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% Document Info
\author{Arshdeep}
\pdfinfo{
  /Title (pandas.pdf)
  /Creator (Cheatography)
  /Author (Arshdeep)
  /Subject (Pandas Cheat Sheet)
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\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{Pandas Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{Arshdeep} via \textcolor{DarkBackground}{\uline{cheatography.com/201979/cs/42963/}}}
\end{tabulary}
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\noindent
\begin{multicols}{3}
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  \SetRowColor{FootBackground}
  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}Arshdeep \\
  \uline{cheatography.com/arshdeep} \\
  \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 6th April, 2024.\\
   Page {\thepage} of \pageref{LastPage}.
\end{tabulary}
\vfill
\columnbreak
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  \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}
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\begin{multicols*}{2}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Introduction to Pandas}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Pandas is a powerful open-source data analysis and manipulation library for Python.} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{It provides data structures and functions to efficiently work with structured data.} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Developed by Wes McKinney in 2008, Pandas is widely used in data science, finance, and research.} \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Key components include Series (1-dimensional labeled array) and DataFrame (2-dimensional labeled data structure).} \tn 
% Row Count 9 (+ 3)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Pandas simplifies data manipulation tasks such as cleaning, filtering, grouping, and transforming.} \tn 
% Row Count 11 (+ 2)
% Row 5
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{It integrates seamlessly with other libraries like NumPy, Matplotlib, and Scikit-learn.} \tn 
% Row Count 13 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Pandas is built on top of NumPy, leveraging its fast array processing capabilities.} \tn 
% Row Count 15 (+ 2)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Offers intuitive and flexible functionalities for data exploration and analysis.} \tn 
% Row Count 17 (+ 2)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Ideal for tasks ranging from data cleaning and preprocessing to statistical analysis and visualization.} \tn 
% Row Count 20 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Indexing and Selecting Data}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Use .loc{[}{]} for label-based indexing on rows and columns.} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use .iloc{[}{]} for integer-based indexing on rows and columns.} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Boolean indexing allows selecting data based on conditions.} \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{df{[}column\_name{]} or df.column\_name selects a single column.} \tn 
% Row Count 8 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{df{[}{[}column1, column2{]}{]} selects multiple columns.} \tn 
% Row Count 9 (+ 1)
% Row 5
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{.head(n) returns the first n rows of the DataFrame.} \tn 
% Row Count 11 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{.tail(n) returns the last n rows of the DataFrame.} \tn 
% Row Count 12 (+ 1)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{df.at{[}{]} and df.iat{[}{]} for single value selection based on label or integer.} \tn 
% Row Count 14 (+ 2)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{df.iloc{[}:, {[}0, 1{]}{]} selects all rows and specific columns.} \tn 
% Row Count 16 (+ 2)
% Row 9
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{.query() method for SQL-like queries.} \tn 
% Row Count 17 (+ 1)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{.isin() method for filtering based on multiple values.} \tn 
% Row Count 19 (+ 2)
% Row 11
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Chained indexing should be avoided for assignment (use .loc{[}{]} or .iloc{[}{]} instead).} \tn 
% Row Count 21 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Dealing with Outliers}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Identify outliers using descriptive statistics (mean, median, standard deviation)} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Visualize data distribution using box plots, histograms, or scatter plots} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Use domain knowledge to determine if outliers are valid data points or errors} \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Apply statistical methods like Z-score, IQR (Interquartile Range) to detect outliers} \tn 
% Row Count 8 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Consider different strategies for handling outliers:} \tn 
% Row Count 10 (+ 2)
% Row 5
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\mymulticolumn{1}{x{8.4cm}}{Removing outliers: Drop outliers from the dataset} \tn 
% Row Count 11 (+ 1)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Transforming data: Apply mathematical transformations (log, square root) to reduce the impact of outliers} \tn 
% Row Count 14 (+ 3)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Winsorization: Cap or clamp extreme values to a specified percentile} \tn 
% Row Count 16 (+ 2)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Evaluate the impact of outlier handling on data analysis and modeling} \tn 
% Row Count 18 (+ 2)
% Row 9
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Document the rationale behind outlier treatment for reproducibility and transparency} \tn 
% Row Count 20 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Data Cleaning}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Handling Missing Values:} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{dropna(): Drops rows or columns with missing values.} \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{fillna(): Fills missing values with specified values.} \tn 
