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文章目录

• 内容介绍
• 离散数据处理
• Map类别转换
• One-hot Encoding
• 数值数据基本描述
• 二值特征
• 多项式特征
• 数值区间统计归类特征
• 分位数切分
• 对数变换
• 日期相关特征
• 时间相关特征

内容介绍

本章节为 数值数据 处理总结，其中包括数值特征、Map类别转换、One-hot Encoding、数值数据基本描述、二值特征、多项式特征、数值区统计归类特征、分位数切分、对数变换、日期相关特征、时间相关特征的介绍。

文本介绍关于数据分析工作中常用的 使用Python进行数据预处理 的方法总结。通过对图片数据、数值数字、文本数据、特征提取、特征处理等方面讲解作为一名数据分析师常用的数据处理套路。

import pandas as pd
import numpy as np

离散数据处理

# 读取观察数据
vg_df = pd.read_csv('datasets/vgsales.csv', encoding = "ISO-8859-1")
vg_df[['Name', 'Platform', 'Year', 'Genre', 'Publisher']].iloc[1:7]

# 离散变量字段取唯一值
genres = np.unique(vg_df['Genre'])
genres
>>> array(['Action', 'Adventure', 'Fighting', 'Misc', 'Platform', 
           'Puzzle','Racing', 'Role-Playing', 'Shooter', 'Simulation', 
           'Sports','Strategy'], dtype=object)

# 离散变量变换转换
from sklearn.preprocessing import LabelEncoder

gle = LabelEncoder()
genre_labels = gle.fit_transform(vg_df['Genre'])
genre_labels
>>> array([10,  4,  6, ...,  6,  5,  4])


# 类别变量字典映射
genre_mappings = {index: label for index, label in enumerate(gle.classes_)}
genre_mappings
>>> {0: 'Action',
     1: 'Adventure',
     2: 'Fighting',
     3: 'Misc',
     4: 'Platform',
     5: 'Puzzle',
     6: 'Racing',
     7: 'Role-Playing',
     8: 'Shooter',
     9: 'Simulation',
     10: 'Sports',
     11: 'Strategy'}

# 数据DF 切片操作
vg_df['GenreLabel'] = genre_labels
vg_df[['Name', 'Platform', 'Year', 'Genre', 'GenreLabel']].iloc[1:7]

Map类别转换

poke_df = pd.read_csv('datasets/Pokemon.csv', encoding='utf-8')
poke_df = poke_df.sample(random_state=1, frac=1).reset_index(drop=True)

np.unique(poke_df['Generation'])
>>> array(['Gen 1', 'Gen 2', 'Gen 3', 'Gen 4', 'Gen 5', 'Gen 6'], dtype=object)

# 构建MAP转换字典 
gen_ord_map = {'Gen 1': 1, 'Gen 2': 2, 'Gen 3': 3, 
               'Gen 4': 4, 'Gen 5': 5, 'Gen 6': 6}
# 哑变量转换
poke_df['GenerationLabel'] = poke_df['Generation'].map(gen_ord_map)
poke_df[['Name', 'Generation', 'GenerationLabel']].iloc[4:10]

One-hot Encoding

# 提取需要转换的数据
poke_df[['Name', 'Generation', 'Legendary']].iloc[4:10]

# 使用MAP将类别变量转换成数值

from sklearn.preprocessing import OneHotEncoder, LabelEncoder

gen_le = LabelEncoder()
gen_labels = gen_le.fit_transform(poke_df['Generation'])
poke_df['Gen_Label'] = gen_labels

leg_le = LabelEncoder()
leg_labels = leg_le.fit_transform(poke_df['Legendary'])
poke_df['Lgnd_Label'] = leg_labels

poke_df_sub = poke_df[['Name', 'Generation', 'Gen_Label', 'Legendary', 'Lgnd_Label']]
poke_df_sub.iloc[4:10]

