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\author{datamansam}
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  /Title (python-snippits.pdf)
  /Creator (Cheatography)
  /Author (datamansam)
  /Subject (Python Snippits Cheat Sheet)
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\noindent
\begin{multicols}{3}
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    {\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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    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Python Snippits Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{datamansam} via \textcolor{DarkBackground}{\uline{cheatography.com/139410/cs/29923/}}}
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\begin{multicols}{3}
\begin{tabulary}{5.8cm}{LL}
  \SetRowColor{FootBackground}
  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}datamansam \\
  \uline{cheatography.com/datamansam} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Published 15th May, 2022.\\
   Updated 9th July, 2022.\\
   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
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\begin{multicols*}{3}

\begin{tabularx}{5.377cm}{x{2.18988 cm} x{2.78712 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Operators}}  \tn
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Arithmetic & (Addition(+), Substraction(-), Multiplication(*), Division(/), Modulus(\%) \tn 
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Relational & \textless{}, \textgreater{}, \textless{}=, \textgreater{}=, ==, != (not equal), \tn 
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Assignment & =. +=, -=, /=, *=, \%= \tn 
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Logical & and. or, not \tn 
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Membership & in, not in \tn 
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Identity (same memory location) & is, is not \tn 
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\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Functions}}  \tn
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len() & determine the length of a string, a list, an array &  \tn 
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split() & split a string into shorter string using defined seperatos & \seqsplit{string.split("},") \tn 
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sum(),mean(), count(),std(). & functions that can be used by grouby in pandas & \seqsplit{grouped\_multiple} = df.groupby({[}'Team', 'Pos'{]}).agg(\{'Age': {[}'mean', 'min', 'max'{]}\}) \seqsplit{grouped\_multiple}.columns = {[}'age\_mean', 'age\_min', 'age\_max'{]} \seqsplit{grouped\_multiple} = \seqsplit{grouped\_multiple}.reset\_index() \tn 
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 &  & df.groupby({[}"Team","College"{]}){[}"Salary"{]}.max() \tn 
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agg() & Allows for multiple or custom aggregations & \seqsplit{defpct30(column):} \tn 
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\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Functions (cont)}}  \tn
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 &  & return \seqsplit{column.quantile(0.3)} \tn 
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 &  & dogs{[}"weight\_kg"{]}.agg(pct30) \tn 
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keys() & We can use the Keys function of a Group By object to describe how rows of a dataset has been split & data.groupby({[}'month'{]}).groups.keys() \tn 
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join() and ravel() & An effective way to rename columns after a group & grouped = \seqsplit{data.groupby('month').agg("duration":} {[}min, max, mean{]}) \tn 
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 &  & \seqsplit{grouped.columns} = {[}"\_".join(x) for x in \seqsplit{grouped.columns}.ravel(){]} \tn 
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Custom Functions}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{User-Defined Functions \newline  \newline By adding {\emph{ to a parameter, we can add any number of arguments to that parameters \newline  \newline def func\_with\_var\_pos\_args(}}args): \newline for arg in args: \newline print(arg) \newline  \newline Simiarly, by adding {\emph{ to an argument, we can add any number of arguments to that parameters \newline  \newline def func\_with\_var\_pos\_args(}}args): \newline for arg in args: \newline print(arg)} \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Naming Conventions}}  \tn