% Row Count 5 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{isna() / notna(): Checks for missing or non-missing values.} \tn 
% Row Count 7 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Removing Duplicates:} \tn 
% Row Count 8 (+ 1)
% Row 5
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{duplicated(): Identifies duplicate rows.} \tn 
% Row Count 9 (+ 1)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{drop\_duplicates(): Removes duplicate rows.} \tn 
% Row Count 10 (+ 1)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Data Imputation:} \tn 
% Row Count 11 (+ 1)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Replace missing values with the mean, median, or mode.} \tn 
% Row Count 13 (+ 2)
% Row 9
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use interpolation methods for time series data.} \tn 
% Row Count 14 (+ 1)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Data Validation:} \tn 
% Row Count 15 (+ 1)
% Row 11
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Validate data types using dtype.} \tn 
% Row Count 16 (+ 1)
% Row 12
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Use regular expressions to validate string data.} \tn 
% Row Count 17 (+ 1)
% Row 13
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Data Standardization:} \tn 
% Row Count 18 (+ 1)
% Row 14
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Convert data to a consistent format (e.g., lowercase).} \tn 
% Row Count 20 (+ 2)
% Row 15
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Normalize numeric data to a common scale.} \tn 
% Row Count 21 (+ 1)
% Row 16
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Data Transformation:} \tn 
% Row Count 22 (+ 1)
% Row 17
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Convert data types using astype().} \tn 
% Row Count 23 (+ 1)
% Row 18
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Apply custom functions using apply().} \tn 
% Row Count 24 (+ 1)
% Row 19
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Outlier Detection:} \tn 
% Row Count 25 (+ 1)
% Row 20
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Visualize data distribution with histograms and box plots.} \tn 
% Row Count 27 (+ 2)
% Row 21
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use statistical methods like z-score or IQR to detect outliers.} \tn 
% Row Count 29 (+ 2)
% Row 22
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Error Correction:} \tn 
% Row Count 30 (+ 1)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Data Cleaning (cont)}}  \tn
% Row 23
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Handle erroneous values based on domain knowledge.} \tn 
% Row Count 1 (+ 1)
% Row 24
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use external datasets or references for validation.} \tn 
% Row Count 3 (+ 2)
% Row 25
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Handling Inconsistent Data:} \tn 
% Row Count 4 (+ 1)
% Row 26
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Standardize categorical data.} \tn 
% Row Count 5 (+ 1)
% Row 27
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Resolve inconsistencies in naming conventions.} \tn 
% Row Count 6 (+ 1)
% Row 28
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Handling Data Integrity Issues:} \tn 
% Row Count 7 (+ 1)
% Row 29
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Identify and rectify data inconsistencies.} \tn 
% Row Count 8 (+ 1)
% Row 30
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use data profiling tools for anomaly detection.} \tn 
% Row Count 9 (+ 1)
% Row 31
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Error Handling:} \tn 
% Row Count 10 (+ 1)
% Row 32
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use try-except blocks to handle errors during data processing.} \tn 
% Row Count 12 (+ 2)
% Row 33
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Log errors for debugging and tracking purposes.} \tn 
% Row Count 13 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Grouping and Aggregating Data}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Grouping Data:} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Grouping data based on one or more columns using the groupby() function.} \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Example: df.groupby('Column') or df.groupby({[}'Column1', 'Column2'{]}).} \tn 
% Row Count 5 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Aggregating Data:} \tn 
% Row Count 6 (+ 1)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Applying aggregate functions like sum, mean, count, etc., to grouped data.} \tn 
% Row Count 8 (+ 2)
% Row 5
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Example: \seqsplit{df.groupby('Column').sum()} or df.groupby('Column').agg(\{'Column2': 'mean', 'Column3': 'sum'\}).} \tn 
% Row Count 11 (+ 3)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Common Aggregate Functions:} \tn 
% Row Count 12 (+ 1)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{sum(): Calculates the sum of numeric values.} \tn 
% Row Count 13 (+ 1)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{mean(): Calculates the mean of numeric values.} \tn 
% Row Count 14 (+ 1)
% Row 9
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{count(): Counts non-null values.} \tn 
% Row Count 15 (+ 1)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{min(), max(): Finds the minimum or maximum value.} \tn 
% Row Count 16 (+ 1)
% Row 11
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{agg(): Allows specifying multiple aggregate functions for different columns.} \tn 
% Row Count 18 (+ 2)
% Row 12
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Custom Aggregation:} \tn 
% Row Count 19 (+ 1)
% Row 13
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Defining custom aggregation functions using agg() or apply().} \tn 
% Row Count 21 (+ 2)
% Row 14
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Example: \seqsplit{df.groupby('Column').agg(custom\_function).}} \tn 
% Row Count 23 (+ 2)
% Row 15
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Grouping with Multiple Functions:} \tn 
% Row Count 24 (+ 1)
% Row 16
