1 import pandas as pd 2 import numpy as np 3 4 # merge合并 ，类似于Excel中的vlookup 5 6 df1 = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'], 7 'A': ['A0', 'A1', 'A2', 'A3'], 8 'B': ['B0', 'B1', 'B2', 'B3']}) 9 df2 = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'], 10 'C': ['C0', 'C1', 'C2', 'C3'], 11 'D': ['D0', 'D1', 'D2', 'D3']}) 12 df3 = pd.DataFrame({'key1': ['K0', 'K0', 'K2', 'K3'], 13 'key2': ['K0', 'K1', 'K0', 'K1'], 14 'A': ['A0', 'A1', 'A2', 'A3'], 15 'B': ['B0', 'B1', 'B2', 'B3']}) 16 df4 = pd.DataFrame({'key1': ['K0', 'K0', 'K2', 'K3'], 17 'key2': ['K0', 'K0', 'K0', 'K0'], 18 'C': ['C0', 'C1', 'C2', 'C3'], 19 'D': ['D0', 'D1', 'D2', 'D3']}) 20 print(pd.merge(df1,df2,on='key')) 21 # 第一个DataFrame为拼接后左边的 22 # 第二个DataFrame为拼接后右边的 23 # on 为参考键 24 ''' 25 key A B C D 26 0 K0 A0 B0 C0 D0 27 1 K1 A1 B1 C1 D1 28 2 K2 A2 B2 C2 D2 29 3 K3 A3 B3 C3 D3 30 ''' 31 # 多个键连接 32 print(pd.merge(df3, df4, on=['key1', 'key2'])) 33 # 当两个DataFrame中的key1和key2都相同时，才会连，否则不连 34 ''' 35 key1 key2 A B C D 36 0 K0 K0 A0 B0 C0 D0 37 1 K0 K0 A0 B0 C1 D1 38 2 K2 K0 A2 B2 C2 D2 39 ''' 40 # 参数how ， 合并方式 41 # 默认，取交集 42 print(pd.merge(df3, df4, on=['key1', 'key2'], how='inner')) 43 print('-' * 8) 44 ''' 45 key1 key2 A B C D 46 0 K0 K0 A0 B0 C0 D0 47 1 K0 K0 A0 B0 C1 D1 48 2 K2 K0 A2 B2 C2 D2 49 -------- 50 ''' 51 # 取并集，outer，数据缺失范围NaN 52 print(pd.merge(df3, df4, on=['key1', 'key2'], how='outer')) 53 print('-' * 8) 54 ''' 55 key1 key2 A B C D 56 0 K0 K0 A0 B0 C0 D0 57 1 K0 K0 A0 B0 C1 D1 58 2 K0 K1 A1 B1 NaN NaN 59 3 K2 K0 A2 B2 C2 D2 60 4 K3 K1 A3 B3 NaN NaN 61 5 K3 K0 NaN NaN C3 D3 62 -------- 63 ''' 64 # 参照df3为参考合并，数据缺失范围NaN 65 print(pd.merge(df3, df4, on=['key1', 'key2'], how='left')) 66 print('-' * 8) 67 ''' 68 key1 key2 A B C D 69 0 K0 K0 A0 B0 C0 D0 70 1 K0 K0 A0 B0 C1 D1 71 2 K0 K1 A1 B1 NaN NaN 72 3 K2 K0 A2 B2 C2 D2 73 4 K3 K1 A3 B3 NaN NaN 74 -------- 75 ''' 76 # 参照df4为参考合并，数据缺失范围NaN 77 print(pd.merge(df3, df4, on=['key1', 'key2'], how='right')) 78 print('-' * 8) 79 ''' 80 key1 key2 A B C D 81 0 K0 K0 A0 B0 C0 D0 82 1 K0 K0 A0 B0 C1 D1 83 2 K2 K0 A2 B2 C2 D2 84 3 K3 K0 NaN NaN C3 D3 85 -------- 86 ''' 87 # 参数left_on，right_on，left_index, right_index ,当键不为一个列时，可以单独设置左键与右键 88 df5 = pd.DataFrame({'lkey': list('bbacaab'), 89 'data1': range(7)}) 90 df6 = pd.DataFrame({'rkey': list('abd'), 91 'date2': range(3)}) 92 print(df5) 93 print(df6) 94 print(pd.merge(df5,df6,left_on='lkey',right_on='rkey')) 95 ''' 96 lkey data1 97 0 b 0 98 1 b 1 99 2 a 2 100 3 c 3 101 4 a 4 102 5 a 5 103 6 b 6 104 rkey date2 