Hands-On Exploratory Data Analysis with Python: (Record no. 2342)
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| 000 -LEADER | |
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| fixed length control field | 04435nam a22001817a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240723121523.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240723b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781789537253 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.133 |
| Item number | SUR |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Mukhiya, Suresh Kumar |
| 245 ## - TITLE STATEMENT | |
| Title | Hands-On Exploratory Data Analysis with Python: |
| Remainder of title | Perform EDA techniques to understand, summarize, and investigate your data |
| Statement of responsibility, etc. | Usman Ahmed , Suresh Kumar Mukhiya |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | UK |
| Name of publisher, distributor, etc. | Packt> |
| Date of publication, distribution, etc. | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Page number | 336 |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Title | 1. Section 1: The Fundamentals of EDA<br/>Section 1: The Fundamentals of EDA<br/>2. Exploratory Data Analysis Fundamentals<br/>Exploratory Data Analysis Fundamentals<br/>Understanding data science<br/>The significance of EDA<br/>Making sense of data<br/>Comparing EDA with classical and Bayesian analysis<br/>Software tools available for EDA<br/>Getting started with EDA<br/>Summary<br/>Further reading<br/>3. Visual Aids for EDA<br/>Visual Aids for EDA<br/>Technical requirements<br/>Line chart<br/>Bar charts<br/>Scatter plot<br/>Area plot and stacked plot<br/>Pie chart<br/>Table chart<br/>Polar chart<br/>Histogram<br/>Lollipop chart<br/>Choosing the best chart<br/>Other libraries to explore<br/>Summary<br/>Further reading<br/>4. EDA with Personal Email<br/>EDA with Personal Email<br/>Technical requirements<br/>Loading the dataset<br/>Data transformation<br/>Data analysis<br/>Summary<br/>Further reading<br/>5. Data Transformation<br/>Data Transformation<br/>Technical requirements<br/>Background<br/>Merging database-style dataframes<br/>Transformation techniques<br/>Benefits of data transformation<br/>Summary<br/>Further reading<br/>6. Section 2: Descriptive Statistics<br/>Section 2: Descriptive Statistics<br/>7. Descriptive Statistics<br/>Descriptive Statistics<br/>Technical requirements<br/>Understanding statistics<br/>Measures of central tendency<br/>Measures of dispersion<br/>Summary<br/>Further reading<br/>8. Grouping Datasets<br/>Grouping Datasets<br/>Technical requirements<br/>Understanding groupby()<br/>Groupby mechanics<br/>Data aggregation<br/>Pivot tables and cross-tabulations<br/>Summary<br/>Further reading<br/>9. Correlation<br/>Correlation<br/>Technical requirements<br/>Introducing correlation<br/>Types of analysis<br/>Discussing multivariate analysis using the Titanic dataset<br/>Outlining Simpson's paradox<br/>Correlation does not imply causation<br/>Summary<br/>Further reading<br/>10. Time Series Analysis<br/>Time Series Analysis<br/>Technical requirements<br/>Understanding the time series dataset<br/>TSA with Open Power System Data<br/>Summary<br/>Further reading<br/>11. Section 3: Model Development and Evaluation<br/>Section 3: Model Development and Evaluation<br/>12. Hypothesis Testing and Regression<br/>Hypothesis Testing and Regression<br/>Technical requirements<br/>Hypothesis testing<br/>p-hacking<br/>Understanding regression<br/>Model development and evaluation<br/>Summary<br/>Further reading<br/>13. Model Development and Evaluation<br/>Model Development and Evaluation<br/>Technical requirements<br/>Types of machine learning<br/>Understanding supervised learning<br/>Understanding unsupervised learning<br/>Understanding reinforcement learning<br/>Unified machine learning workflow<br/>Summary<br/>Further reading<br/>14. EDA on Wine Quality Data Analysis<br/>EDA on Wine Quality Data Analysis<br/>Technical requirements<br/>Disclosing the wine quality dataset<br/>Analyzing red wine<br/>Analyzing white wine<br/>Model development and evaluation<br/>Summary<br/>Further reading<br/> |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Usman Ahmed |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| -- | 6847 |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| -- | 6848 |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| -- | 6849 |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| -- | 6850 |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| -- | 6851 |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Inventory number | Total Checkouts | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Currency | Koha item type | Total Renewals | Date last checked out |
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| Dewey Decimal Classification | Non-fiction | IIITDM Kurnool | IIITDM Kurnool | COMPUTER SCIENCE ENGINEERING | 23.07.2024 | Technical Bureau India | 3299.00 | TB889 DT 6/7/2024 | 005.133 SUR | 0005844 | 23.07.2024 | 3299.00 | 23.07.2024 | INR | Books | |||||||
| Dewey Decimal Classification | Non-fiction | IIITDM Kurnool | IIITDM Kurnool | COMPUTER SCIENCE ENGINEERING | 23.07.2024 | Technical Bureau India | 3299.00 | TB889 DT 6/7/2024 | 1 | 005.133 SUR | 0005845 | 09.09.2024 | 3299.00 | 23.07.2024 | INR | Books | 1 | 30.07.2024 | ||||
| Dewey Decimal Classification | Non-fiction | IIITDM Kurnool | IIITDM Kurnool | COMPUTER SCIENCE ENGINEERING | 23.07.2024 | Technical Bureau India | 3299.00 | TB889 DT 6/7/2024 | 005.133 SUR | 0005846 | 23.07.2024 | 3299.00 | 23.07.2024 | INR | Books | |||||||
| Dewey Decimal Classification | Non-fiction | IIITDM Kurnool | IIITDM Kurnool | COMPUTER SCIENCE ENGINEERING | 23.07.2024 | Technical Bureau India | 3299.00 | TB889 DT 6-7-2024 | 1 | 005.133 SUR | 0005847 | 23.09.2024 | 3299.00 | 23.07.2024 | INR | Books | 1 | 02.08.2024 | ||||
| Dewey Decimal Classification | Not For Loan | Non-fiction | IIITDM Kurnool | IIITDM Kurnool | Reference | 23.07.2024 | Technical Bureau India | 3299.00 | TB889 DT 6-7-2024 | 005.133 SUR | 0005848 | 23.07.2024 | 3299.00 | 23.07.2024 | INR | Reference |