How To Draw A Graphical Count Table In Pandas

I have a dataframe df with two columns customer1 and customer2 which are string valued. I would like to make a square graphical representation of the count number for each pair f

Solution 1:

UPDATE:

Is it possible to sort the rows/columns so the highest count rows are at the top ? In this case the order would be b,a,c

IIUC you can do it this way (where ):

In [80]: x = df.pivot_table(index='customer1',columns='customer2',aggfunc='size',fill_value=0)

In [81]: idx = x.max(axis=1).sort_values(ascending=0).index

In [82]: idx
Out[82]: Index(['b', 'a', 'c'], dtype='object', name='customer1')

In [87]: sns.heatmap(x[idx].reindex(idx), annot=True)
Out[87]: <matplotlib.axes._subplots.AxesSubplot at 0x9ee3f98>

OLD answer:

you can use heatmap() method from seaborn module:

In [42]: import seaborn as sns

In [43]: df
Out[43]:
  customer1 customer2
0         a         b
1         a         c2         a         c3         b         a
4         b         c5         b         c6cc7         a         a
8         b         c9         b         c

In [44]: x = df.pivot_table(index='customer1',columns='customer2',aggfunc='size',fill_value=0)

In [45]: x
Out[45]:
customer2  a  b  c
customer1
a          112
b          104c001

In [46]: sns.heatmap(x)
Out[46]:<matplotlib.axes._subplots.AxesSubplot at 0xb150b70>

or with annotations:

In [48]: sns.heatmap(x, annot=True)
Out[48]: <matplotlib.axes._subplots.AxesSubplot at 0xc596d68>

Solution 2:

As @MaxU mentioned, seaborn.heatmap should work. It appears that you can use the Pandas DataFrame as the input.

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, ax=None, xticklabels=True, yticklabels=True, mask=None, **kwargs)

https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html#seaborn.heatmap