From 397ba81d210dad7331626e4bbaccfca3b745576c Mon Sep 17 00:00:00 2001
From: Christian Kolset <christian.kolset@gmail.com>
Date: Wed, 5 Nov 2025 10:13:24 -0700
Subject: Removed book/ from tracking

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 book/module1/1_excel_to_python.tex | 119 -------------------------------------
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-\section{Excel to Python}\label{excel-to-python}
-
-\begin{itemize}
-\tightlist
-\item
-  Importing
-\item
-  Plotting
-\item
-  Statistical analysis
-\end{itemize}
-
-\subsection{\texorpdfstring{\textbf{How Excel Translates to
-Python}}{How Excel Translates to Python}}\label{how-excel-translates-to-python}
-
-Here's how common Excel functionalities map to Python:
-
-\begin{longtable}[]{@{}
-  >{\raggedright\arraybackslash}p{(\columnwidth - 2\tabcolsep) * \real{0.2911}}
-  >{\raggedright\arraybackslash}p{(\columnwidth - 2\tabcolsep) * \real{0.7089}}@{}}
-\toprule\noalign{}
-\begin{minipage}[b]{\linewidth}\raggedright
-\textbf{Excel Feature}
-\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
-\textbf{Python Equivalent}
-\end{minipage} \\
-\midrule\noalign{}
-\endhead
-\bottomrule\noalign{}
-\endlastfoot
-Formulas (SUM, AVERAGE) & \texttt{numpy}, \texttt{pandas}
-(\texttt{df.sum()}, \texttt{df.mean()}) \\
-Sorting \& Filtering & \texttt{pandas.sort\_values()},
-\texttt{df{[}df{[}\textquotesingle{}col\textquotesingle{}{]}\ \textgreater{}\ value{]}} \\
-Conditional Formatting & \texttt{matplotlib} for highlighting \\
-Pivot Tables & \texttt{pandas.pivot\_table()} \\
-Charts \& Graphs & \texttt{matplotlib}, \texttt{seaborn},
-\texttt{plotly} \\
-Regression Analysis & \texttt{scipy.stats.linregress},
-\texttt{sklearn.linear\_model} \\
-Solver/Optimization & \texttt{scipy.optimize} \\
-VBA Macros & Python scripting with \texttt{openpyxl}, \texttt{pandas},
-or \texttt{xlwings} \\
-\end{longtable}
-
-\subsection{Statistical functions}\label{statistical-functions}
-
-\paragraph{SUM}\label{sum}
-
-Built-in:
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\NormalTok{my\_array }\OperatorTok{=}\NormalTok{ [}\DecValTok{1}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{3}\NormalTok{, }\DecValTok{4}\NormalTok{, }\DecValTok{5}\NormalTok{]}
-\NormalTok{total }\OperatorTok{=} \BuiltInTok{sum}\NormalTok{(my\_array)}
-\BuiltInTok{print}\NormalTok{(total)  }\CommentTok{\# Output: 15}
-\end{Highlighting}
-\end{Shaded}
-
-Numpy:
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np}
-
-\NormalTok{my\_array }\OperatorTok{=}\NormalTok{ np.array([}\DecValTok{1}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{3}\NormalTok{, }\DecValTok{4}\NormalTok{, }\DecValTok{5}\NormalTok{])}
-\NormalTok{total }\OperatorTok{=}\NormalTok{ np.}\BuiltInTok{sum}\NormalTok{(my\_array)}
-\BuiltInTok{print}\NormalTok{(total)  }\CommentTok{\# Output: 15}
-\end{Highlighting}
-\end{Shaded}
-
-\subsubsection{Average}\label{average}
-
-Built-in:
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\NormalTok{my\_array }\OperatorTok{=}\NormalTok{ [}\DecValTok{1}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{3}\NormalTok{, }\DecValTok{4}\NormalTok{, }\DecValTok{5}\NormalTok{]}
-\NormalTok{average }\OperatorTok{=} \BuiltInTok{sum}\NormalTok{(my\_array) }\OperatorTok{/} \BuiltInTok{len}\NormalTok{(my\_array)}
-\BuiltInTok{print}\NormalTok{(average)  }\CommentTok{\# Output: 3.0}
-\end{Highlighting}
-\end{Shaded}
-
-Numpy:
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np}
-
-\NormalTok{my\_array }\OperatorTok{=}\NormalTok{ np.array([}\DecValTok{1}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{3}\NormalTok{, }\DecValTok{4}\NormalTok{, }\DecValTok{5}\NormalTok{])}
-\NormalTok{average }\OperatorTok{=}\NormalTok{ np.mean(my\_array)}
-\BuiltInTok{print}\NormalTok{(average)  }\CommentTok{\# Output: 3.0}
-\end{Highlighting}
-\end{Shaded}
-
-\subsection{Plotting}\label{plotting}
-
-We can use the package \emph{matplotlib} to plot our graphs in python.
-Matplotlib provides data visualization tools for the Scientific Python
-Ecosystem. You can make very professional looking figures with this
-tool.
-
-Here is a section from the matplotlib documentation page that you can
-run in python.
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\ImportTok{import}\NormalTok{ matplotlib.pyplot }\ImportTok{as}\NormalTok{ plt}
-
-\NormalTok{fig, ax }\OperatorTok{=}\NormalTok{ plt.subplots()             }\CommentTok{\# Create a figure containing a single Axes.}
-\NormalTok{ax.plot([}\DecValTok{1}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{3}\NormalTok{, }\DecValTok{4}\NormalTok{], [}\DecValTok{1}\NormalTok{, }\DecValTok{4}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{3}\NormalTok{])  }\CommentTok{\# Plot some data on the Axes.}
-\NormalTok{plt.show()                           }\CommentTok{\# Show the figure.}
-\end{Highlighting}
-\end{Shaded}
-
-Check out the documentation pages for a
-\href{https://matplotlib.org/stable/users/explain/quick_start.html\#a-simple-example}{simple
-example} or more information on the types of plots you came create
-\href{https://matplotlib.org/stable/plot_types/index.html}{here}.
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