python - 如何为水平 Lollipop 图/哑铃图选择最小值和最大值？

我已经创建了一个哑铃图，但是每种类别类型的最小值和最大值都太多了。我只想在每个区域中显示一个天蓝色点（最低价格）和一个绿色点（最高价格）。

这是到目前为止的图表：

My dumbbell chart

这是我的DataFrame：

The DataFrame

这是完整数据集的链接：

https://drive.google.com/open?id=1PpI6PlO8ox2vKfM4aGmEUexCPPWa59S_

到目前为止，这是我的代码：

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

    db = df[['minPrice','maxPrice', 'neighbourhood_hosts']]
    ordered_db = db.sort_values(by='minPrice')
    my_range=db['neighbourhood_hosts']

    plt.figure(figsize=(8,6))
    plt.hlines(y=my_range, xmin=ordered_db['minPrice'], xmax=ordered_db['maxPrice'], color='grey', alpha=0.4)
    plt.scatter(ordered_db['minPrice'], my_range, color='skyblue', alpha=1, label='minimum price')
    plt.scatter(ordered_db['maxPrice'], my_range, color='green', alpha=0.4 , label='maximum price')
    plt.legend()


    plt.title("Comparison of the minimum and maximum prices")
    plt.xlabel('Value range')
    plt.ylabel('Area')

如何格式化我的代码，以便每个区域只有一个最小值和一个最大值？

最佳答案

根据对话，这是脚本：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('dumbbell data.csv')
db = df[['minPrice','maxPrice', 'neighbourhood_hosts']]
#create max and min price based on area name
max_price = db.groupby(['neighbourhood_hosts'])['maxPrice'].max().reset_index()
min_price = db.groupby(['neighbourhood_hosts'])['minPrice'].min().reset_index()
var_price = pd.DataFrame()
var_price['range'] = max_price.maxPrice-min_price.minPrice
var_price['neighbourhood_hosts'] = min_price['neighbourhood_hosts']
var_price = var_price.sort_values(by='range')

#sort max and min price according to variance
max_price = max_price.reindex(var_price.index)
min_price = min_price.reindex(var_price.index)

plt.figure(figsize=(8,6))
plt.hlines(y=min_price['neighbourhood_hosts'], xmin=min_price['minPrice'], xmax=max_price['maxPrice'], color='grey', alpha=0.4)
plt.scatter(min_price['minPrice'], min_price['neighbourhood_hosts'], color='skyblue', alpha=1, label='minimum price')
plt.scatter(max_price['maxPrice'], max_price['neighbourhood_hosts'], color='green', alpha=0.4 , label='maximum price')
plt.legend()


plt.title("Comparison of the minimum and maximum prices")
plt.xlabel('Value range')
plt.ylabel('Area')