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Viser: Pandas for Everyone: Python Data Analysis

Pandas for Everyone, 1. udgave

Pandas for Everyone Vital Source e-bog

Daniel Y. Chen
(2017)
Pearson International
395,00 kr. 355,50 kr.
Leveres umiddelbart efter køb
Pandas for Everyone, 1. udgave

Pandas for Everyone Vital Source e-bog

Daniel Y. Chen
(2017)
Pearson International
136,00 kr.
Leveres umiddelbart efter køb
Pandas for Everyone, 1. udgave

Pandas for Everyone Vital Source e-bog

Daniel Y. Chen
(2017)
Pearson International
272,00 kr.
Leveres umiddelbart efter køb
Pandas for Everyone, 1. udgave

Pandas for Everyone Vital Source e-bog

Daniel Y. Chen
(2017)
Pearson International
190,00 kr.
Leveres umiddelbart efter køb
Pandas for Everyone: Python Data Analysis

Pandas for Everyone: Python Data Analysis

Daniel Chen
(2017)
Pearson Education, Limited
330,00 kr.
ikke på lager, Bestil nu og få den leveret
om ca. 10 hverdage
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Detaljer Om Varen

  • 1. Udgave
  • Vital Source E-book
  • Udgiver: Pearson International (December 2017)
  • ISBN: 9780134547053
The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python   Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.   Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems.   Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.   Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem.  Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning
Licens varighed:
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Detaljer Om Varen

  • 1. Udgave
  • Vital Source leje e-bog 90 dage
  • Udgiver: Pearson International (December 2017)
  • ISBN: 9780134547053R90
The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python   Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.   Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems.   Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.   Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem.  Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning
Licens varighed:
Online udgaven er tilgængelig: 90 dage fra købsdato.
Offline udgaven er tilgængelig: 90 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer Om Varen

  • 1. Udgave
  • Vital Source leje e-bog 365 dage
  • Udgiver: Pearson International (December 2017)
  • ISBN: 9780134547053R365
The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python   Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.   Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems.   Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.   Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem.  Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning
Licens varighed:
Online udgaven er tilgængelig: 365 dage fra købsdato.
Offline udgaven er tilgængelig: 365 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer Om Varen

  • 1. Udgave
  • Vital Source leje e-bog 180 dage
  • Udgiver: Pearson International (December 2017)
  • ISBN: 9780134547053R180
The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python   Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.   Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems.   Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.   Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem.  Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning
Licens varighed:
Online udgaven er tilgængelig: 180 dage fra købsdato.
Offline udgaven er tilgængelig: 180 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer Om Varen

  • Paperback: 416 sider
  • Udgiver: Pearson Education, Limited (December 2017)
  • ISBN: 9780134546933
Today, analysts must manage data characterised by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualise it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you're new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualisation. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem.
  • Work with DataFrames and Series, and import or export data
  • Create plots with matplotlib, seaborn, and pandas
  • Combine datasets and handle missing data
  • Reshape, tidy, and clean datasets so they're easier to work with
  • Convert data types and manipulate text strings
  • Apply functions to scale data manipulations
  • Aggregate, transform, and filter large datasets with groupby
  • Leverage Pandas' advanced date and time capabilities
  • Fit linear models using statsmodels and scikit-learn libraries
  • Use generalised linear modeling to fit models with different response variables
  • Compare multiple models to select the "best"
  • Regularise to overcome overfitting and improve performance

Part I. Introduction
0. Setting Up
1. Introduction to Panda's Dataframes
2. Dataframe Components
3. Performing Statistics and Calculations on Sliced and Grouped Dataframes
4. Plotting in Matplotlib

Part II. Data Munging
5. Basic Data Cleaning
6. Reshaping Dataframes
7. Missing Values
8. Working with Dates
9. Working with Multiple Dataframes
10. Working with Databases

Part III. Modeling
11. Basic Statistics
12. Linear Models and Regression
13. Survival Analysis
14. Model Selection and Diagnostics
15. Time Series

Part IV. Machine Learning
16. Supervised Learning
17. Unsupervised Learning

Part V. Reproducible Documents (Literate Programming)
18. Jupyter Notebook
19. Pweave Appendices
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