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Added missing brackets

tutorial
tommyod 2018-01-07 18:52:48 +01:00
parent 7e28f63728
commit df2f1cdc44
4 changed files with 1026 additions and 1 deletions
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README.md
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@ -42,7 +42,7 @@ quantified roughly as follows:
| Title | Speaker | Uploader | Time | Views | Year | Level |
| ----- | ------- | -------- | ---- | ----- | ---- | ----- |
| :star: [Pandas From The Ground Up](https://www.youtube.com/watch?v=5JnMutdy6Fw) [[repo](https://github.com/brandon-rhodes/pycon-pandas-tutorial)] | Brandon Rhodes | PyCon 2015 | 2:24 | 91000 | 2015 | :smiley: |
| [Introduction Into Pandas](https://www.youtube.com/watch?v=-NR-ynQg0YM) [repo](https://github.com/chendaniely/2016-pydata-carolinas-pandas) | Daniel Chen | Python Tutorial | 1:28 | 46000 | 2017 | :smiley: |
| [Introduction Into Pandas](https://www.youtube.com/watch?v=-NR-ynQg0YM) [[repo](https://github.com/chendaniely/2016-pydata-carolinas-pandas)] | Daniel Chen | Python Tutorial | 1:28 | 46000 | 2017 | :smiley: |
| [Introduction To Data Analytics With Pandas](https://www.youtube.com/watch?v=5XGycFIe8qE) [[repo](https://github.com/QCaudron/pydata_pandas)] | Quentin Caudron | Python Tutorial | 1:51 | 25000 | 2017 | :smiley: |
| [Pandas for Data Analysis](https://www.youtube.com/watch?v=oGzU688xCUs) [[repo](https://github.com/chendaniely/scipy-2017-tutorial-pandas)] | Daniel Chen | Enthought | 3:45 | 13000 | 2017 | :sweat_smile: |
| [Optimizing Pandas Code](https://www.youtube.com/watch?v=HN5d490_KKk) [[repo](https://github.com/sversh/pycon2017-optimizing-pandas)] | Sofia Heisler | PyCon 2017 | 0:29 | 12000 | 2017 | :sweat_smile: |

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data/Metadata_analysis.ipynb 100644
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% Default to the notebook output style
% Inherit from the specified cell style.
\documentclass[11pt]{article}
\usepackage[T1]{fontenc}
% Nicer default font (+ math font) than Computer Modern for most use cases
\usepackage{mathpazo}
% Basic figure setup, for now with no caption control since it's done
% automatically by Pandoc (which extracts ![](path) syntax from Markdown).
\usepackage{graphicx}
% We will generate all images so they have a width \maxwidth. This means
% that they will get their normal width if they fit onto the page, but
% are scaled down if they would overflow the margins.
\makeatletter
\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
\else\Gin@nat@width\fi}
\makeatother
\let\Oldincludegraphics\includegraphics
% Set max figure width to be 80% of text width, for now hardcoded.
\renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
% Ensure that by default, figures have no caption (until we provide a
% proper Figure object with a Caption API and a way to capture that
% in the conversion process - todo).
\usepackage{caption}
\DeclareCaptionLabelFormat{nolabel}{}
\captionsetup{labelformat=nolabel}
\usepackage{adjustbox} % Used to constrain images to a maximum size
\usepackage{xcolor} % Allow colors to be defined
\usepackage{enumerate} % Needed for markdown enumerations to work
\usepackage{geometry} % Used to adjust the document margins
\usepackage{amsmath} % Equations
\usepackage{amssymb} % Equations
\usepackage{textcomp} % defines textquotesingle
% Hack from http://tex.stackexchange.com/a/47451/13684:
\AtBeginDocument{%
\def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
}
\usepackage{upquote} % Upright quotes for verbatim code
\usepackage{eurosym} % defines \euro
\usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
\usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
\usepackage{fancyvrb} % verbatim replacement that allows latex
\usepackage{grffile} % extends the file name processing of package graphics
% to support a larger range
% The hyperref package gives us a pdf with properly built
% internal navigation ('pdf bookmarks' for the table of contents,
% internal cross-reference links, web links for URLs, etc.)
