1DadaFrame和Series创建

通过GroupBy创建DF对象

sn_group=data.groupby('SN')

purchase_count=sn_group.count().Price

average_purchase_price=sn_group.mean().Price.round(2)

total_purchase_price=sn_group.sum().Price

spender_summary=pd.DataFrame({"Purchase Count":purchase_count,

                          "Average Purchase":average_purchase_price,

                          "Total Purchase Value":total_purchase_price})

spender_summary.sort_values('Total Purchase Value',ascending=False,inplace=True)

spender_summary.head(10)

注意：purchase_count和average_purchase_price、total_purchase_price都是Series对象，并且它们的index都是一样的

第二种处理Series组成一个DF

#Age Demographics
age_bins = [0, 9, 14, 19, 24, 29, 34, 39, 100]
group_labels = ["<10", "10-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40+"]
#9个数字 8个区间
data['Age_group']=pd.cut(data.Age,age_bins,labels=group_labels) #后面增加一列Age_group 原始数据780行就增加780个年龄区间

age_groupy=data.groupby('Age_group')

age_df=age_groupy["SN"].nunique()

age_df.name='Total Count' #age_df和age_percent_df的name都是SN所以要改为不同的名字作为列名

age_df #Series类型

age_percent_df=round((age_df/age_df.sum())*100,2)

age_percent_df.name='%Percentage of Players'

avg_purchase_price=age_groupy['Price'].mean()#等价avg_purchase_price=age_groupy.mean().Price avg_purchase_price=age_groupy.mean()是一个DF对象

avg_purchase_price.name='avg_purchase_price'

total_purchase_price=age_groupy['Price'].sum()

total_purchase_price.name='total_purchase_price'

summary_age_df=pd.concat([age_df,age_percent_df,avg_purchase_price,total_purchase_price],axis=1) #列拼接

summary_age_df.reset_index(inplace=True)

　　任意举出一个age_groupy.nunique()就是DF的例子。age_groupy.mean();age_groupy.count()...都是一个DF

把不同的字段组成一个DF,各项之间没有关系，字段用[]阔起来，外面是一个字典{}

#Number of Unique Items

unique_items=data['Item ID'].nunique()

#Average Purchase Price

avg_purchase=data.Price.mean()

#Total Number of Purchases

total_purchases=data.SN.count()

#Total Revenue

total_revenue=data.Price.sum()

summary_df = pd.DataFrame({"Number of Unique Items":[unique_items],

      "Average Price":[avg_purchase],

      "Number of Purchases":[total_purchases],

      "Total Revenue":[total_revenue]})

summary_df

列表list[]转为DF 形如[(),(),(),]列表里面套元组形式

#Gender Demographics 性别特征

def gender_Percentage1(gender,):

    gender_count=data.loc[data.Gender==gender,'SN'].nunique()

    gender_perc = ((gender_count/total_count)*100)

    return  gender,gender_count,gender_perc

gender_laberls=np.sort(data.Gender.unique()).tolist()

total_count=data.SN.nunique()

result=[]

for gender in gender_laberls:

    result.append(gender_Percentage1(gender))

gender_df1=pd.DataFrame(result,columns =  ["Gender", "Total Count", "%Percentage of Players"])

gender_df1

最简单的通过字典创建DF

单个字典创建一个DF

Genre_temp=data.Genre.str.split(',').tolist()  #[[],[],[]...]

genre_list=[i for j in Genre_temp for i in j] #里面有重复的但是没关系

import nltk

fdist=nltk.FreqDist(genre_list)

df=pd.DataFrame(fdist,index=[0]) #index=[0]一定要加一个索引是不是0无所谓

#df.T转置看起来更舒服一点
#genre=pd.Series(fdist).sort_values(ascending=False)  #dist-->pd.Series 其实单个字典转为Series好一点