大熊猫多指标绘图

在 DataFrame上执行了 groupby.sum()之后，我在尝试创建我预期的情节时遇到了一些麻烦。

grouped dataframe with multi-index

import pandas as pd
import numpy as np


np.random.seed(365)
rows = 100
data = {'Month': np.random.choice(['2014-01', '2014-02', '2014-03', '2014-04'], size=rows),
'Code': np.random.choice(['A', 'B', 'C'], size=rows),
'ColA': np.random.randint(5, 125, size=rows),
'ColB': np.random.randint(0, 51, size=rows),}
df = pd.DataFrame(data)


Month Code  ColA  ColB
0  2014-03    C    59    47
1  2014-01    A    24     9
2  2014-02    C    77    50


dfg = df.groupby(['Code', 'Month']).sum()


ColA  ColB
Code Month
A    2014-01   124   102
2014-02   398   282
2014-03   474   198
2014-04   830   237
B    2014-01   477   300
2014-02   591   167
2014-03   522   192
2014-04   367   169
C    2014-01   412   180
2014-02   275   205
2014-03   795   291
2014-04   901   309

如何为每个 Code创建一个子图(kind='bar') ，其中 x 轴是 Month，条是 ColA和 ColB？

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小开

Using the following DataFrame ...

DataFrame

# using pandas version 0.14.1
from pandas import DataFrame
import pandas as pd
import matplotlib.pyplot as plt


data = {'ColB': {('A', 4): 3.0,
('C', 2): 0.0,
('B', 4): 51.0,
('B', 1): 0.0,
('C', 3): 0.0,
('B', 2): 7.0,
('Code', 'Month'): '',
('A', 3): 5.0,
('C', 1): 0.0,
('C', 4): 0.0,
('B', 3): 12.0},
'ColA': {('A', 4): 66.0,
('C', 2): 5.0,
('B', 4): 125.0,
('B', 1): 5.0,
('C', 3): 41.0,
('B', 2): 52.0,
('Code', 'Month'): '',
('A', 3): 22.0,
('C', 1): 14.0,
('C', 4): 51.0,
('B', 3): 122.0}}


df = DataFrame(data)

... you can plot the following (using cross-section):

f, a = plt.subplots(3,1)
df.xs('A').plot(kind='bar',ax=a[0])
df.xs('B').plot(kind='bar',ax=a[1])
df.xs('C').plot(kind='bar',ax=a[2])

enter image description here

One for A, one for B and one for C, x-axis: 'Month', the bars are ColA and ColB. Maybe this is what you are looking for.

小开
最佳答案

I found the unstack(level) method to work perfectly, which has the added benefit of not needing a priori knowledge about how many Codes there are.

ax = dfg.unstack(level=0).plot(kind='bar', subplots=True, rot=0, figsize=(9, 7), layout=(2, 3))
plt.tight_layout()

enter image description here

小开
  • Creating the desired visualization is all about shaping the dataframe to fit the plotting API.
    • seaborn can easily aggregate long form data from a dataframe without .groupby or .pivot_table.
  • Given the original dataframe df, the easiest option is the convert it to a long form with pandas.DataFrame.melt, and then plot with seaborn.catplot, which is a high-level API for matplotlib.
    • Change the default estimator from mean to sum
  • The 'Month' column in the OP is a string type. In general, it's better to convert the column to datetime dtype with pd._to_datetime
  • Tested in python 3.8.11, pandas 1.3.2, matplotlib 3.4.2, seaborn 0.11.2

seaborn.catplot

import seaborn as sns


dfm = df.melt(id_vars=['Month', 'Code'], var_name='Cols')


Month Code  Cols  value
0  2014-03    C  ColA     59
1  2014-01    A  ColA     24
2  2014-02    C  ColA     77
3  2014-04    B  ColA    114
4  2014-01    C  ColA     67


# specify row and col to get a plot like that produced by the accepted answer
sns.catplot(kind='bar', data=dfm, col='Code', x='Month', y='value', row='Cols', order=sorted(dfm.Month.unique()),
col_order=sorted(df.Code.unique()), estimator=sum, ci=None, height=3.5)

enter image description here

sns.catplot(kind='bar', data=dfm, col='Code', x='Month', y='value', hue='Cols', estimator=sum, ci=None,
order=sorted(dfm.Month.unique()), col_order=sorted(df.Code.unique()))

enter image description here

pandas.DataFrame.plot

  • pandas uses matplotlib and the default plotting backend.
  • To produce the plot like the accepted answer, it's better to use pandas.DataFrame.pivot_table instead of .groupby, because the resulting dataframe is in the correct shape, without the need to unstack.
dfp = df.pivot_table(index='Month', columns='Code', values=['ColA', 'ColB'], aggfunc='sum')


dfp.plot(kind='bar', subplots=True, rot=0, figsize=(9, 7), layout=(2, 3))
plt.tight_layout()

enter image description here


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