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Applying multiple aggregate functions simultaneously.} \tn 
% Row Count 26 (+ 2)
% Row 17
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Example: df.groupby('Column').agg({[}'mean', 'sum'{]}).} \tn 
% Row Count 28 (+ 2)
% Row 18
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Named Aggregation:} \tn 
% Row Count 29 (+ 1)
% Row 19
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Providing custom names for aggregated columns.} \tn 
% Row Count 30 (+ 1)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Grouping and Aggregating Data (cont)}}  \tn
% Row 20
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Example: \seqsplit{df.groupby('Column').agg(avg\_salary=('Salary'}, 'mean'), total\_sales=('Sales', 'sum')).} \tn 
% Row Count 2 (+ 2)
% Row 21
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Grouping by Time Periods:} \tn 
% Row Count 3 (+ 1)
% Row 22
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Grouping time series data by specific time periods like months or years.} \tn 
% Row Count 5 (+ 2)
% Row 23
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Example: \seqsplit{df.groupby(pd.Grouper(freq='M')).}} \tn 
% Row Count 6 (+ 1)
% Row 24
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Grouping with Categorical Data:} \tn 
% Row Count 7 (+ 1)
% Row 25
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Grouping based on categorical data types.} \tn 
% Row Count 8 (+ 1)
% Row 26
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Example: \seqsplit{df.groupby('Category').sum().}} \tn 
% Row Count 9 (+ 1)
% Row 27
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Handling Grouped Data:} \tn 
% Row Count 10 (+ 1)
% Row 28
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Accessing grouped data using get\_group() method.} \tn 
% Row Count 11 (+ 1)
% Row 29
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Example: \seqsplit{grouped.get\_group('Group\_Name').}} \tn 
% Row Count 12 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
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\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Working with Excel Files}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Reading Excel Files:} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{pd.read\_excel() function to read Excel files into DataFrame.} \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Specify sheet name, header, index, and column names.} \tn 
% Row Count 5 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Writing Excel Files:} \tn 
% Row Count 6 (+ 1)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{DataFrame.to\_excel() method to write DataFrame to an Excel file.} \tn 
% Row Count 8 (+ 2)
% Row 5
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Specify sheet name, index, and column names.} \tn 
% Row Count 9 (+ 1)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Working with Multiple Sheets:} \tn 
% Row Count 10 (+ 1)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{pd.ExcelFile() to work with multiple sheets in a single Excel file.} \tn 
% Row Count 12 (+ 2)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Read specific sheets using parse() or read\_excel().} \tn 
% Row Count 14 (+ 2)
% Row 9
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Handling Excel Formatting:} \tn 
% Row Count 15 (+ 1)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Preserve formatting while reading with pd.ExcelFile() and xlrd engine.} \tn 
% Row Count 17 (+ 2)
% Row 11
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Formatting may be lost when writing to Excel.} \tn 
% Row Count 18 (+ 1)
% Row 12
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Excel Data Manipulation:} \tn 
% Row Count 19 (+ 1)
% Row 13
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Apply pandas operations (filtering, sorting, grouping) to Excel data after reading.} \tn 
% Row Count 21 (+ 2)
% Row 14
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Convert Excel data into pandas DataFrame for manipulation and analysis.} \tn 
% Row Count 23 (+ 2)
% Row 15
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Exporting DataFrame to Specific Excel Formats:} \tn 
% Row Count 24 (+ 1)
% Row 16
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Specify Excel file format (xls, xlsx) while writing.} \tn 
% Row Count 26 (+ 2)
% Row 17
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use appropriate file extension (.xls or .xlsx) for compatibility.} \tn 
% Row Count 28 (+ 2)
% Row 18
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Handling Large Excel Files:} \tn 
% Row Count 29 (+ 1)
% Row 19
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Utilize chunksize parameter when reading large Excel files to load data in manageable chunks.} \tn 
% Row Count 31 (+ 2)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Working with Excel Files (cont)}}  \tn
% Row 20
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Process data incrementally to avoid memory overflow.} \tn 
% Row Count 2 (+ 2)
% Row 21
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Excel File Metadata:} \tn 
% Row Count 3 (+ 1)
% Row 22
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Retrieve Excel file information (sheet names, data types, etc.) using pandas metadata functions.} \tn 
% Row Count 5 (+ 2)
% Row 23
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Access metadata through pd.ExcelFile() object or DataFrame attributes.} \tn 
% Row Count 7 (+ 2)
% Row 24
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Excel File Validation:} \tn 
% Row Count 8 (+ 1)
% Row 25
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Validate Excel data integrity using pandas functions (e.g., checking for missing values, data types).} \tn 
% Row Count 11 (+ 3)
% Row 26
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Ensure consistency between Excel data and expected data types for analysis.} \tn 
% Row Count 13 (+ 2)
% Row 27
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Excel File Performance Optimization:} \tn 
% Row Count 14 (+ 1)
% Row 28
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Optimize Excel file reading and writing performance by specifying appropriate options (e.g., engine, dtype).} \tn 
