Создание структурированной сетки участков с помощью Seaborn FacetGrid

Некоторые значения 2018 года действительно велики по сравнению с любым из значений, где has_cus_id_but_not_acc_id равно 1. Следовательно, правый сюжет почти пуст. Возможно, имеет смысл использовать логарифмическую шкалу.

Теперь у вас 6 лет, поэтому каждый месяц нужно отображать 6 полосок рядом друг с другом. Это сделает столбцы довольно маленькими и затруднит чтение диаграммы. Тем не менее, это возможно.

Следующее использует не seaborn, а pandas и matplotlib:

import matplotlib.pyplot as plt
import pandas as pd


toy = '{"has_cus_id_but_not_acc_id":{"0":0,"1":0,"2":0,"3":0,"4":0,"5":0,"6":0,"7":0,"8":0,"9":0,"10":0,"11":0,"12":0,"13":0,"14":0,"15":0,"16":0,"17":0,"18":1,"19":0,"20":0,"21":0,"22":1,"23":0,"24":0,"25":1,"26":0,"27":1,"28":0,"29":1,"30":0,"31":1,"32":0,"33":1,"34":0,"35":1,"36":0,"37":1,"38":0,"39":0,"40":1,"41":1,"42":0,"43":1,"44":0,"45":1,"46":0,"47":1,"48":0,"49":1,"50":0,"51":1,"52":0,"53":1,"54":0,"55":1,"56":0,"57":1,"58":0,"59":1,"60":0,"61":1,"62":0,"63":1,"64":0,"65":1,"66":0,"67":1,"68":0,"69":1,"70":0,"71":1,"72":0,"73":1,"74":0,"75":1,"76":0,"77":0,"78":1,"79":0,"80":1,"81":0,"82":0,"83":1,"84":0,"85":1},"reg_year":{"0":2014.0,"1":2014.0,"2":2014.0,"3":2014.0,"4":2014.0,"5":2014.0,"6":2014.0,"7":2014.0,"8":2015.0,"9":2015.0,"10":2015.0,"11":2015.0,"12":2015.0,"13":2015.0,"14":2015.0,"15":2015.0,"16":2015.0,"17":2016.0,"18":2016.0,"19":2016.0,"20":2016.0,"21":2016.0,"22":2016.0,"23":2016.0,"24":2016.0,"25":2016.0,"26":2016.0,"27":2016.0,"28":2016.0,"29":2016.0,"30":2016.0,"31":2016.0,"32":2016.0,"33":2016.0,"34":2016.0,"35":2016.0,"36":2016.0,"37":2016.0,"38":2017.0,"39":2017.0,"40":2017.0,"41":2017.0,"42":2017.0,"43":2017.0,"44":2017.0,"45":2017.0,"46":2017.0,"47":2017.0,"48":2017.0,"49":2017.0,"50":2017.0,"51":2017.0,"52":2017.0,"53":2017.0,"54":2017.0,"55":2017.0,"56":2017.0,"57":2017.0,"58":2017.0,"59":2017.0,"60":2018.0,"61":2018.0,"62":2018.0,"63":2018.0,"64":2018.0,"65":2018.0,"66":2018.0,"67":2018.0,"68":2018.0,"69":2018.0,"70":2018.0,"71":2018.0,"72":2018.0,"73":2018.0,"74":2018.0,"75":2018.0,"76":2018.0,"77":2018.0,"78":2018.0,"79":2018.0,"80":2018.0,"81":2018.0,"82":2019.0,"83":2019.0,"84":2019.0,"85":2019.0},"reg_month":{"0":3.0,"1":5.0,"2":6.0,"3":7.0,"4":9.0,"5":10.0,"6":11.0,"7":12.0,"8":1.0,"9":3.0,"10":5.0,"11":6.0,"12":7.0,"13":8.0,"14":9.0,"15":11.0,"16":12.0,"17":1.0,"18":1.0,"19":2.0,"20":3.0,"21":4.0,"22":4.0,"23":5.0,"24":6.0,"25":6.0,"26":7.0,"27":7.0,"28":8.0,"29":8.0,"30":9.0,"31":9.0,"32":10.0,"33":10.0,"34":11.0,"35":11.0,"36":12.0,"37":12.0,"38":1.0,"39":2.0,"40":2.0,"41":3.0,"42":4.0,"43":4.0,"44":5.0,"45":5.0,"46":6.0,"47":6.0,"48":7.0,"49":7.0,"50":8.0,"51":8.0,"52":9.0,"53":9.0,"54":10.0,"55":10.0,"56":11.0,"57":11.0,"58":12.0,"59":12.0,"60":1.0,"61":1.0,"62":2.0,"63":2.0,"64":3.0,"65":3.0,"66":4.0,"67":4.0,"68":5.0,"69":5.0,"70":6.0,"71":6.0,"72":7.0,"73":7.0,"74":8.0,"75":8.0,"76":9.0,"77":10.0,"78":10.0,"79":11.0,"80":11.0,"81":12.0,"82":1.0,"83":1.0,"84":2.0,"85":2.0},"Total_Revenue":{"0":35852.02,"1":2623.97,"2":3526.67,"3":21466.71,"4":72784.1200000003,"5":103921.2899999999,"6":10852.87,"7":16522.07,"8":7443.76,"9":68962.1600000002,"10":10956.38,"11":193856.8799999985,"12":110766.6099999997,"13":123861.8599999987,"14":2722.34,"15":303488.6900000007,"16":6876.58,"17":17729.5,"18":4687.93,"19":26914.06,"20":2228.12,"21":15708.93,"22":859.58,"23":19164.89,"24":163164.4799999995,"25":33180.7300000001,"26":10033.01,"27":1114.48,"28":462613.2900000042,"29":9822.95,"30":70901.4400000003,"31":22370.29,"32":46711.8900000002,"33":2335.02,"34":7259.28,"35":11.83,"36":13590.51,"37":7677.77,"38":282.01,"39":358522.7900000003,"40":5844.0,"41":7027.28,"42":1908.71,"43":4032.35,"44":11072.6,"45":3973.15,"46":30706.23,"47":2644.13,"48":23831.75,"49":670.12,"50":6949.54,"51":4687.7,"52":9672.69,"53":7333.01,"54":12814.33,"55":689.39,"56":6962.86,"57":2283.16,"58":1259.5,"59":224.84,"60":12812.12,"61":247.68,"62":25452.65,"63":1245.02,"64":24211.36,"65":5255.25,"66":28402.76,"67":9148.55,"68":14822.61,"69":345.37,"70":12408.13,"71":989.93,"72":10601.33,"73":730.32,"74":169020.5000000001,"75":697.54,"76":3862038.6799997138,"77":6148750.9899984254,"78":194.06,"79":2379382.4500000761,"80":1174.11,"81":1729567.9000000793,"82":889650.029999995,"83":95.8,"84":415996.6999999974,"85":654.78}}'
df = pd.read_json(toy)
df['reg_year'].astype(int)


u = df["has_cus_id_but_not_acc_id"].unique()
y = df['reg_year'].unique()

fig, axes = plt.subplots(1,len(u), sharey=True)
axes[0].set_yscale("log")
for ax, (n, grp) in zip(axes.flat, df.groupby("has_cus_id_but_not_acc_id")):
    piv = grp.pivot('reg_month', 'reg_year', 'Total_Revenue')
    empty = pd.DataFrame(index=range(1,12), columns=y)
    empty.combine_first(piv).plot.bar(ax=ax, width=0.8, legend=False)
axes[1].legend()

plt.show()