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Pandas for Everyone

Python Data Analysis

Paperback Engels 2023 9780137891153
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

Manage and Automate Data Analysis with Pandas 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 data sets.

Pandas for Everyone, 2nd Edition, 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 data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set.

New features to the second edition include:  Extended coverage of plotting and the seaborn data visualization library Expanded examples and resources Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries Online bonus material on geopandas, Dask, and creating interactive graphics with Altair

Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets 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 data sets and handle missing data Reshape, tidy, and clean data sets 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 data sets 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” one Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning

Specificaties

ISBN13:9780137891153
Taal:Engels
Bindwijze:Paperback

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Inhoudsopgave

<p>Foreword by Anne M. Brown &nbsp;&nbsp;&nbsp;&nbsp;xxiii</p> <p>Foreword by Jared Lander &nbsp;&nbsp;&nbsp;&nbsp;xxv</p> <p>Preface &nbsp;&nbsp;&nbsp;&nbsp;xxvii</p> <p>Changes in the Second Edition &nbsp;&nbsp;&nbsp;&nbsp;xxxix</p> <p>&nbsp;</p> <p><strong>Part I: Introduction</strong>&nbsp;&nbsp;&nbsp; 1</p> <p><strong>Chapter 1. Pandas DataFrame Basics</strong> &nbsp;&nbsp;&nbsp;&nbsp;3</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;1.1 Introduction &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;1.2 Load Your First Data Set &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;1.3 Look at Columns, Rows, and Cells &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;1.4 Grouped and Aggregated Calculations &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;23</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;1.5 Basic Plot &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;27</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;28</p> <p>&nbsp;</p> <p><strong>Chapter 2. Pandas Data Structures Basics</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;31</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;31</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;2.1 Create Your Own Data &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;31</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;2.2 The Series &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;33</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;2.3 The DataFrame &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;42</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;2.4 Making Changes to Series and DataFrames &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;45</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;2.5 Exporting and Importing Data &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;52</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;63</p> <p>&nbsp;</p> <p><strong>Chapter 3. Plotting Basics</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;65</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;65</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;3.1 Why Visualize Data? &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;65</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;3.2 Matplotlib Basics &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;66</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;3.3 Statistical Graphics Using matplotlib &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;72</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;3.4 Seaborn &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;78</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;3.5 Pandas Plotting Method &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;111</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;115</p> <p>&nbsp;</p> <p><strong>Chapter 4. Tidy Data</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;117</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;117</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Note About This Chapter &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;117</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;4.1 Columns Contain Values, Not Variables &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;118</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;4.2 Columns Contain Multiple Variables &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;122</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;4.3 Variables in Both Rows and Columns &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;126</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;129</p> <p>&nbsp;</p> <p><strong>Chapter 5. Apply Functions</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;131</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;131</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Note About This Chapter &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;131</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;5.1 Primer on Functions &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;131</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;5.2 Apply (Basics) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;133</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;5.3 Vectorized Functions &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;138</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;5.4 Lambda Functions (Anonymous Functions) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;141</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;142</p> <p>&nbsp;</p> <p><strong>Part II: Data Processing</strong>&nbsp;&nbsp;&nbsp;&nbsp; 143</p> <p><strong>Chapter 6. Data Assembly</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;145</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;145</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;6.1 Combine Data Sets &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;145</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;6.2 Concatenation &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;146</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;6.3 Observational Units Across Multiple Tables &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;154</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;6.4 Merge Multiple Data Sets &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;160</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;167</p> <p>&nbsp;</p> <p><strong>Chapter 7. Data Normalization</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;169</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;169</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;7.1 Multiple Observational Units in a Table (Normalization) &nbsp;&nbsp;&nbsp;&nbsp;169</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;173</p> <p>&nbsp;</p> <p><strong>Chapter 8. Groupby Operations: Split-Apply-Combine</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;175</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;175</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;8.1 Aggregate &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;176</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;8.2 Transform &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;184</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;8.3 Filter &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;188</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;8.4 The pandas.core.groupby.DataFrameGroupBy object &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;190</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;8.5 Working with a MultiIndex &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;195</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;199</p> <p>&nbsp;</p> <p><strong>Part III: Data Types</strong>&nbsp;&nbsp;&nbsp; 203</p> <p><strong>Chapter 9. Missing Data</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;203</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;203</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;9.1 What Is a NaN Value? &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;203</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;9.2 Where Do Missing Values Come From? &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;205</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;9.3 Working with Missing Data &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;210</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;9.4 Pandas Built-In NA Missing &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;216</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;218</p> <p>&nbsp;</p> <p><strong>Chapter 10. Data Types</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;219</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;219</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;10.1 Data Types &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;219</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;10.2 Converting Types &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;220</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;10.3 Categorical Data &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;225</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;227</p> <p>&nbsp;</p> <p><strong>Chapter 11. Strings and Text Data</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;229</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Introduction &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;229</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;229</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;11.1 Strings &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;229</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;11.2 String Methods &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;233</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;11.3 More String Methods &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;234</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;11.4 String Formatting (F-Strings) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;236</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;11.5 Regular Expressions (RegEx) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;239</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;11.6 The regex Library &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;247</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;247</p> <p>&nbsp;</p> <p><strong>Chapter 12. Dates and Times </strong>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;249</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Learning Objectives &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;249</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.1 Python's datetime Object &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;249</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.2 Converting to datetime &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;250</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.3 Loading Data That Include Dates &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;253</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.4 Extracting Date Components &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;254</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.5 Date Calculations and Timedeltas &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;257</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.6 Datetime Methods &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;259</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.7 Getting Stock Data &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;261</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.8 Subsetting Data Based on Dates &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;263</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.9 Date Ranges &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;266</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.10 Shifting Values &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;270</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.11 Resampling &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;276</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.12 Time Zones &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;278</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;12.13 Arrow for Better Dates and Times &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;280</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;280</p> <p>&nbsp;</p> <p><strong>Part IV: Data Modeling</strong>&nbsp;&nbsp;&nbsp; 281</p> <p><strong>Chapter 13. Linear Regression (Continuous Outcome Variable)</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;283</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;13.1 Simple Linear Regression &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;283</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;13.2 Multiple Regression &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;287</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;13.3 Models with Categorical Variables &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;289</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;13.4 One-Hot Encoding in scikit-learn with Transformer Pipelines &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;294</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;296</p> <p>&nbsp;</p> <p><strong>Chapter 14. Generalized Linear Models</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;297</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;About This Chapter &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;297</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;14.1 Logistic Regression (Binary Outcome Variable) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;297</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;14.2 Poisson Regression (Count Outcome Variable) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;304</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;14.3 More Generalized Linear Models &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;308</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;309</p> <p>&nbsp;</p> <p><strong>Chapter 15. Survival Analysis</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;311</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;15.1 Survival Data &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;311</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;15.2 Kaplan Meier Curves &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;312</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;15.3 Cox Proportional Hazard Model &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;314</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;317</p> <p>&nbsp;</p> <p><strong>Chapter 16. Model Diagnostics</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;319</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;16.1 Residuals &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;319</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;16.2 Comparing Multiple Models &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;324</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;16.3 k-Fold Cross-Validation &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;329</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;334</p> <p>&nbsp;</p> <p><strong>Chapter 17. Regularization</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;335</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;17.1 Why Regularize? &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;335</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;17.2 LASSO Regression &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;337</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;17.3 Ridge Regression &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;338</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;17.4 Elastic Net &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;340</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;17.5 Cross-Validation &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;341</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;343</p> <p>&nbsp;</p> <p><strong>Chapter 18. Clustering</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;345</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;18.1 k-Means &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;345</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;18.2 Hierarchical Clustering &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;351</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;356</p> <p>&nbsp;</p> <p><strong>Part V. Conclusion</strong>&nbsp;&nbsp;&nbsp; 357</p> <p><strong>Chapter 19. Life Outside of Pandas</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;359</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;19.1 The (Scientific) Computing Stack &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;359</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;19.2 Performance &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;360</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;19.3 Dask &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;360</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;19.4 Siuba &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;360</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;19.5 Ibis &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;361</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;19.6 Polars &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;361</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;19.7 PyJanitor &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;361</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;19.8 Pandera &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;361</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;19.9 Machine Learning &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;361</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;19.10 Publishing &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;362</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;19.11 Dashboards &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;362</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;362</p> <p>&nbsp;</p> <p><strong>Chapter 20. It's Dangerous To Go Alone!</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;363</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;20.1 Local Meetups &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;363</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;20.2 Conferences &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;363</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;20.3 The Carpentries &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;364</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;20.4 Podcasts &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;364</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;20.5 Other Resources &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;365</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;Conclusion &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;365</p> <p>&nbsp;</p> <p><strong>Appendices</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;367</p> <p>A.&nbsp; &nbsp; &nbsp; Concept Maps &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;369<br>B.&nbsp; &nbsp; &nbsp; Installation and Setup &nbsp;&nbsp;&nbsp;&nbsp;373<br>C.&nbsp; &nbsp; &nbsp; Command Line &nbsp;&nbsp;&nbsp;&nbsp;377<br>D.&nbsp; &nbsp; &nbsp; Project Templates &nbsp;&nbsp;&nbsp;&nbsp;379<br>E.&nbsp; &nbsp; &nbsp; Using Python &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;381<br>F.&nbsp; &nbsp; &nbsp; &nbsp;Working Directories &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;383<br>G.&nbsp; &nbsp; &nbsp; Environments &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;385<br>H.&nbsp; &nbsp; &nbsp; Install Packages &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;389<br>I.&nbsp; &nbsp; &nbsp; &nbsp;Importing Libraries &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;391<br>J.&nbsp; &nbsp; &nbsp; &nbsp;Code Style &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;393<br>K.&nbsp; &nbsp; &nbsp; Containers: Lists, Tuples, and Dictionaries &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;395<br>L.&nbsp; &nbsp; &nbsp; Slice Values &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;399<br>M.&nbsp; &nbsp; &nbsp;Loops &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;401<br>N.&nbsp; &nbsp; &nbsp;Comprehensions &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;403<br>O.&nbsp; &nbsp; &nbsp;Functions &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;405<br>P.&nbsp; &nbsp; &nbsp; Ranges and Generators &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;409<br>Q.&nbsp; &nbsp; &nbsp;Multiple Assignment &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;413<br>R.&nbsp; &nbsp; &nbsp;Numpy ndarray &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;415<br>S.&nbsp; &nbsp; &nbsp;Classes &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;417<br>T.&nbsp; &nbsp; &nbsp; SettingWithCopyWarning &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;419<br>U.&nbsp; &nbsp; &nbsp;Method Chaining &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;423<br>V.&nbsp; &nbsp; &nbsp; Timing Code &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;427<br>W.&nbsp; &nbsp; &nbsp;String Formatting &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;429<br>X.&nbsp; &nbsp; &nbsp; Conditionals (if-elif-else) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;433<br>Y.&nbsp; &nbsp; &nbsp; New York ACS Logistic Regression Example &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;435<br>Z.&nbsp; &nbsp; &nbsp; Replicating Results in R &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;443<br><br></p> <p>Index &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;451</p>

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