\usepackage{hyperref}
\usepackage{longtable} % longtable support required by pandoc >1.10
\usepackage{booktabs} % table support for pandoc > 1.12.2
\usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
\usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
% normalem makes italics be italics, not underlines
% Colors for the hyperref package
\definecolor{urlcolor}{rgb}{0,.145,.698}
\definecolor{linkcolor}{rgb}{.71,0.21,0.01}
\definecolor{citecolor}{rgb}{.12,.54,.11}
% ANSI colors
\definecolor{ansi-black}{HTML}{3E424D}
\definecolor{ansi-black-intense}{HTML}{282C36}
\definecolor{ansi-red}{HTML}{E75C58}
\definecolor{ansi-red-intense}{HTML}{B22B31}
\definecolor{ansi-green}{HTML}{00A250}
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\definecolor{ansi-yellow-intense}{HTML}{B27D12}
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\definecolor{ansi-blue-intense}{HTML}{0065CA}
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\definecolor{ansi-cyan-intense}{HTML}{258F8F}
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\definecolor{ansi-white-intense}{HTML}{A1A6B2}
% commands and environments needed by pandoc snippets
% extracted from the output of `pandoc -s`
\providecommand{\tightlist}{%
\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
% Add ',fontsize=\small' for more characters per line
\newenvironment{Shaded}{}{}
\newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
\newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
\newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
\newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
\newcommand{\RegionMarkerTok}[1]{{#1}}
\newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\NormalTok}[1]{{#1}}
% Additional commands for more recent versions of Pandoc
\newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
\newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}}
\newcommand{\ImportTok}[1]{{#1}}
\newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
\newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
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\newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}}
\newcommand{\BuiltInTok}[1]{{#1}}
\newcommand{\ExtensionTok}[1]{{#1}}
\newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}}
\newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}}
\newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
% Define a nice break command that doesn't care if a line doesn't already
% exist.
\def\br{\hspace*{\fill} \\* }
% Math Jax compatability definitions
\def\gt{>}
\def\lt{<}
% Document parameters
\title{Metadata\_analysis}
% Pygments definitions
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\let\PY@bc=\relax \let\PY@ff=\relax}
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\def\PY@toks#1+{\ifx\relax#1\empty\else%
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\expandafter\def\csname PY@tok@ge\endcsname{\let\PY@it=\textit}
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\def\PYZbs{\char`\\}
\def\PYZus{\char`\_}
\def\PYZob{\char`\{}
\def\PYZcb{\char`\}}
\def\PYZca{\char`\^}
\def\PYZam{\char`\&}
\def\PYZlt{\char`\<}
\def\PYZgt{\char`\>}
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% for compatibility with earlier versions
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% Exact colors from NB
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% Prevent overflowing lines due to hard-to-break entities
\sloppy
% Setup hyperref package
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breaklinks=true, % so long urls are correctly broken across lines
colorlinks=true,
urlcolor=urlcolor,
linkcolor=linkcolor,
citecolor=citecolor,
}
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\begin{document}
\maketitle
\section{Metadata analysis}\label{metadata-analysis}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}1}]:} \PY{k+kn}{import} \PY{n+nn}{pandas} \PY{k}{as} \PY{n+nn}{pd}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{k+kn}{import} \PY{n+nn}{bs4}
\PY{k+kn}{import} \PY{n+nn}{requests}
\PY{k+kn}{import} \PY{n+nn}{string}
\PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}2}]:} \PY{k}{def} \PY{n+nf}{int\PYZus{}str}\PY{p}{(}\PY{n}{a\PYZus{}string}\PY{p}{)}\PY{p}{:}