# 在原有DF中创建 One-hot Encoding 字段
gen_ohe = OneHotEncoder()
gen_feature_arr = gen_ohe.fit_transform(poke_df[['Gen_Label']]).toarray()
gen_feature_labels = list(gen_le.classes_)
print (gen_feature_labels)
gen_features = pd.DataFrame(gen_feature_arr, columns=gen_feature_labels)
>>> ['Gen 1', 'Gen 2', 'Gen 3', 'Gen 4', 'Gen 5', 'Gen 6']


leg_ohe = OneHotEncoder()
leg_feature_arr = leg_ohe.fit_transform(poke_df[['Lgnd_Label']]).toarray()
leg_feature_labels = ['Legendary_'+str(cls_label) for cls_label in leg_le.classes_]
print (leg_feature_labels)
leg_features = pd.DataFrame(leg_feature_arr, columns=leg_feature_labels)
>>> ['Legendary_False', 'Legendary_True']

# 进行转换
poke_df_ohe = pd.concat([poke_df_sub, gen_features, leg_features], axis=1)
columns = sum([['Name', 'Generation', 'Gen_Label'],gen_feature_labels,
              ['Legendary', 'Lgnd_Label'],leg_feature_labels], [])
poke_df_ohe[columns].iloc[4:10]

数值数据基本描述

poke_df = pd.read_csv('datasets/Pokemon.csv', encoding='utf-8')
poke_df.head()

poke_df[['HP', 'Attack', 'Defense']].head()

poke_df[['HP', 'Attack', 'Defense']].describe()

二值特征

watched = np.array(popsong_df['listen_count']) 
watched[watched >= 1] = 1
popsong_df['watched'] = watched
popsong_df.head(10)

# 基于阈值判断转换类别
from sklearn.preprocessing import Binarizer

bn = Binarizer(threshold=0.9)
pd_watched = bn.transform([popsong_df['listen_count']])[0]
popsong_df['pd_watched'] = pd_watched
popsong_df.head(11)

多项式特征

atk_def = poke_df[['Attack', 'Defense']]
atk_def.head()

# 2 次多项式的次数为 [1,a,b,a方,ab,b方]
from sklearn.preprocessing import PolynomialFeatures

pf = PolynomialFeatures(degree=2, interaction_only=False, include_bias=False)
res = pf.fit_transform(atk_def)
res

intr_features = pd.DataFrame(res, columns=['Attack', 'Defense', 'Attack^2', 'Attack x Defense', 'Defense^2'])
intr_features.head(5)

数值区间统计归类特征

fcc_survey_df = pd.read_csv('datasets/fcc_2016_coder_survey_subset.csv', encoding='utf-8')
fcc_survey_df[['ID.x', 'EmploymentField', 'Age', 'Income']].head()

# 构建年龄直方图
fig, ax = plt.subplots()
fcc_survey_df['Age'].hist(color='#A9C5D3')
ax.set_title('Developer Age Histogram', fontsize=12)
ax.set_xlabel('Age', fontsize=12)
ax.set_ylabel('Frequency', fontsize=12)

# 以年龄除以10为阶段进行划分
fcc_survey_df['Age_bin_round'] = np.array(np.floor(np.array(fcc_survey_df['Age']) / 10.))
fcc_survey_df[['ID.x', 'Age', 'Age_bin_round']].iloc[1071:1076]

分位数切分

fcc_survey_df[['ID.x', 'Age', 'Income']].iloc[4:9]

# 构建直方图
fig, ax = plt.subplots()
fcc_survey_df['Income'].hist(bins=30, color='#A9C5D3')
ax.set_title('Developer Income Histogram', fontsize=12)
ax.set_xlabel('Developer Income', fontsize=12)
ax.set_ylabel('Frequency', fontsize=12)

# 四分位区分
quantile_list = [0, .25, .5, .75, 1.]
quantiles = fcc_survey_df['Income'].quantile(quantile_list)
quantiles

# 四分卫可视化
fig, ax = plt.subplots()
fcc_survey_df['Income'].hist(bins=30, color='#A9C5D3')

for quantile in quantiles:
    qvl = plt.axvline(quantile, color='r')
ax.legend([qvl], ['Quantiles'], fontsize=10)

ax.set_title('Developer Income Histogram with Quantiles', fontsize=12)
ax.set_xlabel('Developer Income', fontsize=12)
ax.set_ylabel('Frequency', fontsize=12)