% Row Count 17 (+ 3)
% Row 29
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Utilize parallel processing or asynchronous methods for faster data processing.} \tn 
% Row Count 19 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{3.76 cm} x{4.24 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Reshaping Data}}  \tn
% Row 0
\SetRowColor{LightBackground}
Pivot Tables & Restructuring data using one or more columns as new columns. \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
Melting & Unpivoting data from wide to long format. \tn 
% Row Count 5 (+ 2)
% Row 2
\SetRowColor{LightBackground}
Stacking and Unstacking & Manipulating hierarchical indices. \tn 
% Row Count 7 (+ 2)
% Row 3
\SetRowColor{white}
Reshaping with Hierarchical Indexing & Restructuring data with MultiIndex. \tn 
% Row Count 9 (+ 2)
% Row 4
\SetRowColor{LightBackground}
Transposing Data & Swapping rows and columns. \tn 
% Row Count 11 (+ 2)
% Row 5
\SetRowColor{white}
Merging and Joining DataFrames & Combining data horizontally based on common columns or indices. \tn 
% Row Count 14 (+ 3)
% Row 6
\SetRowColor{LightBackground}
Appending DataFrames & Concatenating data vertically. \tn 
% Row Count 16 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{2.72 cm} x{5.28 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Input/Output}}  \tn
% Row 0
\SetRowColor{LightBackground}
\seqsplit{pd.read\_csv()} & Read CSV files into DataFrame. \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\seqsplit{pd.read\_excel()} & Read Excel files into DataFrame. \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\seqsplit{pd.read\_sql()} & Read SQL query or database table into DataFrame. \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
\seqsplit{pd.read\_json()} & Read JSON files into DataFrame. \tn 
% Row Count 8 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\seqsplit{pd.read\_html()} & Read HTML tables into DataFrame. \tn 
% Row Count 10 (+ 2)
% Row 5
\SetRowColor{white}
\seqsplit{pd.read\_pickle()} & Read pickled (serialized) objects into DataFrame. \tn 
% Row Count 12 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\seqsplit{DataFrame.to\_csv()} & Write DataFrame to a CSV file. \tn 
% Row Count 14 (+ 2)
% Row 7
\SetRowColor{white}
\seqsplit{DataFrame.to\_excel()} & Write DataFrame to an Excel file. \tn 
% Row Count 16 (+ 2)
% Row 8
\SetRowColor{LightBackground}
\seqsplit{DataFrame.to\_sql()} & Write DataFrame to a SQL database. \tn 
% Row Count 18 (+ 2)
% Row 9
\SetRowColor{white}
\seqsplit{DataFrame.to\_json()} & Write DataFrame to a JSON file. \tn 
% Row Count 20 (+ 2)
% Row 10
\SetRowColor{LightBackground}
\seqsplit{DataFrame.to\_html()} & Write DataFrame to an HTML file. \tn 
% Row Count 22 (+ 2)
% Row 11
\SetRowColor{white}
\seqsplit{DataFrame.to\_pickle()} & Write DataFrame to a pickled (serialized) object file. \tn 
% Row Count 25 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{3.68 cm} x{4.32 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Performance Optimization}}  \tn
% Row 0
\SetRowColor{LightBackground}
Use Vectorized Operations & Avoid looping through DataFrame rows; instead, utilize Pandas' built-in vectorized operations for faster computations. \tn 
% Row Count 6 (+ 6)
% Row 1
\SetRowColor{white}
Optimize Memory Usage & Convert data types to more memory-efficient ones (e.g., using int8 instead of int64 for smaller integers). \tn 
% Row Count 12 (+ 6)
% Row 2
\SetRowColor{LightBackground}
Leverage Caching & Utilize caching mechanisms like df.eval() and df.query() for repetitive computations on large datasets to improve performance. \tn 
% Row Count 18 (+ 6)
% Row 3
\SetRowColor{white}
Use DataFrame.apply() with caution & It can be slow; explore alternatives like \seqsplit{DataFrame.transform()} or vectorized operations whenever possible. \tn 
% Row Count 24 (+ 6)
% Row 4
\SetRowColor{LightBackground}
Pandas Built-in Methods & Utilize built-in Pandas methods that are optimized for performance (e.g., df.groupby().agg() instead of custom aggregation functions). \tn 
% Row Count 31 (+ 7)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{x{3.68 cm} x{4.32 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Performance Optimization (cont)}}  \tn
% Row 5
\SetRowColor{LightBackground}
Chunking & When working with large datasets, process data in smaller, manageable chunks to avoid memory errors and improve performance. \tn 
% Row Count 6 (+ 6)
% Row 6
\SetRowColor{white}
Parallelization & Use libraries like Dask or Modin to parallelize Pandas operations across multiple cores for faster execution. \tn 
% Row Count 12 (+ 6)
% Row 7
\SetRowColor{LightBackground}
Profile and Benchmark & Identify bottlenecks in your code using tools like pandas\_profiling or Python's built-in cProfile module, and optimize accordingly. \tn 
% Row Count 19 (+ 7)
% Row 8
\SetRowColor{white}
Avoid Method Chaining & While method chaining can make code concise, it can also hinder performance; consider breaking chains into separate statements for better performance. \tn 
% Row Count 27 (+ 8)
% Row 9
\SetRowColor{LightBackground}
Pandas Built-in I/O & Use Pandas' optimized file I/O methods (e.g., pd.read\_csv() with appropriate parameters) to efficiently read and write data from various sources. \tn 
% Row Count 34 (+ 7)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Advanced Indexing}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{MultiIndexing} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Creating hierarchical indexes with multiple levels.} \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Accessing and manipulating data with MultiIndexes.} \tn 
% Row Count 4 (+ 1)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Hierarchical Indexing:} \tn 
% Row Count 5 (+ 1)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Understanding hierarchical indexes.} \tn 
% Row Count 6 (+ 1)
% Row 5
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Using hierarchical indexes for advanced data organization and analysis.} \tn 
% Row Count 8 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Indexing with Boolean Masks:} \tn 
