\documentclass[10pt,a4paper]{article}

% Packages
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\usepackage{multicol}           % Allows multicols in tables
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%\usepackage[utf8x]{inputenc}    % For unicode character support
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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
% Most of the following are not required for the majority
% of cheat sheets but are needed for some symbol support.
\usepackage{amsmath}            % Symbols
\usepackage{MnSymbol}           % Symbols
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%\usepackage[english,german,french,spanish,italian]{babel}              % Languages

% Document Info
\author{Taissir Boukrouba (taissir2002)}
\pdfinfo{
  /Title (pandas-essentials.pdf)
  /Creator (Cheatography)
  /Author (Taissir Boukrouba (taissir2002))
  /Subject (Pandas Essentials Cheat Sheet)
}

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\fancyhead{} % Set header to blank
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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{Pandas Essentials Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{Taissir Boukrouba (taissir2002)} via \textcolor{DarkBackground}{\uline{cheatography.com/193976/cs/41332/}}}
\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}Taissir Boukrouba (taissir2002) \\
  \uline{cheatography.com/taissir2002} \\
  \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 19th November, 2023.\\
   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
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\begin{multicols*}{3}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Introduction}}  \tn
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{{\bf{Pandas}} is a package built on top of NumPy, and provides an efficient implementation of many features :  \newline % Row Count 3 (+ 3)
- DataFrames \newline % Row Count 4 (+ 1)
- Series \newline % Row Count 5 (+ 1)
- Data Alignement \newline % Row Count 6 (+ 1)
- Handling Missing Data  \newline % Row Count 7 (+ 1)
- Grouping and Aggregation \newline % Row Count 8 (+ 1)
- Data Input and Output \newline % Row Count 9 (+ 1)
- Handling Time Series% Row Count 10 (+ 1)
} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.55618 cm} x{1.00694 cm} x{2.01388 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Pandas General Methods}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Accessing values}} & \seqsplit{pd\_ds.values} & DataFrame.values\{\{nl\}\}Series.values \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
{\bf{Accessing Indices}} & \seqsplit{pd\_ds.index} & DataFrame.index\{\{nl\}\}Series.index \tn 
% Row Count 5 (+ 2)
% Row 2
\SetRowColor{LightBackground}
{\bf{Accessing specific element}} & pd\_ds{[}idx{]} & DataFrame{[}1{]}\{\{nl\}\}Series{[}1{]} \tn 
% Row Count 8 (+ 3)
% Row 3
\SetRowColor{white}
{\bf{Accessing range of elements}} & pd\_ds{[}start : end{]} & DataFrame{[}1:4{]}\{\{nl\}\}Series{[}2:5{]} \tn 
% Row Count 11 (+ 3)
% Row 4
\SetRowColor{LightBackground}
{\bf{Implicit Indexing}} & df.iloc{[}rows , cols{]} & data.iloc{[}1:3{]}\{\{nl\}\}\#last index is not included \tn 
% Row Count 14 (+ 3)
% Row 5
\SetRowColor{white}
{\bf{Explicit Indexing}} & df.loc{[}rows,cols{]} & data.loc{[}'California' : 'Texas'{]}\{\{nl\}\}\#last index is included \tn 
% Row Count 18 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.60195 cm} x{1.3731 cm} x{1.60195 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Pandas Series}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Creating Series with lists}} & pd.Series({[}values{]}, index= list) & data = pd.Series({[}0.25, 0.5, 0.75, 1.0{]}, \{\{nl\}\}index={[}'a', 'b', 'c', 'd'{]}) \tn 
