如何在python中做华夫饼图？(方形饼图)

Question

像这样的东西:

在R中有一个非常好的包.在python中,我能想到的最好的就是这个,使用squarify包(灵感来自关于如何做树图的帖子):

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns # just to have better line color and width
import squarify
# for those using jupyter notebooks
%matplotlib inline 


df = pd.DataFrame({
                  'v1': np.ones(100), 
                  'v2': np.random.randint(1, 4, 100)})
df.sort_values(by='v2', inplace=True)

# color scale
cmap = mpl.cm.Accent
mini, maxi = df['v2'].min(), df['v2'].max()
norm = mpl.colors.Normalize(vmin=mini, vmax=maxi)
colors = [cmap(norm(value)) for value in df['v2']]

# figure
fig = plt.figure()
ax = fig.add_subplot(111, aspect="equal")
ax = squarify.plot(df['v1'], color=colors, ax=ax)
ax.set_xticks([])
ax.set_yticks([]);

但是当我创建的不是100个而是200个元素(或其他非方形数字)时,方块会变得不对齐.

另一个问题是,如果我将v2更改为某个分类变量(例如,一百个As,Bs,Cs和Ds),我会收到此错误:

无法将字符串转换为浮点数:'a'

那么,任何人都可以帮助我解决这两个问题:

• 如何用非平方数的观测值解决对齐问题？
• 如何在v2中使用分类变量？

除此之外,如果有任何其他python包可以更有效地创建华夫饼图,我真的很开放.

Answer 1

我花了几天时间来构建一个更通用的解决方案PyWaffle.

你可以安装它

pip install pywaffle

源代码:https://github.com/ligyxy/PyWaffle

PyWaffle不使用matshow()方法,而是逐个构建这些方块.这使得定制更容易.此外,它提供的是一个自定义的Figure类,它返回一个figure对象.通过更新图形的属性,您基本上可以控制图表中的所有内容.

一些例子:

彩色或透明背景:

import matplotlib.pyplot as plt
from pywaffle import Waffle

data = {'Democratic': 48, 'Republican': 46, 'Libertarian': 3}
fig = plt.figure(
    FigureClass=Waffle, 
    rows=5, 
    values=data, 
    colors=("#983D3D", "#232066", "#DCB732"),
    title={'label': 'Vote Percentage in 2016 US Presidential Election', 'loc': 'left'},
    labels=["{0} ({1}%)".format(k, v) for k, v in data.items()],
    legend={'loc': 'lower left', 'bbox_to_anchor': (0, -0.4), 'ncol': len(data), 'framealpha': 0}
)
fig.gca().set_facecolor('#EEEEEE')
fig.set_facecolor('#EEEEEE')
plt.show()

使用图标替换正方形:

data = {'Democratic': 48, 'Republican': 46, 'Libertarian': 3}
fig = plt.figure(
    FigureClass=Waffle, 
    rows=5, 
    values=data, 
    colors=("#232066", "#983D3D", "#DCB732"),
    legend={'loc': 'upper left', 'bbox_to_anchor': (1, 1)},
    icons='child', icon_size=18, 
    icon_legend=True
)

一个图表中的多个子图:

import pandas as pd
data = pd.DataFrame(
    {
        'labels': ['Hillary Clinton', 'Donald Trump', 'Others'],
        'Virginia': [1981473, 1769443, 233715],
        'Maryland': [1677928, 943169, 160349],
        'West Virginia': [188794, 489371, 36258],
    },
).set_index('labels')

fig = plt.figure(
    FigureClass=Waffle,
    plots={
        '311': {
            'values': data['Virginia'] / 30000,
            'labels': ["{0} ({1})".format(n, v) for n, v in data['Virginia'].items()],
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 8},
            'title': {'label': '2016 Virginia Presidential Election Results', 'loc': 'left'}
        },
        '312': {
            'values': data['Maryland'] / 30000,
            'labels': ["{0} ({1})".format(n, v) for n, v in data['Maryland'].items()],
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.2, 1), 'fontsize': 8},
            'title': {'label': '2016 Maryland Presidential Election Results', 'loc': 'left'}
        },
        '313': {
            'values': data['West Virginia'] / 30000,
            'labels': ["{0} ({1})".format(n, v) for n, v in data['West Virginia'].items()],
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.3, 1), 'fontsize': 8},
            'title': {'label': '2016 West Virginia Presidential Election Results', 'loc': 'left'}
        },
    },
    rows=5,
    colors=("#2196f3", "#ff5252", "#999999"),  # Default argument values for subplots
    figsize=(9, 5)  # figsize is a parameter of plt.figure
)

Answer 2

我在下面汇总了一个工作示例,我认为这符合您的需求.需要做一些工作才能完全概括这种方法,但我认为你会发现这是一个好的开始.诀窍是用于matshow()解决非方形问题,并构建自定义图例以轻松考虑分类值.

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

# Let's make a default data frame with catagories and values.
df = pd.DataFrame({ 'catagories': ['cat1', 'cat2', 'cat3', 'cat4'], 
                    'values': [84911, 14414, 10062, 8565] })
# Now, we define a desired height and width.
waffle_plot_width = 20
waffle_plot_height = 7

classes = df['catagories']
values = df['values']

def waffle_plot(classes, values, height, width, colormap):

    # Compute the portion of the total assigned to each class.
    class_portion = [float(v)/sum(values) for v in values]

    # Compute the number of tiles for each catagories.
    total_tiles = width * height
    tiles_per_class = [round(p*total_tiles) for p in class_portion]

    # Make a dummy matrix for use in plotting.
    plot_matrix = np.zeros((height, width))

    # Popoulate the dummy matrix with integer values.
    class_index = 0
    tile_index = 0

    # Iterate over each tile.
    for col in range(waffle_plot_width):
        for row in range(height):
            tile_index += 1

            # If the number of tiles populated is sufficient for this class...
            if tile_index > sum(tiles_per_class[0:class_index]):

                # ...increment to the next class.
                class_index += 1       

            # Set the class value to an integer, which increases with class.
            plot_matrix[row, col] = class_index

    # Create a new figure.
    fig = plt.figure()

    # Using matshow solves your "non-square" problem. 
    plt.matshow(plot_matrix, cmap=colormap)
    plt.colorbar()

    # Get the axis.
    ax = plt.gca()

    # Minor ticks
    ax.set_xticks(np.arange(-.5, (width), 1), minor=True);
    ax.set_yticks(np.arange(-.5, (height), 1), minor=True);

    # Gridlines based on minor ticks
    ax.grid(which='minor', color='w', linestyle='-', linewidth=2)

    # Manually constructing a legend solves your "catagorical" problem.
    legend_handles = []
    for i, c in enumerate(classes):
        lable_str = c + " (" + str(values[i]) + ")"
        color_val = colormap(float(i+1)/len(classes))
        legend_handles.append(mpatches.Patch(color=color_val, label=lable_str))

    # Add the legend. Still a bit of work to do here, to perfect centering.
    plt.legend(handles=legend_handles, loc=1, ncol=len(classes),
               bbox_to_anchor=(0., -0.1, 0.95, .10))

    plt.xticks([])
    plt.yticks([])

# Call the plotting function.
waffle_plot(classes, values, waffle_plot_height, waffle_plot_width,
            plt.cm.coolwarm)

下面是此脚本生成的输出示例.正如您所看到的,它对我来说相当不错,并且满足您所有的需求.如果它给你带来任何麻烦,请告诉我.请享用!