Question

我有一个数据框，其中包含顾客对他们去过的餐馆的评价以及其他一些属性。

我要做的是计算去年的平均星级与该年度的平均星级之间的差异餐馆的第一年。


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，第一年和最后一年的平均值完全相同，没有任何意义，我真的不知道自己在这里的思考过程做错了什么。.我怀疑{ {1}}，因为这是我第一次使用pandas lib。

有什么建议吗？

Answer 1

您提供的数据以这样的方式提供：每个用户/餐厅对具有单个评分，并且您在第一年和去年的汇总中都使用它-因此，自然来说，这两年都是相等的。我首先使用rating_year == first_year条件过滤数据，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匹配。因此，显示适当的示例将需要更多数据。我假设您在更大的数据框中有它。 –

这里是一个示例，我添加了更多行并更改了年份以具有更多匹配项：

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

熊猫：如何使用.agg（）

1 个答案: