python图分组条形图

Question

我有3列数据，如下所示

 clm1                       clm2     clm3
 |["shared","connect"]       13297  |aaaa|
 |["stopped","failed]        25002  |aaaa|
 |["success","obtained"]     11189  |aaaa|
 |["shared","connect"]       16770  |bbbb|
 |["stopped","failed]        81777  |bbbb|
 |["success","obtained"]     9555   |bbbb|

我想在python中使用下面的条形图，我能够编写简单的图，但是无法获得可以对clm3和图进行分组的逻辑

Answer 1

这里的主要问题是matplotlib认为您所有的分类数据“ A”代表相同的类别，因此将其在“ A”的相同位置绘制。我们必须发明一个额外的类别来区分所有那些“ A”值。例如，我们可以使用cumcount()进行此操作，该数字将所有值“ A”从0编号为n。一个例子是：

from matplotlib import pyplot as plt
import pandas as pd

#create toy dataframe
#this part you should have included in your question
#as a Minimal, Complete, and Verifiable example
np.random.seed(1234)
df = pd.DataFrame({"cat": ["A", "B", "C", "C", "B", "C", "A"], "val": np.random.randint(1, 100, 7)})

#add column for multiple cat values and rearrange dataframe
df["cols"] = df.groupby("cat").cumcount()
df1 = df.pivot(index = "cat", columns = "cols", values = "val")
print(df1)

#plot this table
df1.plot.bar(color = "blue", edgecolor = "white")
plt.legend().set_visible(False)
plt.xticks(rotation = 0)
plt.show()

示例数据框：

cols     0     1     2
cat                   
A     48.0  16.0   NaN
B     84.0  77.0   NaN
C     39.0  54.0  25.0

样本图：

编辑： 我只是注意到，在您的情况下它甚至更容易，因为尽管您的问题中从未提到过，但您可能希望将其作为“ clm1”类别。因此，您可以简化过程：

from matplotlib import pyplot as plt
import pandas as pd

#create toy dataframe
np.random.seed(1234)
df = pd.DataFrame({"clm1": ["X", "Y", "Z", "X", "Y", "Z"], "clm2": np.random.randint(1, 100, 6), "clm3": ["A", "A", "A", "B", "B", "B"]})

#rearrange dataframe and plot
df.pivot(index = "clm3", columns = "clm1", values = "clm2").plot.bar(edgecolor = "white")
plt.xticks(rotation = 0)
plt.show()

示例输出：