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% Document Info
\author{DarioPittera (aggialavura)}
\pdfinfo{
  /Title (python-pandas.pdf)
  /Creator (Cheatography)
  /Author (DarioPittera (aggialavura))
  /Subject (Python - Pandas Cheat Sheet)
}

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\fancyhead[L]{
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{C}
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    \vspace{-7pt}
    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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\columnbreak
\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Python - Pandas Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{DarioPittera (aggialavura)} via \textcolor{DarkBackground}{\uline{cheatography.com/83764/cs/19829/}}}
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\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{LL}
  \SetRowColor{FootBackground}
  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}DarioPittera (aggialavura) \\
  \uline{cheatography.com/aggialavura} \\
        \uline{\seqsplit{www}.dariopittera.com}
  \end{tabulary}
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 24th June, 2019.\\
   Page {\thepage} of \pageref{LastPage}.
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  \vspace{-5pt}
  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
\end{tabulary}
\end{multicols}}




\begin{document}
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\begin{multicols*}{3}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{TO START}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{import numpy as np \newline import pandas as pd} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.58804 cm} x{2.38896 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{SERIES (similar to numpy array)}}  \tn
% Row 0
\SetRowColor{LightBackground}
pd.Series(data = list)\{\{nobreak\}\} & create series from list \{\{nobreak\}\} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
{\bf{pd.Series(data=list,\{\{nl\}\} index=labels)}} & create series \{\{nl\}\} with index \tn 
% Row Count 5 (+ 3)
% Row 2
\SetRowColor{LightBackground}
pd.Series(np\_arr) & create series from \{\{nl\}\} numpy array \tn 
% Row Count 7 (+ 2)
% Row 3
\SetRowColor{white}
pd.Series(np\_arr,\{\{nl\}\} labels) & create series \{\{nl\}\} with index \tn 
% Row Count 9 (+ 2)
% Row 4
\SetRowColor{LightBackground}
pd.Series(dict) & create series \{\{nl\}\} from dictionary \tn 
% Row Count 11 (+ 2)
% Row 5
\SetRowColor{white}
{\bf{pd.Series{[}num{]}}} & indexing \tn 
% Row Count 12 (+ 1)
% Row 6
\SetRowColor{LightBackground}
ser1 + ser2 & sum two Series \tn 
% Row Count 13 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{{\bf{Pandas series}} differs from numpy arrays because series {\bf{can have axis labels}}, instead of just a number location. It also {\bf{doesn't need to hold numeric data}}, it can hold any arbitrary Python Object, also functions (although unlikely used). \newline  \newline {\bf{Note:}} the terms "data=" and "index=" can be omitted.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{DATAFRAMES and INDEXING}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{df = pd.DataFrame()}} *\{\{nobreak\}\} & create dataframe \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
{\bf{df{[}'col'{]}}} * & select col \tn 
% Row Count 3 (+ 1)
% Row 2
\SetRowColor{LightBackground}
{\bf{df.loc{[}'row'{]}}} & select row \tn 
% Row Count 4 (+ 1)
% Row 3
\SetRowColor{white}
df.iloc{[}'row'{]} & select a row \{\{nl\}\} by its index \tn 
% Row Count 6 (+ 2)
% Row 4
\SetRowColor{LightBackground}
df.col & select a column \{\{nl\}\} (opt.2 - avoid) \tn 
% Row Count 8 (+ 2)
% Row 5
\SetRowColor{white}
df{[}{[}'col1','col2'{]}{]} * & take two columns \tn 
% Row Count 10 (+ 2)
% Row 6
\SetRowColor{LightBackground}
{\bf{type(df{[}'col'{]})}} & column type \tn 
% Row Count 11 (+ 1)
% Row 7
\SetRowColor{white}
{\bf{df{[}'new\_col'{]} = {[}1,2,3{]}}} & insert column \tn 
% Row Count 13 (+ 2)
% Row 8
\SetRowColor{LightBackground}
df.drop('row',axis=0)* & drop row \tn 
% Row Count 15 (+ 2)
% Row 9
\SetRowColor{white}
df.drop('col',axis=1) & drop column \tn 
% Row Count 17 (+ 2)
% Row 10
\SetRowColor{LightBackground}
df.drop('col',axis=1,\{\{nl\}\}inplace=True)* & permanent drop \tn 
% Row Count 20 (+ 3)
% Row 11
\SetRowColor{white}
{\bf{df.loc{[}'row1','col1'{]}}} & select a row \{\{nl\}\} and a column \tn 
% Row Count 22 (+ 2)