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Funciton & function, my\_function \tn 
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Method & class\_method, method \tn 
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from pprint import pprint & pprint(dir(my\_dict)) \tn 
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Pychecker & detects bugs from the source code and warns about its style and complexity \tn 
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Pylint & Checks whether the module matches upto a coding standard. \tn 
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Modules & Each Python program file is a module, importing other attributes and objects. \tn 
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Package & folder of modules \tn 
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\mymulticolumn{4}{x{5.377cm}}{\bf\textcolor{white}{Map, Filter and Lambda}}  \tn
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Map & Applies a function to the input list & \seqsplit{map(function\_to\_apply}, \seqsplit{list\_of\_inputs)} & items = {[}1, 2, 3, 4, 5{]} squared = \seqsplit{list(map(lambda} x: x**2, items)) \tn 
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\seqsplit{filter} & creates a list of elements for which a function returns true. & \seqsplit{filter(function\_to\_apply},n \seqsplit{lisst\_to\_select\_From)} & \seqsplit{number\_list} = range(-5, 5) \seqsplit{less\_than\_zero} = \seqsplit{list(filter(lambda} x: x \textless{} 0, \seqsplit{number\_list))} \tn 
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\seqsplit{Reduce} & applies a rolling \seqsplit{computation} to \seqsplit{sequential} pairs of values in a list & from functools import reduce & product = \seqsplit{reduce((lambda} x, y: x * y), {[}1, 2, 3, 4{]}) \tn 
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Scikit Learn - Regression}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{poly\_reg = \seqsplit{PolynomialFeatures(degree} = 2) \newline X\_poly = \seqsplit{poly\_reg.fit\_transform(xtrain)} \newline X\_poly.predict(xtest) \newline  \newline xtrainp= X\_poly{[}:11900*3{]} \newline  \newline  \newline \# polynomial regression model \newline poly\_reg\_model = LinearRegression() \newline poly\_reg\_model.fit(xtrainp, ytrain) \newline poly\_reg\_model.predict(xtest) \newline print( \seqsplit{metrics.mean\_squared\_error(y\_test}, \seqsplit{poly\_reg\_model.predict(xtest)} ) ) \newline  \newline  \newline svr\_regressor = SVR(kernel='rbf', gamma='auto') \newline svr\_regressor.fit(xtrain, ytrain) \newline  \newline tree\_regressor = \seqsplit{DecisionTreeRegressor(random\_state} = 0) \newline tree\_regressor.fit(xtrain, ytrain) \newline  \newline forest\_regressor = \seqsplit{RandomForestRegressor(n\_estimators} = 300, random\_state = 0) \newline forest\_regressor.fit(xtrain, ytrain) \newline  \newline from sklearn import linear\_model \newline reg = \seqsplit{linear\_model.LassoLars(alpha=.1}, normalize=False) \newline reg.fit(xtrain, ytrain)  \newline  \newline reg.coef\_ \newline reg.predict(xtest) \newline  \newline est = SGDClassifier() \newline est.fit(xtrain, ytrain) \newline est.predict(xtest) \newline  \newline linear\_regression = LinearRegression() \newline  \newline y\_pred\_lr = \seqsplit{linear\_regression.fit(xtrain}, ytrain).predict(xtest) \newline  \newline  \newline xgbmodel = \seqsplit{xgboost.XGBRegressor(colsample\_bytree=0.4}, \newline                  gamma=0,                  \newline                  learning\_rate=0.07, \newline                  max\_depth=3, \newline                  min\_child\_weight=1.5, \newline                  n\_estimators=10000,                                                                     \newline                  reg\_alpha=0.75, \newline                  reg\_lambda=0.45, \newline                  subsample=0.6, \newline                  seed=42)  \newline xgbmodel.fit(xtrain, ytrain) \newline  \newline  \newline print( \seqsplit{svr\_regressor.predict(xtest))} \newline print( \seqsplit{tree\_regressor.predict(xtest))} \newline print( y\_pred\_lr)  \newline print( \seqsplit{forest\_regressor.predict(xtest))} \newline model.predict(xtest) \newline  \newline  \newline print( \seqsplit{metrics.mean\_squared\_error(y\_test}, \seqsplit{svr\_regressor.predict(xtest)} )  ) \newline  \newline print( \seqsplit{metrics.mean\_squared\_error(y\_test}, \seqsplit{tree\_regressor.predict(xtest)} ) ) \newline