Merge branch 'master' of https://github.com/Dekker1/ResearchMethods
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wk8/.ipynb_checkpoints/
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*~
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## Core latex/pdflatex auxiliary files:
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*.aux
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*.lof
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*.log
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*.lot
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*.fot
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*.cb2
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## Intermediate documents:
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*-converted-to.*
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# these rules might exclude image files for figures etc.
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# *.ps
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# *.pdf
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wk7/week7.pdf
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wk8/week8.pdf
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wk9/week9.pdf
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## Bibliography auxiliary files (bibtex/biblatex/biber):
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*.bbl
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*.bcf
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*.blg
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*-blx.aux
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*-blx.bib
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*.run.xml
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*.fdb_latexmk
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*.synctex
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*.synctex(busy)
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*.synctex.gz(busy)
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*.pdfsync
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## Auxiliary and intermediate files from other packages:
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# algorithms
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*.alg
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*.loa
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# achemso
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*.thm
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# changes
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*.mp
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# glossaries
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*.acn
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*.acr
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*.glg
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*.glo
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*-gnuplottex-*
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# knitr
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*-concordance.tex
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# TODO Comment the next line if you want to keep your tikz graphics files
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*.tikz
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*-tikzDictionary
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# listings
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*.lol
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# makeidx
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*.idx
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*.ind
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# minted
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*.mw
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*.nlo
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# pythontex
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pythontex-files-*/
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*.dpth
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*.auxlock
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*.lod
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# Latexian
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TSWLatexianTemp*
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## Editors:
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# WinEdt
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*.bak
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*.sav
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# expex forward references with \gathertags
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*-tags.tex
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31
wk7/week7.tex
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\documentclass[a4paper]{article}
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% To compile PDF run: latexmk -pdf {filename}.tex
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% Math package
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\usepackage{amsmath}
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%enable \cref{...} and \Cref{...} instead of \ref: Type of reference included in the link
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\usepackage[capitalise,nameinlink]{cleveref}
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% Enable that parameters of \cref{}, \ref{}, \cite{}, ... are linked so that a reader can click on the number an jump to the target in the document
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\usepackage{hyperref}
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% UTF-8 encoding
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\usepackage[T1]{fontenc}
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\usepackage[utf8]{inputenc} %support umlauts in the input
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% Easier compilation
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\usepackage{bookmark}
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\begin{document}
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\title{Week 7 - Evidence and experiments}
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\author{
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Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony
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Silvestere
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}
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\maketitle
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\section{Introduction} \label{sec:introduction}
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\section{Method} \label{sec:method}
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\section{Results} \label{sec:results}
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\section{Discussion} \label{sec:discussion}
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\end{document}
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\documentclass[a4paper]{article}
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% To compile PDF run: latexmk -pdf {filename}.tex
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% Math package
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%enable \cref{...} and \Cref{...} instead of \ref: Type of reference included in the link
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\usepackage[capitalise,nameinlink]{cleveref}
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% Enable that parameters of \cref{}, \ref{}, \cite{}, ... are linked so that a reader can click on the number an jump to the target in the document
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\usepackage{hyperref}
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% UTF-8 encoding
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% Easier compilation
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\usepackage{bookmark}
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\begin{document}
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\title{Week 8 - Quantitative data analysis}
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\author{
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Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony
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Silvestere
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}
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\maketitle
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\section{Introduction} \label{sec:introduction}
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\section{Method} \label{sec:method}
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\section{Results} \label{sec:results}
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\section{Discussion} \label{sec:discussion}
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\end{document}
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\documentclass[a4paper]{article}
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% To compile PDF run: latexmk -pdf {filename}.tex
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% Math package
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\usepackage{amsmath}
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\usepackage[capitalise,nameinlink]{cleveref}
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% Enable that parameters of \cref{}, \ref{}, \cite{}, ... are linked so that a reader can click on the number an jump to the target in the document
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\usepackage{hyperref}
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% UTF-8 encoding
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\usepackage[T1]{fontenc}
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\usepackage[utf8]{inputenc} %support umlauts in the input
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% Easier compilation
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\usepackage{bookmark}
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\usepackage{graphicx}
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\begin{document}
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\title{Week 9 - Correlation and Regression}
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\author{
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Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony
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Silvestere
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}
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\maketitle
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\section{Introduction} \label{sec:introduction}
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We present a report on the relationship between the heights and weights of the
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top tennis players as catalogued in provided data. We use statistical analysis
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techniques to numerically describe the characteristics of the data, to see how
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trends are exhibited within the data set. We conclude the report with a brief
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discussion of the implications of the analysis and provide insights on
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potential correlations that may exist.
