「Python 数据处理基础」数据预处理连续变量10种常用方法

青旅半醒 2022-10-08 02:24 406阅读 0赞

### 文章目录 ###

*  内容介绍
 *  离散数据处理

# 内容介绍 #

在日常的数据分析工作中，经常需要把数据变量转换成模型需要的样子，比如我们经常遇见的数据都是标签化、文字化等内容，需要将这些数据转换成计算机看得懂的内容，就是需要进行标签变量的转换。

不管是离散数据还是连续数据，在某些特定的环境下可能都需要进行类别的转换处理。

通过简单的数值特征、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、时间相关特征操作。

# 离散数据处理 #

import pandas as pd
    import numpy as np
    
    # 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]

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# 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]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 1]

**1.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]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 2]  
**2.One-hot Encoding**

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

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 3]

# 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]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 4]

# 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]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 5]

**3.数值数据基本描述**

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

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 6]

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

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 7]

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

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 8]  
**4.二值特征**

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

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 9]

# 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)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 10]  
**5.多项式特征**

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

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 11]

# 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

![在这里插入图片描述][20210619180318974.png]

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

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 12]  
**6.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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 13]

# 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)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 14]  
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# 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]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 16]  
**7.分位数切分**

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

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 17]

# 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)

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

![在这里插入图片描述][20210619181417235.png]

# 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)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 19]

# 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]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 20]  
**8.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]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 21]

# 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)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIwMjg4MzI3_size_16_color_FFFFFF_t_70 22]  
**9.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

![在这里插入图片描述][20210619181846937.png]

# 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

![在这里插入图片描述][20210619181904788.png]

# 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']]

![在这里插入图片描述][20210619181939439.png]

**10.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']]

![在这里插入图片描述][20210619182042192.png]

# 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']]

![在这里插入图片描述][20210619182059303.png]

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