seaborn.heatmap

Heat maps display numeric tabular data where the cells are colored depending upon the contained value. Heat maps are great for making trends in this kind of data more readily apparent, particularly when the data is ordered and there is clustering.

dataset: Seaborn - flights



In [1]:

    
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
plt.rcParams['figure.figsize'] = (20.0, 10.0)
plt.rcParams['font.family'] = "serif"



In [2]:

    
df = pd.pivot_table(data=sns.load_dataset("flights"),
                    index='month',
                    values='passengers',
                    columns='year')
df.head()

Default plot



In [3]:

    
sns.heatmap(df)









    Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x10efafdd8>

cmap adjusts the colormap used. I like diverging colormaps for heatmaps because they provide good contrast.



In [4]:

    
sns.heatmap(df, cmap='coolwarm')









    Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x10f0e1eb8>

center can be used to indicate at which numeric value to use the center of the colormap. Above we see most of the map using blues, so by setting the value of center equal to the midpoint of the data then we can create a map where there are more equal amounts of red and blue shades.



In [5]:

    
midpoint = (df.values.max() - df.values.min()) / 2
sns.heatmap(df, cmap='coolwarm', center=midpoint)









    Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x10fec2048>

Adjust the lower and upper contrast bounds with vmin and vmax. Everything below vmin will be the same color. Likewise for above vmax.



In [6]:

    
midpoint = (df.values.max() - df.values.min()) / 2
sns.heatmap(df, cmap='coolwarm', center=midpoint, vmin=150, vmax=400)









    Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x1104a0e80>

Alternatively, you can set `vmin` and `vmax` to lie outside of the range of the data for a more muted, washed-out look



In [7]:

    
midpoint = (df.values.max() - df.values.min()) / 2
sns.heatmap(df, cmap='coolwarm', center=midpoint, vmin=-100, vmax=800)









    Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x110aac6d8>



In [8]:

    
midpoint = (df.values.max() - df.values.min()) / 2
p = sns.heatmap(df, cmap='coolwarm', center=midpoint)



In [9]:

    
p.get_figure().savefig('../../figures/heatmap.png')



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In [10]:

    
flights_long.pivot(index="month", columns="year", values='passengers')









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-10-ed58a0e1a61a> in <module>()
----> 1 flights_long.pivot(index="month", columns="year", values='passengers')
      2 

NameError: name 'flights_long' is not defined



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year	1949	1950	1951	1952	1953	1954	1955	1956	1957	1958	1959	1960
month
January	112	115	145	171	196	204	242	284	315	340	360	417
February	118	126	150	180	196	188	233	277	301	318	342	391
March	132	141	178	193	236	235	267	317	356	362	406	419
April	129	135	163	181	235	227	269	313	348	348	396	461
May	121	125	172	183	229	234	270	318	355	363	420	472