\PY{k}{try}\PY{p}{:}
\PY{k}{return} \PY{n+nb}{int}\PY{p}{(}\PY{n}{a\PYZus{}string}\PY{p}{)}
\PY{k}{except}\PY{p}{:}
\PY{k}{return} \PY{l+m+mi}{0}
\PY{k}{def} \PY{n+nf}{info\PYZus{}from\PYZus{}youtube}\PY{p}{(}\PY{n}{youtube\PYZus{}video\PYZus{}url}\PY{p}{)}\PY{p}{:}
\PY{l+s+sd}{\PYZdq{}\PYZdq{}\PYZdq{}}
\PY{l+s+sd}{ Retrieve information from a YouTube url.\PYZdq{}\PYZdq{}\PYZdq{}}
\PY{k}{try}\PY{p}{:}
\PY{n}{soup} \PY{o}{=} \PY{n}{bs4}\PY{o}{.}\PY{n}{BeautifulSoup}\PY{p}{(}\PY{n}{requests}\PY{o}{.}\PY{n}{get}\PY{p}{(}\PY{n}{youtube\PYZus{}video\PYZus{}url}\PY{p}{)}\PY{o}{.}\PY{n}{text}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{lxml}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{title} \PY{o}{=} \PY{n}{soup}\PY{o}{.}\PY{n}{title}\PY{o}{.}\PY{n}{text}\PY{o}{.}\PY{n}{strip}\PY{p}{(}\PY{p}{)}
\PY{n}{views\PYZus{}text} \PY{o}{=} \PY{n}{soup}\PY{o}{.}\PY{n}{find}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{div}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{attrs} \PY{o}{=} \PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{class}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{watch\PYZhy{}view\PYZhy{}count}\PY{l+s+s1}{\PYZsq{}}\PY{p}{\PYZcb{}}\PY{p}{)}\PY{o}{.}\PY{n}{text}
\PY{n}{views} \PY{o}{=} \PY{n}{int\PYZus{}str}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZsq{}}\PY{o}{.}\PY{n}{join}\PY{p}{(}\PY{p}{[}\PY{n}{c} \PY{k}{for} \PY{n}{c} \PY{o+ow}{in} \PY{n}{views\PYZus{}text} \PY{k}{if} \PY{n}{c} \PY{o+ow}{in} \PY{n}{string}\PY{o}{.}\PY{n}{digits}\PY{p}{]}\PY{p}{)}\PY{p}{)}
\PY{n}{published\PYZus{}text} \PY{o}{=} \PY{n}{soup}\PY{o}{.}\PY{n}{find}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{strong}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{attrs} \PY{o}{=} \PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{class}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{watch\PYZhy{}time\PYZhy{}text}\PY{l+s+s1}{\PYZsq{}}\PY{p}{\PYZcb{}}\PY{p}{)}\PY{o}{.}\PY{n}{text}
\PY{n}{published} \PY{o}{=} \PY{n}{int\PYZus{}str}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZsq{}}\PY{o}{.}\PY{n}{join}\PY{p}{(}\PY{p}{[}\PY{n}{c} \PY{k}{for} \PY{n}{c} \PY{o+ow}{in} \PY{n}{published\PYZus{}text}\PY{p}{[}\PY{o}{\PYZhy{}}\PY{l+m+mi}{4}\PY{p}{:}\PY{p}{]} \PY{k}{if} \PY{n}{c} \PY{o+ow}{in} \PY{n}{string}\PY{o}{.}\PY{n}{digits}\PY{p}{]}\PY{p}{)}\PY{p}{)}
\PY{n}{publisher} \PY{o}{=} \PY{n}{soup}\PY{o}{.}\PY{n}{find}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{div}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{attrs} \PY{o}{=} \PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{class}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{yt\PYZhy{}user\PYZhy{}info}\PY{l+s+s1}{\PYZsq{}}\PY{p}{\PYZcb{}}\PY{p}{)}\PY{o}{.}\PY{n}{text}\PY{o}{.}\PY{n}{strip}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+se}{\PYZbs{}n}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{num\PYZus{}likes\PYZus{}text} \PY{o}{=} \PY{n}{soup}\PY{o}{.}\PY{n}{find}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{button}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{attrs} \PY{o}{=} \PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{class}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{like\PYZhy{}button\PYZhy{}renderer\PYZhy{}like\PYZhy{}button}\PY{l+s+s1}{\PYZsq{}}\PY{p}{\PYZcb{}}\PY{p}{)}\PY{o}{.}\PY{n}{text}
\PY{n}{num\PYZus{}likes} \PY{o}{=} \PY{n}{int\PYZus{}str}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZsq{}}\PY{o}{.}\PY{n}{join}\PY{p}{(}\PY{p}{[}\PY{n}{c} \PY{k}{for} \PY{n}{c} \PY{o+ow}{in} \PY{n}{num\PYZus{}likes\PYZus{}text} \PY{k}{if} \PY{n}{c} \PY{o+ow}{in} \PY{n}{string}\PY{o}{.}\PY{n}{digits}\PY{p}{]}\PY{p}{)}\PY{p}{)}
\PY{n}{percentage\PYZus{}likes\PYZus{}text} \PY{o}{=} \PY{n}{soup}\PY{o}{.}\PY{n}{find}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{div}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{attrs} \PY{o}{=} \PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{class}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{video\PYZhy{}extras\PYZhy{}sparkbar\PYZhy{}likes}\PY{l+s+s1}{\PYZsq{}}\PY{p}{\PYZcb{}}\PY{p}{)}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{style}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
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\PY{k}{return} \PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{url}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{n}{youtube\PYZus{}video\PYZus{}url}\PY{o}{.}\PY{n}{strip}\PY{p}{(}\PY{p}{)}\PY{p}{,}
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\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{publisher}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{n}{publisher}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{num\PYZus{}likes}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{n}{num\PYZus{}likes}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{percentage\PYZus{}likes}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{n}{percentage\PYZus{}likes}\PY{p}{\PYZcb{}}
\PY{k}{except}\PY{p}{:}
\PY{k}{return} \PY{k+kc}{None}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
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\PY{n}{urls} \PY{o}{=} \PY{n+nb}{list}\PY{p}{(}\PY{n+nb}{set}\PY{p}{(}\PY{n}{urls}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}4}]:} \PY{c+c1}{\PYZsh{} Parse the URLS}
\PY{n}{data\PYZus{}inn} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{for} \PY{n}{url} \PY{o+ow}{in} \PY{n}{urls}\PY{p}{:}
\PY{n}{info} \PY{o}{=} \PY{n}{info\PYZus{}from\PYZus{}youtube}\PY{p}{(}\PY{n}{url}\PY{p}{)}
\PY{k}{if} \PY{n}{info}\PY{p}{:}
\PY{n}{data\PYZus{}inn}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{info}\PY{p}{)}
\PY{k}{else}\PY{p}{:}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Error with:}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{url}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
Error with: https://www.youtube.com/watch?v=5-qadlG7tWo
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}11}]:} \PY{n}{df} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{DataFrame}\PY{p}{(}\PY{n}{data\PYZus{}inn}\PY{p}{)}
\PY{n}{df}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{l+m+mi}{3}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}11}]:} num\_likes percentage\_likes published\_yr publisher \textbackslash{}
0 6527 98.953911 2013 Next Day Video
1 142 94.666667 2016 PyData
2 88 100.000000 2015 PyCon Australia
title \textbackslash{}
0 Transforming Code into Beautiful, Idiomatic Py{\ldots}
1 Christopher Roach | Visualizing Geographic Dat{\ldots}
2 Predicting sports winners using data analytics{\ldots}
url views
0 https://www.youtube.com/watch?v=OSGv2VnC0go 340228
1 https://www.youtube.com/watch?v=ZIEyHdvF474 13912
2 https://www.youtube.com/watch?v=k7hSD\_-gWMw 12867
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}12}]:} \PY{k}{def} \PY{n+nf}{rating\PYZus{}func}\PY{p}{(}\PY{n}{df}\PY{p}{)}\PY{p}{:}
\PY{l+s+sd}{\PYZdq{}\PYZdq{}\PYZdq{}}
\PY{l+s+sd}{ A rating heuristic.}
\PY{l+s+sd}{ \PYZdq{}\PYZdq{}\PYZdq{}}
\PY{n}{num\PYZus{}likes}\PY{p}{,} \PY{n}{percentage\PYZus{}likes} \PY{o}{=} \PY{n}{df}\PY{o}{.}\PY{n}{num\PYZus{}likes}\PY{p}{,} \PY{n}{df}\PY{o}{.}\PY{n}{percentage\PYZus{}likes}
\PY{n}{views}\PY{p}{,} \PY{n}{published\PYZus{}yr} \PY{o}{=} \PY{n}{df}\PY{o}{.}\PY{n}{views}\PY{o}{/}\PY{l+m+mi}{10}\PY{p}{,} \PY{n}{df}\PY{o}{.}\PY{n}{published\PYZus{}yr}
\PY{n}{a}\PY{p}{,} \PY{n}{b} \PY{o}{=} \PY{n}{num\PYZus{}likes}\PY{o}{*}\PY{n}{percentage\PYZus{}likes}\PY{p}{,} \PY{n}{np}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{n}{views}\PY{p}{)}
\PY{n}{age} \PY{o}{=} \PY{n+nb}{abs}\PY{p}{(}\PY{l+m+mi}{2018} \PY{o}{\PYZhy{}} \PY{n}{df}\PY{o}{.}\PY{n}{published\PYZus{}yr}\PY{p}{)}
\PY{k}{return} \PY{p}{(}\PY{n}{a}\PY{o}{*}\PY{n}{b} \PY{o}{/} \PY{p}{(}\PY{n}{a} \PY{o}{+} \PY{n}{b}\PY{p}{)}\PY{p}{)} \PY{o}{\PYZhy{}} \PY{n}{age}\PY{o}{*}\PY{o}{*}\PY{l+m+mf}{0.8}
\PY{n}{df} \PY{o}{=} \PY{n}{df}\PY{o}{.}\PY{n}{assign}\PY{p}{(}\PY{n}{rating} \PY{o}{=} \PY{n}{rating\PYZus{}func}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}13}]:} \PY{n+nb}{len}\PY{p}{(}\PY{n}{df}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}13}]:} 73
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}15}]:} \PY{n}{temp} \PY{o}{=} \PY{n}{df}\PY{o}{.}\PY{n}{sort\PYZus{}values}\PY{p}{(}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{rating}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{n}{ascending} \PY{o}{=} \PY{k+kc}{False}\PY{p}{)}\PY{o}{.}\PY{n}{drop\PYZus{}duplicates}\PY{p}{(}\PY{p}{)}
\PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n+nb}{len}\PY{p}{(}\PY{n}{temp}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{n}{values\PYZus{}dict} \PY{o}{=} \PY{n}{temp}\PY{o}{.}\PY{n}{iloc}\PY{p}{[}\PY{n}{i}\PY{p}{,} \PY{p}{:}\PY{p}{]}\PY{o}{.}\PY{n}{to\PYZus{}dict}\PY{p}{(}\PY{p}{)}
\PY{n}{string} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{| [}\PY{l+s+si}{\PYZob{}Title\PYZcb{}}\PY{l+s+s1}{](}\PY{l+s+si}{\PYZob{}url\PYZcb{}}\PY{l+s+s1}{) | }\PY{l+s+si}{\PYZob{}Speaker\PYZcb{}}\PY{l+s+s1}{ | }\PY{l+s+si}{\PYZob{}Uploader\PYZcb{}}\PY{l+s+s1}{ | }\PY{l+s+si}{\PYZob{}Duration\PYZcb{}}\PY{l+s+s1}{ | }\PY{l+s+si}{\PYZob{}Views\PYZcb{}}\PY{l+s+s1}{ | }\PY{l+s+si}{\PYZob{}Keywords\PYZcb{}}\PY{l+s+s1}{ | }\PY{l+s+si}{\PYZob{}Year\PYZcb{}}\PY{l+s+s1}{ | }\PY{l+s+si}{\PYZob{}Level\PYZcb{}}\PY{l+s+s1}{ |}\PY{l+s+s1}{\PYZsq{}}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{string}\PY{o}{.}\PY{n}{format}\PY{p}{(}
\PY{n}{Title} \PY{o}{=} \PY{n}{values\PYZus{}dict}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{title}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{|}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{ }\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{n}{Speaker} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{NAME}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{n}{Uploader} \PY{o}{=} \PY{n}{values\PYZus{}dict}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{publisher}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,}
\PY{n}{Duration} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{DURATION}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{n}{Views} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n+nb}{round}\PY{p}{(}\PY{n}{values\PYZus{}dict}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{views}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{/}\PY{l+m+mi}{1000}\PY{p}{)}\PY{o}{*}\PY{l+m+mi}{1000}\PY{p}{)}\PY{p}{,}
\PY{n}{Keywords} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{None}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{n}{Year} \PY{o}{=} \PY{n}{values\PYZus{}dict}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{published\PYZus{}yr}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,}
\PY{n}{url} \PY{o}{=} \PY{n}{values\PYZus{}dict}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{url}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,}
\PY{n}{Level} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Novice}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
| [Tetiana Ivanova - How to become a Data Scientist in 6 months a hacker’s approach to career planning - YouTube](https://www.youtube.com/watch?v=rIofV14c0tc) | NAME | PyData | DURATION | 148000 | None | 2016 | Novice |
| [Introduction Into Pandas: Python Tutorial - YouTube](https://www.youtube.com/watch?v=-NR-ynQg0YM) | NAME | Python Tutorial | DURATION | 46000 | None | 2017 | Novice |
| [James Powell - So you want to be a Python expert? - YouTube](https://www.youtube.com/watch?v=cKPlPJyQrt4) | NAME | PyData | DURATION | 28000 | None | 2017 | Novice |
| [NumPy Beginner SciPy 2016 Tutorial Alexandre Chabot LeClerc - YouTube](https://www.youtube.com/watch?v=gtejJ3RCddE) | NAME | Enthought | DURATION | 56000 | None | 2016 | Novice |
| [Introduction To Data Analytics With Pandas - YouTube](https://www.youtube.com/watch?v=5XGycFIe8qE) | NAME | Python Tutorial | DURATION | 25000 | None | 2017 | Novice |
| [Transforming Code into Beautiful, Idiomatic Python - YouTube](https://www.youtube.com/watch?v=OSGv2VnC0go) | NAME | Next Day Video | DURATION | 340000 | None | 2013 | Novice |
| [Machine Learning Part 1 SciPy 2016 Tutorial Andreas Mueller \& Sebastian Raschka - YouTube](https://www.youtube.com/watch?v=OB1reY6IX-o) | NAME | Enthought | DURATION | 47000 | None | 2016 | Novice |
| [Brandon Rhodes - Pandas From The Ground Up - PyCon 2015 - YouTube](https://www.youtube.com/watch?v=5JnMutdy6Fw) | NAME | PyCon 2015 | DURATION | 91000 | None | 2015 | Novice |
| [Modern Dictionaries by Raymond Hettinger - YouTube](https://www.youtube.com/watch?v=p33CVV29OG8) | NAME | SF Python | DURATION | 44000 | None | 2016 | Novice |
| [Jake VanderPlas The Python Visualization Landscape PyCon 2017 - YouTube](https://www.youtube.com/watch?v=FytuB8nFHPQ) | NAME | PyCon 2017 | DURATION | 21000 | None | 2017 | Novice |
| [Raymond Hettinger, Keynote on Concurrency, PyBay 2017 - YouTube](https://www.youtube.com/watch?v=9zinZmE3Ogk) | NAME | SF Python | DURATION | 15000 | None | 2017 | Novice |
| [Pandas for Data Analysis SciPy 2017 Tutorial Daniel Chen - YouTube](https://www.youtube.com/watch?v=oGzU688xCUs) | NAME | Enthought | DURATION | 13000 | None | 2017 | Novice |
| [JupyterLab: Building Blocks for Interactive Computing SciPy 2016 Brian Granger - YouTube](https://www.youtube.com/watch?v=Ejh0ftSjk6g) | NAME | Enthought | DURATION | 28000 | None | 2016 | Novice |
| [Sofia Heisler No More Sad Pandas Optimizing Pandas Code for Speed and Efficiency PyCon 2017 - YouTube](https://www.youtube.com/watch?v=HN5d490\_KKk) | NAME | PyCon 2017 | DURATION | 12000 | None | 2017 | Novice |
| [A Visual Guide To Pandas - YouTube](https://www.youtube.com/watch?v=9d5-Ti6onew) | NAME | Next Day Video | DURATION | 49000 | None | 2015 | Novice |
| [Machine Learning with Scikit Learn SciPy 2015 Tutorial Andreas Mueller \& Kyle Kastner Part I - YouTube](https://www.youtube.com/watch?v=80fZrVMurPM) | NAME | Enthought | DURATION | 48000 | None | 2015 | Novice |
| [Machine Learning for Time Series Data in Python SciPy 2016 Brett Naul - YouTube](https://www.youtube.com/watch?v=ZgHGCfwExw0) | NAME | Enthought | DURATION | 24000 | None | 2016 | Novice |
| [The Fun of Reinvention (Screencast) - YouTube](https://www.youtube.com/watch?v=js\_0wjzuMfc) | NAME | David Beazley | DURATION | 11000 | None | 2017 | Novice |
| [Analyzing and Manipulating Data with Pandas Beginner SciPy 2016 Tutorial Jonathan Rocher - YouTube](https://www.youtube.com/watch?v=6ohWS7J1hVA) | NAME | Enthought | DURATION | 22000 | None | 2016 | Novice |
| [Super Advanced Python - YouTube](https://www.youtube.com/watch?v=u2KZJzoz-qI) | NAME | Next Day Video | DURATION | 143000 | None | 2013 | Novice |
| [Computational Statistics SciPy 2017 Tutorial Allen Downey - YouTube](https://www.youtube.com/watch?v=He9MCbs1wgE) | NAME | Enthought | DURATION | 10000 | None | 2017 | Novice |
| [Raymond Hettinger, "Being a Core Developer in Python", PyBay2016 - YouTube](https://www.youtube.com/watch?v=voXVTjwnn-U) | NAME | SF Python | DURATION | 19000 | None | 2016 | Novice |
| [Aileen Nielsen - Time Series Analysis - PyCon 2017 - YouTube](https://www.youtube.com/watch?v=zmfe2RaX-14) | NAME | PyCon 2017 | DURATION | 9000 | None | 2017 | Novice |
| [Learning TensorFlow - YouTube](https://www.youtube.com/watch?v=bvHgESVuS6Q) | NAME | PyCon Australia | DURATION | 18000 | None | 2016 | Novice |
| [JupyterHub: Deploying Jupyter Notebooks for students and researchers - YouTube](https://www.youtube.com/watch?v=gSVvxOchT8Y) | NAME | PyData | DURATION | 17000 | None | 2016 | Novice |
| [Jeffrey Yau Applied Time Series Econometrics in Python and R - YouTube](https://www.youtube.com/watch?v=tJ-O3hk1vRw) | NAME | PyData | DURATION | 17000 | None | 2016 | Novice |
| [Machine Learning with scikit learn Part One SciPy 2017 Tutorial Andreas Mueller \& Alexandre Gram - YouTube](https://www.youtube.com/watch?v=2kT6QOVSgSg) | NAME | Enthought | DURATION | 8000 | None | 2017 | Novice |
| [Introduction to Numerical Computing with NumPy SciPy 2017 Tutorial Dillon Niederhut - YouTube](https://www.youtube.com/watch?v=lKcwuPnSHIQ) | NAME | Enthought | DURATION | 8000 | None | 2017 | Novice |
| [Matthew Rocklin Dask A Pythonic Distributed Data Science Framework PyCon 2017 - YouTube](https://www.youtube.com/watch?v=RA\_2qdipVng) | NAME | PyCon 2017 | DURATION | 7000 | None | 2017 | Novice |
| [Christopher Fonnesbeck - Introduction to Statistical Modeling with Python - PyCon 2017 - YouTube](https://www.youtube.com/watch?v=TMmSESkhRtI) | NAME | PyCon 2017 | DURATION | 7000 | None | 2017 | Novice |
| [Fully Convolutional Networks for Image Segmentation SciPy 2017 Daniil Pakhomov - YouTube](https://www.youtube.com/watch?v=-lXfsWP7DJ8) | NAME | Enthought | DURATION | 7000 | None | 2017 | Novice |