print( \seqsplit{metrics.mean\_squared\_error(y\_test}, y\_pred\_lr)  ) \newline print( \seqsplit{metrics.mean\_squared\_error(y\_test}, \seqsplit{forest\_regressor.predict(xtest)} ) ) \newline  \newline  \newline  \newline forestrev = \seqsplit{forest\_regressor.predict(xtest)} \newline  \newline xgbmodel.predict(xtest).mean()  \newline print( \seqsplit{metrics.mean\_squared\_error(y\_test}, xgbmodel.predict(xtest) ) ) \newline  \newline  \newline ytest.mean()  \newline  \newline bas.REVENUE.mean()  \newline  \newline  \newline  \newline  \newline  \newline xtrain, ytrain = np.array(xtrain), np.array(ytrain) \newline  \newline xtrain = np.reshape(xtrain, (xtrain.shape{[}0{]},xtrain.shape{[}1{]},1)) \newline  \newline \# create and fit the LSTM network \newline model = Sequential() \newline model.add(LSTM(units=50, return\_sequences=True, input\_shape=(xtrain.shape{[}1{]},1))) \newline model.add(LSTM(units=50)) \newline model.add(Dense(1)) \newline  \newline ytrain = \seqsplit{ytrain.astype(np.float32)} \newline xtrain =  \seqsplit{xtrain.astype(np.float32)} \newline xtrain = np.reshape(xtrain, (xtrain.shape{[}0{]},xtrain.shape{[}1{]},1))} \tn 
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\begin{tabularx}{5.377cm}{X}
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Looping Data Structures}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{1) With One Column: \newline import pandas as pd \newline \#The column to look through  \newline brics = pd.read\_csv("brics.csv", index\_col = 0) \newline     for val in brics : \newline          print(val) \newline  \newline 2) Index then all cols in row: \newline for lab, row in brics.iterrows(): \newline    print(lab) \newline    print(row) \newline  \newline 3) Index then one col in row: \newline for lab, row in brics.iterrows(): \newline     brics.loc{[}lab, "name\_length"{]} = len(row{[}"country"{]}) \newline  \newline 4) Apply  \newline brics{[}"name\_length"{]} = brics{[}"country"{]}.apply(len)} \tn 
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Scikit Learn - Classification}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{\#\# Classifier imports \newline from sklearn.neighbors import KNeighborsClassifier \newline from sklearn.naive\_bayes import GaussianNB, MultinomialNB, BernoulliNB \newline from sklearn.linear\_model import LogisticRegression, SGDClassifier \newline from sklearn.svm import SVC, LinearSVC, NuSVC \newline from sklearn.linear\_model import Ridge  \newline from sklearn.ensemble import AdaBoostClassifier \newline from sklearn.ensemble import \seqsplit{GradientBoostingClassifier} \newline  \newline \# Defining our models \newline gnb = GaussianNB() \newline KNN = \seqsplit{KNeighborsClassifier(n\_neighbors=1)} \newline MNB = MultinomialNB() \newline BNB = BernoulliNB() \newline LR = LogisticRegression() \newline SDG = SGDClassifier() \newline \#SVC = SVC(kernel='linear', C=1e3) \newline LSVC = LinearSVC() \newline NSVC = NuSVC() \newline  \newline \# Train our classifier and print accuracy scores \newline gnb.fit(x1, y1) \newline y2\_GNB\_model = gnb.predict(x2) \newline print("GaussianNB Accuracy :", accuracy\_score(y2, y2\_GNB\_model)) \newline  \newline KNN.fit(x1,y1) \newline y2\_KNN\_model = KNN.predict(x2) \newline print("KNN Accuracy :", accuracy\_score(y2, y2\_KNN\_model)) \newline  \newline \#MNB.fit(x1,y1) \newline \#y2\_MNB\_model = MNB.predict(x2) \newline \#print("MNB Accuracy :", accuracy\_score(y2, y2\_MNB\_model)) \newline  \newline BNB.fit(x1,y1) \newline y2\_BNB\_model = BNB.predict(x2) \newline print("BNB Accuracy :", accuracy\_score(y2, y2\_BNB\_model)) \newline  \newline LR.fit(x1,y1) \newline y2\_LR\_model = LR.predict(x2) \newline print("LR Accuracy :", accuracy\_score(y2, y2\_LR\_model)) \newline  \newline SDG.fit(x1,y1) \newline y2\_SDG\_model = SDG.predict(x2) \newline print("SDG Accuracy :", accuracy\_score(y2, y2\_SDG\_model)) \newline  \newline \# SVC.fit(x1,y1) \newline \# y2\_SVC\_model = SVC.predict(x2) \newline \# print("SVC Accuracy :", accuracy\_score(y2, y2\_SVC\_model)) \newline  \newline LSVC.fit(x1,y1) \newline y2\_LSVC\_model = LSVC.predict(x2) \newline print("LSVC Accuracy :", accuracy\_score(y2, y2\_LSVC\_model)) \newline  \newline NSVC.fit(x1,y1) \newline y2\_NSVC\_model = NSVC.predict(x2) \newline print("NSVC Accuracy :", accuracy\_score(y2, y2\_NSVC\_model))} \tn 
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% That's all folks
\end{multicols*}

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