python_等频分箱_等距分箱
data_temp = data
# 分箱：等距等频分箱
# 等距分箱
#bins=10 分箱数
data_temp['deposit_cur_balance_bins'] = binning(df = data_temp,col = 'deposit_cur_balance',method='frequency',bins=10)
data_temp['deposit_year_total_balance_bins'] = binning(df = data_temp,col = 'deposit_year_total_balance',method='frequency',bins=10)
def binning(df,col,method,bins):
# 需要判断类型
uniqs=df[col].nunique()
if uniqs<=bins:
raise KeyError('nunique is smaller than bins: '+col)
# print('nunique is smaller than bins: '+col)
return
# 左开右闭
def ff(x,fre_list):
if x<=fre_list[0]:
return 0
elif x>fre_list[-1]:
return len(fre_list)-1
else :
for i in range(len(fre_list)-1):
if x>fre_list[i] and x<=fre_list[i+1]:
return i
# 等距分箱
if method=='distance':
umax=np.percentile(df[col],99.99)
umin=np.percentile(df[col],0.01)
step=(umax-umin)/bins
fre_list=[umin+i*step for i in range(bins+1)]
return df[col].map(lambda x:ff(x,fre_list))
#等频分箱
elif method=='frequency' :
fre_list=[np.percentile(df[col],100/bins*i) for i in range(bins+1)]
fre_list=sorted(list(set(fre_list)))
return df[col].map(lambda x:ff(x,fre_list))
