Python 3.x 根据python中其他列的计算平均值更改列值

我是熊猫的新手。我问了很多问题，但没有找到答案

我有以下数据集

Name    || Price    || Cuisine Category || City || Region || Cuisine Types || Rating Types || Rating

Pizza  || 600  || Fast Food,Pizza || Ajmer ||   Ana Saga || Quick Bites || Good || 3.9

...     ...     ...     ...     ...     ...     ...     ...     ...

Chawla's || 300 || Beverages || Ajmer || Sagar Lake     || Cafe || Average || 3.3

Masala || 0 || North,South Indian || Ajmer || Ram Ganj || Mess || None || NEW

我想更改以下内容的值：

• 根据特定烹饪类型的平均评分，然后根据计算出的评分，对新烹饪类型进行评分

• 价格为0时，基于该特定区域的平均价格

我尝试改变价格：

读取CSV文件

data = pd.read_csv('/content/Ajmer.csv')

计算区域平均价格

gregion = round(data.groupby('Region')['Price'].mean()) 

正在尝试替换价格列的0

data['Price'] = data['Price'].replace(0, gregion[data['Region']])

但我的价格栏是不变的

我尝试更改评级：

读取CSV文件

data2 = pd.read_csv('/content/Ajmer.csv')

创建单独的数据框，使其不会影响平均值

filtered_rating = data2[(data2['Rating'] == 'NEW') | (data2['Rating'] == '-') | (data2['Rating'] == 'Opening')]

从原始数据中删除2

data2.drop(data2.loc[data['Rating']=='NEW'].index, inplace=True)
data2.drop(data2.loc[data['Rating']=='-'].index, inplace=True)
data2.drop(data2.loc[data['Rating']=='Opening'].index, inplace=True)

按等级平均数计算烹饪类型

c = round(data2.groupby('Cuisine Types')['Rating'].mean(),1)

这给了我如下输出：

Cuisine Types

Bakery            3.4

Confectionery     3.4

Dessert Parlor    3.5

...

Quick Bites       3.4

Sweet Shop        3.4

Name: Rating, dtype: float64

试图替换值

filtered_rating['Rating'].replace('NEW', c[data2['Region']], inplace=True)
filtered_rating['Rating'].replace('-', c[data2['Region']], inplace=True)
filtered_rating['Rating'].replace('Opening', c[data2['Region']], inplace=True)

但我的评级栏没有改变

预期产量

• 价格列中价格为零的行的特定区域的平均价格

data['Price'] = data['Price'].replace(0, gregion[data['Region']])

• 评级列中新评级的特定烹饪类型行的平均评级

有人能帮我做这件事吗

提前谢谢！

---

非常感谢您的帮助

您可以尝试以下代码：

gregion = round(data.groupby('Region')['Price'].mean()) 
# convert your group by to DataFrame
gregion = pd.DataFrame(gregion)
gregion.reset_index(inplace=True)

# merge the datas and drop the new column that is created
data = data.merge(gregion, left_on='Region', right_on='Region', suffixes=('_x', ''))
data = data.drop(columns={'Price_x'})

filtered_rating = data[(data['Rating'] == 'NEW') | (data['Rating'] == '-') | (data['Rating'] == 'Opening')]

# you don't need to re-upload the file
data2 = data.copy()

data2.drop(data2.loc[data2['Rating']=='NEW'].index, inplace=True)
data2.drop(data2.loc[data2['Rating']=='-'].index, inplace=True)
data2.drop(data2.loc[data['Rating']=='Opening'].index, inplace=True)

# do the same with c
c = round(data2.groupby('Cuisine Types')['Rating'].mean(),1)
c = pd.DataFrame(c)
c.reset_index(inplace=True)

filtered_rating = filtered_rating.merge(c, left_on='Cuisine Types', right_on='Cuisine Types', how='left', suffixes=('_x', ''))
filtered_rating = filtered_rating.drop(columns={'Rating_x'})

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希望这有帮助。

假设您有如下数据

data
    name            region     price    cuisine_type          rating_type   rating
0   pizza           NY          500      fast food              average        3.3
1   burger          NY          350      fast food              good           4.1
2   lobster         LA          1500     seafood                good           4.5
3   mussels         LA          1000     seafood                average        3.9
4   shawarma        NY          300    mediterranean            average        3.4
5   kabab           LA          600    mediterranean            good             4
6   pancake         NY          250      breakfast              average        3.7
7   waffle          LA          450      breakfast              good           4.2
8   fries           NY          0        fast food              None           NEW
9   crab            LA          0        seafood                None        Opening
10  tuna sandwich   NY          0        seafood                None           NEW
11  onion rings     LA          0        fast food              None        Opening

现在，根据您的问题，我们需要将新的或开放的评级替换为相应类型的平均评级。以及当其为0时的价格，以及相应区域的平均价格。并在末尾更新“无”的评级类型

#get a list of cuisine types
cuisine_type_list=data.cuisine_type.unique().tolist()

cuisine_type_list
['fast food', 'seafood', 'mediterranean', 'breakfast']

#get a list of regions
region_list=data.region.unique().tolist()

region_list
['NY', 'LA']

这是更新后的数据

data
    name            region     price    cuisine_type          rating_type   rating
0   pizza           NY          500      fast food              average        3.3
1   burger          NY          350      fast food              good           4.1
2   lobster         LA          1500     seafood                good           4.5
3   mussels         LA          1000     seafood                average        3.9
4   shawarma        NY          300    mediterranean            average        3.4
5   kabab           LA          600    mediterranean            good             4
6   pancake         NY          250      breakfast              average        3.7
7   waffle          LA          450      breakfast              good           4.2
8   fries           NY          350      fast food              average        3.7
9   crab            LA          887.5    seafood                good           4.2
10  tuna sandwich   NY          350      seafood                good           4.2
11  onion rings     LA          887.5    fast food              average        3.7

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谢谢！这是一个绝妙的方法