# 基于分位数的数据描述，添加对应的标签
quantile_labels = ['0-25Q', '25-50Q', '50-75Q', '75-100Q']
fcc_survey_df['Income_quantile_range'] = pd.qcut(fcc_survey_df['Income'], q=quantile_list)
fcc_survey_df['Income_quantile_label'] = pd.qcut(fcc_survey_df['Income'], q=quantile_list, labels=quantile_labels)
fcc_survey_df[['ID.x', 'Age', 'Income', 'Income_quantile_range', 'Income_quantile_label']].iloc[4:9]

对数变换

fcc_survey_df['Income_log'] = np.log((1+ fcc_survey_df['Income']))
fcc_survey_df[['ID.x', 'Age', 'Income', 'Income_log']].iloc[4:9]

# 数值数据取LOG后 可视化直方图
income_log_mean = np.round(np.mean(fcc_survey_df['Income_log']), 2)

fig, ax = plt.subplots()
fcc_survey_df['Income_log'].hist(bins=30, color='#A9C5D3')
plt.axvline(income_log_mean, color='r')
ax.set_title('Developer Income Histogram after Log Transform', fontsize=12)
ax.set_xlabel('Developer Income (log scale)', fontsize=12)
ax.set_ylabel('Frequency', fontsize=12)
ax.text(11.5, 450, r'$\mu$='+str(income_log_mean), fontsize=10)

日期相关特征

import datetime
import numpy as np
import pandas as pd
from dateutil.parser import parse
import pytz

time_stamps = ['2015-03-08 10:30:00.360000+00:00', '2017-07-13 15:45:05.755000-07:00',
               '2012-01-20 22:30:00.254000+05:30', '2016-12-25 00:30:00.000000+10:00']
df = pd.DataFrame(time_stamps, columns=['Time'])
df

# 转换日期类型
ts_objs = np.array([pd.Timestamp(item) for item in np.array(df.Time)])
df['TS_obj'] = ts_objs
ts_objs

# 提取日期中的字段信息构建新的日期分类字段
df['Year'] = df['TS_obj'].apply(lambda d: d.year)
df['Month'] = df['TS_obj'].apply(lambda d: d.month)
df['Day'] = df['TS_obj'].apply(lambda d: d.day)
df['DayOfWeek'] = df['TS_obj'].apply(lambda d: d.dayofweek)
df['DayName'] = df['TS_obj'].apply(lambda d: d.weekday_name)
df['DayOfYear'] = df['TS_obj'].apply(lambda d: d.dayofyear)
df['WeekOfYear'] = df['TS_obj'].apply(lambda d: d.weekofyear)
df['Quarter'] = df['TS_obj'].apply(lambda d: d.quarter)

df[['Time', 'Year', 'Month', 'Day', 'Quarter', 
    'DayOfWeek', 'DayName', 'DayOfYear', 'WeekOfYear']]

时间相关特征

df['Hour'] = df['TS_obj'].apply(lambda d: d.hour)
df['Minute'] = df['TS_obj'].apply(lambda d: d.minute)
df['Second'] = df['TS_obj'].apply(lambda d: d.second)
df['MUsecond'] = df['TS_obj'].apply(lambda d: d.microsecond)   #毫秒
df['UTC_offset'] = df['TS_obj'].apply(lambda d: d.utcoffset()) #UTC时间位移

df[['Time', 'Hour', 'Minute', 'Second', 'MUsecond', 'UTC_offset']]

# 按照早晚切分时间 
hour_bins = [-1, 5, 11, 16, 21, 23]
bin_names = ['Late Night', 'Morning', 'Afternoon', 'Evening', 'Night']
df['TimeOfDayBin'] = pd.cut(df['Hour'], 
                            bins=hour_bins, labels=bin_names)
df[['Time', 'Hour', 'TimeOfDayBin']]

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