I have two differents Data Frames:
DF1: with columns A B1 C1 D1 E1
DF2: with columns A B2 C2 D2 E2
With A being the same for the two of them.
then I want to plot two plots in the same figure, one at the right and the other at the left with this information:
Plot 1: x axis = column A
y axis = B1, B2, C1, C2 curves
Plot 2: x axis = column A
y axis = D1, D2, E1, E2 curves
How can I do it without merge the two DF using Pandas and Matplotlib?
The idea would be to create an axes ax and a twin axes ax2 = ax.twinx() and to then plot each dataframe to one of them, df.plot(ax=ax) and df2.plot(ax=ax2).
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
a = np.linspace(-5,5, 11)
data1 = np.sort(np.random.rand(len(a),5))
data1[:,0] =a
data2 = np.sort(np.random.rand(len(a),5))*10
data2[:,0] =a
df = pd.DataFrame(data1, columns=["A", "B1", "C1", "D1", "E1"])
df2 = pd.DataFrame(data2, columns=["A", "B2", "C2", "D2", "E2"])
fig, ax = plt.subplots()
ax2 = ax.twinx()
df.plot(x="A", y=["B1", "C1", "D1", "E1"], ax=ax)
df2.plot(x="A", y=["B2", "C2", "D2", "E2"], ax=ax2, ls="--")
plt.show()
If instead you want to have two separate plots (the question is not clear on this point), you can do it by
fig, (ax, ax2) = plt.subplots(ncols=2)
and removing the twinx call.
You can use
f, (ax1, ax2) = plt.subplots(1,2)
which will create a plot containing 1 row and 2 columns of subplots. It will probably be the easiest way to obtain the columns of the data frames using
A = DF1['A']
...
in case matplotlib does not like to be fed directly with the data frame columns.
You can then plot the columns into the different subplots. This looks like the following example:
f, (ax1, ax2) = plt.subplots(1,2)
ax1.plot(A, B1, label='...')
ax1.plot(A, B2, label='...')
...
ax2.plot(A, D1, label='...')
ax2.plot(A, D2, label='...')
...
ax1.legend(loc='best')
ax2.legend(loc='best')
plt.show()
