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 a matrix dataset as a hierarchically-clustered heatmap. This function requires scipy to be available. Parameters: data 2D array-like. Rectangular data for clustering. Cannot contain NAs. pivot_kws dict, optional. If data is a tidy dataframe, can provide keyword arguments for pivot to create a rectangular dataframe. method str, optional

Scatterplot heatmap# seaborn components used: set_theme(), load_dataset(), relplot()

import matplotlib.pyplot as plt import seaborn as sns sns. set # Load the example flights dataset and convert to long-form flights_long = sns. load_dataset ("flights") flights = flights_long. pivot ("month", "year", "passengers") # Draw a heatmap with the numeric values in each cell f, ax = plt. subplots (figsize = (9, 6)) sns. heatmap (flights ...

seaborn components used: set_theme(), diverging_palette(), heatmap() from string import ascii_letters import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt sns . set_theme ( style = "white" ) # …

Once you have seaborn installed, you’re ready to get started. To test it out, you could load and plot one of the example datasets: import seaborn as sns df = sns . load_dataset ( "penguins" ) sns . pairplot ( df , hue = "species" )

Annotated heatmaps# seaborn components used: set_theme(), load_dataset(), heatmap()

Seaborn includes four perceptually uniform sequential colormaps: "rocket", "mako", "flare", and "crest". The first two have a very wide luminance range and are well suited for applications such as heatmaps, where colors fill the space they are plotted into:

Discovering structure in heatmap data# seaborn components used: set_theme(), load_dataset(), husl_palette(), clustermap()

Use vars or x_vars and y_vars to select the variables to plot: sns . pairplot ( penguins , x_vars = [ "bill_length_mm" , "bill_depth_mm" , "flipper_length_mm" ], y_vars = [ "bill_length_mm" , "bill_depth_mm" ], )