matplotlib - plot merged dataframe with group bar - matplotlib

Question

I try to plot a grouped bar chart from a merged dataframe. below code the bar is stacked, how can I put it side by side just like a grouped bar chart?
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)
df1 = pd.DataFrame({
'key': ['A', 'B', 'C', 'D'],
'value':[ 10 ,6, 6, 8]})
df2 = pd.DataFrame({
'key': ['B', 'D', 'A', 'F'],
'value':[ 3, 5, 5, 7]})
df3 = pd.merge(df1, df2, how='inner', on=['key'])
print(df1)
print(df2)
print(df3)
fig, ax = plt.subplots(figsize=(12, 8))
b1 = ax.bar(df3['key'],df3['value_x'])
b2 = ax.bar(df3['key'],df3['value_y'])
pngname = "demo.png"
fig.savefig(pngname, dpi=fig.dpi)
print("[[./%s]]"%(pngname))
Current output:

Answer 1

The problem is that the x axis data is the same, in your case it aren't numbers, it are the keys: "A", "B", "C". So matplotlib stacks them one onto another.
There's a simple way around it, as some tutorials online show https://www.geeksforgeeks.org/create-a-grouped-bar-plot-in-matplotlib/.
So, what you do is basically enumerate the keys, i.e. A=1, B=2, C=3. After this, choose your desired bar width, I chose 0.4 for example. And now, shift one group of bars to the left by bar_width/2, and shift the other one to the right by bar_width/2.
Perhaps the code explains it better than I did:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)
df1 = pd.DataFrame({
'key': ['A', 'B', 'C', 'D'],
'value':[ 10 ,6, 6, 8]})
df2 = pd.DataFrame({
'key': ['B', 'D', 'A', 'F'],
'value':[ 3, 5, 5, 7]})
df3 = pd.merge(df1, df2, how='inner', on=['key'])
fig, ax = plt.subplots(figsize=(12, 8))
# modifications
x = np.arange(len(df3['key'])) # enumerate the keys
bar_width = 0.4 # choose bar length
b1 = ax.bar(x - bar_width/2,df3['value_x'], width=bar_width, label='value_x') # shift x values left
b2 = ax.bar(x + bar_width/2,df3['value_y'], width=bar_width, label='value_y') # shift x values right
plt.xticks(x, df3['key']) # replace x axis ticks with keys from df3.
plt.legend(['value_x', 'value_y'])
plt.show()
Result: