python实现RFM建模（三）

def functions(x):
    if x.iloc[0]==1 and x.iloc[1]==1 and x.iloc[2]==1:
        return "重要价值客户"
    elif x.iloc[0]==1 and x.iloc[1]==1 and x.iloc[2]==0:
        return "潜力客户"
    elif x.iloc[0]==1 and x.iloc[1]==0 and x.iloc[2]==1:
        return "重要深耕客户"
    elif x.iloc[0]==1 and x.iloc[1]==0 and x.iloc[2]==0:
        return "新客户"
    elif x.iloc[0]==0 and x.iloc[1]==1 and x.iloc[2]==1:
        return "重要唤回客户"
    elif x.iloc[0]==0 and x.iloc[1]==1 and x.iloc[2]==0:
        return "一般客户"
    elif x.iloc[0]==0 and x.iloc[1]==0 and x.iloc[2]==1:
        return "重要挽回客户"
    elif x.iloc[0]==0 and x.iloc[1]==0 and x.iloc[2]==0:
        return "流失客户"
df2["标签"] = df2[["R-SCORE是否大于均值","F-SCORE是否大于均值","M-SCORE是否大于均值"]].apply(functions,axis=1)
df2.sample(10)

df3 = df2.groupby("标签").agg({"标签":"count"})
df3["不同客户的占比"] = df3["标签"].apply(lambda x:x/np.sum(df3["标签"]))
df3 = df3.sort_values(by="标签",ascending=True)
plt.figure(figsize=(6,4),dpi=100)
x = df3.index
y = df3["标签"]
plt.barh(x,height=0.5,width=y,align="center")
plt.title("不同类型客户的人数对比")
for x,y in enumerate(y):
    plt.text(y+450,x,y,ha="center",va="center",fontsize=14)
plt.xticks(np.arange(0,10001,2000))
plt.tight_layout()
plt.savefig("不同类型客户的人数对比",dpi=300)

df3 = df2.groupby("标签").agg({"标签":"count"})
df3["不同客户的占比"] = df3["标签"].apply(lambda x:x/np.sum(df3["标签"]))
df3 = df3.sort_values(by="标签",ascending=True)
plt.figure(figsize=(7,4),dpi=100)
x = df3["不同客户的占比"]
labels = ['潜力客户', '一般客户', '重要价值客户', '重要唤回客户', '重要深耕客户', '新客户', '重要挽回客户', '流失客户']
colors = ['#9999ff','#ff9999','#7777aa','#2442aa','#dd5555','deeppink','yellowgreen','lightskyblue']
explode = [0,0,0,0,0,0,0,0]
patches,l_text = plt.pie(x,labels=labels,colors=colors,
        explode=explode,startangle=90,counterclock=False)
for t in l_text:
    t.set_size(0)
plt.axis("equal")
plt.legend(loc=(0.001,0.001),frameon=False)
plt.title("不同类型客户人数占比图")
plt.savefig("不同类型客户人数占比图",dpi=300)