pandas 条形图-按列指定条形颜色 [英] Pandas bar plot -- specify bar color by column

问题描述

是否有一种简单的方法使用Pandas DataFrame.plot(kind='bar')方法按列名指定条形颜色?

Is there a simply way to specify bar colors by column name using Pandas DataFrame.plot(kind='bar') method?

我有一个脚本，可以从目录中的几个不同数据文件生成多个DataFrame.例如，它执行以下操作:

I have a script that generates multiple DataFrames from several different data files in a directory. For example it does something like this:

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

data_files = ['a', 'b', 'c', 'd']

df1 = pds.DataFrame(np.random.rand(4,3), columns=data_files[:-1])
df2 = pds.DataFrame(np.random.rand(4,3), columns=data_files[1:])

df1.plot(kind='bar', ax=plt.subplot(121))
df2.plot(kind='bar', ax=plt.subplot(122))

plt.show()

具有以下输出:

不幸的是，不同图中每个标签的列颜色不一致.是否可以传入(filenames:colors)的字典，以便任何特定的列始终具有相同的颜色.例如，我可以想象通过使用Matplotlib color_cycle压缩文件名来创建该文件:

Unfortunately, the column colors aren't consistent for each label in the different plots. Is it possible to pass in a dictionary of (filenames:colors), so that any particular column always has the same color. For example, I could imagine creating this by zipping up the filenames with the Matplotlib color_cycle:

data_files = ['a', 'b', 'c', 'd']
colors = plt.rcParams['axes.color_cycle']
print zip(data_files, colors)

[('a', u'b'), ('b', u'g'), ('c', u'r'), ('d', u'c')]

我可以弄清楚如何使用Matplotlib直接执行此操作:我只是认为可能会有一个更简单的内置解决方案.

I could figure out how to do this directly with Matplotlib: I just thought there might be a simpler, built-in solution.

下面是在纯Matplotlib中工作的部分解决方案.但是，我在IPython笔记本中使用了该笔记本，该笔记本将分发给非程序员同事，并且我希望尽量减少过多的绘图代码.

Below is a partial solution that works in pure Matplotlib. However, I'm using this in an IPython notebook that will be distributed to non-programmer colleagues, and I'd like to minimize the amount of excessive plotting code.

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

data_files = ['a', 'b', 'c', 'd']
mpl_colors = plt.rcParams['axes.color_cycle']
colors = dict(zip(data_files, mpl_colors))

def bar_plotter(df, colors, sub):
    ncols = df.shape[1]
    width = 1./(ncols+2.)
    starts = df.index.values - width*ncols/2.
    plt.subplot(120+sub)
    for n, col in enumerate(df):
        plt.bar(starts + width*n, df[col].values, color=colors[col],
                width=width, label=col)
    plt.xticks(df.index.values)
    plt.grid()
    plt.legend()

df1 = pds.DataFrame(np.random.rand(4,3), columns=data_files[:-1])
df2 = pds.DataFrame(np.random.rand(4,3), columns=data_files[1:])

bar_plotter(df1, colors, 1)
bar_plotter(df2, colors, 2)

plt.show()

推荐答案

您可以传递一个列表作为颜色.与您可以通过字典不同，这需要一点点手工工作才能使它排成一行，但这可能是实现目标的一种较为简洁的方法.

You can pass a list as the colors. This will require a little bit of manual work to get it to line up, unlike if you could pass a dictionary, but may be a less cluttered way to accomplish your goal.

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

data_files = ['a', 'b', 'c', 'd']

df1 = pds.DataFrame(np.random.rand(4,3), columns=data_files[:-1])
df2 = pds.DataFrame(np.random.rand(4,3), columns=data_files[1:])

color_list = ['b', 'g', 'r', 'c']


df1.plot(kind='bar', ax=plt.subplot(121), color=color_list)
df2.plot(kind='bar', ax=plt.subplot(122), color=color_list[1:])

plt.show()

编辑 阿让提出了一种简单的方法，可以从字典中返回正确颜色的列表:

EDIT Ajean came up with a simple way to return a list of the correct colors from a dictionary:

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

data_files = ['a', 'b', 'c', 'd']
color_list = ['b', 'g', 'r', 'c']
d2c = dict(zip(data_files, color_list))

df1 = pds.DataFrame(np.random.rand(4,3), columns=data_files[:-1])
df2 = pds.DataFrame(np.random.rand(4,3), columns=data_files[1:])

df1.plot(kind='bar', ax=plt.subplot(121), color=map(d2c.get,df1.columns))
df2.plot(kind='bar', ax=plt.subplot(122), color=map(d2c.get,df2.columns))

plt.show()

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