下载软件的app,seo网络推广优化教程,wordpress信息发布系统,宁波seo关键词优化制作文章目录 1.数据加载2.查看数据情况3.数据合并及填充4.查看特征字段之间相关性5.聚合操作6.时间维度上看销售额7.计算用户RFM8.数据保存存储(1).to_csv(1).to_pickle 1.数据加载
import pandas as pd
dataset pd.read_csv(SupplyChain.csv, encodingunicode_escape)
dataset2… 文章目录 1.数据加载2.查看数据情况3.数据合并及填充4.查看特征字段之间相关性5.聚合操作6.时间维度上看销售额7.计算用户RFM8.数据保存存储(1).to_csv(1).to_pickle 1.数据加载
import pandas as pd
dataset pd.read_csv(SupplyChain.csv, encodingunicode_escape)
dataset2.查看数据情况
print(dataset.shape)
print(dataset.isnull().sum())3.数据合并及填充
print(dataset[[Customer Fname, Customer Lname]])
# fistname与lastname进行合并
dataset[Customer Full Name] dataset[Customer Fname] dataset[Customer Lname]
#dataset.head()
dataset[Customer Zipcode].value_counts()
# 查看缺失值发现有3个缺失值
print(dataset[Customer Zipcode].isnull().sum())dataset[Customer Zipcode] dataset[Customer Zipcode].fillna(0)
dataset.head()4.查看特征字段之间相关性
import matplotlib.pyplot as plt
import seaborn as sns
# 特征字段之间相关性 热力图
data dataset
plt.figure(figsize(20,10))
# annotTrue 显示具体数字
sns.heatmap(data.corr(), annotTrue, cmapcoolwarm)
# 结论可以观察到Product Price和SalesOrder Item Total有很高的相关性5.聚合操作
# 基于Market进行聚合
market data.groupby(Market)
# 基于Region进行聚合
region data.groupby(Order Region)
plt.figure(1)
market[Sales per customer].sum().sort_values(ascendingFalse).plot.bar(figsize(12,6), titleSales in different markets)
plt.figure(2)
region[Sales per customer].sum().sort_values(ascendingFalse).plot.bar(figsize(12,6), titleSales in different regions)
plt.show()# 基于Category Name进行聚类
cat data.groupby(Category Name)
plt.figure(1)
# 不同类别的 总销售额
cat[Sales per customer].sum().sort_values(ascendingFalse).plot.bar(figsize(12,6), titleTotal sales)
plt.figure(2)
# 不同类别的 平均销售额
cat[Sales per customer].mean().sort_values(ascendingFalse).plot.bar(figsize(12,6), titleTotal sales)
plt.show()6.时间维度上看销售额
#data[order date (DateOrders)]
# 创建时间戳索引
temp pd.DatetimeIndex(data[order date (DateOrders)])
temp# 取order date (DateOrders)字段中的year, month, weekday, hour, month_year
data[order_year] temp.year
data[order_month] temp.month
data[order_week_day] temp.weekday
data[order_hour] temp.hour
data[order_month_year] temp.to_period(M)
data.head()# 对销售额进行探索按照不同时间维度 年星期小时月
plt.figure(figsize(10, 12))
plt.subplot(4, 2, 1)
df_year data.groupby(order_year)
df_year[Sales].mean().plot(figsize(12, 12), titleAverage sales in years)
plt.subplot(4, 2, 2)
df_day data.groupby(order_week_day)
df_day[Sales].mean().plot(figsize(12, 12), titleAverage sales in days per week)
plt.subplot(4, 2, 3)
df_hour data.groupby(order_hour)
df_hour[Sales].mean().plot(figsize(12, 12), titleAverage sales in hours per day)
plt.subplot(4, 2, 4)
df_month data.groupby(order_month)
df_month[Sales].mean().plot(figsize(12, 12), titleAverage sales in month per year)
plt.tight_layout()
plt.show()# 探索商品价格与 销售额之间的关系
data.plot(xProduct Price, ySales per customer)
plt.title(Relationship between Product Price and Sales per customer)
plt.xlabel(Product Price)
plt.ylabel(Sales per customer)
plt.show()7.计算用户RFM
# # 用户分层 RFM
data[TotalPrice] data[Order Item Quantity] * data[Order Item Total]
data[[TotalPrice, Order Item Quantity, Order Item Total]]# 时间类型转换
data[order date (DateOrders)] pd.to_datetime(data[order date (DateOrders)])
# 统计最后一笔订单的时间
data[order date (DateOrders)].max()# 假设我们现在是2018-2-1
import datetime
present datetime.datetime(2018,2,1)
# 计算每个用户的RFM指标
# 按照Order Customer Id进行聚合
customer_seg data.groupby(Order Customer Id).agg({order date (DateOrders): lambda x: (present-x.max()).days, Order Id: lambda x:len(x), TotalPrice: lambda x: x.sum()})
customer_seg# 将字段名称改成 RFM
customer_seg.rename(columns{order date (DateOrders): R_Value, Order Id: F_Value, TotalPrice: M_Value}, inplaceTrue)
customer_seg.head()# 将RFM数据划分为4个尺度
quantiles customer_seg.quantile(q[0.25, 0.5, 0.75])
quantiles quantiles.to_dict()
quantiles# R_Value越小越好 R_Score就越大
def R_Score(a, b, c):if a c[b][0.25]:return 4elif a c[b][0.50]:return 3elif a c[b][0.75]:return 2else:return 1# F_Value, M_Value越大越好
def FM_Score(a, b, c):if a c[b][0.25]:return 1elif a c[b][0.50]:return 2elif a c[b][0.75]:return 3else:return 4# 新建R_Score字段用于将R_Value [1,4]
customer_seg[R_Score] customer_seg[R_Value].apply(R_Score, args(R_Value, quantiles))
# 新建F_Score字段用于将F_Value [1,4]
customer_seg[F_Score] customer_seg[F_Value].apply(FM_Score, args(F_Value, quantiles))
# 新建M_Score字段用于将R_Value [1,4]
customer_seg[M_Score] customer_seg[M_Value].apply(FM_Score, args(M_Value, quantiles))
customer_seg.head()# 计算RFM用户分层
def RFM_User(df):if df[M_Score] 2 and df[F_Score] 2 and df[R_Score] 2:return 重要价值用户if df[M_Score] 2 and df[F_Score] 2 and df[R_Score] 2:return 重要发展用户if df[M_Score] 2 and df[F_Score] 2 and df[R_Score] 2:return 重要保持用户if df[M_Score] 2 and df[F_Score] 2 and df[R_Score] 2:return 重要挽留用户if df[M_Score] 2 and df[F_Score] 2 and df[R_Score] 2:return 一般价值用户if df[M_Score] 2 and df[F_Score] 2 and df[R_Score] 2:return 一般发展用户if df[M_Score] 2 and df[F_Score] 2 and df[R_Score] 2:return 一般保持用户if df[M_Score] 2 and df[F_Score] 2 and df[R_Score] 2:return 一般挽留用户
customer_seg[Customer_Segmentation] customer_seg.apply(RFM_User, axis1)
customer_seg8.数据保存存储
(1).to_csv
customer_seg.to_csv(supply_chain_rfm_result.csv, indexFalse)(1).to_pickle
# 数据预处理后将处理后的数据进行保存
data.to_pickle(data.pkl)参考资料开课吧
