Hands-on exploratory data analysis with Python :: perform EDA techniques to understand, summarize, and investigate your data /
Further reading -- Chapter 02: Visual Aids for EDA -- Technical requirements -- Line chart -- Steps involved -- Bar charts -- Scatter plot -- Bubble chart -- Scatter plot using seaborn -- Area plot and stacked plot -- Pie chart -- Table chart -- Polar chart -- Histogram -- Lollipop chart -- Choosing...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2020.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Further reading -- Chapter 02: Visual Aids for EDA -- Technical requirements -- Line chart -- Steps involved -- Bar charts -- Scatter plot -- Bubble chart -- Scatter plot using seaborn -- Area plot and stacked plot -- Pie chart -- Table chart -- Polar chart -- Histogram -- Lollipop chart -- Choosing the best chart -- Other libraries to explore -- Summary -- Further reading -- Chapter 03: EDA with Personal Email -- Technical requirements -- Loading the dataset -- Data transformation -- Data cleansing -- Loading the CSV file -- Converting the date -- Removing NaN values This book provides practical knowledge about the main pillars of EDA including data cleaning, data preparation, data exploration, and data visualization. You can leverage the power of Python to understand, summarize and investigate your data in the best way possible. The book presents a unique approach to exploring hidden features in your data. |
| Beschreibung: | 1 online resource (vii, 336 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781789535624 178953562X |
Internformat
MARC
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| 245 | 1 | 0 | |a Hands-on exploratory data analysis with Python : |b perform EDA techniques to understand, summarize, and investigate your data / |c Suresh Kumar Mukhiya, Usman Ahmed. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2020. | |
| 300 | |a 1 online resource (vii, 336 pages) : |b illustrations | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: The Fundamentals of EDA -- Chapter 01: Exploratory Data Analysis Fundamentals -- Understanding data science -- The significance of EDA -- Steps in EDA -- Making sense of data -- Numerical data -- Discrete data -- Continuous data -- Categorical data -- Measurement scales -- Nominal -- Ordinal -- Interval -- Ratio -- Comparing EDA with classical and Bayesian analysis -- Software tools available for EDA -- Getting started with EDA -- NumPy -- Pandas -- SciPy -- Matplotlib | |
| 505 | 8 | |a Applying descriptive statistics -- Data refactoring -- Dropping columns -- Refactoring timezones -- Data analysis -- Number of emails -- Time of day -- Average emails per day and hour -- Number of emails per day -- Most frequently used words -- Summary -- Further reading -- Chapter 04: Data Transformation -- Technical requirements -- Background -- Merging database-style dataframes -- Concatenating along with an axis -- Using df.merge with an inner join -- Using the pd.merge() method with a left join -- Using the pd.merge() method with a right join -- Using pd.merge() methods with outer join | |
| 505 | 8 | |a Merging on index -- Reshaping and pivoting -- Transformation techniques -- Performing data deduplication -- Replacing values -- Handling missing data -- NaN values in pandas objects -- Dropping missing values -- Dropping by rows -- Dropping by columns -- Mathematical operations with NaN -- Filling missing values -- Backward and forward filling -- Interpolating missing values -- Renaming axis indexes -- Discretization and binning -- Outlier detection and filtering -- Permutation and random sampling -- Random sampling without replacement -- Random sampling with replacement | |
| 505 | 8 | |a Computing indicators/dummy variables -- String manipulation -- Benefits of data transformation -- Challenges -- Summary -- Further reading -- Section 2: Descriptive Statistics -- Chapter 05: Descriptive Statistics -- Technical requirements -- Understanding statistics -- Distribution function -- Uniform distribution -- Normal distribution -- Exponential distribution -- Binomial distribution -- Cumulative distribution function -- Descriptive statistics -- Measures of central tendency -- Mean/average -- Median -- Mode -- Measures of dispersion -- Standard deviation -- Variance -- Skewness | |
| 520 | |a Further reading -- Chapter 02: Visual Aids for EDA -- Technical requirements -- Line chart -- Steps involved -- Bar charts -- Scatter plot -- Bubble chart -- Scatter plot using seaborn -- Area plot and stacked plot -- Pie chart -- Table chart -- Polar chart -- Histogram -- Lollipop chart -- Choosing the best chart -- Other libraries to explore -- Summary -- Further reading -- Chapter 03: EDA with Personal Email -- Technical requirements -- Loading the dataset -- Data transformation -- Data cleansing -- Loading the CSV file -- Converting the date -- Removing NaN values | ||
