<jupyter_start><jupyter_text>Grids= allgemeine Typen von Plots, die es ermögl. mehrerer Diagramme entlang von Zeilen und Spalten darzustellen<jupyter_code>import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
tips = pd.read_csv('../Daten/csv/tips.csv')
tips.head(2)<jupyter_output><empty_output><jupyter_text>Pairgrid<jupyter_code>grid = sns.PairGrid(tips)
grid.map(plt.scatter)
grid = sns.PairGrid(tips)
#Platzierung der Plots ändern
grid.map_diag(plt.hist) #auf der Diagonalen sind Histogramme
grid.map_upper(plt.scatter) #alle Dia über der Diagonalen
grid.map_lower(sns.kdeplot) #alle Dia unter der Diagonalen<jupyter_output>C:\Users\flfr\AppData\Local\Continuum\anaconda3\lib\site-packages\matplotlib\contour.py:967: UserWarning: The following kwargs were not used by contour: 'label', 'color'
  s)<jupyter_text>Pairplot= einfachere Version des PairGrid<jupyter_code>sns.pairplot(tips)
sns.pairplot(tips, hue='smoker')<jupyter_output><empty_output><jupyter_text>FacetGrid= allgemeine Weg um eine Vielzahl an Diagrammen in einem Grid zu erstellen<jupyter_code>grid = sns.FacetGrid(tips, col="time", row="smoker", hue="sex")
#grid.map(plt.hist, 'total_bill') #hist benötigt 1 Argument
grid.map(plt.scatter, 'total_bill', 'tip').add_legend() #scatter benötigt 2 Argumente<jupyter_output><empty_output><jupyter_text>JointGridJointGrid ist die allg. Version für jointplot()<jupyter_code>grid = sns.JointGrid(x="total_bill", y="tip", data=tips)
grid.plot(sns.regplot, sns.distplot) #in einem Grid werden 2 verschiedene Dia. / Plots dargestellt<jupyter_output><empty_output>
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