Finding Natural Breaks in Data with the Fisher-Jenks Algorithm¶

Notebook accompanying article on PB Python

In [1]:

import pandas as pd
import jenkspy

Create an example dataframe

In [2]:

sales = {
    'account': [
        'Jones Inc', 'Alpha Co', 'Blue Inc', 'Super Star Inc', 'Wamo',
        'Next Gen', 'Giga Co', 'IniTech', 'Beta LLC'
    ],
    'Total': [1500, 2100, 50, 20, 75, 1100, 950, 1300, 1400]
}
df = pd.DataFrame(sales)

In [3]:

df.sort_values(by='Total')

Out[3]:

	account	Total
3	Super Star Inc	20
2	Blue Inc	50
4	Wamo	75
6	Giga Co	950
5	Next Gen	1100
7	IniTech	1300
8	Beta LLC	1400
0	Jones Inc	1500
1	Alpha Co	2100

Try cutting the data using qcut

In [4]:

df['quantile'] = pd.qcut(df['Total'], q=2, labels=['bucket_1', 'bucket_2'])

In [5]:

df.sort_values(by='Total')

Out[5]:

	account	Total	quantile
3	Super Star Inc	20	bucket_1
2	Blue Inc	50	bucket_1
4	Wamo	75	bucket_1
6	Giga Co	950	bucket_1
5	Next Gen	1100	bucket_1
7	IniTech	1300	bucket_2
8	Beta LLC	1400	bucket_2
0	Jones Inc	1500	bucket_2
1	Alpha Co	2100	bucket_2

Compare with using cut

In [6]:

df['cut_bins'] = pd.cut(df['Total'],
                         bins=2,
                         labels=['bucket_1', 'bucket_2'])

In [7]:

df.sort_values(by='Total')

Out[7]:

	account	Total	quantile	cut_bins
3	Super Star Inc	20	bucket_1	bucket_1
2	Blue Inc	50	bucket_1	bucket_1
4	Wamo	75	bucket_1	bucket_1
6	Giga Co	950	bucket_1	bucket_1
5	Next Gen	1100	bucket_1	bucket_2
7	IniTech	1300	bucket_2	bucket_2
8	Beta LLC	1400	bucket_2	bucket_2
0	Jones Inc	1500	bucket_2	bucket_2
1	Alpha Co	2100	bucket_2	bucket_2

Show how jenkspy works

In [8]:

breaks = jenkspy.jenks_breaks(df['Total'], nb_class=2)
print(breaks)

[20.0, 75.0, 2100.0]

In [9]:

df['cut_jenks'] = pd.cut(df['Total'],
                         bins=breaks,
                         labels=['bucket_1', 'bucket_2'])
df.sort_values(by='Total')

Out[9]:

	account	Total	quantile	cut_bins	cut_jenks
3	Super Star Inc	20	bucket_1	bucket_1	NaN
2	Blue Inc	50	bucket_1	bucket_1	bucket_1
4	Wamo	75	bucket_1	bucket_1	bucket_1
6	Giga Co	950	bucket_1	bucket_1	bucket_2
5	Next Gen	1100	bucket_1	bucket_2	bucket_2
7	IniTech	1300	bucket_2	bucket_2	bucket_2
8	Beta LLC	1400	bucket_2	bucket_2	bucket_2
0	Jones Inc	1500	bucket_2	bucket_2	bucket_2
1	Alpha Co	2100	bucket_2	bucket_2	bucket_2

Fix the NaN by using include_lowest

In [10]:

df['cut_jenksv2'] = pd.cut(df['Total'],
                         bins=breaks,
                         labels=['bucket_1', 'bucket_2'],
                         include_lowest=True)
df.sort_values(by='Total')

Out[10]:

	account	Total	quantile	cut_bins	cut_jenks	cut_jenksv2
3	Super Star Inc	20	bucket_1	bucket_1	NaN	bucket_1
2	Blue Inc	50	bucket_1	bucket_1	bucket_1	bucket_1
4	Wamo	75	bucket_1	bucket_1	bucket_1	bucket_1
6	Giga Co	950	bucket_1	bucket_1	bucket_2	bucket_2
5	Next Gen	1100	bucket_1	bucket_2	bucket_2	bucket_2
7	IniTech	1300	bucket_2	bucket_2	bucket_2	bucket_2
8	Beta LLC	1400	bucket_2	bucket_2	bucket_2	bucket_2
0	Jones Inc	1500	bucket_2	bucket_2	bucket_2	bucket_2
1	Alpha Co	2100	bucket_2	bucket_2	bucket_2	bucket_2

Try some other examples

In [11]:

df['quantilev2'] = pd.qcut(
    df['Total'], q=4, labels=['bucket_1', 'bucket_2', 'bucket_3', 'bucket_4'])

df['cut_jenksv3'] = pd.cut(
    df['Total'],
    bins=jenkspy.jenks_breaks(df['Total'], nb_class=4),
    labels=['bucket_1', 'bucket_2', 'bucket_3', 'bucket_4'],
    include_lowest=True)


df.sort_values(by='Total')

Out[11]:

	account	Total	quantile	cut_bins	cut_jenks	cut_jenksv2	quantilev2	cut_jenksv3
3	Super Star Inc	20	bucket_1	bucket_1	NaN	bucket_1	bucket_1	bucket_1
2	Blue Inc	50	bucket_1	bucket_1	bucket_1	bucket_1	bucket_1	bucket_1
4	Wamo	75	bucket_1	bucket_1	bucket_1	bucket_1	bucket_1	bucket_1
6	Giga Co	950	bucket_1	bucket_1	bucket_2	bucket_2	bucket_2	bucket_2
5	Next Gen	1100	bucket_1	bucket_2	bucket_2	bucket_2	bucket_2	bucket_2
7	IniTech	1300	bucket_2	bucket_2	bucket_2	bucket_2	bucket_3	bucket_3
8	Beta LLC	1400	bucket_2	bucket_2	bucket_2	bucket_2	bucket_3	bucket_3
0	Jones Inc	1500	bucket_2	bucket_2	bucket_2	bucket_2	bucket_4	bucket_3
1	Alpha Co	2100	bucket_2	bucket_2	bucket_2	bucket_2	bucket_4	bucket_4

In [ ]: