seaborn.clustermap — seaborn 0.13.2 documentation
seaborn.clustermap# seaborn.clustermap(data, *, pivot_kws=None, method='average', metric='euclidean', z_score=None, standard_scale=None, figsize=(10, 10), cbar_kws=None, row_cluster=True, col_cluster=True, row_linkage=None, col_linkage=None, row_colors=None, col_colors=None, mask=None, dendrogram_ratio=0.2, colors_ratio=0.03, cbar_pos=(0.02, 0.8, 0.05, 0.18), tree_kws=None, **kwargs)# Plot a matri
seaborn.heatmap — seaborn 0.13.2 documentation
seaborn.heatmap# seaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt='.2g', annot_kws=None, linewidths=0, linecolor='white', cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels='auto', yticklabels='auto', mask=None, ax=None, **kwargs)# Plot rectangular data as a color-encoded matrix. This is an Axes-level function and will draw the hea
Example gallery — seaborn 0.13.2 documentation
Ctrl+K Site Navigation Installing Gallery Tutorial API Releases Citing FAQ GitHub StackOverflow Twitter Example gallery# lmplot scatterplot lineplot displot relplot catplot boxplot violinplot relplot jointplot histplot boxplot stripplot JointGrid jointplot FacetGrid boxenplot scatterplot lmplot FacetGrid heatmap JointGrid kdeplot displot displot lmplot PairGrid PairGrid PairGrid barplot kdeplot ba
Facetting histograms by subsets of data — seaborn 0.13.2 documentation
Facetting histograms by subsets of data# seaborn components used: set_theme(), load_dataset(), displot() import seaborn as sns sns.set_theme(style="darkgrid") df = sns.load_dataset("penguins") sns.displot( df, x="flipper_length_mm", col="species", row="sex", binwidth=3, height=3, facet_kws=dict(margin_titles=True), )
Visualizing categorical data — seaborn 0.13.2 documentation
Visualizing categorical data# In the relational plot tutorial we saw how to use different visual representations to show the relationship between multiple variables in a dataset. In the examples, we focused on cases where the main relationship was between two numerical variables. If one of the main variables is “categorical” (divided into discrete groups) it may be helpful to use a more specialize
seaborn.countplot — seaborn 0.13.2 documentation
seaborn.countplot# seaborn.countplot(data=None, *, x=None, y=None, hue=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, fill=True, hue_norm=None, stat='count', width=0.8, dodge='auto', gap=0, log_scale=None, native_scale=False, formatter=None, legend='auto', ax=None, **kwargs)# Show the counts of observations in each categorical bin using bars. A count plot
seaborn.swarmplot — seaborn 0.13.2 documentation
seaborn.swarmplot# seaborn.swarmplot(data=None, *, x=None, y=None, hue=None, order=None, hue_order=None, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor=None, linewidth=0, hue_norm=None, log_scale=None, native_scale=False, formatter=None, legend='auto', warn_thresh=0.05, ax=None, **kwargs)# Draw a categorical scatterplot with points adjusted to be non-overlapping. This functi
API reference — seaborn 0.13.2 documentation
An interface for declaratively specifying statistical graphics.
Discovering structure in heatmap data — seaborn 0.13.0 documentation
Discovering structure in heatmap data# seaborn components used: set_theme(), load_dataset(), husl_palette(), clustermap() import pandas as pd import seaborn as sns sns.set_theme() # Load the brain networks example dataset df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0) # Select a subset of the networks used_networks = [1, 5, 6, 7, 8, 12, 13, 17] used_columns = (df.columns.ge
seaborn.pairplot — seaborn 0.13.0 documentation
seaborn.pairplot# seaborn.pairplot(data, *, hue=None, hue_order=None, palette=None, vars=None, x_vars=None, y_vars=None, kind='scatter', diag_kind='auto', markers=None, height=2.5, aspect=1, corner=False, dropna=False, plot_kws=None, diag_kws=None, grid_kws=None, size=None)# Plot pairwise relationships in a dataset. By default, this function will create a grid of Axes such that each numeric variab
seaborn.FacetGrid — seaborn 0.13.2 documentation
seaborn.FacetGrid# class seaborn.FacetGrid(data, *, row=None, col=None, hue=None, col_wrap=None, sharex=True, sharey=True, height=3, aspect=1, palette=None, row_order=None, col_order=None, hue_order=None, hue_kws=None, dropna=False, legend_out=True, despine=True, margin_titles=False, xlim=None, ylim=None, subplot_kws=None, gridspec_kws=None)# Multi-plot grid for plotting conditional relationships.
Distribution plot options — seaborn 0.10.1 documentation
import numpy as np import seaborn as sns import matplotlib.pyplot as plt sns.set(style="white", palette="muted", color_codes=True) rs = np.random.RandomState(10) # Set up the matplotlib figure f, axes = plt.subplots(2, 2, figsize=(7, 7), sharex=True) sns.despine(left=True) # Generate a random univariate dataset d = rs.normal(size=100) # Plot a simple histogram with binsize determined automatically
seaborn.lmplot — seaborn 0.13.2 documentation
seaborn.lmplot# seaborn.lmplot(data, *, x=None, y=None, hue=None, col=None, row=None, palette=None, col_wrap=None, height=5, aspect=1, markers='o', sharex=None, sharey=None, hue_order=None, col_order=None, row_order=None, legend=True, legend_out=None, x_estimator=None, x_bins=None, x_ci='ci', scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None, seed=None, order=1, logistic=False, lowess=Fal
Example gallery — seaborn 0.13.2 documentation
Ctrl+K Site Navigation Installing Gallery Tutorial API Releases Citing FAQ GitHub StackOverflow Twitter Example gallery# lmplot scatterplot lineplot displot relplot catplot boxplot violinplot relplot jointplot histplot boxplot stripplot JointGrid jointplot FacetGrid boxenplot scatterplot lmplot FacetGrid heatmap JointGrid kdeplot displot displot lmplot PairGrid PairGrid PairGrid barplot kdeplot ba
seaborn: statistical data visualization — seaborn 0.13.2 documentation
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery
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seaborn.lineplot — seaborn 0.13.2 documentation
seaborn.lmplot — seaborn 0.13.2 documentation
seaborn.distplot — seaborn 0.13.2 documentation
seaborn.barplot — seaborn 0.13.2 documentation
https://seaborn.pydata.org/generated/seaborn.factorplot.html