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<>常用库导入
import pandas as pd import numpy as np import matplotlib.pyplot as plt import
seabornas sns import warnings warnings.filterwarnings("ignore") pd.options.
display.max_columns = None #显示所有列 pd.set_option('display.float_format', lambda x
: '%.2f' % x) #取消科学计数法
<>1、数据整合（concat）
train_data = pd.read_csv('training30.csv') test_data = pd.read_csv('test30.csv'
) total_data = pd.concat([train_data, test_data], axis=0) total_data.info()
<>2、数据筛选
cdma = pd.read_csv('cdma.xls', encoding='gbk', sep='\t') print(cdma.shape) cdma
= cdma[(cdma['销售区局'] == '浦东电信局') & (cdma['渠道管理细分'].isin(['专营渠道', '中小渠道', '开放渠道']
))] print(cdma.shape) # cdma = cdma[~cdma['发展部门名称'].str.contains('千秋')] #
print(cdma.shape)
<>3、数据匹配（merge）
match_table = pd.read_excel('数据说明与匹配公式.xlsx', sheet_name='部门匹配表') new_cdma =
cdma.merge(match_table, how='left', on=['发展部门名称', '渠道管理细分']) new_cdma = new_cdma
[new_cdma['渠道管理细分'] == '专营渠道'] new_cdma[['统计日期', '订单号', '所属部门', '所属代理商', '所属分局',
'渠道经理']].head()
<>4、数据透视表（pivot_table）
cdma_pivot = new_cdma.pivot_table(index='所属代理商', values='订单号', columns='所属分局',
aggfunc='count', fill_value=0, margins=True, margins_name='合计') cdma_pivot
<>5、数据排序（sort_values）
cdma_pivot.sort_values(by='合计',inplace=True, ascending=False) cdma_pivot
<>6、数据替换（replace）
train_data = train_data.replace('?', np.nan) #精准匹配 train_data.head(10)
train_data2= train_data.replace('Tai', 'Cy', regex=True) #模糊匹配 train_data2.head(
10)
<>7、数据删除（dropna）
print(train_data.shape) train_data3 = train_data.dropna(subset=['gender', 'age'
]) print(train_data3.shape)
<>8、降采样
def lower_sample_data(df, labelname, percent=1): '''
percent:多数类别下采样的数量相对于少数类别样本数量的比例 ''' data1 = df[df[labelname] == 1] #
将少数类别的样本放在data1 data0 = df[df[labelname] == 0] # 将多数类别的样本放在data0 index = np.
random.randint( len(data0), size=percent * (len(data1))) # 随机给定下采样取出样本的序号
lower_data0= data0.iloc[list(index)] # 下采样 return(pd.concat([lower_data0, data1]
)) print(train_data["'Purchase or not'"].value_counts()) train_data4 =
lower_sample_data(train_data, "'Purchase or not'", percent=1) print(train_data4[
"'Purchase or not'"].value_counts())
<>9、缺失值处理（fillna）
train_data5 = pd.read_csv('cs-training.csv') per_columns = set(train_data5.
columns) - set(['CustomerID', 'SeriousDlqin2yrs']) for column in per_columns:
temp_mean= train_data5[column].mean() #如果是中位数的话是median，众数的话是mode train_data5[
column] = train_data5[column].fillna(temp_mean) train_data5.describe()
<>10、噪声处理
<>方法一：四分位法
def cap(x, quantile=[0.05, 0.95]): """盖帽法处理异常值 Args： x：pd.Series列，连续变量
quantile：指定盖帽法的上下分位数范围 """ # 生成分位数 Q05, Q95=x.quantile(quantile).values.tolist()
# 替换异常值为指定的分位数 if Q05 > x.min(): x = x.copy() x.loc[x<Q05] = Q05 if Q95 < x.max(
): x = x.copy() x.loc[x>Q95] = Q95 return(x) train_data6 = train_data5[
per_columns] train_data6 = train_data6.apply(cap) train_data7 = pd.concat([
train_data5[['CustomerID', 'SeriousDlqin2yrs']], train_data6], axis=1)
train_data7= train_data7[train_data5.columns] train_data7.describe()
<>方法二：平均值法
def cap_mean(x): """盖帽法处理异常值 Args： x：pd.Series列，连续变量 """ # 生成平均值和标准差的上下界限 x_up
= x.mean() + 3*x.std() x_down = x.mean() - 3*x.std() # 替换异常值 if x_down > x.min()
: x = x.copy() x.loc[x<x_down] = x_down if x_up < x.max(): x = x.copy() x.loc[x>
x_up] = x_up return(x) train_data8 = train_data5[per_columns] train_data8 =
train_data8.apply(cap_mean) train_data9 = pd.concat([train_data5[['CustomerID',
'SeriousDlqin2yrs']], train_data8], axis=1) train_data9 = train_data9[
train_data5.columns] train_data9.describe()
<>11、数据正规化/标准化
from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing
import StandardScaler mm_scaler = MinMaxScaler() ss_scaler = StandardScaler()
print(train_data9['age'].head()) train_data9['age'] = mm_scaler.fit_transform(
train_data9[['age']]) print(train_data9['age'].head()) print(
'-------------------------------------------------') print(train_data9[
'MonthlyIncome'].head()) train_data9['MonthlyIncome'] = ss_scaler.fit_transform(
train_data9[['MonthlyIncome']]) print(train_data9['MonthlyIncome'].head())
<>12、数据泛化（map）
print(cdma['发展渠道小类'].value_counts()) qd_map = {'自营营业厅': '自营渠道', '专营店': '专营渠道',
'合作营业厅': '专营渠道', '核心渠道专区专柜':'专营渠道', '天翼小店':'中小渠道', '外包营业厅':'专营渠道', '全国连锁卖场':
'开放渠道', '全网通（专营）':'专营渠道', '商圈店':'专营渠道', '天翼合作店':'中小渠道', '终端零售店（开放）':'中小渠道'}
cdma_2= cdma.copy() cdma_2['渠道统计归类'] = cdma_2['发展渠道小类'].map(qd_map) print(cdma_2
['渠道统计归类'].value_counts())
<>13、连续性指派（LabelEncoder）
from sklearn.preprocessing import LabelEncoder le = LabelEncoder() cdma_2[
'渠道统计归类'] = le.fit_transform(cdma_2[['渠道统计归类']]) cdma_2['渠道统计归类'].value_counts()
<>14、数据离散化（cut/qcut）
<>方法一：人工分离法
age_range = list(range(0,111,10)) train_data5['age_cut1'] = pd.cut(train_data5[
'age'], age_range, include_lowest=True, right=False) train_data5['age_cut1'].
value_counts().sort_index()
<>方法二：等宽装箱法
train_data5['age_cut2'] = pd.cut(train_data5['age'], bins=10, include_lowest=
True, right=False, precision=0) train_data5['age_cut2'].value_counts().
sort_index()
<>方法三：等深装箱法
train_data5['age_cut3'] = pd.qcut(train_data5['age'], 10, precision=1)
train_data5['age_cut3'].value_counts().sort_index()