熊猫：如何使用 .agg()

Question

我有一个数据框，其中包含顾客对他们去过的餐馆的评分以及其他一些属性。

• 我要做的是计算去年的平均星级和平均星级之间的差异 餐厅的第一年。

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

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

df.head()

df['star_rating'] = df['star_rating'].astype(float)

# calculate the average of the stars of the first year 

ratings_mean_firstYear= df.groupby(['restaurant_id','first_year']).agg({'star_rating':[np.mean]})
ratings_mean_firstYear.columns = ['avg_firstYear']
ratings_mean_firstYear.reset_index()

# calculate the average of the stars of the last year 

ratings_mean_lastYear= df.groupby(['restaurant_id','last_year']).agg({'star_rating':[np.mean]})
ratings_mean_lastYear.columns = ['avg_lastYear']
ratings_mean_lastYear.reset_index()

# merge the means into a single table

ratings_average = ratings_mean_firstYear.merge(
    ratings_mean_lastYear.groupby('restaurant_id')['avg_lastYear'].max()
    , on='restaurant_id'
)

ratings_average.head(20)

我的问题是第一年和最后几年的平均值完全相同，这毫无意义，我真的不知道我在这里的思考过程中做错了什么..我怀疑@有什么问题987654325@因为这是我第一次使用pandas lib。

有什么建议吗？

Answer 1

您的数据是以这样一种方式提供的，即每个用户/餐厅对具有单一评级，并且您在第一年和去年的聚合中都使用它 - 所以自然而然地在这两年中它是相等的。我首先使用 rating_year == first_year 标准过滤数据，然后应用 groupby 和 agg。然后对去年重复相同的操作，然后合并 2 个结果。在您的示例中，没有一条评论的数据与任何餐厅的第一年或去年相匹配。因此，要显示适当的示例将需要更多数据。我假设您在较大的数据框中拥有它。 ——

这里是一个例子，我添加了更多的行并改变了年份以有更多的匹配：

data = {'rating_id': ['1', '2','3','4','5','6','7','8','9'],
        'user_id': ['56', '56','56','56', '99','99','99','99','99'],
        'restaurant_id':  ['xxx', 'xxx','yyy','yyy','xxx', 'xxx','yyy','yyy','xxx'],
        'star_rating': ['2.3', '3.7','1.2','5.0','1.0','3.2','4.0','2.5','3.0'],
        'rating_year': ['2012', '2020','2001','2020', '2012', '2020','2001','2020','2019'],
        'first_year': ['2012', '2012','2001','2001','2012', '2012','2001','2001','2012'],
        'last_year': ['2020', '2020','2020','2020','2020','2020','2020','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)

ratings_mean_firstYear = df[df.rating_year == df.first_year].groupby('restaurant_id').agg({'star_rating':'mean'})
ratings_mean_firstYear.columns = ['avg_firstYear']
ratings_mean_lastYear= df[df.rating_year == df.last_year].groupby('restaurant_id').agg({'star_rating':'mean'})
ratings_mean_lastYear.columns = ['avg_lastYear']

结果：

ratings_mean_firstYear.merge(ratings_mean_lastYear, left_index=True, right_index=True)

               avg_firstYear  avg_lastYear
restaurant_id                             
xxx                     1.65          3.45
yyy                     2.60          3.75