| 520 | |a This book provides practical knowledge about the main pillars of EDA including data cleaning, data preparation, data exploration, and data visualization. You can leverage the power of Python to understand, summarize and investigate your data in the best way possible. The book presents a unique approach to exploring hidden features in your data. | ||
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 0 | |a Electronic data processing |x Distributed processing. |0 http://id.loc.gov/authorities/subjects/sh85042293 | |
| 650 | 0 | |a Information visualization. |0 http://id.loc.gov/authorities/subjects/sh2002000243 | |
| 650 | 2 | |a Data Mining |0 https://id.nlm.nih.gov/mesh/D057225 | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Traitement réparti. | |
| 650 | 6 | |a Visualisation de l'information. | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Electronic data processing |x Distributed processing |2 fast | |
| 650 | 7 | |a Information visualization |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 655 | 4 | |a Electronic book. | |
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| 776 | 0 | 8 | |i Print version: |a Mukhiya, Suresh Kumar. |t Hands-On Exploratory Data Analysis with Python : Perform EDA Techniques to Understand, Summarize, and Investigate Your Data. |d Birmingham : Packt Publishing, Limited, ©2020 |
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| contents | Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: The Fundamentals of EDA -- Chapter 01: Exploratory Data Analysis Fundamentals -- Understanding data science -- The significance of EDA -- Steps in EDA -- Making sense of data -- Numerical data -- Discrete data -- Continuous data -- Categorical data -- Measurement scales -- Nominal -- Ordinal -- Interval -- Ratio -- Comparing EDA with classical and Bayesian analysis -- Software tools available for EDA -- Getting started with EDA -- NumPy -- Pandas -- SciPy -- Matplotlib Applying descriptive statistics -- Data refactoring -- Dropping columns -- Refactoring timezones -- Data analysis -- Number of emails -- Time of day -- Average emails per day and hour -- Number of emails per day -- Most frequently used words -- Summary -- Further reading -- Chapter 04: Data Transformation -- Technical requirements -- Background -- Merging database-style dataframes -- Concatenating along with an axis -- Using df.merge with an inner join -- Using the pd.merge() method with a left join -- Using the pd.merge() method with a right join -- Using pd.merge() methods with outer join Merging on index -- Reshaping and pivoting -- Transformation techniques -- Performing data deduplication -- Replacing values -- Handling missing data -- NaN values in pandas objects -- Dropping missing values -- Dropping by rows -- Dropping by columns -- Mathematical operations with NaN -- Filling missing values -- Backward and forward filling -- Interpolating missing values -- Renaming axis indexes -- Discretization and binning -- Outlier detection and filtering -- Permutation and random sampling -- Random sampling without replacement -- Random sampling with replacement Computing indicators/dummy variables -- String manipulation -- Benefits of data transformation -- Challenges -- Summary -- Further reading -- Section 2: Descriptive Statistics -- Chapter 05: Descriptive Statistics -- Technical requirements -- Understanding statistics -- Distribution function -- Uniform distribution -- Normal distribution -- Exponential distribution -- Binomial distribution -- Cumulative distribution function -- Descriptive statistics -- Measures of central tendency -- Mean/average -- Median -- Mode -- Measures of dispersion -- Standard deviation -- Variance -- Skewness |
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| discipline | Allgemeines |
| format | Electronic eBook |
