python - 如何在python中制作华夫饼图？（方形饼图）

Question

像这样的东西：

在 R 中有一个非常好的包可以做到这一点。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形图的帖子）：

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（或其他非正方形数字）时，正方形就会错位。

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，谁能帮我解决这两个问题：

如何解决非平方观测值的对齐问题？
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。

score 42 · Accepted Answer

我花了几天时间来构建一个更通用的解决方案 PyWaffle。

你可以通过安装它

pip install pywaffle

源代码：https ://github.com/gyli/PyWaffle

PyWaffle 不使用 matshow() 方法，而是一个一个地构建这些正方形。这使得定制更容易。此外，它提供的是一个自定义的 Figure 类，它返回一个图形对象。通过更新图形的属性，基本上可以控制图表中的一切。

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()

使用图标替换方块：

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
)

一张图表中的多个子图：

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
)

score 14 · Accepted Answer

我在下面整理了一个工作示例，我认为它可以满足您的需求。需要做一些工作来完全概括该方法，但我认为您会发现这是一个好的开始。诀窍是用来matshow()解决您的非平方问题，并构建自定义图例以轻松解释分类值。

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)

以下是此脚本生成的输出示例。如您所见，它对我来说效果很好，并且可以满足您提出的所有需求。如果它给你带来任何麻烦，请告诉我。享受！

score 2 · Accepted Answer

您可以使用此功能自动创建带有简单参数的华夫饼：

def create_waffle_chart(categories, values, height, width, colormap, value_sign=''):

    # compute the proportion of each category with respect to the total
    total_values = sum(values)
    category_proportions = [(float(value) / total_values) for value in values]

    # compute the total number of tiles
    total_num_tiles = width * height # total number of tiles
    print ('Total number of tiles is', total_num_tiles)

    # compute the number of tiles for each catagory
    tiles_per_category = [round(proportion * total_num_tiles) for proportion in category_proportions]

    # print out number of tiles per category
    for i, tiles in enumerate(tiles_per_category):
        print (df_dsn.index.values[i] + ': ' + str(tiles))

    # initialize the waffle chart as an empty matrix
    waffle_chart = np.zeros((height, width))

    # define indices to loop through waffle chart
    category_index = 0
    tile_index = 0

    # populate the waffle chart
    for col in range(width):
        for row in range(height):
            tile_index += 1

            # if the number of tiles populated for the current category 
            # is equal to its corresponding allocated tiles...
            if tile_index > sum(tiles_per_category[0:category_index]):
                # ...proceed to the next category
                category_index += 1       

            # set the class value to an integer, which increases with class
            waffle_chart[row, col] = category_index

    # instantiate a new figure object
    fig = plt.figure()

    # use matshow to display the waffle chart
    colormap = plt.cm.coolwarm
    plt.matshow(waffle_chart, cmap=colormap)
    plt.colorbar()

    # get the axis
    ax = plt.gca()

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

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

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

    # compute cumulative sum of individual categories to match color schemes between chart and legend
    values_cumsum = np.cumsum(values)
    total_values = values_cumsum[len(values_cumsum) - 1]

    # create legend
    legend_handles = []
    for i, category in enumerate(categories):
        if value_sign == '%':
            label_str = category + ' (' + str(values[i]) + value_sign + ')'
        else:
            label_str = category + ' (' + value_sign + str(values[i]) + ')'

        color_val = colormap(float(values_cumsum[i])/total_values)
        legend_handles.append(mpatches.Patch(color=color_val, label=label_str))

    # add legend to chart
    plt.legend(
        handles=legend_handles,
        loc='lower center', 
        ncol=len(categories),
        bbox_to_anchor=(0., -0.2, 0.95, .1)
    )

python - 如何在python中制作华夫饼图？（方形饼图）

3 回答 3

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