% Row Count 9 (+ 1)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Using boolean arrays to filter data.} \tn 
% Row Count 10 (+ 1)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Applying boolean masks for advanced data selection.} \tn 
% Row Count 12 (+ 2)
% Row 9
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Indexing with .loc and .iloc:} \tn 
% Row Count 13 (+ 1)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Utilizing .loc for label-based indexing.} \tn 
% Row Count 14 (+ 1)
% Row 11
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Utilizing .iloc for integer-based indexing.} \tn 
% Row Count 15 (+ 1)
% Row 12
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Setting and Resetting Index:} \tn 
% Row Count 16 (+ 1)
% Row 13
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Setting new indexes for DataFrames.} \tn 
% Row Count 17 (+ 1)
% Row 14
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Resetting indexes to default integer index.} \tn 
% Row Count 18 (+ 1)
% Row 15
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Indexing Performance Optimization:} \tn 
% Row Count 19 (+ 1)
% Row 16
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Techniques for optimizing indexing performance.} \tn 
% Row Count 20 (+ 1)
% Row 17
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Avoiding common pitfalls for efficient indexing.} \tn 
% Row Count 21 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{3.68 cm} x{4.32 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Tips and Tricks for Efficient Pandas Usage}}  \tn
% Row 0
\SetRowColor{LightBackground}
Use Vectorized Operations & Utilize built-in functions and operations for faster computation \tn 
% Row Count 4 (+ 4)
% Row 1
\SetRowColor{white}
Avoid Iteration over Rows & Use apply() with vectorized functions instead of looping through rows. \tn 
% Row Count 8 (+ 4)
% Row 2
\SetRowColor{LightBackground}
Use Method Chaining & Combine multiple operations in a single statement for cleaner code. \tn 
% Row Count 12 (+ 4)
% Row 3
\SetRowColor{white}
Optimize Memory Usage & Convert data types to appropriate ones (int64 to int32, etc.) to reduce memory usage. \tn 
% Row Count 17 (+ 5)
% Row 4
\SetRowColor{LightBackground}
Utilize Pandas Built-in Functions: & Explore and leverage the extensive set of built-in functions for common tasks. \tn 
% Row Count 21 (+ 4)
% Row 5
\SetRowColor{white}
Explore Pandas Documentation & Refer to the official documentation for detailed explanations and examples. \tn 
% Row Count 25 (+ 4)
% Row 6
\SetRowColor{LightBackground}
Profile Code & Use profiling tools like cProfile to identify bottlenecks and optimize performance. \tn 
% Row Count 29 (+ 4)
% Row 7
\SetRowColor{white}
Leverage Cython and Numba & For computationally intensive tasks, consider using Cython or Numba to speed up operations. \tn 
% Row Count 34 (+ 5)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{x{3.68 cm} x{4.32 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Tips and Tricks for Efficient Pandas Usage (cont)}}  \tn
% Row 8
\SetRowColor{LightBackground}
Parallelize Operations & Utilize parallel processing with libraries like Dask or Modin for large datasets. \tn 
% Row Count 4 (+ 4)
% Row 9
\SetRowColor{white}
Keep Code Readable & Prioritize readability and maintainability while optimizing performance. \tn 
% Row Count 8 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Working with JSON and XML Data}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Reading JSON Data:} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{pd.read\_json() to read JSON files into a DataFrame.} \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Specify orient parameter for different JSON structures ('records', 'split', 'index', 'columns').} \tn 
% Row Count 5 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Writing JSON Data:} \tn 
% Row Count 6 (+ 1)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{to\_json() method to convert DataFrame to JSON format.} \tn 
% Row Count 8 (+ 2)
% Row 5
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Specify orient parameter for desired JSON structure.} \tn 
% Row Count 10 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Reading XML Data:} \tn 
% Row Count 11 (+ 1)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use xml.etree.ElementTree or lxml library to parse XML data.} \tn 
% Row Count 13 (+ 2)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Convert XML structure to DataFrame manually.} \tn 
% Row Count 14 (+ 1)
% Row 9
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Writing XML Data:} \tn 
% Row Count 15 (+ 1)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{No direct method in Pandas for writing XML.} \tn 
% Row Count 16 (+ 1)
% Row 11
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Convert DataFrame to XML using libraries like xml.etree.ElementTree or lxml.} \tn 
% Row Count 18 (+ 2)
% Row 12
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Handling Nested JSON/XML:} \tn 
% Row Count 19 (+ 1)
% Row 13
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use normalization techniques like pd.json\_normalize() to handle nested JSON structures.} \tn 
% Row Count 21 (+ 2)
% Row 14
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{For XML, flatten the hierarchical structure manually or use appropriate libraries.} \tn 
% Row Count 23 (+ 2)
% Row 15
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Working with APIs:} \tn 
% Row Count 24 (+ 1)
% Row 16
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Retrieve JSON data from APIs using libraries like requests.} \tn 
% Row Count 26 (+ 2)
% Row 17
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Convert JSON responses to DataFrame for analysis.} \tn 
% Row Count 27 (+ 1)
% Row 18
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Performance Considerations:} \tn 
% Row Count 28 (+ 1)
% Row 19
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{JSON and XML parsing can be slower compared to other formats like CSV.} \tn 
% Row Count 30 (+ 2)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Working with JSON and XML Data (cont)}}  \tn
% Row 20
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Optimize parsing methods for large datasets to improve performance.} \tn 
% Row Count 2 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Working with Text Data}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Pandas provides powerful tools for working with text data within Series and DataFrame objects.} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{str accessor allows accessing string methods for Series containing strings.} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Common string methods include lower(), upper(), strip(), split(), replace(), etc.} \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{contains() method checks if a pattern or substring exists in each element of a Series.} \tn 
% Row Count 8 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{extract() method extracts substrings using regular expressions.} \tn 
% Row Count 10 (+ 2)
% Row 5
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{split() method splits strings into lists of substrings based on a delimiter.} \tn 
% Row Count 12 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{join() method joins lists of strings into a single string with a specified delimiter.} \tn 
% Row Count 14 (+ 2)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{get\_dummies() method creates dummy variables for categorical text data.} \tn 
% Row Count 16 (+ 2)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{replace() method replaces values based on a mapping or regular expression.} \tn 
% Row Count 18 (+ 2)
% Row 9
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{find() method finds the first occurrence of a substring in each element of a Series.} \tn 
% Row Count 20 (+ 2)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{count() method counts occurrences of a substring in each element of a Series.} \tn 
% Row Count 22 (+ 2)
% Row 11
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{startswith() and endswith() methods check if each element in a Series starts or ends with a specified substring.} \tn 
% Row Count 25 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Handling Categorical Data}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Convert categorical data to numerical representation using pd.factorize() or pd.get\_dummies()} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Utilize astype() method to convert categorical data to categorical dtype} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Handle ordinal data using Categorical dtype with specified order} \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use pd.cut() for binning numerical data into discrete intervals} \tn 
% Row Count 8 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Employ pd.qcut() for quantile-based discretization} \tn 
% Row Count 9 (+ 1)
% Row 5
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Encode categorical variables using LabelEncoder or OneHotEncoder from sklearn.preprocessing} \tn 
% Row Count 11 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Handle high cardinality categorical data using techniques like frequency encoding or target encoding} \tn 
% Row Count 13 (+ 2)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use pd.Categorical() to create categorical data with custom categories and ordering} \tn 
% Row Count 15 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{2.72 cm} x{5.28 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Visualization with Pandas}}  \tn
% Row 0
\SetRowColor{LightBackground}
Plotting Functions: & Pandas provides easy-to-use plotting functions that leverage Matplotlib under the hood. Use .plot() method on Series or DataFrame to create various types of plots like line, bar, histogram, scatter, etc. \tn 
% Row Count 8 (+ 8)
% Row 1
\SetRowColor{white}
\seqsplit{Customization:} & You can customize plots by passing parameters to the plotting functions such as title, labels, colors, styles, etc. Additionally, you can directly use Matplotlib functions to fine-tune your plots further. \tn 
% Row Count 16 (+ 8)
% Row 2
\SetRowColor{LightBackground}
Subplots: & Pandas supports creating subplots from DataFrame or Series. Simply call .plot() on different columns or subsets of data to create multiple plots in the same figure. \tn 
% Row Count 23 (+ 7)
% Row 3
\SetRowColor{white}
Interactive Plots: & Pandas supports integration with libraries like Plotly and Bokeh for creating interactive plots. Simply install these libraries and Pandas will use them to generate interactive visualizations. \tn 
% Row Count 31 (+ 8)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{x{2.72 cm} x{5.28 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Visualization with Pandas (cont)}}  \tn
% Row 4
\SetRowColor{LightBackground}
Time Series Plotting: & Pandas makes it easy to plot time series data with intelligent date formatting and labeling. Use .plot() with time-indexed data to create informative time series plots. \tn 
% Row Count 7 (+ 7)
% Row 5
\SetRowColor{white}
Seaborn Integration: & Seaborn, a statistical data visualization library, integrates seamlessly with Pandas. You can use Seaborn functions directly on Pandas objects to create more complex and visually appealing plots. \tn 
% Row Count 15 (+ 8)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Time Series Data}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Introduction:} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Time series data is sequential data indexed by timestamps.} \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Pandas provides robust tools for working with time series data efficiently.} \tn 
% Row Count 5 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Date-Time Index:} \tn 
% Row Count 6 (+ 1)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Pandas offers specialized data structures like DatetimeIndex to handle time series indexing.} \tn 
% Row Count 8 (+ 2)
% Row 5
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Convert date strings to DatetimeIndex using pd.to\_datetime().} \tn 
% Row Count 10 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Resampling and Frequency Conversion:} \tn 
% Row Count 11 (+ 1)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Adjust time series data to different frequencies using resample().} \tn 
