如何创建一个字典，其字典值会根据在主数据集上找到多少重复项而扩展？

如何解决如何创建一个字典，其字典值会根据在主数据集上找到多少重复项而扩展？

我正在使用的数据集可以在这里找到：https://www.kaggle.com/lava18/google-play-store-apps 此数据集有两列可对App的类型进行分类（第1列和第9列-我从第一列开始计数为0）。也许下面的图片会有所帮助：

第1列的数据比第9列的数据粒度小，因此字典键将是Column1，而值将是Column9。我已经有一个函数可以查看第1列到第9列中每个类别的百分比。

def freq_table(dataset,index_category):
table = {}
total = 0

for row in dataset:
    total += 1
    category = row[index_category]
    if category in table:
        table[category] += 1
    else:
        table[category] = 1

table_percentages = {}
cat_num=0
for key in table:
    cat_num+=1
    percentage = (table[key] / total) * 100
    table_percentages[key] = percentage 
print(f'Total Number of Categories: {cat_num}')
return table_percentages

#Removing from being a dictionary and putting in a Descending Order
def display_table(dataset,index_category):
    table = freq_table(dataset,index_category)
    table_display = []
    for key in table:
        key_val_as_tuple = (table[key],key) 
        #The order of this sentence is - Percentage and Category,because the function sorted gets the first element to sort it
        #And this is the Percentage since we want a Descending Order
        #This is a Tuple since we will not need to change these values and it is easy to pack values together
        table_display.append(key_val_as_tuple)
        #In order to pack everything in one object,we use List Append (Tuples don't have Append)
    table_sorted = sorted(table_display,reverse = True) #We choose the Descending Order in the Percentage Field here
    for entry in table_sorted:
        print(entry[1],':',entry[0],'%') 
        #Before the order was Percentage : Category,Now to be more user friendly we change to Category : Percentage

但是我如何创建一个可以告诉我以下内容的函数？

家庭（column0）的流派（column9）：“休闲；脑力游戏”占35％，“教育；创造力”占20％，“教育；教育”占45％

让我知道是否需要进一步的信息，并非常感谢您的帮助。'

解决方法

对于给定的文件，最好使用pandas。熊猫将以表格格式读取文件，并且提供了许多分组选项-

import pandas as pd

x = pd.read_csv("googleplaystore.csv")
x = x.groupby(["Category","Genres"],as_index=False)[['App']].count().rename(columns={'App' : 'cnt'})
x_tot = x.groupby(["Category"],as_index=False)[['cnt']].sum().rename(columns={'cnt' : 'tot'})
x = x.merge(x_tot,on = ['Category'])
x['pct'] = x['cnt']/x['tot']