% Row 12
\SetRowColor{LightBackground}
{\bf{df.loc{[}{[}'r1', 'r2'{]},{[}'c1','c2'{]}{]}* }}\{\{nobreak\}\} & select 2 rows \{\{nl\}\} and 2 columns \tn 
% Row Count 25 (+ 3)
% Row 13
\SetRowColor{white}
df\textgreater{}condition & return boolean \tn 
% Row Count 26 (+ 1)
% Row 14
\SetRowColor{LightBackground}
df{[}df\textgreater{}cond{]} & return values \tn 
% Row Count 27 (+ 1)
% Row 15
\SetRowColor{white}
{\bf{df{[}df{[}'col'{]}\textgreater{}0{]}}} & return rows of col that satisfy condition \tn 
% Row Count 30 (+ 3)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{DATAFRAMES and INDEXING (cont)}}  \tn
% Row 16
\SetRowColor{LightBackground}
df{[}df{[}'col1'{]}\textgreater{}0{]}{[}'col2'{]} & return col2 that satisfy cond. \{\{nl\}\} on col1 \tn 
% Row Count 3 (+ 3)
% Row 17
\SetRowColor{white}
df{[}df{[}'c1'{]}\textgreater{}0{]}{[}{[}'c2','c3'{]}{]} & return c2 \& c3 \{\{nl\}\} that satisfy \{\{nl\}\} cond. on col1 \tn 
% Row Count 6 (+ 3)
% Row 18
\SetRowColor{LightBackground}
df{[}(cond1) \& (cond2){]} & return values that satisfy cond1 \& cond2 \tn 
% Row Count 8 (+ 2)
% Row 19
\SetRowColor{white}
df{[}(cond1) | (cond2){]} & return values that satisfy cond1 | cond2 \tn 
% Row Count 10 (+ 2)
% Row 20
\SetRowColor{LightBackground}
{\bf{df.reset\_index()}} & add num index \tn 
% Row Count 11 (+ 1)
% Row 21
\SetRowColor{white}
df{[}'new\_col'{]} = 'NY LA'\{\{nl\}\}.split() & add col quickly \tn 
% Row Count 13 (+ 2)
% Row 22
\SetRowColor{LightBackground}
df.set\_index('col') & set a column \{\{nl\}\} as index \tn 
% Row Count 15 (+ 2)
% Row 23
\SetRowColor{white}
df.set\_index('col',\{\{nl\}\} inplace=True) & make it permanent \tn 
% Row Count 17 (+ 2)
% Row 24
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{!!THERE IS ALSO MULTI-INDEXING"} \tn 
% Row Count 18 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{{\bf{DataFrame}} function take a {\bf{data}} (the values), {\bf{index}} (the name of the index column), {\bf{columns}} (the name of the column) parameters. \newline  \newline {\bf{Columns}} are series. \newline  \newline {\bf{take two columns}}: note the double brackets {[}{[} {]}{]} \newline  \newline {\bf{axis=0}} can be omitted, is the default value. \newline  \newline {\bf{inplace=True}} will apply the result to the original dataframe. Without it, you are not changing the dataframe. \newline  \newline {\bf{r}} = row. \newline {\bf{c}} = column.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.53827 cm} x{2.43873 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{MISSING DATA}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{df.isnull()}} & check for na \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
{\bf{df.dropna()}} & drop all rows with \{\{nl\}\} at least 1 na \tn 
% Row Count 4 (+ 3)
% Row 2
\SetRowColor{LightBackground}
df.dropna(axis=1)\{\{nobreak\}\} & drop all cols with \{\{nl\}\} at least 1 na \tn 
% Row Count 7 (+ 3)
% Row 3
\SetRowColor{white}
df.dropna(thresh=n)\{\{nobreak\}\} & keep with at least \{\{nl\}\} n value/s \tn 
% Row Count 9 (+ 2)
% Row 4
\SetRowColor{LightBackground}
{\bf{df.fillna(value='value')}}\{\{nobreak\}\} & replace na \tn 
% Row Count 11 (+ 2)
% Row 5
\SetRowColor{white}
df{[}'col'{]}.fillna(value=\{\{nl\}\}df{[}'col'{]}.mean()) & replace using funct \tn 
% Row Count 14 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.63781 cm} x{2.33919 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{GROUPBY}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{df.groupby('col')}} & group rows by a col \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
grouped\_df{\bf{.count()}} & use cnt function \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
grouped\_df{\bf{.mean()}} & use mean function \tn 
% Row Count 5 (+ 1)
% Row 3
\SetRowColor{white}
grouped\_df.std() & use std function \tn 
% Row Count 6 (+ 1)
% Row 4
\SetRowColor{LightBackground}
grouped\_df.min() & use min function \tn 
% Row Count 7 (+ 1)
% Row 5
\SetRowColor{white}
grouped\_df.max() & use max function \tn 
% Row Count 8 (+ 1)
% Row 6
\SetRowColor{LightBackground}
grouped\_df{\bf{.describe()}} & df descriptives \tn 
% Row Count 10 (+ 2)
% Row 7
\SetRowColor{white}
grouped\_df('col')\{\{nl\}\}.count().loc{[}'row{]} & apply function and \{\{nl\}\} take a row \tn 
% Row Count 12 (+ 2)
% Row 8
\SetRowColor{LightBackground}
... {\bf{.transpose()}} & rotate results \tn 
% Row Count 13 (+ 1)
% Row 9
\SetRowColor{white}
