DataFrame和Series练习

短命女 2022-04-25 04:24 268阅读 0赞

主要练习DataFrame的

import pandas as pd
    zhou=[[1,2,3,4],[5,6,7,8]]
    df=pd.DataFrame(zhou,columns=['x1','x2','x3','x4'])
    df1=df.copy()                 #拷贝一个DataFrame的副本
    
    import os
    import datetime
    import numpy as np
    from scipy import sparse
    from scipy.stats import mstats
    import pandas as pd
    from pandas.api.types import is_numeric_dtype
    import re
    import pickle
    import shutil
    
    import xgboost as xgb
    import lightgbm as lgb
    from sklearn.model_selection import KFold
    
    s=pd.Series(['-10.0121','+11.222','9.087'])
    s = s.str.extract('([-+]?\d*\.\d+|\d+)', expand=False).astype(float).round(2)
        # deal with outliers
    “”“
    0   -10.01
    1    11.22
    2     9.09
    dtype: float64
    ”“”
    s = pd.Series(mstats.winsorize(s, limits=[0.01, 0.01]))    
        
        
    tmp_s=pd.DataFrame([['row1','y1'],['row2','y1'],['row3','y2'],['row4','y1'],['row5','y1'],['row6','y2'],['row7','y1'],['row8','y2'],['row9','y3'],['row10','y2']],columns=['feature','label'])
    tmp_counts = tmp_s['label'].value_counts() #对训练数据的标签进行计数
    “”“
    Out[81]: 
    y1    5
    y2    4
    y3    1
    Name: label, dtype: int64
    ”“”
    num_top=2
    tmp_counts_top = tmp_counts[:num_top]  #筛选(过滤):选出Series中最高的几个
    tmp_num_unique = np.sum(tmp_counts>4)  #筛选出次数异常的(大于某个阈值)
    tmp_counts_num = pd.to_numeric(tmp_counts_top.index.to_series(), errors='coerce')
    tmp_counts_num_extract = tmp_counts_top.index.to_series().str.extract('([-+]?\d*\.\d+|\d+)', expand=False).astype(float).round(2)    #链式法则调用
    #y1    1.0
    #y2    2.0
    
    import numpy as np    
    df=pd.DataFrame([[1,2,3,4,5],[6,7,8,9,10],[11,12,np.nan,14,15]],columns=['0120', '0125', '0981', '0984', '0983'])
    np.sum(df[['0120', '0125', '0981', '0984', '0983']].notnull(), axis=1) #统计每一行的空值数目
    #0    5
    #1    5
    #2    4
    np.sum(df['0981'].notnull()) #统计列的空值数目
    df['0981'].name              #取出某列(Series)的名字
    
    
    keywords_ch = ['糖尿病', '高血压', '血脂', '治疗中', '肥胖', '血糖', '血压高', '血脂偏高', '血压高偏高', '冠心病',
                      '脂肪肝', '不齐', '过缓', '血管弹性', '脂',
                      '硬化', '舒张期杂音', '收缩期杂音', '低盐', '低脂']
    keywords_en = ['disease_'+str(i) for i in range(len(keywords_ch))]
    
    
    #1.apply()
    #2.applymap()
    #3.map()
    
    #1.apply()
    frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])
    f = lambda x: x.max() - x.min()
    frame.apply(f,axis=1) #让方程作用在一维的向量(行向量axis=0,or列向量axis=1)上
    
    #2.applymap()
    format = lambda x: '%.2f' % x
    frame.applymap(format)  #让方程作用于DataFrame中的每一个元素
    
    #3.map()
    frame['e'].map(format)  #将函数作用于一个Series的每一个元素
    """
    df_to_merge['if_subhealth'] = (df['2302'] == '亚健康').astype(int)  #直接将bool值转为类别值(强制类型转换)
    df_to_merge['if_ill'] = (df['2302'] == '疾病').astype(int)
    df_to_merge['if_health'] = (~(df_to_merge['if_subhealth']&df_to_merge['if_ill'])).astype(int)  #只有三类,第三类=总体-一二类即可。
    
