如何通过大于考虑索引来过滤列

Question

我有一个代表餐厅顾客评分的数据框。star_rating是该数据框中客户的评级。

• 我想要做的是nb_fave_rating在同一数据框中添加一列，表示餐厅的好评总数。如果其星星数为 ，我认为“赞成”意见> = 3。

data = {'rating_id': ['1', '2','3','4','5','6','7','8','9'],
        'user_id': ['56', '13','56','99','99','13','12','88','45'],
        'restaurant_id':  ['xxx', 'xxx','yyy','yyy','xxx','zzz','zzz','eee','eee'],
        'star_rating': ['2.3', '3.7','1.2','5.0','1.0','3.2','1.0','2.2','0.2'],
        'rating_year': ['2012','2012','2020','2001','2020','2015','2000','2003','2004'],
        'first_year': ['2012', '2012','2001','2001','2012','2000','2000','2001','2001'],
        'last_year': ['2020', '2020','2020','2020','2020','2015','2015','2020','2020'],
        }


df = pd.DataFrame (data, columns = ['rating_id','user_id','restaurant_id','star_rating','rating_year','first_year','last_year'])
df['star_rating'] = df['star_rating'].astype(float)

positive_reviews = df[df.star_rating >= 3.0 ].groupby('restaurant_id')
positive_reviews.head()

从这里开始，我不知道要计算餐厅的正面评论数量并将其添加到我的初始数据框的新列中df。

预期的输出会是这样的。

data = {'rating_id': ['1', '2','3','4','5','6','7','8','9'],
        'user_id': ['56', '13','56','99','99','13','12','88','45'],
        'restaurant_id':  ['xxx', 'xxx','yyy','yyy','xxx','zzz','zzz','eee','eee'],
        'star_rating': ['2.3', '3.7','1.2','5.0','1.0','3.2','1.0','2.2','0.2'],
        'rating_year': ['2012','2012','2020','2001','2020','2015','2000','2003','2004'],
        'first_year': ['2012', '2012','2001','2001','2012','2000','2000','2001','2001'],
        'last_year': ['2020', '2020','2020','2020','2020','2015','2015','2020','2020'],
        'nb_fave_rating': ['1', '1','1','1','1','1','1','0','0'],
        }

所以我尝试了这个并得到了一堆 NaN

data = {'rating_id': ['1', '2','3','4','5','6','7','8','9'],
        'user_id': ['56', '13','56','99','99','13','12','88','45'],
        'restaurant_id':  ['xxx', 'xxx','yyy','yyy','xxx','zzz','zzz','eee','eee'],
        'star_rating': ['2.3', '3.7','1.2','5.0','1.0','3.2','1.0','2.2','0.2'],
        'rating_year': ['2012','2012','2020','2001','2020','2015','2000','2003','2004'],
        'first_year': ['2012', '2012','2001','2001','2012','2000','2000','2001','2001'],
        'last_year': ['2020', '2020','2020','2020','2020','2015','2015','2020','2020'],
        'nb_fave_rating': ['1', '1','1','1','1','1','1','0','0'],
        }

Answer 1

groupby这是和的潜在解决方案map：

#filtering the data with >=3 ratings 
filtered_data = df[df['star_rating'] >= 3]

#creating a dict containing the counts of the all the favorable reviews
d = filtered_data.groupby('restaurant_id')['star_rating'].count().to_dict()

#mapping the dictionary to the restaurant_id to generate 'nb_fave_rating'
df['nb_fave_rating'] = df['restaurant_id'].map(d)

#taking care of `NaN` values 
df.fillna(0,inplace=True)

#making the column integer (just to match the requirements)
df['nb_fave_rating'] = df['nb_fave_rating'].astype(int)

print(df)

输出：

  rating_id user_id restaurant_id  star_rating rating_year first_year last_year  nb_fave_rating
0         1      56           xxx          2.3        2012       2012      2020               1
1         2      13           xxx          3.7        2012       2012      2020               1
2         3      56           yyy          1.2        2020       2001      2020               1
3         4      99           yyy          5.0        2001       2001      2020               1
4         5      99           xxx          1.0        2020       2012      2020               1
5         6      13           zzz          3.2        2015       2000      2015               1
6         7      12           zzz          1.0        2000       2000      2015               1
7         8      88           eee          2.2        2003       2001      2020               0
8         9      45           eee          0.2        2004       2001      2020               0