数据挖掘实践（33）：实战--高潜用户购买画像（二）EDA/探索性数据分析

2.EDA/探索性数据分析

2.1 周一到周日每天购买情况

# 导入相关包
%matplotlib inline
# 绘图包
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

#定义文件名
ACTION_201602_FILE = "data/Data_Action_201602.csv"
ACTION_201603_FILE = "data/Data_Action_201603.csv"
ACTION_201604_FILE = "data/Data_Action_201604.csv"
COMMENT_FILE = "data/Data_Comment.csv"
PRODUCT_FILE = "data/Data_Product.csv"
USER_FILE = "data/Data_User.csv"
USER_TABLE_FILE = "data/User_table.csv"
ITEM_TABLE_FILE = "data/Item_table.csv"

# 提取购买(type=4)的下单行为数据
def get_from_action_data(fname, chunk_size=50000):
    reader = pd.read_csv(fname, header=0, iterator=True)
    chunks = []
    loop = True
    while loop:
        try:
            chunk = reader.get_chunk(chunk_size)[
                ["user_id", "sku_id", "type", "time"]]
            chunks.append(chunk)
        except StopIteration:
            loop = False
            print("Iteration is stopped")

    df_ac = pd.concat(chunks, ignore_index=True)
    # type=4,为购买/下单
    df_ac = df_ac[df_ac['type'] == 4]

    return df_ac[["user_id", "sku_id", "time"]]

df_ac = []
df_ac.append(get_from_action_data(fname=ACTION_201602_FILE))
df_ac.append(get_from_action_data(fname=ACTION_201603_FILE))
df_ac.append(get_from_action_data(fname=ACTION_201604_FILE))
df_ac = pd.concat(df_ac, ignore_index=True)

Iteration is stopped
Iteration is stopped
Iteration is stopped

print(df_ac.dtypes) # 将time字段转换为datetime类型

user_id     int64
sku_id      int64
time       object
dtype: object

# 将time字段转换为datetime类型
df_ac['time'] = pd.to_datetime(df_ac['time'])

# 使用lambda匿名函数将时间time转换为星期(周一为1, 周日为７)
df_ac['time'] = df_ac['time'].apply(lambda x: x.weekday() + 1)

df_ac.head()

# 周一到周日每天购买用户个数
df_user = df_ac.groupby('time')['user_id'].nunique()
df_user = df_user.to_frame().reset_index() # DataFrame可以通过set_index方法，可以设置索引
df_user.columns = ['weekday', 'user_num']

# 周一到周日每天购买商品个数
df_item = df_ac.groupby('time')['sku_id'].nunique()
df_item = df_item.to_frame().reset_index()
df_item.columns = ['weekday', 'item_num']

# 周一到周日每天购买记录个数
df_ui = df_ac.groupby('time', as_index=False).size()
df_ui = df_ui.to_frame().reset_index()
df_ui.columns = ['weekday', 'user_item_num']

# 条形宽度
bar_width = 0.2
# 透明度
opacity = 0.4

plt.bar(df_user['weekday'], df_user['user_num'], bar_width, 
        alpha=opacity, color='c', label='user')
plt.bar(df_item['weekday']+bar_width, df_item['item_num'], 
        bar_width, alpha=opacity, color='g', label='item')
plt.bar(df_ui['weekday']+bar_width*2, df_ui['user_item_num'], 
        bar_width, alpha=opacity, color='m', label='user_item')

plt.xlabel('weekday')
plt.ylabel('number')
plt.title('A Week Purchase Table')
plt.xticks(df_user['weekday'] + bar_width * 3 / 2., (1,2,3,4,5,6,7))
plt.tight_layout() 
plt.legend(prop={'size':10})

# 分析：周六，周日购买量较少，配送问题

<matplotlib.legend.Legend at 0x12fc35b90>

2.2 2016年2月中各天购买量

df_ac = get_from_action_data(fname=ACTION_201602_FILE)

# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天
df_ac['time'] = pd.to_datetime(df_ac['time']).apply(lambda x: x.day)

Iteration is stopped

df_ac.head()

df_ac.tail()

df_user = df_ac.groupby('time')['user_id'].nunique()
df_user = df_user.to_frame().reset_index()
df_user.columns = ['day', 'user_num']

df_item = df_ac.groupby('time')['sku_id'].nunique()
df_item = df_item.to_frame().reset_index()
df_item.columns = ['day', 'item_num']

df_ui = df_ac.groupby('time', as_index=False).size()
df_ui = df_ui.to_frame().reset_index()
df_ui.columns = ['day', 'user_item_num']

# 条形宽度
bar_width = 0.2
# 透明度
opacity = 0.4
# 天数
day_range = range(1,len(df_user['day']) + 1, 1)
# 设置图片大小
plt.figure(figsize=(14,10))

plt.bar(df_user['day'], df_user['user_num'], bar_width, 
        alpha=opacity, color='c', label='user')
plt.bar(df_item['day']+bar_width, df_item['item_num'], 
        bar_width, alpha=opacity, color='g', label='item')
plt.bar(df_ui['day']+bar_width*2, df_ui['user_item_num'], 
        bar_width, alpha=opacity, color='m', label='user_item')

plt.xlabel('day')
plt.ylabel('number')
plt.title('February Purchase Table')
plt.xticks(df_user['day'] + bar_width * 3 / 2., day_range)
# plt.ylim(0, 80)
plt.tight_layout() 
plt.legend(prop={'size':9})

