使用Pandas数据框架中的特征创建计算器

Question

我想创建一个计算器来计算Airbnb房间的平均价格，当我们把邻居、床、浴室、卧室的数量作为输入时（这些特性已经在数据集中给出） 邻里、床、卧室、浴室和价格是数据集中的特征，，，请帮助

Answer 1

如果你能提供更多的细节并提出具体的问题，这会有所帮助。你知道吗

大熊猫平均价格的计算方法如下：

import pandas as pd

df = pd.read_csv(path_to_file.csv) # assuming the file has all the relevant fields

def calculate_price(row):
    return row['price_per_room'] * row['number_of_rooms'] * row['number_of_nights']

df['price'] = df.apply(calculate_price)

average_price = df['price'].mean()

print(f"The average price is {average_price }")

## use group by to aggregate across categories

希望这有帮助！你知道吗

Answer 2

我不确定你到底需要什么（你应该更详细地说明你的问题，添加示例数据，首选输出，你的代码……），但groupby可能有用。。。像这样：

df = pd.DataFrame({
    'neighbourhood' : ['nice', 'not so nice', 'nice', 'awesome', 'not so nice'],
    'room_type' : ['a', 'a', 'b', 'b', 'a']
    'beds': [7,2,1,6,6],
    'bedrooms': [3,1,1,3,2],
    'bathrooms': [2,1,1,1,1],
    'price': [220,100,125,320,125]
})

print('Mean of all prices:\n', df['price'].mean())
print('\nMean grouped by neighbourhood:\n', df.groupby(['neighborhood']).mean().price)
print('\nMean grouped by more cols:\n', df.groupby(['neighbourhood', 'beds', 'bedrooms']).mean().price) 

输出：

Mean of all prices:
 178.0

Mean grouped by neighbourhood:
 neighbourhood
awesome        320.0
nice           172.5
not so nice    112.5

Mean grouped by more cols:
 neighbourhood  beds  bedrooms
awesome         6     3           320
nice            1     1           125
                7     3           220
not so nice     2     1           100
                6     2           125

您还可以在应用groupy之前过滤数据帧，例如：

# select requested data data in loc[...] and then apply groupby
df_filtered = df.loc[(df['neighbourhood']=='nice') & (df['beds']==1)]
df_filtered.groupby('neighbourhood')['price'].mean()
# or the same on one line:
df.loc[(df['neighbourhood']=='nice') & (df['beds']==1)].groupby('neighbourhood')['price'].mean()

您的函数（来自上一条评论）可能如下所示：

def calculate_price(air_df):
    a = str(input("Enter the Neighbourhood : "))
    b = str(input("Enter the Room Type : "))
    c = float(input("Enter number of Beds : "))
    d = float(input("Enter number of Bedrooms : "))
    e = float(input("Enter number of Bathrooms : "))
    return air_df.loc[
        (air_df['neighbourhood']==a) & 
        (air_df['room_type']==b) &
        (air_df['beds']==c) &
        (air_df['bedrooms']==d) &
        (air_df['bathrooms']==e)
    ].groupby('neighbourhood')['price'].mean()