% Row Count 13 (+ 2)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Aggregating or downsampling time series data to a lower frequency or upsampling to a higher frequency.} \tn 
% Row Count 16 (+ 3)
% Row 9
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Time Shifting:} \tn 
% Row Count 17 (+ 1)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Shift index by a specified number of periods with shift().} \tn 
% Row Count 19 (+ 2)
% Row 11
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Useful for calculating differences over time or shifting data for alignment.} \tn 
% Row Count 21 (+ 2)
% Row 12
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Rolling and Expanding Windows:} \tn 
% Row Count 22 (+ 1)
% Row 13
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Compute rolling statistics (mean, sum, etc.) over a specified window with rolling().} \tn 
% Row Count 24 (+ 2)
% Row 14
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Calculate expanding statistics over the entire history of a time series with expanding().} \tn 
% Row Count 26 (+ 2)
% Row 15
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Time Zone Handling:} \tn 
% Row Count 27 (+ 1)
% Row 16
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Localize timestamps to a specific time zone using tz\_localize().} \tn 
% Row Count 29 (+ 2)
% Row 17
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Convert timestamps between time zones with tz\_convert().} \tn 
% Row Count 31 (+ 2)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Time Series Data (cont)}}  \tn
% Row 18
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Offset Aliases:} \tn 
% Row Count 1 (+ 1)
% Row 19
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Use offset aliases like 'D' for day, 'M' for month, 'Y' for year to perform frequency conversions easily.} \tn 
% Row Count 4 (+ 3)
% Row 20
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Time Series Plotting:} \tn 
% Row Count 5 (+ 1)
% Row 21
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Pandas provides convenient methods for plotting time series data directly from DataFrames.} \tn 
% Row Count 7 (+ 2)
% Row 22
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Use plot() function with a datetime index for quick visualization.} \tn 
% Row Count 9 (+ 2)
% Row 23
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Date Range Generation:} \tn 
% Row Count 10 (+ 1)
% Row 24
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Generate date ranges using date\_range() for easy creation of time series indices.} \tn 
% Row Count 12 (+ 2)
% Row 25
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Specify start date, end date, frequency, and time zone parameters.} \tn 
% Row Count 14 (+ 2)
% Row 26
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{Time Series Analysis:} \tn 
% Row Count 15 (+ 1)
% Row 27
\SetRowColor{white}
\mymulticolumn{1}{x{8.4cm}}{Perform time series analysis including trend analysis, seasonality detection, and forecasting using Pandas in conjunction with other libraries like Statsmodels.} \tn 
% Row Count 19 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{3.6 cm} x{4.4 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Merging and Joining DataFrames}}  \tn
% Row 0
\SetRowColor{LightBackground}
Concatenation & Combining DataFrames along rows or columns. \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
Merge & Combining DataFrames based on common columns using SQL-like joins such as inner, outer, left, and right joins. \tn 
% Row Count 7 (+ 5)
% Row 2
\SetRowColor{LightBackground}
Join & Convenient method for merging DataFrames based on index labels. \tn 
% Row Count 10 (+ 3)
% Row 3
\SetRowColor{white}
Handling Duplicate Columns & Dealing with duplicate column names when merging DataFrames. \tn 
% Row Count 13 (+ 3)
% Row 4
\SetRowColor{LightBackground}
Suffixes & Specifying suffixes for overlapping column names in the merged DataFrame. \tn 
% Row Count 17 (+ 4)
% Row 5
\SetRowColor{white}
Merging on Index & Merging DataFrames based on their index values. \tn 
% Row Count 20 (+ 3)
% Row 6
\SetRowColor{LightBackground}
Joining on Index & Joining DataFrames based on their index labels. \tn 
% Row Count 23 (+ 3)
% Row 7
\SetRowColor{white}
Concatenating DataFrames & Combining multiple DataFrames along rows or columns using the pd.concat() function. \tn 
% Row Count 27 (+ 4)
% Row 8
\SetRowColor{LightBackground}
Merging with Different Join Types & Utilizing different types of joins (inner, outer, left, right) to merge DataFrames using the pd.merge() function. \tn 
% Row Count 33 (+ 6)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{x{3.6 cm} x{4.4 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Merging and Joining DataFrames (cont)}}  \tn
% Row 9
\SetRowColor{LightBackground}
Joining on Index & Merging DataFrames based on their index labels using the .join() method. \tn 
% Row Count 4 (+ 4)
% Row 10
\SetRowColor{white}
Handling Overlapping Column Names & Managing duplicate or overlapping column names during merging. \tn 
% Row Count 7 (+ 3)
% Row 11
\SetRowColor{LightBackground}
Merging on Multiple Columns & Performing merges based on multiple columns in the DataFrames. \tn 
% Row Count 10 (+ 3)
% Row 12
\SetRowColor{white}
Suffixes & Specifying suffixes for overlapping column names to distinguish them in the merged DataFrame. \tn 
% Row Count 15 (+ 5)
% Row 13
\SetRowColor{LightBackground}
Merging on Index & Merging DataFrames based on their index values using the .merge() method with the 'left\_index' and 'right\_index' parameters. \tn 
% Row Count 21 (+ 6)
% Row 14
\SetRowColor{white}
Joining on Index & Joining DataFrames based on their index labels using the .join() method. \tn 
% Row Count 25 (+ 4)
% Row 15
\SetRowColor{LightBackground}
Handling Overlapping Column Names & Managing duplicate or overlapping column names during merging. \tn 
% Row Count 28 (+ 3)
% Row 16
\SetRowColor{white}
Merging on Multiple Columns & Performing merges based on multiple columns in the DataFrames. \tn 
% Row Count 31 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{3.6 cm} x{4.4 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Data Transformation}}  \tn