% Row Count 6 (+ 6)
% Row 1
\SetRowColor{white}
{\bf{Creating Series with dictionaries}} & pd.Series(\{index:value\}) & \seqsplit{population\_dict} = \{'California': 38332521, 'Texas': 26448193, 'New York': 19651127, 'Florida': 19552860, 'Illinois': 12882135\}\{\{nl\}\}population = \seqsplit{pd.Series(population\_dict)} \tn 
% Row Count 19 (+ 13)
% Row 2
\SetRowColor{LightBackground}
{\bf{Slicing Series}} & Series{[}from\_idx : to\_idx{]} & population{[}'Texas':'Florida'{]} \tn 
% Row Count 22 (+ 3)
% Row 3
\SetRowColor{white}
{\bf{Slicing Indices with Dictionary Series}} & pd.Series(\{index:value\} , index={[}{]}) & pd.Series(\{2:'a', 1:'b', 3:'c'\}, index={[}3, 2{]}) \{\{nl\}\} \# only returns the third and second index respectfully \tn 
% Row Count 30 (+ 8)
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\SetRowColor{LightBackground}
\mymulticolumn{3}{x{5.377cm}}{Pandas Series Index Can be a list of string or list of integers (or any desired type) unlike numpy arrays}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.19002 cm} x{1.69349 cm} x{1.69349 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Pandas DataFrames}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Creating DataFrame}} & pd.DataFrame(\{index : iterable\}) & pd.DataFrame(\{'population': population, 'area': area\}) \tn 
% Row Count 4 (+ 4)
% Row 1
\SetRowColor{white}
{\bf{Adding Column names}} & \seqsplit{pd.DataFrame(dict} , columns = {[}list\_of\_col\_names{]}) & \seqsplit{pd.DataFrame(population}, columns={[}'population'{]}) \tn 
% Row Count 8 (+ 4)
% Row 2
\SetRowColor{LightBackground}
{\bf{Slicing DataFrame Index}} & \seqsplit{pd.DataFrame(dict} , columns = {[} {]} , index = {[} {]}) & \seqsplit{pd.DataFrame(np.random.rand(3}, 2), columns={[}'foo', 'bar'{]}, index={[}'a', 'b', 'c'{]}) \tn 
% Row Count 14 (+ 6)
% Row 3
\SetRowColor{white}
{\bf{Reading CSV Files}} & \seqsplit{pd.read\_csv(source} , index\_col = col) & \seqsplit{pd.read\_csv("data/president\_heights.csv"}, \seqsplit{index\_col="order")} \tn 
% Row Count 19 (+ 5)
% Row 4
\SetRowColor{LightBackground}
{\bf{Saving DF to CSV}} & \seqsplit{dataframe.to\_csv(source)} & \seqsplit{df.to\_csv("data/president\_heights\_copy.csv")} \tn 
% Row Count 23 (+ 4)
% Row 5
\SetRowColor{white}
{\bf{Reading Excel Files}} & \seqsplit{pd.read\_excel(source)} & \seqsplit{pd.read\_excel("data/president\_heights.xlsx")} \tn 
% Row Count 27 (+ 4)
% Row 6
\SetRowColor{LightBackground}
{\bf{Saving DF to Excel}} & \seqsplit{dataframe.to\_excel(source)} & \seqsplit{df.to\_excel("data/president\_heights\_copy.xlsx")} \tn 
% Row Count 31 (+ 4)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{1.19002 cm} x{1.69349 cm} x{1.69349 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Pandas DataFrames (cont)}}  \tn
% Row 7
\SetRowColor{LightBackground}
{\bf{Access DataFrame Columns}} & \seqsplit{dataframe.columns} & df.columns \tn 
% Row Count 3 (+ 3)
% Row 8
\SetRowColor{white}
{\bf{Transposing DataFrames}} & dataframe.T & df.T \tn 
% Row Count 6 (+ 3)
% Row 9
\SetRowColor{LightBackground}
{\bf{Subsetting Using loc}} & dataframe.loc{[}condition , cols{]} & data.loc{[}data.density \textgreater{} 100, {[}'pop', 'density'{]}{]} \tn 
% Row Count 10 (+ 4)
% Row 10
\SetRowColor{white}
{\bf{Masking}} & dataframe{[}mask{]} & data{[}data.density \textgreater{} 100{]} \tn 