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| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Mukhiya, Suresh Kumar, author. http://id.loc.gov/authorities/names/nb2022004017 Hands-on exploratory data analysis with Python : perform EDA techniques to understand, summarize, and investigate your data / Suresh Kumar Mukhiya, Usman Ahmed. Birmingham, UK : Packt Publishing, 2020. 1 online resource (vii, 336 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: The Fundamentals of EDA -- Chapter 01: Exploratory Data Analysis Fundamentals -- Understanding data science -- The significance of EDA -- Steps in EDA -- Making sense of data -- Numerical data -- Discrete data -- Continuous data -- Categorical data -- Measurement scales -- Nominal -- Ordinal -- Interval -- Ratio -- Comparing EDA with classical and Bayesian analysis -- Software tools available for EDA -- Getting started with EDA -- NumPy -- Pandas -- SciPy -- Matplotlib Applying descriptive statistics -- Data refactoring -- Dropping columns -- Refactoring timezones -- Data analysis -- Number of emails -- Time of day -- Average emails per day and hour -- Number of emails per day -- Most frequently used words -- Summary -- Further reading -- Chapter 04: Data Transformation -- Technical requirements -- Background -- Merging database-style dataframes -- Concatenating along with an axis -- Using df.merge with an inner join -- Using the pd.merge() method with a left join -- Using the pd.merge() method with a right join -- Using pd.merge() methods with outer join Merging on index -- Reshaping and pivoting -- Transformation techniques -- Performing data deduplication -- Replacing values -- Handling missing data -- NaN values in pandas objects -- Dropping missing values -- Dropping by rows -- Dropping by columns -- Mathematical operations with NaN -- Filling missing values -- Backward and forward filling -- Interpolating missing values -- Renaming axis indexes -- Discretization and binning -- Outlier detection and filtering -- Permutation and random sampling -- Random sampling without replacement -- Random sampling with replacement Computing indicators/dummy variables -- String manipulation -- Benefits of data transformation -- Challenges -- Summary -- Further reading -- Section 2: Descriptive Statistics -- Chapter 05: Descriptive Statistics -- Technical requirements -- Understanding statistics -- Distribution function -- Uniform distribution -- Normal distribution -- Exponential distribution -- Binomial distribution -- Cumulative distribution function -- Descriptive statistics -- Measures of central tendency -- Mean/average -- Median -- Mode -- Measures of dispersion -- Standard deviation -- Variance -- Skewness Further reading -- Chapter 02: Visual Aids for EDA -- Technical requirements -- Line chart -- Steps involved -- Bar charts -- Scatter plot -- Bubble chart -- Scatter plot using seaborn -- Area plot and stacked plot -- Pie chart -- Table chart -- Polar chart -- Histogram -- Lollipop chart -- Choosing the best chart -- Other libraries to explore -- Summary -- Further reading -- Chapter 03: EDA with Personal Email -- Technical requirements -- Loading the dataset -- Data transformation -- Data cleansing -- Loading the CSV file -- Converting the date -- Removing NaN values This book provides practical knowledge about the main pillars of EDA including data cleaning, data preparation, data exploration, and data visualization. You can leverage the power of Python to understand, summarize and investigate your data in the best way possible. The book presents a unique approach to exploring hidden features in your data. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Data Mining https://id.nlm.nih.gov/mesh/D057225 Python (Langage de programmation) Exploration de données (Informatique) Traitement réparti. Visualisation de l'information. Data mining fast Electronic data processing Distributed processing fast Information visualization fast Python (Computer program language) fast Electronic book. Ahmed, Usman, author. http://id.loc.gov/authorities/names/n88142341 has work: Hands-on exploratory data analysis with Python (Text) https://id.oclc.org/worldcat/entity/E39PCGtKtfkpVTfPH3JmDxHkCP https://id.oclc.org/worldcat/ontology/hasWork Print version: Mukhiya, Suresh Kumar. Hands-On Exploratory Data Analysis with Python : Perform EDA Techniques to Understand, Summarize, and Investigate Your Data. Birmingham : Packt Publishing, Limited, ©2020 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2411474 Volltext |