% Row 0
\SetRowColor{LightBackground}
Applying Functions & Use .apply() to apply a function along an axis of the DataFrame or Series. \tn 
% Row Count 4 (+ 4)
% Row 1
\SetRowColor{white}
Mapping & Transform values in a Series or DataFrame using a mapping or a function. \tn 
% Row Count 8 (+ 4)
% Row 2
\SetRowColor{LightBackground}
Replacing Values & Replace specific values in a DataFrame or Series with other values. \tn 
% Row Count 12 (+ 4)
% Row 3
\SetRowColor{white}
Dropping Columns or Rows & Use .drop() to remove specified rows or columns from a DataFrame. \tn 
% Row Count 15 (+ 3)
% Row 4
\SetRowColor{LightBackground}
Adding/Removing Columns & Add or remove columns from a DataFrame using assignment or the .drop() method. \tn 
% Row Count 19 (+ 4)
% Row 5
\SetRowColor{white}
Renaming Columns & Rename columns in a DataFrame using the .rename() method. \tn 
% Row Count 22 (+ 3)
% Row 6
\SetRowColor{LightBackground}
Duplicating Data & Create copies of data using the .copy() method. \tn 
% Row Count 25 (+ 3)
% Row 7
\SetRowColor{white}
Changing Data Types & Convert data types of columns using the .astype() method. \tn 
% Row Count 28 (+ 3)
% Row 8
\SetRowColor{LightBackground}
Discretization and Binning & Convert continuous data into discrete intervals using the .cut() function. \tn 
% Row Count 32 (+ 4)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{x{3.6 cm} x{4.4 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Data Transformation (cont)}}  \tn
% Row 9
\SetRowColor{LightBackground}
Encoding Categorical Variables & Convert categorical variables into numerical representations using techniques like one-hot encoding or label encoding. \tn 
% Row Count 6 (+ 6)
% Row 10
\SetRowColor{white}
Normalization and Standardization & Scale numeric data to a standard range or distribution. \tn 
% Row Count 9 (+ 3)
% Row 11
\SetRowColor{LightBackground}
\seqsplit{Merging/Concatenating} DataFrames & Combine multiple DataFrames either by concatenating or merging based on common columns or indices. \tn 
% Row Count 14 (+ 5)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{3.12 cm} x{4.88 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Basic Operations}}  \tn
% Row 0
\SetRowColor{LightBackground}
Slicing & Selecting subsets of data using row and column labels or positions. \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
Filtering & Applying conditions to extract specific rows or columns from a DataFrame. \tn 
% Row Count 7 (+ 4)
% Row 2
\SetRowColor{LightBackground}
Sorting & Arranging data in ascending or descending order based on one or more columns. \tn 
% Row Count 11 (+ 4)
% Row 3
\SetRowColor{white}
Applying Functions & Applying functions element-wise to data, either built-in or custom functions. \tn 
% Row Count 15 (+ 4)
% Row 4
\SetRowColor{LightBackground}
Descriptive Statistics & Calculating basic statistical measures like mean, median, mode, etc., for data exploration. \tn 
% Row Count 19 (+ 4)
% Row 5
\SetRowColor{white}
Data Alignment & Automatically aligning data based on row and column labels when performing operations between different DataFrames or Series. \tn 
% Row Count 25 (+ 6)
% Row 6
\SetRowColor{LightBackground}
Element-wise Operations & Performing operations like addition, subtraction, multiplication, and division on individual elements of a DataFrame or Series. \tn 
% Row Count 31 (+ 6)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{x{3.12 cm} x{4.88 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Basic Operations (cont)}}  \tn
% Row 7
\SetRowColor{LightBackground}
Aggregating Data & Computing summary statistics like sum, mean, count, etc., over specified axes of the data. \tn 
% Row Count 4 (+ 4)
% Row 8
\SetRowColor{white}
Filling Missing Values & Handling missing or NaN values by filling them with a specified value or using methods like forward-fill or backward-fill. \tn 
% Row Count 10 (+ 6)
% Row 9
\SetRowColor{LightBackground}
Applying Conditional Logic & Using conditions to assign values or modify data based on certain criteria. \tn 
% Row Count 14 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{3.44 cm} x{4.56 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Data Structures}}  \tn
% Row 0
\SetRowColor{LightBackground}
Series & One-dimensional labeled array that can hold any data type. \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
DataFrame & Two-dimensional labeled data structure with columns of potentially different types, akin to a spreadsheet or SQL table. \tn 
% Row Count 9 (+ 6)
% Row 2
\SetRowColor{LightBackground}
Indexing and Selecting Data & Techniques for accessing specific elements, rows, or columns within Series or DataFrame. \tn 
% Row Count 13 (+ 4)
% Row 3
\SetRowColor{white}
Basic Operations & Fundamental operations such as slicing, filtering, and sorting data for effective manipulation. \tn 
% Row Count 18 (+ 5)
% Row 4
\SetRowColor{LightBackground}
Data Cleaning & Strategies for handling missing values, duplicates, and other inconsistencies within the data. \tn 
% Row Count 23 (+ 5)
% Row 5
\SetRowColor{white}
Data Transformation & Methods for applying functions, mapping values, and transforming data for analysis. \tn 
% Row Count 27 (+ 4)
% Row 6
\SetRowColor{LightBackground}
Grouping and Aggregating Data & Techniques for grouping data based on specified criteria and performing aggregations like sum, mean, count, etc. \tn 
% Row Count 33 (+ 6)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{8.4cm}{x{3.44 cm} x{4.56 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Data Structures (cont)}}  \tn
% Row 7
\SetRowColor{LightBackground}
Merging and Joining DataFrames & Methods for combining multiple DataFrames based on common columns or indices. \tn 
% Row Count 4 (+ 4)
% Row 8
\SetRowColor{white}
Reshaping Data & Tools for reshaping data using pivot tables, melting, and other techniques to suit analytical needs. \tn 
% Row Count 9 (+ 5)
% Row 9
\SetRowColor{LightBackground}
Time Series Data & Handling and analyzing time-based data using pandas' specialized functionalities. \tn 
% Row Count 13 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}


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

\end{document}