... .transpose(){[}'row'{]} & rotate and \{\{nl\}\} take a row \tn 
% Row Count 15 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.83689 cm} x{2.14011 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{MERGING, JOINING, CONCATENATING}}  \tn
% Row 0
\SetRowColor{LightBackground}
pd.concat({[}df1,df2,df3{]}) & concatenate dfs \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
pd.concat({[}...{]},axis=1) & concatenate by col \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
pd.merge()* & merge two dfs \tn 
% Row Count 5 (+ 1)
% Row 3
\SetRowColor{white}
df1.join(df2) & join two dfs \tn 
% Row Count 6 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{{\bf{pd.merge()}} takes "{\bf{df1}}", "{\bf{df2}}", "{\bf{how=}}", "{\bf{on=}}" parameters. "how=" can be \seqsplit{"inner"/"outer"/"left"/"right"}, "on=" has to be a column/s key. \newline  \newline {\bf{join()}} is similar to merge but works on indexes that can be different. It also cn take the "how=" argument.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.58804 cm} x{2.38896 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{METHODS and FUNCTIONS}}  \tn
% Row 0
\SetRowColor{LightBackground}
df{[}'c'{]}{\bf{.unique()}} & return unique values \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
df{[}'c'{]}{\bf{.nunique()}} & count unique val \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
df{[}'c'{]}{\bf{.value\_counts()}}\{\{nobreak\}\} & count how many \{\{nl\}\} of same values \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
df{[}'c1'{]}{\bf{.apply(func)}} & apply func to df \tn 
% Row Count 8 (+ 2)
% Row 4
\SetRowColor{LightBackground}
df{[}'c1'{]}.apply(len) & apply len \tn 
% Row Count 9 (+ 1)
% Row 5
\SetRowColor{white}
df{[}'c1'{]}.apply(sum) & apply sum \tn 
% Row Count 10 (+ 1)
% Row 6
\SetRowColor{LightBackground}
... {\bf{.apply(lambda x:x+2)}}\{\{nobreak\}\} & apply lambda \tn 
% Row Count 12 (+ 2)
% Row 7
\SetRowColor{white}
df.index & return idx names \tn 
% Row Count 13 (+ 1)
% Row 8
\SetRowColor{LightBackground}
{\bf{df.info()}} & return df info \tn 
% Row Count 14 (+ 1)
% Row 9
\SetRowColor{white}
{\bf{df.describe()}} & return df stats \tn 
% Row Count 15 (+ 1)
% Row 10
\SetRowColor{LightBackground}
{\bf{df.columns}} & return col names \tn 
% Row Count 16 (+ 1)
% Row 11
\SetRowColor{white}
{\bf{del df{[}'col'{]}}}* & delete col from df \tn 
% Row Count 17 (+ 1)
% Row 12
\SetRowColor{LightBackground}
{\bf{df.sort\_values(by='col')}} & sort df values \tn 
% Row Count 19 (+ 2)
% Row 13
\SetRowColor{white}
df.pivot\_table()* & create a pivot tbl \tn 
% Row Count 20 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{{\bf{del}} differs from ".drop()" because it will permanently remove a column from the df. \newline  \newline {\bf{pivot\_table()}} takes "{\bf{values=}}", "{\bf{index=}}", "{\bf{columns=}}" parameters. It reads: "Create a table from df, with values of colx, index of colx2, and divided by values in colx3"}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{INPUT and OUPUT code to start}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\# to import HTML tables \newline conda install lxml \newline conda install html5lib \newline conda install BeautifulSoup4 \newline  \newline \# to use SQL \newline from sqlalchemy import create\_engine \newline engine = \seqsplit{create\_engine('sqlite:///:memory:')}  \newline df.to\_sql('data', engine) \newline sql\_df = pd.read\_sql('data',con=engine)} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{3.83229 cm} x{1.14471 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{INPUT and OUPUT operations}}  \tn
% Row 0
\SetRowColor{LightBackground}
pwd & ask nb route \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
df = {\bf{pd.read\_csv('example')}} & read csv \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
df = {\bf{pd.read\_excel\{\{nl\}\}}}('name',sheet\_name='name') & read excel \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
df = pd.read\_html('address') & read html \tn 
% Row Count 7 (+ 1)
% Row 4
\SetRowColor{LightBackground}
df{\bf{.to\_csv('str',index=False)\{\{nobreak\}\}}} & save as csv \tn 
% Row Count 9 (+ 2)
% Row 5
\SetRowColor{white}
df{\bf{.to\_excel}}('name',\{\{nl\}\}sheet\_name='name', \{\{nl\}\}index = False) & save as xlsx \tn 
% Row Count 12 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}


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

\end{document}