\documentclass[10pt,a4paper]{article}

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\usepackage{seqsplit}           % Splits long words.
%\usepackage{opensans}          % Can't make this work so far. Shame. Would be lovely.
\usepackage[normalem]{ulem}     % For underlining links
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\usepackage{amsmath}            % Symbols
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% Document Info
\author{ryurk}
\pdfinfo{
  /Title (exam-3.pdf)
  /Creator (Cheatography)
  /Author (ryurk)
  /Subject (Exam \#3 Cheat Sheet)
}

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\fancyhead[L]{
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{C}
    \SetRowColor{DarkBackground}
    \vspace{-7pt}
    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
    }
\end{tabulary}
\columnbreak
\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Exam \#3 Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{ryurk} via \textcolor{DarkBackground}{\uline{cheatography.com/208729/cs/44997/}}}
\end{tabulary}
\end{multicols}}

\fancyfoot[L]{ \footnotesize
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{LL}
  \SetRowColor{FootBackground}
  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}ryurk \\
  \uline{cheatography.com/ryurk} \\
  \end{tabulary}
\vfill
\columnbreak
\begin{tabulary}{5.8cm}{L}
  \SetRowColor{FootBackground}
  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 21st November, 2024.\\
   Page {\thepage} of \pageref{LastPage}.
\end{tabulary}
\vfill
\columnbreak
\begin{tabulary}{5.8cm}{L}
  \SetRowColor{FootBackground}
  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Sponsor}}  \\
  \SetRowColor{white}
  \vspace{-5pt}
  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
\end{tabulary}
\end{multicols}}




\begin{document}
\raggedright
\raggedcolumns

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% www.emerson.emory.edu/services/latex/latex_169.html
\footnotesize % Small font.

\begin{multicols*}{2}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Day 14}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{def hello(): \newline        print("hello world") \newline invoke it with hello() \newline  \newline def welcome(name): \newline         print(f'Hello, \{name\}") \newline invoke it with welcom("amy" \newline  \newline def welcome\_greeting(name, greeting) \newline         print(f' Hey\{name\}. \{greeting\}") \newline invoke it with welcome\_greeting('Liz', "How are you?") -{}-\textgreater{} Kwargs \newline  \newline def exponent(base,exponent): \newline        power = base {\emph{* exponent \newline        return power \newline num1 = 2 \newline num2 = 3 \newline answer = exponent(num1,num2) \newline print(answer) \newline print(exponent(2,3)) \newline  \newline def sum(}}parameters): -{}-\textgreater{} unlimited parameters \newline         total = 0 \newline for i in parameters: \newline         total = total + each\_number \newline         return total \newline sum = sum(1,2,3,4,5) \newline print(sum)} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Day 16}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Df = pd.read\_csv(url) \newline  \newline print(df.to\_string()) \newline  \newline Null is null, a null is something = a special creation to indicate the absence of a value - its a made up value \newline  \newline Df.shape = not a method does not need () \newline  \newline Look at only one column = df{[}'School Name'{]} \newline  \newline print( df{[} 'School Name' {]}.to\_string( )) \newline  \newline Find the unique names= df{[}'School Name'{]}.unique() \newline  \newline type(unique\_schools) shows the type.. This is not a data frame \newline  \newline Statistics: \newline  \newline df{[}'Starting Salary'{]}.max() or df{[}'Starting Salary'{]}.mean() or df{[}'Starting Salary'{]}.min() \newline  \newline Find the NAs = df{[}'Starting Salary'{]}.isna() then to count the trues = na\_rows.sum() \newline  \newline Based on a condition \newline  \newline Df2 = df.query(" `Starting Salary` \textgreater{} 75000 ")} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Day 20}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Change individual values \newline  \newline Df.loc{[}20, 'Starting Salary'{]} = ' ' \newline  \newline Convert to numeric type = df{[}'Starting Salary'{]} = pd.to\_numeric( df{[}'Starting Salary'{]} ) \newline  \newline Df.loc{[}139, 'Starting Salary'{]} = 46000 \newline  \newline  \newline \#Convert starting salary to numeric FORCE CONVERT or "COERCE" conversion \newline  \newline Error\_columns = pd.to\_numeric( df{[}'Starting Salary'{]}, errors= 'coerce') \newline  \newline print(error\_columns) \newline  \newline \#find the NAs \newline  \newline Nas = error\_columns.isna() \newline  \newline print(Nas) \newline  \newline Df{[}20:25{]} \newline  \newline \#fix columns \newline  \newline Df.loc{[}70, 'Starting Salary'{]} = 42600 \newline  \newline df{[}Nas{]} \newline  \newline  \newline Save it to the original by overwriting \newline  \newline df{[}'Starting Salary'{]} = pd.to\_numeric(df{[}'Starting Salary'{]})} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Day 15}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{import pandas as pd \newline data\_list = {[}45,74,78{]} \newline series\_of\_numbers = pd.series(data\_list) \newline print(serties\_of\_numbers{[}1{]}) -{}-\textgreater{} 74 \newline  \newline Years = {[}2021, 2022, 2023{]} \newline series\_of\_numbers = \seqsplit{pd.series(data=data\_list}, index=years) \newline print(series\_of\_numbers) \newline print(series\_of\_numbers{[}2021{]} -{}-\textgreater{} 2021 \newline  \newline grades = \{'A': 34, 'B': 56\} \newline grade\_series = pd.series(data=grades) \newline print(grade\_series) or print(grade\_series{[}'A'{]} \newline  \newline quiz\_scores = \{ \newline              'Quiz1': {[}32,56,56{]}, \newline              'Quiz2': {[}78,34,32{]}\} \newline df = \seqsplit{pd.DataFrame(data=quiz\_scores)} \newline print(df) \newline \# to overwrite -{}-\textgreater{} \newline df = \seqsplit{pd.DataFrame(data=quiz\_scores.} index={[}'mike', 'susan', 'amy'{]} \newline  \newline df.head() -{}-\textgreater{} top 5 rows \newline df.tail() -{}-\textgreater{} bottom 5 rows \newline df{[}40:60{]} -{}-\textgreater{} rows 40-59} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Day 18}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Find all the schools with the name Pitt \newline  \newline Df2 = df.query( " `School Name` == 'Pitt' ") \newline  \newline df2.head() \newline  \newline Remove a column: \seqsplit{df.drop(columns='Starting} Salary', inplace = True) \newline  \newline Or df = \seqsplit{df.drop(columns='Starting} Salary') \newline  \newline Drop a row df.drop(index=2, inplace = True) \newline  \newline Delete entire row of data when one column had missing data df=df.dropna()} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Day 19}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Load descriptives for the df = df.describe() \newline  \newline Load tab-delimitted file \newline  \newline Df2 = pd.read\_csv(URL, sep='\textbackslash{}t' ) \newline  \newline Replace function: \newline  \newline Df{[} 'School Name'{]}.replace('-', ' -', regex=True, inplace=True) \newline  \newline  \newline Fillnas = df{[}'Starting Salary'{]}.fillna(0, inplace=True) \newline  \newline  \newline How many unique school names are there: \newline  \newline len( df{[}'School Name'{]}.unique()) \newline  \newline  \newline Show only the rows in which df are duplicate: \newline  \newline Duplicates = df.duplicated(subset= 'School Name') \newline  \newline Boolean series = df{[}duplicates{]} \newline  \newline Df2 = \seqsplit{df.drop\_duplicates(subset='School} Name', keep='first') \newline  \newline  \newline Find out schools with specified \newline  \newline PA\_schools = df2{[}'School Name'{]}.str.contains('Pennsylvania') \newline  \newline Use a boolean series df2{[}PA\_schools{]} \newline  \newline Overwrite instead on inplace \newline  \newline Df2 = \seqsplit{df2.sort\_values('Starting} Salary', ascending = False) \newline  \newline Fix one bad value: \newline  \newline Df2.loc{[}2, 'Starting Salary'{]} = df2{[}'Starting Salary'{]}.mean()} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}


% That's all folks
\end{multicols*}

\end{document}