105 0 a 0 106 1 b 1 107 2 d 2 108 lkey data1 rkey date2 109 0 b 0 b 1 110 1 b 1 b 1 111 2 b 6 b 1 112 3 a 2 a 0 113 4 a 4 a 0 114 5 a 5 a 0 115 ''' 116 117 # concat() 连接，默认axis=0 行+行，当axis=1时，列+列 成为Dataframe 118 s1 = pd.Series([2, 3, 4]) 119 s2 = pd.Series([1, 2, 3]) 120 print(pd.concat([s1, s2])) 121 ''' 122 0 2 123 1 3 124 2 4 125 0 1 126 1 2 127 2 3 128 dtype: int64 129 ''' 130 print(pd.concat([s1,s2],axis=1)) 131 ''' 132 0 1 133 0 2 1 134 1 3 2 135 2 4 3 136 ''' 137 snew = pd.concat([s1, s2], axis=1) 138 snew.reset_index(inplace=True) 139 print(snew) 140 ''' 141 index 0 1 142 0 0 2 1 143 1 1 3 2 144 2 2 4 3 145 ''' 146 snew2 = pd.concat([s1, s2], axis=1) 147 snew2.reset_index(inplace=True, drop=True) 148 print(snew2) 149 ''' 150 0 1 151 0 2 1 152 1 3 2 153 2 4 3 154 ''' 155 156 # 去重 .duplicated() 157 s3 = pd.Series([1, 2, 2, 4, 4, 6, 7, 6, 87]) 158 # 判断是否重复 159 print(s3.duplicated()) 160 ''' 161 0 False 162 1 False 163 2 True 164 3 False 165 4 True 166 5 False 167 6 False 168 7 True 169 8 False 170 dtype: bool 171 ''' 172 # 取出重复的值 173 s4 = s3[s3.duplicated()] 174 print(s4) 175 # 取出唯一的元素 176 s5 = s3[s3.duplicated() == False] 177 print(s5) 178 ''' 179 0 1 180 1 2 181 3 4 182 5 6 183 6 7 184 8 87 185 dtype: int64 186 ''' 187 s5 = s3.drop_duplicates() 188 # 可以通过设置参数：inplace控制是否替换原先的值 189 print(s5) 190 ''' 191 0 1 192 1 2 193 3 4 194 5 6 195 6 7 196 8 87 197 dtype: int64 198 ''' 199 df7 = pd.DataFrame({'key1':['a','a',3,4,3], 200 'key2':['a','a','b','b',5]}) 201 print(df7.duplicated()) 202 # 按行检测，第二次出现时，返回True 203 ''' 204 0 1 205 1 2 206 3 4 207 5 6 208 6 7 209 8 87 210 dtype: int64 211 ''' 212 # 今查看key2列 213 print(df7['key2'].duplicated()) 214 ''' 215 0 False 216 1 True 217 2 False 218 3 True 219 4 False 220 Name: key2, dtype: bool 221 ''' 222 # 直接去重 223 print(df7.drop_duplicates()) 224 ''' 225 key1 key2 226 0 a a 227 2 3 b 228 3 4 b 229 4 3 5 230 ''' 231 print(df7['key2'].drop_duplicates()) 232 ''' 233 0 a 234 2 b 235 4 5 236 Name: key2, dtype: object 237 ''' 238 239 # 替换 .replace() 240 s6 = pd.Series(list('askjdghs')) 241 # 一次性替换一个值 242 # print(s6.replace('s','dsd')) 243 ''' 244 0 a 245 1 dsd 246 2 k 247 3 j 248 4 d 249 5 g 250 6 h 251 7 dsd 252 dtype: object 253 ''' 254 # 一次性替换多个值 255 print(s6.replace(['a','s'],np.nan)) 256 ''' 257 0 NaN 258 1 NaN 259 2 k 260 3 j 261 4 d 262 5 g 263 6 h 264 7 NaN 265 dtype: object 266 ''' 267 # 通过字典的形式替换值 268 print(s6.replace({'a':np.nan})) 269 ''' 270 0 NaN 271 1 s 272 2 k 273 3 j 274 4 d 275 5 g 276 6 h 277 7 s 278 dtype: object 279 280 '''
