Matplotlib Different Scaled Y-Axes

Question

I have a dataframe with the data below.

ex_dict = {'revenue': [613663,  1693667,  2145183,  2045065,  2036406,  
1708862,  1068232,  1196899,  2185852,  2165778,  2144738,  2030337,  
1784067],
'abs_percent_diff': [0.22279211315310588,  0.13248909660765254,  
0.12044821447874667,  0.09438674840975962,  0.1193588387687364,  
0.062100921139322744,  0.05875297161175445,  0.06240362963749895,  
0.05085338590212515,  0.034877614941165744,  0.012263947005671703,  
0.029227374323993634,  0.023411816504907524],
'ds': [dt.date(2017,1,1),  dt.date(2017,1,2),  dt.date(2017,1,3),  
dt.date(2017,1,4),  dt.date(2017,1,5),  dt.date(2017,1,6),  
dt.date(2017,1,7),  dt.date(2017,1,8),  dt.date(2017,1,9),  
dt.date(2017,1,10),  dt.date(2017,1,11),  dt.date(2017,1,12),  
dt.date(2017,1,13)], 
'yhat_normal': [501853.9074623253,  1952329.3521464923,  1914575.7673396615,  
1868685.8215084015,  1819261.1068672044,  1608945.031482406,  
1008953.0123101478,  1126595.36037955,  2302965.598289115,  
2244044.9351591542,  2171367.536396199,  2091465.0313570146,  
1826836.562382966]}

df_vis=pd.DataFrame.from_dict(ex_dict)

I want to graph yhat_normal and revenue on the same y-axis and abs_percent_diff on a y-axis with a different scale.

df_vis = df_vis.set_index('ds')
df_vis[['rev', 'yhat_normal']].plot(figsize=(20, 12))

I can easily graph rev and yhat_normal with the code above, but I am struggling to get abs_percent_diff on a different y-axis scale. I tried converting my columns to numpy arrays and doing this, but it looks terrible.

npdate = df_vis.as_matrix(columns= ['ds'])
nppredictions = df_vis.as_matrix(columns= ['yhat_normal'])
npactuals = df_vis.as_matrix(columns= ['rev'])
npmape = df_vis.as_matrix(columns=['abs_percent_diff'])

fig, ax1 = plt.subplots()

ax2 = ax1.twinx()
fig.set_size_inches(20,10)
ax1.plot_date(npdate, nppredictions, ls= '-', color= 'b')
ax1.plot_date(npdate, npactuals, ls='-', color='g')
ax2.plot_date(npdate, npmape, 'r-')

ax1.set_xlabel('X data')
ax1.set_ylabel('Y1 data', color='g')
ax2.set_ylabel('Y2 data', color='b')

plt.show()

This is what I want. Where the red line is the abs_percent_diff. Obviously, I drew the line by hand so it is not accurate.

Answer 1

I'm not sure if I got the problem correclty, but it seems you simply want to draw one of the dataframe columns at the bottom of the plot area.

import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt

ex_dict = {'revenue': [613663,  1693667,  2145183,  2045065,  2036406,  
1708862,  1068232,  1196899,  2185852,  2165778,  2144738,  2030337,  
1784067],
'abs_percent_diff': [0.22279211315310588,  0.13248909660765254,  
0.12044821447874667,  0.09438674840975962,  0.1193588387687364,  
0.062100921139322744,  0.05875297161175445,  0.06240362963749895,  
0.05085338590212515,  0.034877614941165744,  0.012263947005671703,  
0.029227374323993634,  0.023411816504907524],
'ds': [dt.date(2017,1,1),  dt.date(2017,1,2),  dt.date(2017,1,3),  
dt.date(2017,1,4),  dt.date(2017,1,5),  dt.date(2017,1,6),  
dt.date(2017,1,7),  dt.date(2017,1,8),  dt.date(2017,1,9),  
dt.date(2017,1,10),  dt.date(2017,1,11),  dt.date(2017,1,12),  
dt.date(2017,1,13)], 
'yhat_normal': [501853.9074623253,  1952329.3521464923,  1914575.7673396615,  
1868685.8215084015,  1819261.1068672044,  1608945.031482406,  
1008953.0123101478,  1126595.36037955,  2302965.598289115,  
2244044.9351591542,  2171367.536396199,  2091465.0313570146,  
1826836.562382966]}

df_vis=pd.DataFrame.from_dict(ex_dict)

df_vis = df_vis.set_index('ds')
ax = df_vis[['revenue','yhat_normal']].plot(figsize=(13, 8))
ax2 = df_vis['abs_percent_diff'].plot(secondary_y=True, ax=ax)
ax2.set_ylim(0,1)


plt.show()