使用groupby和pandas dataframe中的多个列从字符串数据创建条形图

Question

我想在python中创建一个条形图,其中包含多个x类别,数据计数为"是"或"否".我已经开始使用一些代码,但我相信我正在以一种缓慢的方式获得我想要的解决方案.我可以使用seaborn,Matplotlib或pandas但不是 Bokeh 的解决方案,因为我想制作可扩展的出版品质数字.

最终我想要的是:

• 在x轴上标有"独木舟","巡航","皮划艇"和"船"类别的条形图
• 分组 - "颜色",所以绿色或红色
• 显示"是"响应的比例:所以是行的数量除以"红色"和"绿色"的数量,在这种情况下是4红色和4绿色,但这可能会改变.

这是我正在使用的数据集:

import pandas as pd
data = [{'ship': 'Yes','canoe': 'Yes', 'cruise': 'Yes', 'kayak': 'No','color': 'Red'},{'ship': 'Yes', 'cruise': 'Yes', 'kayak': 'Yes','canoe': 'No','color': 'Green'},{'ship': 'Yes', 'cruise': 'Yes', 'kayak': 'No','canoe': 'No','color': 'Green'},{'ship': 'Yes', 'cruise': 'Yes', 'kayak': 'No','canoe': 'No','color': 'Red'},{'ship': 'Yes', 'cruise': 'Yes', 'kayak': 'Yes','canoe': 'No','color': 'Red'},{'ship': 'No', 'cruise': 'Yes', 'kayak': 'No','canoe': 'Yes','color': 'Green'},{'ship': 'No', 'cruise': 'No', 'kayak': 'No','canoe': 'No','color': 'Green'},{'ship': 'No', 'cruise': 'No', 'kayak': 'No','canoe': 'No','color': 'Red'}]
df = pd.DataFrame(data)

这是我开始的:

print(df['color'].value_counts())

red = 4 # there must be a better way to code this rather than manually. Perhaps using len()?
green = 4

# get count per type
ca = df['canoe'].value_counts()
cr = df['cruise'].value_counts()
ka = df['kayak'].value_counts()
sh = df['ship'].value_counts()
print(ca, cr, ka, sh)

# group by color
cac = df.groupby(['canoe','color'])
crc = df.groupby(['cruise','color'])
kac = df.groupby(['kayak','color'])
shc = df.groupby(['ship','color'])

# make plots 
cac2 = cac['color'].value_counts().unstack()
cac2.plot(kind='bar', title = 'Canoe by color')

但实际上我想要的是在一个图上的所有x类别,仅显示"是"响应的结果,并将其视为"是"而不仅仅是计数的比例.救命？

Answer 1

咱们试试吧。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from itertools import groupby

data = [{'ship': 'Yes','canoe': 'Yes', 'cruise': 'Yes', 'kayak': 'No','color': 'Red'},{'ship': 'Yes', 'cruise': 'Yes', 'kayak': 'Yes','canoe': 'No','color': 'Green'},{'ship': 'Yes', 'cruise': 'Yes', 'kayak': 'No','canoe': 'No','color': 'Green'},{'ship': 'Yes', 'cruise': 'Yes', 'kayak': 'No','canoe': 'No','color': 'Red'},{'ship': 'Yes', 'cruise': 'Yes', 'kayak': 'Yes','canoe': 'No','color': 'Red'},{'ship': 'No', 'cruise': 'Yes', 'kayak': 'No','canoe': 'Yes','color': 'Green'},{'ship': 'No', 'cruise': 'No', 'kayak': 'No','canoe': 'No','color': 'Green'},{'ship': 'No', 'cruise': 'No', 'kayak': 'No','canoe': 'No','color': 'Red'}]
df = pd.DataFrame(data)
df1 = df.replace(["Yes","No"],[1,0]).groupby("color").mean().stack().rename('% Yes').to_frame()


def add_line(ax, xpos, ypos):
    line = plt.Line2D([xpos, xpos], [ypos + .1, ypos],
                      transform=ax.transAxes, color='gray')
    line.set_clip_on(False)
    ax.add_line(line)

def label_len(my_index,level):
    labels = my_index.get_level_values(level)
    return [(k, sum(1 for i in g)) for k,g in groupby(labels)]

def label_group_bar_table(ax, df):
    ypos = -.1
    scale = 1./df.index.size
    for level in range(df.index.nlevels)[::-1]:
        pos = 0
        for label, rpos in label_len(df.index,level):
            lxpos = (pos + .5 * rpos)*scale
            ax.text(lxpos, ypos, label, ha='center', transform=ax.transAxes)
            add_line(ax, pos*scale, ypos)
            pos += rpos
        add_line(ax, pos*scale , ypos)
        ypos -= .1


colorlist = ['green','red']
cp = sns.color_palette(colorlist)

ax = sns.barplot(x=df1.index, y='% Yes', hue = df1.index.get_level_values(0), data=df1, palette=cp)
#Below 2 lines remove default labels
ax.set_xticklabels('')
ax.set_xlabel('')
label_group_bar_table(ax, df1)

输出：