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  /Title (encoding-categorical-variables-in-python.pdf)
  /Creator (Cheatography)
  /Author ({[}deleted{]})
  /Subject (Encoding Categorical Variables in Python Cheat Sheet)
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    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Encoding Categorical Variables in Python Cheat Sheet}}}} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 10th December, 2021.\\
   Page {\thepage} of \pageref{LastPage}.
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  Measure your website readability!\\
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\begin{multicols*}{3}

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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Why do we Encode?}}  \tn
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{- Most of the models only accept numeric values. \newline % Row Count 1 (+ 1)
- We cannot afford to loose important features because of their data types. \newline % Row Count 3 (+ 2)
- It is required to ensure correct and good performance of the model.% Row Count 5 (+ 2)
} \tn 
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\begin{tabularx}{5.377cm}{X}
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Types of Encoding}}  \tn
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{- Ordinal Encoding \newline % Row Count 1 (+ 1)
- One Hot Encoding \newline % Row Count 2 (+ 1)
- Label Encoding% Row Count 3 (+ 1)
} \tn 
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Ordinal Encoding}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{- Used for encoding Ordinal Variables. \newline % Row Count 1 (+ 1)
- Numbers are assigned to each category based on their order hierarchy of the variable. \newline % Row Count 3 (+ 2)
- Assigned numbers can be any numbers as long as original order is unchanged. \newline % Row Count 5 (+ 2)
Code: \newline % Row Count 6 (+ 1)
!pip install category\_encoders \newline % Row Count 7 (+ 1)
import category\_encoders as ce \newline % Row Count 8 (+ 1)
encoder = ce.OrdinalEncoder(mapping={[}\{'col': 'feedback', 'mapping': \{'bad': 1, 'okay': 2, 'good':3\}\}{]}) \newline % Row Count 11 (+ 3)
encoder.fit(X) \newline % Row Count 12 (+ 1)
X = encoder.transform(X) \newline % Row Count 13 (+ 1)
X{[}'feedback'{]} \newline % Row Count 14 (+ 1)
Output: \newline % Row Count 15 (+ 1)
feedback \newline % Row Count 16 (+ 1)
1 \newline % Row Count 17 (+ 1)
2 \newline % Row Count 18 (+ 1)
3 \newline % Row Count 19 (+ 1)
2 \newline % Row Count 20 (+ 1)
3 \newline % Row Count 21 (+ 1)
. \newline % Row Count 22 (+ 1)
.% Row Count 23 (+ 1)
} \tn 
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\mymulticolumn{1}{x{5.377cm}}{Documentation:  \seqsplit{https://contrib.scikit-learn.org/category\_encoders/ordinal.html}}  \tn 
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{One-Hot Encoding}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{- Used when number of categories in the variable are low, max 3 or 4. Anymore will seriously increase the size of your dataset and decrease performance of your model. \newline % Row Count 4 (+ 4)
- Assigns 0 and 1 to the categories based on their presence in the columns. \newline % Row Count 6 (+ 2)
- Creates extra columns based on the number of categorical elements in the main column. \newline % Row Count 8 (+ 2)
i.e if there are 3 categories in the column Shipping - Standard, One Day, Two Day, 3 extra columns are created in place of the original column, 1 for each category and 1 will be assigned for each unique value. \newline % Row Count 13 (+ 5)
Usage: \newline % Row Count 14 (+ 1)
import category\_encoders as ce \newline % Row Count 15 (+ 1)
encoder = ce.OneHotEncoder(cols={[}'Column Name'{]}) \newline % Row Count 17 (+ 2)
encoder.fit(df) \newline % Row Count 18 (+ 1)
df = encoder.transform(df) \newline % Row Count 19 (+ 1)
df{[}'Shipping'{]}% Row Count 20 (+ 1)
} \tn 
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\mymulticolumn{1}{x{5.377cm}}{- Converts each category in a column to a number directly. \newline % Row Count 2 (+ 2)
- Can also be used for non-numerical values as long as they are relevant and usable to the target variable. \newline % Row Count 5 (+ 3)
- Different Methods can be applied according to your requirements. \newline % Row Count 7 (+ 2)
from sklearn.preprocessing import LabelEncoder \newline % Row Count 9 (+ 2)
le = LabelEncoder() \newline % Row Count 10 (+ 1)
df{[}'Column Name\_Cat'{]} = le.fit\_transform(df{[}'Column Name'{]}) \newline % Row Count 12 (+ 2)
df% Row Count 13 (+ 1)
} \tn 
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% That's all folks
\end{multicols*}

\end{document}