Grouped bar chart by three column using matplotlib-CodePudding

I am trying to display a grouped bar chart by categorical values on columns.

An example data is below.

df = pd.DataFrame({'Type': ['A','A','A','A','A','A','A','A','A','B','B','B','B','B','B','B','B','B'],
                   'Ratio': [3, 3, 3, 5, 5, 5, 7, 7, 7,3, 3, 3, 5, 5, 5, 7, 7, 7],
                   'Method': ['X','Y','Z','X','Y','Z','X','Y','Z','X','Y','Z','X','Y','Z','X','Y','Z'],
                   'Result': [90, 85, 96, 89, 82, 80, 78, 72, 75, 91, 82, 94, 87, 86, 84, 71, 78, 86]})

Values in "Type" column can be on the same chart or two subplot graphs. The Y axis must display the value in "Result" and the legend of the bar chart must display the value in "Method"

CodePudding user response：

This should work multiple types in your Type column creating vertical subplots for each.

item_list = list(df.Type.unique())
fig, ax = plt.subplots(len(item_list), figsize=(12, 8))
for t in range(len(item_list)):    
    df1 = df.loc[df["Type"] == item_list[t]]    
    for r, w in zip([3,5,7], [5, 10, 15]):
        
        df2 = df1.loc[df1["Ratio"] == r]
        
        for m, i in zip(["X","Y","Z"], range(3)):
            
            df3 = df2.loc[df2["Method"] == m]
            
            label = str(m)
            ax[t].bar((w i), df3["Result"], label=label)
            ax[t].set_title(f'Type{item_list[t]}')

see plots here