<matplotlib.legend.Legend at 0x15a590550>

• 分析： 2月份5,6,7,8,9,10 这几天购买量非常少，原因可能是中国农历春节，快递不营业

2.3 2016年3月中各天购买量

df_ac = get_from_action_data(fname=ACTION_201603_FILE)

# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天
df_ac['time'] = pd.to_datetime(df_ac['time']).apply(lambda x: x.day)

Iteration is stopped

df_user = df_ac.groupby('time')['user_id'].nunique()
df_user = df_user.to_frame().reset_index()
df_user.columns = ['day', 'user_num']

df_item = df_ac.groupby('time')['sku_id'].nunique()
df_item = df_item.to_frame().reset_index()
df_item.columns = ['day', 'item_num']

df_ui = df_ac.groupby('time', as_index=False).size()
df_ui = df_ui.to_frame().reset_index()
df_ui.columns = ['day', 'user_item_num']

# 条形宽度
bar_width = 0.2
# 透明度
opacity = 0.4
# 天数
day_range = range(1,len(df_user['day']) + 1, 1)
# 设置图片大小
plt.figure(figsize=(14,10))

plt.bar(df_user['day'], df_user['user_num'], bar_width, 
        alpha=opacity, color='c', label='user')
plt.bar(df_item['day']+bar_width, df_item['item_num'], 
        bar_width, alpha=opacity, color='g', label='item')
plt.bar(df_ui['day']+bar_width*2, df_ui['user_item_num'], 
        bar_width, alpha=opacity, color='m', label='user_item')

plt.xlabel('day')
plt.ylabel('number')
plt.title('March Purchase Table')
plt.xticks(df_user['day'] + bar_width * 3 / 2., day_range)
# plt.ylim(0, 80)
plt.tight_layout() 
plt.legend(prop={'size':9})

<matplotlib.legend.Legend at 0x175b74f50>

2.4 2016年4月中各天购买量

df_ac = get_from_action_data(fname=ACTION_201604_FILE)

# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天
df_ac['time'] = pd.to_datetime(df_ac['time']).apply(lambda x: x.day)

Iteration is stopped

df_user = df_ac.groupby('time')['user_id'].nunique()
df_user = df_user.to_frame().reset_index()
df_user.columns = ['day', 'user_num']

df_item = df_ac.groupby('time')['sku_id'].nunique()
df_item = df_item.to_frame().reset_index()
df_item.columns = ['day', 'item_num']

df_ui = df_ac.groupby('time', as_index=False).size()
df_ui = df_ui.to_frame().reset_index()
df_ui.columns = ['day', 'user_item_num']

# 条形宽度
bar_width = 0.2
# 透明度
opacity = 0.4
# 天数
day_range = range(1,len(df_user['day']) + 1, 1)
# 设置图片大小
plt.figure(figsize=(14,10))

plt.bar(df_user['day'], df_user['user_num'], bar_width, 
        alpha=opacity, color='c', label='user')
plt.bar(df_item['day']+bar_width, df_item['item_num'], 
        bar_width, alpha=opacity, color='g', label='item')
plt.bar(df_ui['day']+bar_width*2, df_ui['user_item_num'], 
        bar_width, alpha=opacity, color='m', label='user_item')

plt.xlabel('day')
plt.ylabel('number')
plt.title('April Purchase Table')
plt.xticks(df_user['day'] + bar_width * 3 / 2., day_range)
# plt.ylim(0, 80)
plt.tight_layout() 
plt.legend(prop={'size':9})

<matplotlib.legend.Legend at 0x138b4db50>

2.5 商品类别销售统计

• 周一到周日各商品类别销售情况

# 从行为记录中提取商品类别数据
def get_from_action_data(fname, chunk_size=50000):
    reader = pd.read_csv(fname, header=0, iterator=True)
    chunks = []
    loop = True
    while loop:
        try:
            chunk = reader.get_chunk(chunk_size)[
                ["cate", "brand", "type", "time"]]
            chunks.append(chunk)
        except StopIteration:
            loop = False
            print("Iteration is stopped")

    df_ac = pd.concat(chunks, ignore_index=True)
    # type=4,为购买
    df_ac = df_ac[df_ac['type'] == 4]

    return df_ac[["cate", "brand", "type", "time"]]

df_ac = []
df_ac.append(get_from_action_data(fname=ACTION_201602_FILE))
df_ac.append(get_from_action_data(fname=ACTION_201603_FILE))
df_ac.append(get_from_action_data(fname=ACTION_201604_FILE))
df_ac = pd.concat(df_ac, ignore_index=True)