| spellingShingle | Mukhiya, Suresh Kumar Ahmed, Usman Hands-on exploratory data analysis with Python : perform EDA techniques to understand, summarize, and investigate your data / Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: The Fundamentals of EDA -- Chapter 01: Exploratory Data Analysis Fundamentals -- Understanding data science -- The significance of EDA -- Steps in EDA -- Making sense of data -- Numerical data -- Discrete data -- Continuous data -- Categorical data -- Measurement scales -- Nominal -- Ordinal -- Interval -- Ratio -- Comparing EDA with classical and Bayesian analysis -- Software tools available for EDA -- Getting started with EDA -- NumPy -- Pandas -- SciPy -- Matplotlib Applying descriptive statistics -- Data refactoring -- Dropping columns -- Refactoring timezones -- Data analysis -- Number of emails -- Time of day -- Average emails per day and hour -- Number of emails per day -- Most frequently used words -- Summary -- Further reading -- Chapter 04: Data Transformation -- Technical requirements -- Background -- Merging database-style dataframes -- Concatenating along with an axis -- Using df.merge with an inner join -- Using the pd.merge() method with a left join -- Using the pd.merge() method with a right join -- Using pd.merge() methods with outer join Merging on index -- Reshaping and pivoting -- Transformation techniques -- Performing data deduplication -- Replacing values -- Handling missing data -- NaN values in pandas objects -- Dropping missing values -- Dropping by rows -- Dropping by columns -- Mathematical operations with NaN -- Filling missing values -- Backward and forward filling -- Interpolating missing values -- Renaming axis indexes -- Discretization and binning -- Outlier detection and filtering -- Permutation and random sampling -- Random sampling without replacement -- Random sampling with replacement Computing indicators/dummy variables -- String manipulation -- Benefits of data transformation -- Challenges -- Summary -- Further reading -- Section 2: Descriptive Statistics -- Chapter 05: Descriptive Statistics -- Technical requirements -- Understanding statistics -- Distribution function -- Uniform distribution -- Normal distribution -- Exponential distribution -- Binomial distribution -- Cumulative distribution function -- Descriptive statistics -- Measures of central tendency -- Mean/average -- Median -- Mode -- Measures of dispersion -- Standard deviation -- Variance -- Skewness Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Data Mining https://id.nlm.nih.gov/mesh/D057225 Python (Langage de programmation) Exploration de données (Informatique) Traitement réparti. Visualisation de l'information. Data mining fast Electronic data processing Distributed processing fast Information visualization fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh85042293 http://id.loc.gov/authorities/subjects/sh2002000243 https://id.nlm.nih.gov/mesh/D057225 |
| title | Hands-on exploratory data analysis with Python : perform EDA techniques to understand, summarize, and investigate your data / |
| title_auth | Hands-on exploratory data analysis with Python : perform EDA techniques to understand, summarize, and investigate your data / |
| title_exact_search | Hands-on exploratory data analysis with Python : perform EDA techniques to understand, summarize, and investigate your data / |
| title_full | Hands-on exploratory data analysis with Python : perform EDA techniques to understand, summarize, and investigate your data / Suresh Kumar Mukhiya, Usman Ahmed. |
| title_fullStr | Hands-on exploratory data analysis with Python : perform EDA techniques to understand, summarize, and investigate your data / Suresh Kumar Mukhiya, Usman Ahmed. |
| title_full_unstemmed | Hands-on exploratory data analysis with Python : perform EDA techniques to understand, summarize, and investigate your data / Suresh Kumar Mukhiya, Usman Ahmed. |
| title_short | Hands-on exploratory data analysis with Python : |
| title_sort | hands on exploratory data analysis with python perform eda techniques to understand summarize and investigate your data |
| title_sub | perform EDA techniques to understand, summarize, and investigate your data / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Data Mining https://id.nlm.nih.gov/mesh/D057225 Python (Langage de programmation) Exploration de données (Informatique) Traitement réparti. Visualisation de l'information. Data mining fast Electronic data processing Distributed processing fast Information visualization fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Data mining. Electronic data processing Distributed processing. Information visualization. Data Mining Python (Langage de programmation) Exploration de données (Informatique) Traitement réparti. Visualisation de l'information. Data mining Electronic data processing Distributed processing Information visualization Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2411474 |
| work_keys_str_mv | AT mukhiyasureshkumar handsonexploratorydataanalysiswithpythonperformedatechniquestounderstandsummarizeandinvestigateyourdata AT ahmedusman handsonexploratorydataanalysiswithpythonperformedatechniquestounderstandsummarizeandinvestigateyourdata |