% Row Count 12 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.32733 cm} x{1.60195 cm} x{1.64772 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Pandas Index}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Creating Index}} & \seqsplit{pd.Index(list)} & pd.Index({[}2, 3, 5, 7, 11{]}) \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
{\bf{Accessing Index}} & Index{[}idx{]} & ind{[}1{]} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
{\bf{Slicing Index}} & Index{[}from : to : step{]} & ind{[} : : 2{]} \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
{\bf{Intersection Between Indices}} & \seqsplit{index\_1.intersection(index\_2)} & indA = pd.Index({[}1, 3, 5, 7, 9{]})\{\{nl\}\}indB = pd.Index({[}2, 3, 5, 7, 11{]})\{\{nl\}\}indA.intersection(indB) \tn 
% Row Count 14 (+ 8)
% Row 4
\SetRowColor{LightBackground}
{\bf{Union Between Indices}} & \seqsplit{index\_1.union(index\_2)} & \seqsplit{indA.union(indB)} \tn 
% Row Count 17 (+ 3)
% Row 5
\SetRowColor{white}
{\bf{Symmetric Difference}} & index\_1. \seqsplit{symmetric\_difference(index\_2)} & \seqsplit{indA.symmetric\_difference(indB)} \tn 
% Row Count 20 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\SetRowColor{LightBackground}
\mymulticolumn{3}{x{5.377cm}}{The {\bf{Index}} has many of the attributes familiar from NumPy arrays such as :  \newline ind.size, ind.shape, ind.ndim, ind.dtype}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{p{0.4977 cm} x{4.4793 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Pandas Universal Functions}}  \tn
% Row 0
\SetRowColor{LightBackground}
+ & add() \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
- & sub() , subtract() \tn 
% Row Count 2 (+ 1)
% Row 2
\SetRowColor{LightBackground}
* & mul(), multiply() \tn 
% Row Count 3 (+ 1)
% Row 3
\SetRowColor{white}
/ & truediv(), div(), divide() \tn 
% Row Count 4 (+ 1)
% Row 4
\SetRowColor{LightBackground}
// & floordiv() \tn 
% Row Count 5 (+ 1)
% Row 5
\SetRowColor{white}
\% & mod() \tn 
% Row Count 6 (+ 1)
% Row 6
\SetRowColor{LightBackground}
** & pow() \tn 
% Row Count 7 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{These universal functions are used in the following form :  \newline - \seqsplit{data\_struct.uf(data\_struct\_2)} \newline - data\_struct.uf()}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.94103 cm} x{3.03597 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Datatype Conversions (NaN or None)}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Float}} & No change \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
{\bf{Object}} & No change \tn 
% Row Count 2 (+ 1)
% Row 2
\SetRowColor{LightBackground}
{\bf{Integer}} & Upcast to float64 \tn 
% Row Count 3 (+ 1)
% Row 3
\SetRowColor{white}
{\bf{Boolean}} & Upcast to object \tn 
% Row Count 4 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{These are data type conversion when there is missing values}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.55618 cm} x{1.51041 cm} x{1.51041 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Operating On Missing Values}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Nullability Check}} & \seqsplit{data\_struc.is\_null()} & data = pd.Series({[}1, np.nan, 'hello', None{]})\{\{nl\}\}data.isnull() \tn 
% Row Count 5 (+ 5)
% Row 1
\SetRowColor{white}
{\bf{Non-Nullability Check}} & \seqsplit{data\_struc.not\_null()} & \seqsplit{data.not\_null()} \tn 
% Row Count 7 (+ 2)
% Row 2
\SetRowColor{LightBackground}
{\bf{Slicing Non-Null Values}} & data\_struct{[}data\_struc.not\_null(){]} & data{[}data.not\_null(){]} \tn 
% Row Count 10 (+ 3)
% Row 3
\SetRowColor{white}
{\bf{Dropping Null Values}} & \seqsplit{data\_struct.dropna(axis=0/1} , how = 'any'/'all' , thresh = n) & \seqsplit{data.dropna(axis} = 0 , thresh = 2)\{\{nl\}\}\# the tresh means each row has at least 2 non-null values \tn 
