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% Document Info
\author{Comtive45}
\pdfinfo{
  /Title (pandas-plotting-y-bootstrap-en-python-cheat-sheet.pdf)
  /Creator (Cheatography)
  /Author (Comtive45)
  /Subject (Pandas Plotting y Bootstrap en Python 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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\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Pandas Plotting y Bootstrap en Python Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{Comtive45} via \textcolor{DarkBackground}{\uline{cheatography.com/208912/cs/44875/}}}
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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}Comtive45 \\
  \uline{cheatography.com/comtive45} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 3rd November, 2024.\\
   Page {\thepage} of \pageref{LastPage}.
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  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
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\begin{document}
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\begin{multicols*}{4}

\begin{tabularx}{3.833cm}{X}
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\mymulticolumn{1}{x{3.833cm}}{\bf\textcolor{white}{Introducción}}  \tn
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\mymulticolumn{1}{x{3.833cm}}{{\bf{`pandas.plotting`}} es un submódulo en Pandas para gráficos avanzados, como gráficos de dispersión y matrices de correlación. Se usa para análisis exploratorio de datos. \newline % Row Count 4 (+ 4)
{\bf{Bootstrap}} es una técnica de remuestreo que estima la distribución de una estadística sin suponer una distribución específica. Es ideal para calcular intervalos de confianza cuando los datos son limitados.% Row Count 9 (+ 5)
} \tn 
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\begin{tabularx}{3.833cm}{x{1.20155 cm} x{2.23145 cm} }
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\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Funciones Clave}}  \tn
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Función & Descripción \tn 
% Row Count 1 (+ 1)
% Row 1
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\seqsplit{scatter\_matrix()} & Crea una matriz de dispersión para comparar todas las columnas numéricas en un DataFrame. \tn 
% Row Count 5 (+ 4)
% Row 2
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\seqsplit{andrews\_curves()} & Visualiza patrones en un DataFrame usando curvas de Andrews. \tn 
% Row Count 8 (+ 3)
% Row 3
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\seqsplit{parallel\_coordinates()} & Muestra las columnas de un DataFrame como líneas paralelas para ver agrupaciones. \tn 
% Row Count 12 (+ 4)
% Row 4
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lag\_plot() & Grafica una variable contra sus valores desplazados para ver autocorrelación. \tn 
% Row Count 15 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
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\par\addvspace{1.3em}

\begin{tabularx}{3.833cm}{X}
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\mymulticolumn{1}{x{3.833cm}}{\bf\textcolor{white}{¿Cuándo usar Bootstrap?}}  \tn
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\mymulticolumn{1}{x{3.833cm}}{- {\bf{Datos limitados}}: Si tienes pocos datos, Bootstrap permite estimar intervalos de confianza.} \tn 
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% Row 1
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\mymulticolumn{1}{x{3.833cm}}{- {\bf{Estimación de intervalos de confianza}}: Útil cuando se desconoce la distribución de los datos.} \tn 
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\mymulticolumn{1}{x{3.833cm}}{- {\bf{Sin supuestos paramétricos}}: No necesitas asumir normalidad u otras propiedades de los datos.} \tn 
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\mymulticolumn{1}{x{3.833cm}}{\bf\textcolor{white}{Código Básico: Bootstrap con pandas.plotting}}  \tn
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\mymulticolumn{1}{x{3.833cm}}{import pandas as pd \newline import numpy as np \newline import matplotlib.pyplot as plt \newline from pandas.plotting import scatter\_matrix \newline  \newline \# Datos de ejemplo \newline data = pd.DataFrame(\{ \newline     'X': np.random.normal(50, 10, 100), \newline     'Y': np.random.normal(30, 5, 100), \newline \}) \newline  \newline \# Aplicar Bootstrap \newline bootstrap\_means = {[}{]} \newline samples = 1000  \# Número de muestras \newline  \newline for \_ in range(samples): \newline     sample = data.sample(frac=1, replace=True) \newline     \seqsplit{bootstrap\_means.append(sample.mean())} \newline  \newline bootstrap\_df = \seqsplit{pd.DataFrame(bootstrap\_means)} \newline  \newline \# Visualización \newline scatter\_matrix(bootstrap\_df, alpha=0.5, diagonal='kde') \newline plt.show()} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
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\begin{tabularx}{3.833cm}{x{0.99557 cm} x{2.43743 cm} }
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\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Personalización de Gráficos en Matplotlib}}  \tn
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Elemento & Código \tn 
% Row Count 1 (+ 1)
% Row 1
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Título Central & plt.suptitle("Título", fontsize=16, fontweight='bold', color='navy') \tn 
% Row Count 4 (+ 3)
% Row 2
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Título Ejes X/Y & plt.xlabel("Eje X"), plt.ylabel("Eje Y") \tn 
% Row Count 6 (+ 2)
% Row 3
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Cambiar Colores & color='teal' (en funciones de Matplotlib y pandas.plotting) \tn 
% Row Count 9 (+ 3)
% Row 4
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Leyendas & plt.legend({[}"Label 1", "Label 2"{]}) para etiquetas automáticas, o ax.text para leyendas personalizadas. \tn 
% Row Count 13 (+ 4)
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\begin{tabularx}{3.833cm}{X}
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\mymulticolumn{1}{x{3.833cm}}{\bf\textcolor{white}{Ejemplo Completo}}  \tn
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\mymulticolumn{1}{x{3.833cm}}{import pandas as pd \newline import numpy as np \newline import matplotlib.pyplot as plt \newline from pandas.plotting import scatter\_matrix \newline  \newline \# Generar datos \newline np.random.seed(0) \newline data = pd.DataFrame(\{ \newline     'A': np.random.normal(60, 15, 200), \newline     'B': np.random.normal(50, 10, 200) \newline \}) \newline  \newline \# Aplicar Bootstrap \newline bootstrap\_means = {[}{]} \newline samples = 1000  \# Número de muestras Bootstrap \newline  \newline for \_ in range(samples): \newline     sample = data.sample(frac=1, replace=True) \newline     \seqsplit{bootstrap\_means.append(sample.mean())} \newline  \newline bootstrap\_df = \seqsplit{pd.DataFrame(bootstrap\_means)} \newline  \newline \# Visualización y personalización \newline fig, ax = plt.subplots(figsize=(10, 8)) \newline scatter\_matrix(bootstrap\_df, alpha=0.5, figsize=(8, 8), diagonal='kde', color='teal') \newline plt.suptitle("Distribución Bootstrap de Medias", fontsize=16, fontweight='bold', color='navy') \newline plt.xlabel("Variables", fontsize=12, fontweight='bold', color='darkred') \newline plt.ylabel("Valores de la Media", fontsize=12, fontweight='bold', color='darkred') \newline  \newline \# Leyenda personalizada \newline for i, col in \seqsplit{enumerate(bootstrap\_df.columns):} \newline     ax.text(0.9, 0.1 - i*0.1, f"Media de \{col\}", transform=ax.transAxes, fontsize=12, color='teal') \newline  \newline plt.show()} \tn 
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% That's all folks
\end{multicols*}

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