Iteration is stopped
Iteration is stopped
Iteration is stopped

# 将time字段转换为datetime类型
df_ac['time'] = pd.to_datetime(df_ac['time'])

# 使用lambda匿名函数将时间time转换为星期(周一为1, 周日为７)
df_ac['time'] = df_ac['time'].apply(lambda x: x.weekday() + 1)

df_ac.head()

# 观察有几个类别商品
df_ac.groupby(df_ac['cate']).count()

# 周一到周日每天购买商品类别数量统计
df_product = df_ac['brand'].groupby([df_ac['time'],df_ac['cate']]).count()
df_product=df_product.unstack()
df_product.plot(kind='bar',title='Cate Purchase Table in a Week',figsize=(14,10))

<matplotlib.axes._subplots.AxesSubplot at 0x187094350>

• 分析：星期二买类别8的最多，星期天最少。

2.6 每月各类商品销售情况

df_ac2 = get_from_action_data(fname=ACTION_201602_FILE)

# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天
df_ac2['time'] = pd.to_datetime(df_ac2['time']).apply(lambda x: x.day)
df_ac3 = get_from_action_data(fname=ACTION_201603_FILE)

# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天
df_ac3['time'] = pd.to_datetime(df_ac3['time']).apply(lambda x: x.day)
df_ac4 = get_from_action_data(fname=ACTION_201604_FILE)

# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天
df_ac4['time'] = pd.to_datetime(df_ac4['time']).apply(lambda x: x.day)

Iteration is stopped
Iteration is stopped
Iteration is stopped

dc_cate2 = df_ac2[df_ac2['cate']==8]
dc_cate2 = dc_cate2['brand'].groupby(dc_cate2['time']).count()
dc_cate2 = dc_cate2.to_frame().reset_index()
dc_cate2.columns = ['day', 'product_num']

dc_cate3 = df_ac3[df_ac3['cate']==8]
dc_cate3 = dc_cate3['brand'].groupby(dc_cate3['time']).count()
dc_cate3 = dc_cate3.to_frame().reset_index()
dc_cate3.columns = ['day', 'product_num']

dc_cate4 = df_ac4[df_ac4['cate']==8]
dc_cate4 = dc_cate4['brand'].groupby(dc_cate4['time']).count()
dc_cate4 = dc_cate4.to_frame().reset_index()
dc_cate4.columns = ['day', 'product_num']

# 条形宽度
bar_width = 0.2
# 透明度
opacity = 0.4
# 天数
day_range = range(1,len(dc_cate3['day']) + 1, 1)
# 设置图片大小
plt.figure(figsize=(14,10))

plt.bar(dc_cate2['day'], dc_cate2['product_num'], bar_width, 
        alpha=opacity, color='c', label='February')
plt.bar(dc_cate3['day']+bar_width, dc_cate3['product_num'], 
        bar_width, alpha=opacity, color='g', label='March')
plt.bar(dc_cate4['day']+bar_width*2, dc_cate4['product_num'], 
        bar_width, alpha=opacity, color='m', label='April')

plt.xlabel('day')
plt.ylabel('number')
plt.title('Cate-8 Purchase Table')
plt.xticks(dc_cate3['day'] + bar_width * 3 / 2., day_range)
# plt.ylim(0, 80)
plt.tight_layout() 
plt.legend(prop={'size':9})

<matplotlib.legend.Legend at 0x1725cf790>

• 分析：2月份对类别8商品的购买普遍偏低，3，4月份普遍偏高，3月15日购买极其多！可以对比3月份的销售记录，发现类别8将近占了3月15日总销售的一半！同时发现，3,4月份类别8销售记录在前半个月特别相似，除了4月8号，9号和3月15号。

2.7 查看特定用户对特定商品的的轨迹

def spec_ui_action_data(fname, user_id, item_id, chunk_size=100000):
    reader = pd.read_csv(fname, header=0, iterator=True)
    chunks = []
    loop = True
    while loop:
        try:
            chunk = reader.get_chunk(chunk_size)[
                ["user_id", "sku_id", "type", "time"]]
            chunks.append(chunk)
        except StopIteration:
            loop = False
            print("Iteration is stopped")

    df_ac = pd.concat(chunks, ignore_index=True)
    df_ac = df_ac[(df_ac['user_id'] == user_id) & (df_ac['sku_id'] == item_id)]

    return df_ac

def explore_user_item_via_time():
    user_id = 266079
    item_id = 138778
    df_ac = []
    df_ac.append(spec_ui_action_data(ACTION_201602_FILE, user_id, item_id))
    df_ac.append(spec_ui_action_data(ACTION_201603_FILE, user_id, item_id))
    df_ac.append(spec_ui_action_data(ACTION_201604_FILE, user_id, item_id))
    df_ac = pd.concat(df_ac, ignore_index=False)
    print(df_ac.sort_values(by='time'))

explore_user_item_via_time()

Iteration is stopped
Iteration is stopped
Iteration is stopped
    user_id  sku_id  type                 time
0    266079  138778     1  2016-01-31 23:59:02
1    266079  138778     6  2016-01-31 23:59:03
15   266079  138778     6  2016-01-31 23:59:40