% Row Count 18 (+ 8)
% Row 4
\SetRowColor{LightBackground}
{\bf{Filling Missing Values}} & \seqsplit{data\_struct.fillna(value} , method = \seqsplit{'ffil'/'bfill'} , axis = 0/1) & \seqsplit{df.fillna(method='ffill'}, axis=1) \tn 
% Row Count 23 (+ 5)
% Row 5
\SetRowColor{white}
{\bf{Filling Using A Function (Interpolation)}} & \seqsplit{data\_struct.interpolate(method='linear'/'polynomial'...)} & df.interpolate()\{\{nl\}\}\# the method is linear by default \tn 
% Row Count 28 (+ 5)
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\SetRowColor{LightBackground}
\mymulticolumn{3}{x{5.377cm}}{When Working with missing values methods , axis = 0 means rows and 1 columns}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.55618 cm} x{1.51041 cm} x{1.51041 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Pandas Multi-Indexing}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Creating Multi-Index From Tuples}} & \seqsplit{pd.MultiIndex.from\_tuples(tuple)} & index = \seqsplit{pd.MultiIndex}.from\_tuples({[}('California', 2000), \seqsplit{('California'}, 2010){]}) \tn 
% Row Count 7 (+ 7)
% Row 1
\SetRowColor{white}
{\bf{Creating Multi-Index From Arrays}} & \seqsplit{pd.MultiIndex.from\_arrays(list)} & \seqsplit{pd.MultiIndex}.from\_arrays({[}{[}'a', 'a', 'b', 'b'{]}, {[}1, 2, 1, 2{]}{]}) \tn 
% Row Count 12 (+ 5)
% Row 2
\SetRowColor{LightBackground}
{\bf{Creating Multi-Index From Product}} & \seqsplit{pd.MultiIndex}.from\_product({[}index1\_list,index2\_list{]}) & \seqsplit{pd.MultiIndex}.from\_product({[}{[}'a', 'b'{]}, {[}1, 2{]}{]}) \tn 
% Row Count 17 (+ 5)
% Row 3
\SetRowColor{white}
{\bf{Creating Multi-Index From DataFrame Values}} & \seqsplit{pd.MultiIndex.from\_frame(dataframe)} & df = pd.DataFrame({[}{[}'a', 'b'{]}, {[}1, 2{]}{]})\{\{nl\}\}pd.MultiIndex.from\_frame(df) \tn 
% Row Count 23 (+ 6)
% Row 4
\SetRowColor{LightBackground}
{\bf{Applying Muli-Index}} & \seqsplit{data\_struct.reindex(index)} & pop = \seqsplit{pop.reindex(index)} \tn 
% Row Count 25 (+ 2)
% Row 5
\SetRowColor{white}
{\bf{Setting Index From Columns}} & data\_struct.set\_index({[}cols{]}) & pop\_flat.set\_index({[}'population'{]}) \tn 
% Row Count 28 (+ 3)
% Row 6
\SetRowColor{LightBackground}
{\bf{Accessing \seqsplit{Multi-Indexed} Data Structures}} & data\_struct{[}first\_index,second\_index,....., col{]} & pop{[}:, 2010{]}\{\{nl\}\}\# gets all rows from first index and only 2010 rows from second index \tn 
% Row Count 35 (+ 7)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{1.55618 cm} x{1.51041 cm} x{1.51041 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Pandas Multi-Indexing (cont)}}  \tn
% Row 7
\SetRowColor{LightBackground}
{\bf{Unstacking}} & \seqsplit{data\_struct.unstack()} & pop.unstack()\{\{nl\}\}\# this converts the last index (if we have 2 then the second one) values into cols \tn 
% Row Count 8 (+ 8)
% Row 8
\SetRowColor{white}
{\bf{Stacking}} & \seqsplit{data\_struct.stack()} & pop.stack()\{\{nl\}\}\# this converts columns into a second index \tn 
% Row Count 13 (+ 5)
% Row 9
\SetRowColor{LightBackground}
{\bf{Naming Multi-Indexes}} & \seqsplit{data\_struct.index.names} = list & \seqsplit{pop.index.names} = {[}'state', 'year'{]} \tn 
% Row Count 16 (+ 3)
% Row 10
\SetRowColor{white}
{\bf{Swapping Multi-Indexes}} & \seqsplit{data\_struct.swaplevel(0},1) & pop\_df = \seqsplit{pop\_df.swaplevel(0},1) \tn 
% Row Count 19 (+ 3)
% Row 11
\SetRowColor{LightBackground}
{\bf{Dropping Multi-Indexes}} & \seqsplit{data\_struct.droplevel(level=index)} & \seqsplit{pop\_df.droplevel(level=0)} \tn 
% Row Count 22 (+ 3)
% Row 12
