熊猫：如何使用.agg

如何解决熊猫：如何使用.agg

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','12'],'restaurant_id':  ['xxx','xxx','yyy','zzz','zzz'],'star_rating': ['2.3','3.7','1.2','5.0','1.0','3.2','1.0'],'rating_year': ['2012','2012','2020','2001','2015','2000'],'first_year': ['2012','2000','last_year': ['2020','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？

解决方法

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','99','99'],'restaurant_id':  ['xxx','xxx','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','2012','2019'],'first_year': ['2012','2012'],'last_year': ['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