    df_to_merge['if_jianman'] = df['1402'].apply(lambda x: '减慢' in str(x)).astype(int)  #对DataFrame的一列做转换:bool->数值。
    df_to_merge['if_zengkuai'] = df['1402'].apply(lambda x: '增快' in str(x)).astype(int)
    df_to_merge['if_jiangdi'] = df['1402'].apply(lambda x: '降低' in str(x)).astype(int)
    """
    data11=[['x'+str(i) for i in range(1,41)],
            ['检出糖尿病II级','正常','正常','未检查','健康','无','未见','冠心病1',
             '高血压', '血脂', '脂肪肝阳性', '慢性胃炎阳性', '阑尾炎阳性', '甲肝', '肾结石',
             '胆囊切除', '甲肝', '冠心病', '胆结石', '甲状腺', '脑梗塞', '胆囊炎', '脑溢血', 
             '早搏', '杂音', '心动过缓', '心律不齐', '心动过速','正常','正常','未检查','健康','无','未见',
             '正常','正常','未检查','健康','无','未见'],
            ['检出糖尿病II级','正常','正常','未检查','健康','无','未见','冠心病1',
             '高血压', '血脂', '正常','未检查','健康','无','未见',
             '胆囊切除', '甲肝', '冠心病阳性', '胆结石阳性', '甲状腺阳性', '脑梗塞阳性', '胆囊炎', '脑溢血', 
             '早搏', '杂音', '查见心动过缓', '查见心律不齐', '心动过速','正常','正常','未检查','健康','无','未见',
             '正常','正常','未检查','健康','无','未见']
    ]
    
    s=pd.DataFrame(data11).T
    s.columns=['vid','0434', '0409']
    keywords_ch = ['糖尿病', '高血压', '血脂', '脂肪肝', '慢性胃炎', '阑尾炎', '甲肝', '肾结石',
                       '胆囊切除', '甲肝', '冠心病', '胆结石', '甲状腺', '脑梗塞', '胆囊炎', '脑溢血', 
                      '早搏', '杂音', '心动过缓', '心律不齐', '心动过速']
    len(keywords_ch)
    keywords_en = ['disease_'+str(i) for i in range(len(keywords_ch))]
    dict_out = {}
    #将原始文本中的两列转为独热编码的len(keywords_ch)列。
    for i in s.columns:
        sname=i
        if sname in ['0434', '0409']:
            for i, kw in enumerate(keywords_en):
                s_out = pd.Series([np.nan]*len(s))
                s_out[s[sname].str.contains('{}|阳性|查见|检到|检出'.format(keywords_ch[i]), na=False)] = 1
                s_out[s[sname].str.contains('无|未查见|健康|未见', na=False)] = 0
                if np.sum(s_out) > 2: #太少了就忽略不计数了。
                    #dict_out[sname+'_'+kw] = s_out
                    dict_out[kw] = s_out
    df_to_concat = pd.DataFrame(dict_out)
    
    X_test_df=pd.DataFrame([[1,2,3,4],[5,6,7,8]],columns=['vid','x1','x2','x3'])
    X_test_df.drop('vid',axis=1)  #删除特征矩阵部分的vid那一列,这是最简单的方法。亦可以直接筛选想要的所有特征列名作为list传入DataFrame中。
    # x1  x2  x3
    #0   2   3   4
    #1   6   7   8
    
    X_test_df.drop('vid',axis=1).values
    #array([[2, 3, 4],
    #       [6, 7, 8]], dtype=int64)
    
    def log1p_mse(preds, train_data):
        labels = train_data.get_label()
        result = np.mean((np.log1p(preds) - np.log1p(labels))**2)
        return 'error', result, False
    
    
    # 生成四类特征：1:纯文本2:纯数值;3:文本+数值;4:categorical
    X_processed = pd.DataFrame([['A0001',1,2,3],['A0002',4,5,6],['B0003',7,8,9],['B0004',11,12,13]],columns=['vid','t1','t2','t3'])
    # 临时加一波特征
    X_to_merge1 = pd.DataFrame([['A0001',101,102,102],['A0002',104,105,106],['B0003',107,108,109],['B0004',111,112,113]],columns=['vid','f1','f2','f3'])
    X_processed = X_processed.merge(X_to_merge1, how='left', on='vid')
    # 再加一波特征
    X_to_merge2 = pd.DataFrame([['A0001',1,1,0],['A0002',0,0,1],['B0003',1,0,0],['B0004',0,1,0]],columns=['vid','s1','s2','s3'])
    X_processed = X_processed.merge(X_to_merge2, how='left', on='vid')
    # 第三波特征
    X_to_merge3 =  pd.DataFrame([['A0001','f'],['A0002','m'],['B0003','m'],['B0004','f']],columns=['vid','sex'])
    X_processed = X_processed.merge(X_to_merge3, how='left', on='vid')
    # 第四波特征
    X_to_merge3 =  pd.DataFrame([['A0001','Shanghai'],['A0002','Beijing'],['B0003','New York'],['B0004','Tokyo']],columns=['vid','address'])
    X_processed = X_processed.merge(X_to_merge3, how='left', on='vid')

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