![]() Among interpreted languages, for various historical and cultural reasons, Python has developed a large and active scientific computing and data analysis community. I don’t like the term “scripting languages,” as it carries a connotation that they cannot be used for building serious software. Such languages are often called scripting languages, as they can be used to quickly write small programs, or scripts to automate other tasks. Python and Ruby have become especially popular since 2005 or so for building websites using their numerous web frameworks, like Rails (Ruby) and Django (Python). Since its first appearance in 1991, Python has become one of the most popular interpreted programming languages, along with Perl, Ruby, and others. 1.2 Why Python for Data Analysis?įor many people, the Python programming language has strong appeal. Most users of spreadsheet programs like Microsoft Excel, perhaps the most widely used data analysis tool in the world, will not be strangers to these kinds of data. As an example, a collection of news articles could be processed into a word frequency table, which could then be used to perform sentiment analysis. If not, it may be possible to extract features from a dataset into a structured form. Even though it may not always be obvious, a large percentage of datasets can be transformed into a structured form that is more suitable for analysis and modeling. Multiple tables of data interrelated by key columns (what would be primary or foreign keys for a SQL user). ![]() This includes most kinds of data commonly stored in relational databases or tab- or comma-delimited text files. Tabular or spreadsheet-like data in which each column may be a different type (string, numeric, date, or otherwise). When I say “data,” what am I referring to exactly? The primary focus is on structured data, a deliberately vague term that encompasses many different common forms of data, such as: Some might characterize much of the content of the book as "data manipulation" as opposed to "data analysis." We also use the terms wrangling or munging to refer to data manipulation. My hope is that this book serves as adequate preparation to enable you to move on to a more domain-specific resource. There are now many other books which focus specifically on these more advanced methodologies. The Python open source ecosystem for doing data analysis (or data science) has also expanded significantly since then. Sometime after I originally published this book in 2012, people started using the term data science as an umbrella description for everything from simple descriptive statistics to more advanced statistical analysis and machine learning. This is the Python programming you need for data analysis. ![]() While "data analysis" is in the title of the book, the focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. My goal is to offer a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst. This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. The code examples are MIT licensed and can be found on GitHub or Gitee. The content from this website may not be copied or reproduced. If you find the online edition of the book useful, please consider ordering a paper copy or a DRM-free eBook to support the author. ![]() If you encounter any errata, please report them here. This Open Access web version of Python for Data Analysis 3rd Edition is now available as a companion to the print and digital editions.
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is the world's leading source for international market research reports and market data. ![]() The broad perspective of the report coupled with comprehensive, actionable detail will help Telecom, Media, and Technology (TMT) stakeholders, service providers, and other Esports players succeed in growing the Esports market globally.ģ.1.2.2 Event management software providersģ.1.3.4 Training analytics tools providersģ.2 Esports Market Structure - PORTER's Analysisģ.4 Esports - Market Variables & Impact Analysisģ.4.1.1 High Accessibility and Inclusivenessģ.4.1.3 Increasing adoption of PC and mobile gamingģ.4.2.3 Lack of regulation and standardizationģ.4.2.4 Health risks associated with esportsģ.5 Esports - Mergers & Acquisitions (M&A)Ĥ Global Esports Market Revenue OpportunityĤ.2 Global Esports Market By Revenue Stream, 2021-2030($ Mn)Ĥ.3 Global Esports Market By Gaming Genre, 2021-2030 ($ Mn)ĥ.2.1 ESPORTS MARKET BY REVENUE STREAM, 2021 - 2030 ($ MN)ĥ.2.2 ESPORTS MARKET BY GAMING GENRE, 2021-2030 ($ MN)Ħ.2.1 Company overview and financial informationĦ.2.2 Recent developments and Innovationsįor more information about this report visit.The report provides an easily digestible market assessment for decision-makers built around in-depth information gathered from local market players, which enables executives to quickly get up to speed with the current and emerging trends in Esports markets.With more than 20 charts and tables, the report is designed for an executive-level audience, boasting presentation quality.Accompanying the publisher's Forecast products, the report examines the assumptions and drivers behind ongoing and upcoming trends in Esports markets.This market intelligence report offers a thorough, forward-looking analysis of the global Esports market by revenue stream, gaming genre, region, and key opportunities in a concise format to help executives build proactive and profitable growth strategies.The detailed value chain consists of six main aspects: Games, events, teams and players, channels, sponsorship and investment, and end-users.It contains details of M&A and VF deals in the Esports space.It includes global market forecasts for the Esports industry and analysis of patents, company filings, and hiring trends.It identifies the key trends impacting growth of the market over the next 12 to 24 months.This report provides overview and service addressable market for Esports. ![]() The adoption of Esports will be supported by increasing adoption of mobile and PC gaming, high accessibility and inclusiveness, and the development of the mobile 5G connectivity. ![]() This market intelligence report offers a thorough, forward-looking analysis of the global Esports market by revenue stream, gaming genre, region, and key opportunities in a concise format to help executives build proactive and profitable growth strategies. The Esports market report provides an executive-level overview of the Esports market worldwide today, with detailed forecasts of key indicators up to 2030. The market is expected to grow at a CAGR of 16.7% during the forecast period (2022-2030). The Esports market size was valued at US$1.39 billion in 2022. Dublin, Ma(GLOBE NEWSWIRE) - The "Esports Market Size, Share, Trends and Analysis by Region, Revenue Stream (Sponsorship, Media Rights, Digital, Tickets and Merchandise, Publisher Fees, Streaming), Gaming Genre (MOBA, RTS, FPS, Battle Royale, Others) and Segment Forecast, 2022-2030" report has been added to 's offering.
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Finally an orthographic projection of the warping mesh. One orthographic rendering of the fisheye image mapped onto a warping mesh. So in all it is a 6 pass rendering process, 4 perspective views mapped to a fisheye. Note however this is only useful for the exact installation shown here, this final mesh is normally site specific and, for example, looks very different for a planetarium arrangement. The mesh to perform this is also given here as an obj file: warp.zip. An orthographic camera viewing this mesh is the final image sent to the data projector. This is achieved by applying the fisheye image to another mesh. In order to create the correct projection using a spherical mirror the fisheye needs to be warped. In the example here the fisheye render texture is 2Kx2K. The 4 meshes that form the fisheye from the 4 cubic map images is given here as obj files. If one was using a fisheye lens to illuminate the dome then this would be the end of the story. 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