python基础-22-数据分析python——pandas——matplotlib and change time

本章内容包括：matplotlib 、jyputer内显示图片、时间转化

用户个体消费分析

//input1
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
columns = ['user_id','order_dt','order_products','order_amount']
df = pd.read_table('CDNOW_master.txt',names = columns,sep = '\s+')

//user_id:用户ID
//order_dt:购买日期
//orderproducts:购买产品数
//order_amount:购买金额

df['order_dt'] = pd.to_datetime(df.order_dt,format='%Y%m%d')
df['month'] = df.order_dt.values.astype('datetime64[M]')
df.info()




//output1

RangeIndex: 69659 entries, 0 to 69658
Data columns (total 5 columns):
user_id           69659 non-null int64
order_dt          69659 non-null datetime64[ns]
order_products    69659 non-null int64
order_amount      69659 non-null float64
month             69659 non-null datetime64[ns]
dtypes: datetime64[ns](2), float64(1), int64(2)
memory usage: 2.7 MB









//input2
df.head()




//output2
user_id order_dt    order_products  order_amount    month
0   1   1997-01-01  1   11.77   1997-01-01
1   2   1997-01-12  1   12.00   1997-01-01
2   2   1997-01-12  5   77.00   1997-01-01
3   3   1997-01-02  2   20.76   1997-01-01
4   3   1997-03-30  2   20.76   1997-03-01










//input3
//## 1.进行用户消费趋势的分析（按月）
//每月消费的金额
//每月的消费次数
//每月的产品购买量
//每月的消费人数
order_month_amount.plot()

//output3

output3

由上图可知，消费金额在前三个月达到最高峰，后续休息金额较为稳定，有轻微下降趋势。

//input4
grouped_month.user_id.count().plot()





//output4

output4

//input5
grouped_month.order_products.sum().plot()




//output5

output5

//input6
df.groupby('month').user_id.apply(lambda x:len(x.drop_duplicates())).plot()



//output6

output6

//input7
df.pivot_table(index = 'month',
               values = ['order_products','order_amount','user_id'],
               aggfunc = {'order_products':'sum',
                          'order_amount':'sum',
                          'user_id':'count'                   
               }
              ).head()



//output7
    order_amount    order_products  user_id
month           
1997-01-01  299060.17   19416   8928
1997-02-01  379590.03   24921   11272
1997-03-01  393155.27   26159   11598
1997-04-01  142824.49   9729    3781
1997-05-01  107933.30   7275    2895

//除此之外还可以计算
//每月用户平均消费金额的趋势
//每月用户平均消费次数的趋势