library is specifically designed for data manipulation and analysis in Python. It is powerful, flexible, and easy to use. Thanks to Pandas, the Python language can finally be used to load, align, manipulate, or merge data. The performance is particularly impressive when the back-end source code is written in C or Python.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"How does Pandas work?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Pandas is based on \\\"DataFrames\\\": two-dimensional arrays of data, where each column contains the values of a variable and each row contains a set of values from each column. The data stored in a data frame can be numbers or characters.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What are the benefits of Pandas?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"For data scientists and developers, Pandas bring several advantages. This library makes it easy to compensate for missing data. It is a flexible tool, as columns can be easily inserted or removed within DataFrames. The alignment of data with labels can be automated.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Which companies use Pandas?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"On the other hand, Pandas is not necessarily adequate for working with incompatible data formats such as images, audio files, or certain textual data. The structure of these data types is not suitable for use with Pandas. It is therefore important to consider the type of data to be processed before choosing a tool.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Pandas is a library of the Python programming language, entirely dedicated to Data Science. Find out what this tool is used for, and why it is essential for Data Scientists. Created in 1991, Python is the most popular programming language for data analysis and Machine Learning. Several advantages explain its success with Data Scientists. First [&hellip;]<\/p>\n","protected":false},"author":63,"featured_media":138126,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pandas : the Python library dedicated to Data Science<\/title>\n<meta name=\"description\" content=\"Pandas is a library of the Python programming language, entirely dedicated to Data Science. 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