\SetRowColor{white}
{\bf{Multi-Index In Columns}} & \seqsplit{pd.DataFrame(data}, \seqsplit{index=multi\_index\_rows}, \seqsplit{columns=multi\_indx\_cols)} & columns = \seqsplit{pd.MultiIndex}.from\_product({[}{[}'Bob', 'Guido', 'Sue'{]}, {[}'HR', 'Temp'{]}{]}, names={[}'subject', 'type'{]})\{\{nl\}\}health\_data = \seqsplit{pd.DataFrame(data}, index=index, \seqsplit{columns=columns)} \tn 
% Row Count 36 (+ 14)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{1.55618 cm} x{1.51041 cm} x{1.51041 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Pandas Multi-Indexing (cont)}}  \tn
% Row 13
\SetRowColor{LightBackground}
{\bf{Slicing Using Multi-Index Column Values}} & dataframe{[}multi\_ind\_col\_value{]} & health\_data{[}'Guido'{]} \tn 
% Row Count 4 (+ 4)
% Row 14
\SetRowColor{white}
{\bf{Slicing Multi-Index Cols \& Rows Using IndexSlice}} & idx = pd.IndexSlice\{\{nl\}\}df.loc{[}idx{[}index\_row1,index\_row2{]}, idx{[}index\_col1,index\_col2{]}{]} & idx = pd.IndexSlice\{\{nl\}\}health\_data.loc{[}idx{[}:, 1{]}, idx{[}:, 'HR'{]}{]} \tn 
% Row Count 11 (+ 7)
% Row 15
\SetRowColor{LightBackground}
{\bf{Resetting Multi-Index to Cols}} & \seqsplit{data\_struct.reset\_index()} & \seqsplit{pop.reset\_index(name='population')} \tn 
% Row Count 14 (+ 3)
% Row 16
\SetRowColor{white}
{\bf{Sorting Multi-Index}} & \seqsplit{data\_struct.sort\_index()} & \seqsplit{data.sort\_index()} \tn 
% Row Count 16 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\SetRowColor{LightBackground}
\mymulticolumn{3}{x{5.377cm}}{It is a good practice to sort the values after swapping Multi-index Levels}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.55618 cm} x{1.51041 cm} x{1.51041 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Concatenation , Merging and Joins}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Concatenation}} & pd.concat({[}data\_struc , data\_struct2{]} , \seqsplit{ignore\_index} = True/False) & pd.concat({[}ser1, ser2{]}) \tn 
% Row Count 6 (+ 6)
% Row 1
\SetRowColor{white}
{\bf{Adding MultiIndex Keys}} & pd.concat({[}data\_struc , data\_struct2{]} , keys = {[}'a','b'{]} ) & display('x', 'y', "pd.concat({[}x, y{]}, keys={[}'x', 'y'{]})") \tn 
% Row Count 11 (+ 5)
% Row 2
\SetRowColor{LightBackground}
{\bf{Concatenation with Joins}} & pd.concat({[}data\_struc , data\_struct2{]} , join = \seqsplit{'outer'/'inner'} ) & pd.concat({[}df5, df6{]}, join='inner')\{\{nl\}\}\# The intersection of 2 DFs \tn 
% Row Count 17 (+ 6)
% Row 3
\SetRowColor{white}
{\bf{Merging}} & \seqsplit{pd.merge(data\_struc} , \seqsplit{data\_struct2)} & df3 = pd.merge(df1, df2) \tn 
% Row Count 20 (+ 3)
% Row 4
\SetRowColor{LightBackground}
{\bf{Merging on Columns}} & \seqsplit{pd.merge(data\_struc} , \seqsplit{data\_struct2} , on \seqsplit{='col\_name')} & pd.merge(df1, df2, \seqsplit{on='employee')} \tn 
% Row Count 24 (+ 4)
% Row 5
\SetRowColor{white}
{\bf{Specific Merging}} & \seqsplit{pd.merge(data\_struc} , \seqsplit{data\_struct2} , right\_on ='col\_name' , left\_on \seqsplit{='col\_name')} & pd.merge(df1, df3, \seqsplit{left\_on="employee"}, \seqsplit{right\_on="name")}.drop('name', axis=1) \{\{nl\}\}\# When using right and left on we have to drop one of the cols (avoid redundancy) \tn 
% Row Count 37 (+ 13)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{1.55618 cm} x{1.51041 cm} x{1.51041 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Concatenation , Merging and Joins (cont)}}  \tn
% Row 6
\SetRowColor{LightBackground}
{\bf{Joining (Default Merge to indices only)}} & \seqsplit{data\_struct.join(data\_struct2)} & \seqsplit{df1a.join(df2a)} \tn 
% Row Count 4 (+ 4)
% Row 7
\SetRowColor{white}
{\bf{Merging on Indices}} & \seqsplit{pd.merge(data\_struc} , \seqsplit{data\_struct2} , \seqsplit{left\_index=True} , right\_index = True) & \seqsplit{pd.merge(df1a}, df3, \seqsplit{left\_index=True}, \seqsplit{right\_on='name')} \tn 
% Row Count 10 (+ 6)
% Row 8
\SetRowColor{LightBackground}
{\bf{Merging with methods}} & \seqsplit{pd.merge(data\_struct}, \seqsplit{data\_struct2}, \seqsplit{how='inner'/'outer'/'left'/'right')} & pd.merge(df6, df7, how='inner') \tn 
% Row Count 16 (+ 6)
% Row 9
\SetRowColor{white}
{\bf{Merging Conflicting Col Names}} & \seqsplit{pd.merge(data\_struct}, \seqsplit{data\_struct2}, suffixes = {[}'\_suff1', \_'suff2'{]}) & pd.merge(df8, df9, on="name", suffixes={[}"\_Sem1", "\_Sem\_2"{]}) \tn 
% Row Count 22 (+ 6)
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\SetRowColor{LightBackground}
\mymulticolumn{3}{x{5.377cm}}{- Note that when adding multi-index keys in a concatenation , the number of keys should be the same as the number of data structures being concatenated}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.51041 cm} x{1.51041 cm} x{1.55618 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Advanced Group By Methods}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Aggregation using a list (General)}} & \seqsplit{df.groupby('col')}.aggregate({[}list\_of\_methods{]} & \seqsplit{df.groupby('key')}.aggregate({[}'min', np.median, max{]}) \tn 
% Row Count 4 (+ 4)
% Row 1
\SetRowColor{white}
{\bf{Aggregation using a dict (Specific)}} & \seqsplit{df.groupby('col')}.aggregate(\{'col' : 'method' , 'col2' : 'method'\}) & \seqsplit{df.groupby('key')}.aggregate(\{'data1': 'min', 'data2': 'max'\}) \tn 
% Row Count 10 (+ 6)
% Row 2
\SetRowColor{LightBackground}
{\bf{Filtering}} & \seqsplit{df.groupby('col').filter(func)} & def \seqsplit{filter\_func(x):} \{\{nl\}\}   return x{[}'data2'{]}.std() \textgreater{} 4\{\{nl\}\} \seqsplit{df.groupby('key').filter(filter\_func)} \tn 
% Row Count 18 (+ 8)
% Row 3
\SetRowColor{white}
{\bf{Transformation}} & \seqsplit{df.groupby('col').transform(lambda\_func)} & \seqsplit{df.groupby('key').transform(lambda} x: x - x.mean()) \tn 
% Row Count 22 (+ 4)
% Row 4
\SetRowColor{LightBackground}
{\bf{Apply}} & \seqsplit{df.groupby('col').apply(user\_func)} & def norm\_by\_data2(x):\{\{nl\}\} \# x is a DataFrame of group values \{\{nl\}\}x{[}'data1'{]} /= x{[}'data2'{]}.sum()\{\{nl\}\} return x \{\{nl\}\}df.groupby('key').apply(norm\_by\_data2) \tn 
% Row Count 35 (+ 13)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{1.51041 cm} x{1.51041 cm} x{1.55618 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Advanced Group By Methods (cont)}}  \tn
% Row 5
\SetRowColor{LightBackground}
{\bf{Grouping By Custom Mapping}} & \seqsplit{df.groupby(mapping).method} & mapping = \{'A': 'vowel', 'B': 'consonant', 'C': 'consonant'\}\{\{nl\}\}df2.groupby(mapping).sum() \tn 
% Row Count 8 (+ 8)
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\SetRowColor{LightBackground}
\mymulticolumn{3}{x{5.377cm}}{{\bf{aggregate()}} : this method allows using more than one function with groupby for different columns \newline {\bf{filter()}} : this method allows user-defined filter functions to be applied with a groupby (uses boolean operations only) \newline {\bf{transform()}}: mostly uses lambda functions to returned new and transformed version of a columns \newline {\bf{apply()}}: this method allows you to apply arbitrary user-defined functions with groupby}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\end{tabularx}
\par\addvspace{1.3em}


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

\end{document}