| [Chloe Mawer, Jonathan Whitmore - Exploratory data analysis in python - PyCon 2017 - YouTube](https://www.youtube.com/watch?v=W5WE9Db2RLU) | NAME | PyCon 2017 | DURATION | 7000 | None | 2017 | Novice |
| [Christopher Roach Visualizing Geographic Data With Python - YouTube](https://www.youtube.com/watch?v=ZIEyHdvF474) | NAME | PyData | DURATION | 14000 | None | 2016 | Novice |
| [Builtin Superheroes (Screencast) - YouTube](https://www.youtube.com/watch?v=j6VSAsKAj98) | NAME | David Beazley | DURATION | 12000 | None | 2016 | Novice |
| [Python's Class Development Toolkit - YouTube](https://www.youtube.com/watch?v=HTLu2DFOdTg) | NAME | Next Day Video | DURATION | 80000 | None | 2013 | Novice |
| [Sebastian Raschka Learning scikit learn - An Introduction to Machine Learning in Python - YouTube](https://www.youtube.com/watch?v=9fOWryQq9J8) | NAME | PyData | DURATION | 12000 | None | 2016 | Novice |
| [Alex Rubinsteyn: Python Libraries for Deep Learning with Sequences - YouTube](https://www.youtube.com/watch?v=E92jDCmJNek) | NAME | PyData | DURATION | 23000 | None | 2015 | Novice |
| [The Other Async (Threads + Async = ❤️) - YouTube](https://www.youtube.com/watch?v=x1ndXuw7S0s) | NAME | David Beazley | DURATION | 5000 | None | 2017 | Novice |
| [Daniel Chen Introduction to Pandas - YouTube](https://www.youtube.com/watch?v=dye7rDktJ2E) | NAME | PyData | DURATION | 10000 | None | 2016 | Novice |
| [Numba - Tell Those C++ Bullies to Get Lost SciPy 2017 Tutorial Gil Forsyth \& Lorena Barba - YouTube](https://www.youtube.com/watch?v=1AwG0T4gaO0) | NAME | Enthought | DURATION | 5000 | None | 2017 | Novice |
| [Deploying Interactive Jupyter Dashboards for Visualizing Hundreds of Millions of Datapoints, in 30 L - YouTube](https://www.youtube.com/watch?v=8Jktm-Imt-I) | NAME | Enthought | DURATION | 5000 | None | 2017 | Novice |
| [Divya Sardana Building Recommender Systems Using Python - YouTube](https://www.youtube.com/watch?v=39vJRxIPSxw) | NAME | PyData | DURATION | 10000 | None | 2016 | Novice |
| [Joel Grus: Learning Data Science Using Functional Python - YouTube](https://www.youtube.com/watch?v=ThS4juptJjQ) | NAME | PyData | DURATION | 18000 | None | 2015 | Novice |
| [Stephen Simmons Pandas from the Inside - YouTube](https://www.youtube.com/watch?v=CowlcrtSyME) | NAME | PyData | DURATION | 9000 | None | 2016 | Novice |
| [Curious Course on Coroutines and Concurrency - YouTube](https://www.youtube.com/watch?v=Z\_OAlIhXziw) | NAME | David Beazley | DURATION | 9000 | None | 2016 | Novice |
| [Anatomy of matplotlib SciPy 2015 Tutorial Benjamin Root and Joe Kington - YouTube](https://www.youtube.com/watch?v=MKucn8NtVeI) | NAME | Enthought | DURATION | 18000 | None | 2015 | Novice |
| [Anatomy of Matplotlib SciPy 2017 Tutorial Ben Root - YouTube](https://www.youtube.com/watch?v=rARMKS8jE9g) | NAME | Enthought | DURATION | 4000 | None | 2017 | Novice |
| [Data Science is Software SciPy 2016 Tutorial Peter Bull \& Isaac Slavitt - YouTube](https://www.youtube.com/watch?v=EKUy0TSLg04) | NAME | Enthought | DURATION | 9000 | None | 2016 | Novice |
| [Jake VanderPlas: Machine Learning with Scikit Learn - YouTube](https://www.youtube.com/watch?v=HC0J\_SPm9co) | NAME | PyData | DURATION | 16000 | None | 2015 | Novice |
| [Using Jupyter notebooks to develop and share interactive data displays - YouTube](https://www.youtube.com/watch?v=aXR2d9k9-h4) | NAME | PyCon Australia | DURATION | 8000 | None | 2016 | Novice |
| [Parallel Python: Analyzing Large Datasets Intermediate SciPy 2016 Tutorial Matthew Rocklin \& Mi - YouTube](https://www.youtube.com/watch?v=5Md\_sSsN51k) | NAME | Enthought | DURATION | 7000 | None | 2016 | Novice |
| [Functional Programming with Python - YouTube](https://www.youtube.com/watch?v=Ta1bAMOMFOI) | NAME | Next Day Video | DURATION | 44000 | None | 2013 | Novice |
| [Predicting sports winners using data analytics with pandas and scikit-learn by Robert Layton - YouTube](https://www.youtube.com/watch?v=k7hSD\_-gWMw) | NAME | PyCon Australia | DURATION | 13000 | None | 2015 | Novice |
| [Keynote: Project Jupyter SciPy 2016 Brian Granger - YouTube](https://www.youtube.com/watch?v=v5mrwq7yJc4) | NAME | Enthought | DURATION | 7000 | None | 2016 | Novice |
