我是熊猫的新手。
将此视为我的数据帧：
东风
Search Impressions Clicks Transactions ContainsBest ContainsFree Country
Best phone 10 5 1 True False UK
Best free phone 15 4 2 True True UK
free phone 20 3 4 False True UK
good phone 13 1 5 False False US
just a free phone 12 3 4 False True US
我有专栏ContainsBest和ContainsFree.我想对所有Impressions、Clicks和Transactions求和ContainsBestTrue，然后我想对ContainsFree为 True 的Impressions、Clicks和Transactions求和，并对第Country列中的唯一值执行相同的操作。因此，新的数据帧将如下所示：
output_df
Country Impressions Clicks Transactions
UK 45 12 7
ContainsBest 25 9 3
ContainsFree 35 7 6
US 25 4 9
ContainsBest 0 0 0
ContainsFree 12 3 4
为此，我会理解我需要使用类似以下内容的内容：
uk_toal_impressions = df['Impressions'].sum().where(df['Country']=='UK')
uk_best_impressions = df['Impressions'].sum().where(df['Country']=='UK' & df['ContainsBest'])
uk_free_impressions = df['Impressions'].sum().where(df['Country']=='UK' & df['ContainsFree'])
然后我会对Clicks和Transactions应用相同的逻辑，并为CountryUS重做相同的代码。
我要实现的第二件事是为每个Country和Impressions、Clicks和Transactions添加列TopCategories，以便我的final_output_df如下所示：
final_output_df
Country Impressions Clicks Transactions TopCategoriesForImpressions TopCategoriesForClicks TopCategoriesForTransactions
UK 45 12 7 ContainsFree ContainsBest ContainsFree
ContainsBest 25 9 3 ContainsBest ContainsFree ContainsBest
ContainsFree 35 7 6
US 25 4 9 ContainsFree ContainsFree ContainsFree
ContainsBest 0 0 0
ContainsFree 12 3 4
列TopCategoriesForxx逻辑是一种简单的ContainsBest，Country列下ContainsFree行。因此，UK国家的TopCategoriesForImpressions是
- 包含免费
- 包含最佳
虽然UK国家的TopCategoriesForClicks是：
- 包含最佳
- 包含免费
我知道我需要使用这样的东西：
TopCategoriesForImpressions = output_df['Impressions'].sort_values(by='Impressions', ascending=False).where(output_df['Country']=='UK')
我只是觉得很难把所有东西都看成我上final_output_df.另外，我认为我不需要创建output_df，只是想添加它以更好地了解我实现final_output_df的步骤。
所以我的问题是：
- 如何应用基于一个或多个条件的计算？请参阅第
ContainsBest行和第ContainsFree行 - 如何根据条件对列值进行排序？请参阅第
TopCategoriesForImpressions栏 - 实际上，我有 70 个国家和 20 列
Containsxxx，有没有办法在不为 70 个国家和 20 个Containsxxx列添加条件的情况下实现这一目标？
非常感谢您的建议。
解决方案的第一部分应该是：
#removed unnecessary column Search and added ContainAll column filled Trues
df1 = df.drop('Search', 1).assign(ContainAll = True)
#columns for tests
cols1 = ['Impressions','Clicks','Transactions']
cols2 = ['ContainsBest','ContainsFree','ContainAll']
print (df1[cols2].dtypes)
ContainsBest bool
ContainsFree bool
ContainAll bool
dtype: object
print (df1[cols1].dtypes)
Impressions int64
Clicks int64
Transactions int64
dtype: object
print (df1.melt(['Country'] + cols1, var_name='Type', value_name='mask'))
Country Impressions Clicks Transactions Type mask
0 UK 10 5 1 ContainsBest True
1 UK 15 4 2 ContainsBest True
2 UK 20 3 4 ContainsBest False
3 US 13 1 5 ContainsBest False
4 US 12 3 4 ContainsBest False
5 UK 10 5 1 ContainsFree False
6 UK 15 4 2 ContainsFree True
7 UK 20 3 4 ContainsFree True
8 US 13 1 5 ContainsFree False
9 US 12 3 4 ContainsFree True
10 UK 10 5 1 ContainAll True
11 UK 15 4 2 ContainAll True
12 UK 20 3 4 ContainAll True
13 US 13 1 5 ContainAll True
14 US 12 3 4 ContainAll True
print (df1.melt(['Country'] + cols1, var_name='Type', value_name='mask').query('mask'))
Country Impressions Clicks Transactions Type mask
0 UK 10 5 1 ContainsBest True
1 UK 15 4 2 ContainsBest True
6 UK 15 4 2 ContainsFree True
7 UK 20 3 4 ContainsFree True
9 US 12 3 4 ContainsFree True
10 UK 10 5 1 ContainAll True
11 UK 15 4 2 ContainAll True
12 UK 20 3 4 ContainAll True
13 US 13 1 5 ContainAll True
14 US 12 3 4 ContainAll True
#all possible combinations of Country and boolean columns
mux = pd.MultiIndex.from_product([df['Country'].unique(), cols2],
names=['Country','Type'])
#reshape by melt for all boolean column to one mask column
#filter Trues by loc and aggregate sum
#add 0 rows by reindex
df1 = (df1.melt(['Country'] + cols1, var_name='Type', value_name='mask')
.query('mask')
.drop('mask', axis=1)
.groupby(['Country','Type'])
.sum()
.reindex(mux, fill_value=0)
.reset_index())
print (df1)
Country Type Impressions Clicks Transactions
0 UK ContainsBest 25 9 3
1 UK ContainsFree 35 7 6
2 UK ContainAll 45 12 7
3 US ContainsBest 0 0 0
4 US ContainsFree 12 3 4
5 US ContainAll 25 4 9
第二个是可能的过滤器行，用于检查排序，numpy.argsort按每组降序排列：
def f(x):
i = x.index.to_numpy()
a = i[(-x.to_numpy()).argsort(axis=0)]
return pd.DataFrame(a, columns=x.columns)
df2 = (df1[df1['Type'].isin(['ContainsBest','ContainsFree']) &
~df1[cols1].eq(0).all(1)]
.set_index('Type')
.groupby('Country')[cols1]
.apply(f)
.add_prefix('TopCategoriesFor')
.rename_axis(['Country','Type'])
.rename({0:'ContainsBest', 1:'ContainsFree'})
)
print (df2)
TopCategoriesForImpressions TopCategoriesForClicks
Country Type
UK ContainsBest ContainsFree ContainsBest
ContainsFree ContainsBest ContainsFree
US ContainsBest ContainsFree ContainsFree
TopCategoriesForTransactions
Country Type
UK ContainsBest ContainsFree
ContainsFree ContainsBest
US ContainsBest ContainsFree
df3 = df1.join(df2, on=['Country','Type'])
print (df3)
Country Type Impressions Clicks Transactions
0 UK ContainsBest 25 9 3
1 UK ContainsFree 35 7 6
2 UK ContainAll 45 12 7
3 US ContainsBest 0 0 0
4 US ContainsFree 12 3 4
5 US ContainAll 25 4 9
TopCategoriesForImpressions TopCategoriesForClicks
0 ContainsFree ContainsBest
1 ContainsBest ContainsFree
2 NaN NaN
3 ContainsFree ContainsFree
4 NaN NaN
5 NaN NaN
TopCategoriesForTransactions
0 ContainsFree
1 ContainsBest
2 NaN
3 ContainsFree
4 NaN
5 NaN