python - 以数据框行名称作为图例的 matplotlib 条形图

Question

我正在尝试使用 Pandas 数据框的值设置条形图的图例。我搜索但找不到解决方案，我使用了 SO 中的另一个片段来注释条形图。生成的图显示了我想要的不同颜色的系列中的条形图，甚至显示了条形图的值。例如，在 Excel 中，您可以有一个将系列值显示为图例的图例。我正在尝试在此处获得该功能。

这是一个 MWE:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import seaborn, itertools
seaborn.set()

def flip(items, ncol):
    return itertools.chain(*[items[i::ncol] for i in range(ncol)])

def annotateBars(row, ax=ax):
    if row['A'] < 0.2:
        color = 'black'
        vertalign = 'bottom'
        vertpad = 0.02
    else:
        color = 'white'
        vertalign = 'top'
        vertpad = -0.02

    ax.text(row.name, row['A'] + vertpad, "{:.4f}%".format(row['A']),
            zorder=10, rotation=90, color=color,
            horizontalalignment='center',
            verticalalignment=vertalign,
            fontsize=14, weight='heavy')

labels1=["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
width = 0.75
my_colors = 'gbkymc'
arr1 = np.random.random((1, 5))
arr1_ind = np.arange((arr1.shape[1]))
df_arr1 = pd.DataFrame(zip(*arr1), index = arr1_ind, columns = ['A'])
ax = df_arr1.plot(kind='bar', width = 0.85, alpha = 0.5, color = my_colors)
# plt.xticks(arr1_ind+width/4, arr1_ind)

ax.set_xticks(arr1_ind)
ax.set_xticklabels([labels1[i] for i in arr1_ind])
hndls, lbls = ax.get_legend_handles_labels()
plt.legend(flip(hndls, 2), flip(labels1, 2), loc='best', ncol=2)

junk = df_arr1.apply(annotateBars, ax=ax, axis=1)
plt.tick_params(
    axis='x',          # changes apply to the x-axis
    which='both',      # both major and minor ticks are affected
    bottom='off',      # ticks along the bottom edge are off
    top='off',         # ticks along the top edge are off
    labelbottom='off') # labels along the bottom edge are off
plt.tight_layout()
plt.show()

Answer 1

听起来您希望图例中每种颜色都有一个项目。

现在，您只创建了一位艺术家(对 bar 的一次调用)，因此图例将只有一个条目。

做一个类似于你想要做的事情的简单例子:

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

df = pd.DataFrame({
        'value':np.random.random(5),
        'label':['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
        'color':['g', 'b', 'k', 'y', 'm']})

fig, ax = plt.subplots()

# Plot each bar separately and give it a label.
for index, row in df.iterrows():
    ax.bar([index], [row['value']], color=row['color'], label=row['label'],
           alpha=0.5, align='center')

ax.legend(loc='best', frameon=False)

# More reasonable limits for a vertical bar plot...
ax.margins(0.05)
ax.set_ylim(bottom=0)

# Styling similar to your example...
ax.patch.set_facecolor('0.9')
ax.grid(color='white', linestyle='-')
ax.set(axisbelow=True, xticklabels=[])

plt.show()