用 pandas 绘制多索引数据 [英] Plotting multi indexed data with pandas

问题描述

我有一个 Python 字典，格式如下:

I have a Python dictionary in the form of:

dict = {0: {a: 1, b: 2}, 1: {a: 1, b: 2}}

我使用 from_dict() 从中创建了一个 Pandas DataFrame.正如预期的那样，这给了我一个多索引数据帧.

I created a Pandas DataFrame from it, by using from_dict(). This gave me a multiindex dataframe`, as expected.

现在我想绘制这个数据框，外部索引是 x 轴，内部索引是我的图表中的单独线，y 轴上的数据点是值.

Now I want to plot this dataframe with the outer index being the x-axis and the inner index being separate lines in my graph, the data points on the y-axis being the values.

我尝试将它们堆叠起来，但它只给了我一行.

I tried stacking them, but it only gave me a single line.

我当前的代码:

data = pd.DataFrame.from_dict(my_data)
data = data.T.stack()
data.plot()

有人知道如何做到这一点吗?

Anyone has an idea how this could be done?

推荐答案

使用

data.groupby(level=1).plot(stacked=True)

<小时>

import pandas as pd
import matplotlib.pyplot as plt

my_data = {0: {'a': 1, 'b': 2}, 1: {'a': 1, 'b': 2}}
data = pd.DataFrame.from_dict(my_data)
data = data.T.stack()
# 0  a    1
#    b    2
# 1  a    1
#    b    2
# dtype: int64

data.groupby(level=1).plot(stacked=True, legend=True)
plt.show()

<小时>

或者，为了更好地控制配置绘图，直接使用 matplotlib 可能更容易:

---

Or, for more control in configuring the plot it may be easier to use matplotlib directly:

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

data = pd.concat({key:pd.Series(np.random.randint(10, size=(10,))) for key in 'AB'}).swaplevel()

fix, ax = plt.subplots()
for key, grp in data.groupby(level=1):
    ax.plot(grp.index.get_level_values(0), grp.values, label=key)
plt.legend()
plt.plot()
plt.show()

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