From 0fc68d39cf23955f443c75fa536922b45cf2e1dc Mon Sep 17 00:00:00 2001
From: Christian Kolset <christian.kolset@gmail.com>
Date: Thu, 24 Apr 2025 23:28:53 -0600
Subject: Updated .tex files

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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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