使用 matplotlib 按三列分组条形图

Question

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"

Example

My dataset is quite large compared to the example above so a loop or function would be more useful.

My code below is not working as I want.

fig, ax = plt.subplots(figsize=(4,3))

for t in ["A", "B"]:
    
    df1 = df.loc[df["Type"] == 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.bar((w+i), df3["Result"], label=label)
            
plt.savefig("test.svg")

Answer 1

import pandas as pd
import matplotlib.pyplot as plt


grouped_A = df[df['Type'] == 'A']
grouped_B = df[df['Type'] == 'B']

fig, ax = plt.subplots(1, 2, figsize=(15, 5))

for r, w in zip([3,5,7], [5, 10, 15]):
    
    A = grouped_df_A.loc[grouped_df_A["Ratio"] == r]
    B = grouped_df_B.loc[grouped_df_B["Ratio"] == r]
    
    for m, i in zip(["X","Y","Z"], range(3)):
        
        methodA = A.loc[A["Method"] == m]
        methodB = B.loc[B["Method"] == m]
            
        label = str(m)
        ax[0].bar((w+i), methodA["Result"], label=label)
        ax[0].set_title("Type A")
        
        ax[1].bar((w+i), methodB["Result"], label=label)
        ax[1].set_title("Type B")
            
plt.show()

Answer 2

Consider a subset loop with pandas DataFrame.groupby , then pivoting data with DataFrame.pivot_table , and finally running plot with DataFrame.plot :

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

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]
})


num_types = len(df["Type"].unique())
fig, axes = plt.subplots(figsize=(12,4), ncols=num_types, nrows=1)

# FLATTEN MULTI-DIMENSIONAL ARRAY
axes = np.ravel(axes)

# ITERATE THROUGH TYPE GROUPS
for (g, df), ax in zip(df.groupby("Type"), axes):
    # PIVOT FOR  WIDE DATA FOR BAR CHART
    (
        df.pivot_table(index="Ratio", columns="Method", values="Result", aggfunc="sum")
          .plot(kind="bar", ax=ax, rot=0, title=f"{g} - Bar Chart")
    )

plt.show()
fig.savefig("bar_chart.png")

plt.clf()
plt.close()

Answer 3

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