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\section{Method} \label{sec:method}
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Provided with a set of 132 unique records of the top 200 male tennis players,
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we sought to investigate the relationship between the height of particular
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individuals with their respective weights. We conducted basic statistical
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correlation analyses of the two variables with both Pearson's and Spearman's
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correlation coefficients to achieve this. Further, to understand the
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correlations more deeply, we carried out these correlation tests on the full
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population of cleaned data (removed duplicates etc), alongside several random
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samples and samples of ranking ranges within the top 200. To this end, we made
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use of Microsoft Excel tools and functions of the Python library SciPy.
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We specifically have made use of these separate statistical analysis tools in the
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interest of sanity checking our findings. To do this, we simply replicated the
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correlation tests within other software environments.
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\section{Results} \label{sec:results}
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We performed separate statistical analyses on 10 different samples of the
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population, as well as the population itself. This included 11 separate
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subsets of the rankings:
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\begin{itemize}
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\item The top 20 entries
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\item The middle 20 entries
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\item The bottom 20 entries
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\item The top 50 entries
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\item The bottom 50 entries
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\item 5 randomly chosen sets of 20 entries
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\vspace{1em}
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|
Table \ref{tab:excel_results} shows the the results for the conducted tests.
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\begin{table}[ht]
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\centering
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|
\label{tab:excel_results}
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\begin{tabular}{|l|r|r|}
|
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\hline
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\textbf{Test Set} & \textbf{Pearson's Coefficient} & \textbf{Spearman's Coefficient} \\
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\hline
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\textbf{Full Population} & 0.77953 & 0.73925 \\
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\textbf{Top 20} & 0.80743 & 0.80345 \\
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\textbf{Middle 20} & 0.54134 & 0.36565 \\
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\textbf{Bottom 20} & 0.84046 & 0.88172 \\
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\textbf{Top 50} & 0.80072 & 0.78979 \\
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\textbf{Bottom 50} & 0.84237 & 0.81355 \\
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\textbf{Random Set \#1} & 0.84243 & 0.80237 \\
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\textbf{Random Set \#2} & 0.56564 & 0.58714 \\
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\textbf{Random Set \#3} & 0.59223 & 0.63662 \\
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\textbf{Random Set \#4} & 0.65091 & 0.58471 \\
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\textbf{Random Set \#5} & 0.86203 & 0.77832
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|
\\ \hline
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|
\end{tabular}
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\caption{Table showing the correlation coefficients between height and
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|
weight using different test sets. All data is rounded to 5 decimal
|
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|
places}
|
||||||
|
\end{table}
|
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|
|
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|
\begin{figure}[ht]
|
||||||
|
\centering
|
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|
\label{fig:scipy}
|
||||||
|
\includegraphics[width=0.6\textwidth]{pearson.png}
|
||||||
|
\includegraphics[width=0.6\textwidth]{spearman.png}
|
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|
\caption{The Pearsion (top) and Spearman (bottom) correlations coefficients
|
||||||
|
of the data set as computed by the Pandas Python library}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
\section{Discussion} \label{sec:discussion}
|
||||||
|
The results generally indicate that there is a fairly strong positive
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|
correlation between the weight and weight of an individual tennis player,
|
||||||
|
within the top 200 male players. The population maintains a strong positive
|
||||||
|
correlation with both Pearson's and Spearman's correlation coefficients,
|
||||||
|
indicating that a relationship may exist. Our population samples show
|
||||||
|
promising consistency with this, with 6 seperate samples having values above
|
||||||
|
0.6 with both techniques. The sample taken from the middle 20 players,
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|
however, shows a relatively weaker correlation compared with the top 20 and
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||||||
|
middle 20, which provides some insight into the distribution of the strongest
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||||||
|
correlated heights and weights amongst the rankings. All five random samples
|
||||||
|
of 20 taken from the population indicate however that there does appear to be
|
||||||
|
a consistent trend through the population, which corresponds accurately with
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||||||
|
the coefficients on the general population.
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|
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\end{document}
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"cells": [
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{
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||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 1,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Using matplotlib backend: MacOSX\n",
|
||||||
|
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"%pylab\n",
|
||||||
|
"%matplotlib inline\n",
|
||||||
|
"import pandas as pd\n",
|
||||||
|
"import numpy as np\n",
|
||||||
|
"import matplotlib.pyplot as plt\n",
|
||||||
|
"from scipy import stats\n",
|
||||||
|
"from matplotlib import colors\n",
|
||||||
|
"\n",
|
||||||
|
"data = pd.read_csv(\"Tennis players 2017-09.csv\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 2,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<style type=\"text/css\" >\n",
|
||||||
|
" #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col0 {\n",
|
||||||
|
" background-color: #fc7f00;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col1 {\n",
|
||||||
|
" background-color: #ffd20c;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col2 {\n",
|
||||||
|
" background-color: #ffe619;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col3 {\n",
|
||||||
|
" background-color: #f1f44d;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col0 {\n",
|
||||||
|
" background-color: #ffd20c;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col1 {\n",
|
||||||
|
" background-color: #fc7f00;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col2 {\n",
|
||||||
|
" background-color: #e4ff7a;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col3 {\n",
|
||||||
|
" background-color: #e8fc6c;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col0 {\n",
|
||||||
|
" background-color: #ffe619;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col1 {\n",
|
||||||
|
" background-color: #e4ff7a;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col2 {\n",
|
||||||
|
" background-color: #fc7f00;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col3 {\n",
|
||||||
|
" background-color: #fe9800;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col0 {\n",
|
||||||
|
" background-color: #f1f44d;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col1 {\n",
|
||||||
|
" background-color: #e8fc6c;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col2 {\n",
|
||||||
|
" background-color: #fe9800;\n",
|
||||||
|
" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col3 {\n",
|
||||||
|
" background-color: #fc7f00;\n",
|
||||||
|
" }</style> \n",
|
||||||
|
"<table id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2\" > \n",
|
||||||
|
"<thead> <tr> \n",
|
||||||
|
" <th class=\"blank level0\" ></th> \n",
|
||||||
|
" <th class=\"col_heading level0 col0\" >DOB</th> \n",
|
||||||
|
" <th class=\"col_heading level0 col1\" >RANK</th> \n",
|
||||||
|
" <th class=\"col_heading level0 col2\" >HEIGHT</th> \n",
|
||||||
|
" <th class=\"col_heading level0 col3\" >Weight</th> \n",
|
||||||
|
" </tr></thead> \n",
|
||||||
|
"<tbody> <tr> \n",
|
||||||
|
" <th id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2level0_row0\" class=\"row_heading level0 row0\" >DOB</th> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col0\" class=\"data row0 col0\" >1</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col1\" class=\"data row0 col1\" >0.277766</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col2\" class=\"data row0 col2\" >0.139684</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col3\" class=\"data row0 col3\" >-0.030479</td> \n",
|
||||||
|
" </tr> <tr> \n",
|
||||||
|
" <th id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2level0_row1\" class=\"row_heading level0 row1\" >RANK</th> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col0\" class=\"data row1 col0\" >0.277766</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col1\" class=\"data row1 col1\" >1</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col2\" class=\"data row1 col2\" >-0.16755</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col3\" class=\"data row1 col3\" >-0.121946</td> \n",
|
||||||
|
" </tr> <tr> \n",
|
||||||
|
" <th id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2level0_row2\" class=\"row_heading level0 row2\" >HEIGHT</th> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col0\" class=\"data row2 col0\" >0.139684</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col1\" class=\"data row2 col1\" >-0.16755</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col2\" class=\"data row2 col2\" >1</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col3\" class=\"data row2 col3\" >0.779526</td> \n",
|
||||||
|
" </tr> <tr> \n",
|
||||||
|
" <th id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2level0_row3\" class=\"row_heading level0 row3\" >Weight</th> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col0\" class=\"data row3 col0\" >-0.030479</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col1\" class=\"data row3 col1\" >-0.121946</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col2\" class=\"data row3 col2\" >0.779526</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col3\" class=\"data row3 col3\" >1</td> \n",
|
||||||
|
" </tr></tbody> \n",
|
||||||
|
"</table> "
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<pandas.io.formats.style.Styler at 0x1a197d7b38>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 2,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"def background_gradient(s, m, M, cmap='Wistia', low=0, high=0):\n",
|
||||||
|
" rng = M - m\n",
|
||||||
|
" norm = colors.Normalize(m - (rng * low),\n",
|
||||||
|
" M + (rng * high))\n",
|
||||||
|
" normed = norm(s.values)\n",
|
||||||
|
" c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]\n",
|
||||||
|
" return ['background-color: %s' % color for color in c]\n",
|
||||||
|
"\n",
|
||||||
|
"data = data[[\"SEX\", \"DOB\", \"RANK\", \"HANDED\", \"Country\", \"HEIGHT\", \"Weight\"]]\n",
|
||||||
|
"data.drop_duplicates\n",
|
||||||
|
"\n",
|
||||||
|
"pearson = data.corr()\n",
|
||||||
|
"pearson.style.apply(background_gradient,\n",
|
||||||
|
" cmap='Wistia',\n",
|
||||||
|
" m=pearson.min().min(),\n",
|
||||||
|
" M=pearson.max().max()\n",
|
||||||
|
")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 3,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<style type=\"text/css\" >\n",
|
||||||
|
" #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col0 {\n",
|
||||||
|
" background-color: #fc7f00;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col1 {\n",
|
||||||
|
" background-color: #ffd20c;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col2 {\n",
|
||||||
|
" background-color: #fee91d;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col3 {\n",
|
||||||
|
" background-color: #f4f242;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col0 {\n",
|
||||||
|
" background-color: #ffd20c;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col1 {\n",
|
||||||
|
" background-color: #fc7f00;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col2 {\n",
|
||||||
|
" background-color: #e4ff7a;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col3 {\n",
|
||||||
|
" background-color: #eafa63;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col0 {\n",
|
||||||
|
" background-color: #fee91d;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col1 {\n",
|
||||||
|
" background-color: #e4ff7a;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col2 {\n",
|
||||||
|
" background-color: #fc7f00;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col3 {\n",
|
||||||
|
" background-color: #ff9d00;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col0 {\n",
|
||||||
|
" background-color: #f4f242;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col1 {\n",
|
||||||
|
" background-color: #eafa63;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col2 {\n",
|
||||||
|
" background-color: #ff9d00;\n",
|
||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col3 {\n",
|
||||||
|
" background-color: #fc7f00;\n",
|
||||||
|
" }</style> \n",
|
||||||
|
"<table id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2\" > \n",
|
||||||
|
"<thead> <tr> \n",
|
||||||
|
" <th class=\"blank level0\" ></th> \n",
|
||||||
|
" <th class=\"col_heading level0 col0\" >DOB</th> \n",
|
||||||
|
" <th class=\"col_heading level0 col1\" >RANK</th> \n",
|
||||||
|
" <th class=\"col_heading level0 col2\" >HEIGHT</th> \n",
|
||||||
|
" <th class=\"col_heading level0 col3\" >Weight</th> \n",
|
||||||
|
" </tr></thead> \n",
|
||||||
|
"<tbody> <tr> \n",
|
||||||
|
" <th id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2level0_row0\" class=\"row_heading level0 row0\" >DOB</th> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col0\" class=\"data row0 col0\" >1</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col1\" class=\"data row0 col1\" >0.280386</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col2\" class=\"data row0 col2\" >0.122412</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col3\" class=\"data row0 col3\" >0.00769861</td> \n",
|
||||||
|
" </tr> <tr> \n",
|
||||||
|
" <th id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2level0_row1\" class=\"row_heading level0 row1\" >RANK</th> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col0\" class=\"data row1 col0\" >0.280386</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col1\" class=\"data row1 col1\" >1</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col2\" class=\"data row1 col2\" >-0.160006</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col3\" class=\"data row1 col3\" >-0.0908714</td> \n",
|
||||||
|
" </tr> <tr> \n",
|
||||||
|
" <th id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2level0_row2\" class=\"row_heading level0 row2\" >HEIGHT</th> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col0\" class=\"data row2 col0\" >0.122412</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col1\" class=\"data row2 col1\" >-0.160006</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col2\" class=\"data row2 col2\" >1</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col3\" class=\"data row2 col3\" >0.739246</td> \n",
|
||||||
|
" </tr> <tr> \n",
|
||||||
|
" <th id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2level0_row3\" class=\"row_heading level0 row3\" >Weight</th> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col0\" class=\"data row3 col0\" >0.00769861</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col1\" class=\"data row3 col1\" >-0.0908714</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col2\" class=\"data row3 col2\" >0.739246</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col3\" class=\"data row3 col3\" >1</td> \n",
|
||||||
|
" </tr></tbody> \n",
|
||||||
|
"</table> "
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<pandas.io.formats.style.Styler at 0x111a3b198>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 3,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"spearman = data.corr(method=\"spearman\")\n",
|
||||||
|
"spearman.style.apply(background_gradient,\n",
|
||||||
|
" cmap='Wistia',\n",
|
||||||
|
" m=spearman.min().min(),\n",
|
||||||
|
" M=spearman.max().max()\n",
|
||||||
|
")"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.6.4"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user