Cheat Sheet Seaborn Reference Guide
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Python For Data Science Cheat Sheet 3 Seaborn Learn Data Science Interactively at www.DataCamp.com Statistical Data Visualization With Seaborn The Python visualization library Seaborn is based on matplotlib and provides a high-level interface for drawing attractive statistical graphics. The basic steps to creating plots with Seaborn are: 1. Prepare some data 2. Control figure aesthetics 3. Plot with Seaborn 4. Further customize your plot >>> >>> >>> >>> >>> import matplotlib.pyplot as plt import seaborn as sns Step 1 tips = sns.load_dataset("tips") Step 2 sns.set_style("whitegrid") Step 3 g = sns.lmplot(x="tip", y="total_bill", data=tips, aspect=2) >>> g = (g.set_axis_labels("Tip","Total bill(USD)"). set(xlim=(0,10),ylim=(0,100))) Step 4 >>> plt.title("title") >>> plt.show(g) Step 5 1 Data >>> >>> >>> >>> Also see Lists, NumPy & Pandas import pandas as pd import numpy as np uniform_data = np.random.rand(10, 12) data = pd.DataFrame({'x':np.arange(1,101), 'y':np.random.normal(0,4,100)}) Seaborn also offers built-in data sets: >>> titanic = sns.load_dataset("titanic") >>> iris = sns.load_dataset("iris") 2 Axis Grids >>> g = sns.FacetGrid(titanic, col="survived", row="sex") >>> g = g.map(plt.hist,"age") >>> sns.factorplot(x="pclass", y="survived", hue="sex", data=titanic) >>> sns.lmplot(x="sepal_width", y="sepal_length", hue="species", data=iris) Scatterplot Scatterplot with one categorical variable >>> sns.barplot(x="sex", y="survived", hue="class", data=titanic) Show point estimates and confidence intervals with scatterplot glyphs >>> sns.countplot(x="deck", data=titanic, palette="Greens_d") Show count of observations >>> sns.pointplot(x="class", y="survived", hue="sex", data=titanic, palette={"male":"g", "female":"m"}, markers=["^","o"], linestyles=["-","--"]) Show point estimates and confidence intervals as rectangular bars >>> sns.boxplot(x="alive", y="age", hue="adult_male", data=titanic) >>> sns.boxplot(data=iris,orient="h") Boxplot >>> sns.violinplot(x="age", y="sex", hue="survived", data=titanic) Violin plot Categorical scatterplot with non-overlapping points Bar Chart Count Plot >>> sns.regplot(x="sepal_width", y="sepal_length", data=iris, ax=ax) Plot data and a linear regression model fit >>> plot = sns.distplot(data.y, kde=False, color="b") Plot univariate distribution Distribution Plots Matrix Plots >>> sns.heatmap(uniform_data,vmin=0,vmax=1) 4 Boxplot Boxplot with wide-form data Violinplot (Re)set the seaborn default Set the matplotlib parameters Set the matplotlib parameters Return a dict of params or use with Context Functions >>> sns.set_context("talk") >>> sns.set_context("notebook", font_scale=1.5, Set context to "talk" Set context to "notebook", Scale font elements and rc={"lines.linewidth":2.5}) override param mapping Color Palette >>> >>> >>> >>> Further Customizations Heatmap Also see Matplotlib Axisgrid Objects Also see Matplotlib with to temporarily set the style h = sns.PairGrid(iris) Subplot grid for plotting pairwise h = h.map(plt.scatter) relationships sns.pairplot(iris) Plot pairwise bivariate distributions i = sns.JointGrid(x="x", Grid for bivariate plot with marginal y="y", univariate plots data=data) >>> i = i.plot(sns.regplot, sns.distplot) >>> sns.jointplot("sepal_length", Plot bivariate distribution "sepal_width", data=iris, kind='kde') Point Plot Seaborn styles >>> sns.axes_style("whitegrid") Plot data and regression model fits across a FacetGrid >>> sns.stripplot(x="species", y="petal_length", data=iris) >>> sns.swarmplot(x="species", y="petal_length", data=iris) >>> f, ax = plt.subplots(figsize=(5,6)) Create a figure and one subplot {"xtick.major.size":8, "ytick.major.size":8}) Draw a categorical plot onto a Facetgrid >>> >>> >>> >>> Regression Plots Figure Aesthetics >>> sns.set() >>> sns.set_style("whitegrid") >>> sns.set_style("ticks", Subplot grid for plotting conditional relationships Categorical Plots Make use of the following aliases to import the libraries: >>> import matplotlib.pyplot as plt >>> import seaborn as sns Plotting With Seaborn sns.set_palette("husl",3) Define the color palette sns.color_palette("husl") Use with with to temporarily set palette flatui = ["#9b59b6","#3498db","#95a5a6","#e74c3c","#34495e","#2ecc71"] sns.set_palette(flatui) Set your own color palette >>> >>> >>> >>> g.despine(left=True) g.set_ylabels("Survived") g.set_xticklabels(rotation=45) g.set_axis_labels("Survived", "Sex") >>> h.set(xlim=(0,5), ylim=(0,5), xticks=[0,2.5,5], yticks=[0,2.5,5]) Remove left spine Set the labels of the y-axis Set the tick labels for x Set the axis labels Set the limit and ticks of the x-and y-axis Plot >>> >>> >>> >>> >>> >>> >>> 5 plt.title("A Title") plt.ylabel("Survived") plt.xlabel("Sex") plt.ylim(0,100) plt.xlim(0,10) plt.setp(ax,yticks=[0,5]) plt.tight_layout() Add plot title Adjust the label of the y-axis Adjust the label of the x-axis Adjust the limits of the y-axis Adjust the limits of the x-axis Adjust a plot property Adjust subplot params Show or Save Plot >>> plt.show() >>> plt.savefig("foo.png") >>> plt.savefig("foo.png", transparent=True) Also see Matplotlib Show the plot Save the plot as a figure Save transparent figure Close & Clear Also see Matplotlib >>> plt.cla() >>> plt.clf() >>> plt.close() Clear an axis Clear an entire figure Close a window DataCamp Learn Python for Data Science Interactively
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