| [matplotlib (Python Plotting Library) Beginner SciPy 2016 Tutorial Nicolas Rougier - YouTube](https://www.youtube.com/watch?v=p7Mj-4kASmI) | NAME | Enthought | DURATION | 6000 | None | 2016 | Novice |
| [Awesome Big Data Algorithms - YouTube](https://www.youtube.com/watch?v=jKBwGlYb13w) | NAME | Next Day Video | DURATION | 41000 | None | 2013 | Novice |
| [Stephen Simmons - Pandas from the Inside / "Big Pandas" - YouTube](https://www.youtube.com/watch?v=YGk09nK\_xnM) | NAME | PyData | DURATION | 3000 | None | 2017 | Novice |
| [Fear and Awaiting in Async (Screencast) - YouTube](https://www.youtube.com/watch?v=Bm96RqNGbGo) | NAME | David Beazley | DURATION | 5000 | None | 2016 | Novice |
| [Brian Granger: All About Jupyter - YouTube](https://www.youtube.com/watch?v=GMKZD1Ohlzk) | NAME | PyData | DURATION | 11000 | None | 2015 | Novice |
| [Anusua Trivedi: An example of Predictive Analytics: Building a Recommendation Engine using Python - YouTube](https://www.youtube.com/watch?v=E9XTOnEgqRY) | NAME | PyData | DURATION | 11000 | None | 2015 | Novice |
| [Sarah Guido The Wild West of Data Wrangling PyCon 2017 - YouTube](https://www.youtube.com/watch?v=xn9sTXR3Cp8) | NAME | PyCon 2017 | DURATION | 3000 | None | 2017 | Novice |
| [Adventures in scikit-learn's Random Forest by Gregory Saunders - YouTube](https://www.youtube.com/watch?v=YkVscKsV\_qk) | NAME | PyCon Australia | DURATION | 9000 | None | 2015 | Novice |
| [Doing Math with Python - YouTube](https://www.youtube.com/watch?v=XJOt4QQgx0A) | NAME | PyCon Australia | DURATION | 5000 | None | 2016 | Novice |
| [Iterations of Evolution: The Unauthorized Biography of the For-Loop - YouTube](https://www.youtube.com/watch?v=2AXuhgid7E4) | NAME | David Beazley | DURATION | 2000 | None | 2017 | Novice |
| [Alex Martelli, ""Good Enough" IS Good Enough!", PyBay2016 - YouTube](https://www.youtube.com/watch?v=\_Ek3A2b-nHU) | NAME | SF Python | DURATION | 4000 | None | 2016 | Novice |
| [Renee Chu - Python for Social Scientists: Cleaning and Prepping Data - PyCon 2016 - YouTube](https://www.youtube.com/watch?v=u682UpVrMVM) | NAME | PyCon 2016 | DURATION | 3000 | None | 2016 | Novice |
| [PyMC: Markov Chain Monte Carlo in Python SciPy 2014 Chris Fonnesbeck - YouTube](https://www.youtube.com/watch?v=XbxIo7ScVzc) | NAME | Enthought | DURATION | 9000 | None | 2014 | Novice |
| [Alex Martelli, ""The Tower of Abstraction", PyBay2016 - YouTube](https://www.youtube.com/watch?v=zhpWhkW8kcc) | NAME | SF Python | DURATION | 3000 | None | 2016 | Novice |
| [Jupyter Advanced Topics Tutorial SciPy 2015 Tutorial Jonathan Frederic, Matthias Bussonier \& Tho - YouTube](https://www.youtube.com/watch?v=38R7jiCspkw) | NAME | Enthought | DURATION | 4000 | None | 2015 | Novice |
| [Rachel Thomas, "Using randomness to make code much faster", PyBay2017 - YouTube](https://www.youtube.com/watch?v=7i6kBz1kZ-A) | NAME | SF Python | DURATION | 1000 | None | 2017 | Novice |
| [Mahmoud Hashemi, Python Profiling \& Performance: elementary to enterprise, PyBay2016 - YouTube](https://www.youtube.com/watch?v=Dgnp28Ijm\_M) | NAME | SF Python | DURATION | 1000 | None | 2016 | Novice |
| [Dillon Niederhut, "What to do when your data is large, but not big", PyBay2016 - YouTube](https://www.youtube.com/watch?v=g-YCaX3ml2Q) | NAME | SF Python | DURATION | 0 | None | 2016 | Novice |
| [Cynthia Lin, "Opening Up to Open Source", PyBay2017 - YouTube](https://www.youtube.com/watch?v=rfdzOZkDqYk) | NAME | SF Python | DURATION | 0 | None | 2017 | Novice |
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}9}]:} \PY{n}{df}\PY{o}{.}\PY{n}{sort\PYZus{}values}\PY{p}{(}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{rating}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{n}{ascending} \PY{o}{=} \PY{k+kc}{False}\PY{p}{)}\PY{o}{.}\PY{n}{rating}\PY{o}{.}\PY{n}{hist}\PY{p}{(}\PY{n}{bins} \PY{o}{=} \PY{l+m+mi}{25}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}9}]:} <matplotlib.axes.\_subplots.AxesSubplot at 0x7f928bdbf748>
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_9_1.png}
\end{center}
{ \hspace*{\fill} \\}
% Add a bibliography block to the postdoc
\end{document}

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