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Pandas Basics
This book is intended for those who plan to become data scientists as well as anyone who needs to perform data cleaning tasks using Pandas and NumPy. --
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Bloomfield :
Mercury Learning & Information,
2022.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 003 | OCoLC | ||
| 005 | 20231027140348.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 221210s2022 xx o ||| 0 eng d | ||
| 040 | |a EBLCP |b eng |c EBLCP |d UKAHL |d YDX |d K6U |d OCLCF |d OCLCO |d OCLCQ | ||
| 019 | |a 1354515675 | ||
| 020 | |a 9781683928256 | ||
| 020 | |a 1683928253 | ||
| 029 | 1 | |a AU@ |b 000075099795 | |
| 035 | |a (OCoLC)1354205722 |z (OCoLC)1354515675 | ||
| 050 | 1 | 4 | |a QA76.9.D343 |b .C36 2022 |
| 082 | 0 | 4 | |a 006.3/12 |2 23 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Campesato, Oswald. | |
| 245 | 1 | 0 | |a Pandas Basics |h [electronic resource]. |
| 260 | |a Bloomfield : |b Mercury Learning & Information, |c 2022. | ||
| 300 | |a 1 online resource (215 p.) | ||
| 500 | |a Description based upon print version of record. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Chapter 1: Introduction to Python -- Tools for Python -- easy_install and pip -- virtualenv -- IPython -- Python Installation -- Setting the PATH Environment Variable (Windows Only) -- Launching Python on Your Machine -- The Python Interactive Interpreter -- Python Identifiers -- Lines, Indentation, and Multi-lines -- Quotations and Comments -- Saving Your Code in a Module -- Some Standard Modules -- The help() and dir() Functions -- Compile Time and Runtime Code Checking -- Simple Data Types -- Working with Numbers | |
| 505 | 8 | |a Working with Other Bases -- The chr() Function -- The round() Function -- Formatting Numbers -- Working with Fractions -- Unicode and UTF-8 -- Working with Unicode -- Working with Strings -- Comparing Strings -- Formatting Strings -- Uninitialized Variables and the Value None -- Slicing and Splicing Strings -- Testing for Digits and Alphabetic Characters -- Search and Replace a String in Other Strings -- Remove Leading and Trailing Characters -- Printing Text without NewLine Characters -- Text Alignment -- Working with Dates -- Converting Strings to Dates -- Exception Handling | |
| 505 | 8 | |a Handling User Input -- Command-line Arguments -- Summary -- Chapter 2: Working with Data -- Dealing with Data: What Can Go Wrong? -- What is Data Drift? -- What are Datasets? -- Data Preprocessing -- Data Types -- Preparing Datasets -- Discrete Data Versus Continuous Data -- Binning Continuous Data -- Scaling Numeric Data via Normalization -- Scaling Numeric Data via Standardization -- Scaling Numeric Data via Robust Standardization -- What to Look for in Categorical Data -- Mapping Categorical Data to Numeric Values -- Working with Dates -- Working with Currency | |
| 505 | 8 | |a Working with Outliers and Anomalies -- Outlier Detection/Removal -- Finding Outliers with NumPy -- Finding Outliers with Pandas -- Calculating Z-scores to Find Outliers -- Finding Outliers with SkLearn (Optional) -- Working with Missing Data -- Imputing Values: When is Zero a Valid Value? -- Dealing with Imbalanced Datasets -- What is SMOTE? -- SMOTE extensions -- The Bias-Variance Tradeoff -- Types of Bias in Data -- Analyzing Classifiers (Optional) -- What is LIME? -- What is ANOVA? -- Summary -- Chapter 3: Introduction to Probability and Statistics -- What is a Probability? | |
| 505 | 8 | |a Calculating the Expected Value -- Random Variables -- Discrete versus Continuous Random Variables -- Well-known Probability Distributions -- Fundamental Concepts in Statistics -- The Mean -- The Median -- The Mode -- The Variance and Standard Deviation -- Population, Sample, and Population Variance -- Chebyshev's Inequality -- What is a p-value? -- The Moments of a Function (Optional) -- What is Skewness? -- What is Kurtosis? -- Data and Statistics -- The Central Limit Theorem -- Correlation versus Causation -- Statistical Inferences -- Statistical Terms: RSS, TSS, R^2, and F1 Score | |
| 500 | |a What is an F1 score? | ||
| 520 | |a This book is intended for those who plan to become data scientists as well as anyone who needs to perform data cleaning tasks using Pandas and NumPy. -- |c Edited summary from book. | ||
| 590 | |a Knovel |b ACADEMIC - Software Engineering | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Campesato, Oswald |t Pandas Basics |d Bloomfield : Mercury Learning & Information,c2022 |z 9781683928263 |
| 856 | 4 | 0 | |u https://appknovel.uam.elogim.com/kn/resources/kpPB000023/toc |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH41158364 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL30286674 | ||
| 938 | |a YBP Library Services |b YANK |n 18407642 | ||
| 994 | |a 92 |b IZTAP | ||