python之pandas练习

练习代码：

  1 import numpy as np
  2 import pandas as pd
  3 
  4 #显示所有列
  5 pd.set_option('display.max_columns', None)
  6 #显示所有行
  7 pd.set_option('display.max_rows', None)
  8 #设置value的显示长度为100，默认为50
  9 pd.set_option('max_colwidth',100)
 10 
 11 df = pd.DataFrame(pd.read_csv('tags.csv',header=1)) 12 13 df = pd.DataFrame( 14  { 15 "id":[1001,1002,1003,1004,1005,1006], 16 "date":pd.date_range('20130102', periods=6), 17 "city":['Beijing ', 'SH', ' guangzhou ', 'Shenzhen', 'shanghai', 'BEIJING '], 18 "age":[23,44,54,32,34,45], 19 "category":['100-A','100-B','110-A','110-C','210-A','130-F'], 20 "price":[1200.9,np.nan,2133,5433.2,np.nan,4432] 21  }, 22 columns =['id','date','city','category','age','price']) 23 24 df1 = pd.DataFrame({ 25 "id":[1001,1002,1003,1004,1005,1006,1007,108], 26 "gender":['male','female','male','female','male','female','male','female'], 27 "pay":['Y','N','Y','Y','N','Y','N','Y',], 28 "m-point":[10,12,20,40,40,40,30,20]}) 29 30 if __name__ == '__main__': 31 print('********************************') 32 print('一、信息表信息查看') 33 print('********************************') 34 print('查看前2行') 35 print(df.head(2)) 36 print('===============================') 37 print('查看后2行') 38 print(df.tail(2)) 39 print('===============================') 40 print('维度查看') 41 print(df.shape) 42 print('===============================') 43 print('数据表基本信息（维度、列名称、数据格式、所占空间等）') 44 print(df.info()) 45 print('===============================') 46 print('每一列数据的格式') 47 print(df.dtypes) 48 print('===============================') 49 print('category列格式') 50 print(df['category'].dtype) 51 print('===============================') 52 print('空值') 53 print(df.isnull()) 54 print('===============================') 55 print('查看price列空值') 56 print(df['price'].isnull()) 57 print('===============================') 58 print('查看age列的唯一值') 59 print(df['age'].unique()) 60 print('===============================') 61 print('查看数据表的值') 62 print(df.values) 63 print('===============================') 64 print('查看列名称') 65 print(df.columns) 66 print('===============================') 67 print('********************************') 68 print('二、数据表清洗 ') 69 print('********************************') 70 print('使用列prince的均值对NA进行填充') 71 print(df) 72 print(df.fillna(value=100)) 73 #fillna()的参数如下，其中value可以取df.median、df.mean 74 #fillna(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs) 75 print( df['price'].fillna(df['price'].mean()) ) 76 print('===============================') 77 print('清除city字段的字符前后空格,字符的大小写转换') 78 df['city']=df['city'].map(str.strip) 79 #大小写转换（小写lower） 80 df['city'] = df['city'].map(str.upper) 81 print( df ) 82 print('===============================') 83 print('更改数据格式') 84 print(df['price'].fillna(value=100).astype('int')) 85 print('===============================') 86 print('更改列名称') 87 print(df.rename(columns={'category': 'category-size'})) 88 print('===============================') 89 print('删除后出现的重复值') 90 print(df['city'].drop_duplicates()) 91 print('===============================') 92 print('数据替换') 93 df['city'] = df['city'].replace('SH', 'shanghai') 94 print(df) 95 print('===============================') 96 print('********************************') 97 print('三、数据预处理 ') 98 print('********************************') 99 print('内连接') 100 df_inner=pd.merge(df,df1,how='inner') # 匹配合并，交集 101 print( df_inner ) 102 print('===============================') 103 print('左连接') 104 df_left=pd.merge(df,df1,how='left') # 左关联 105 print( df_left ) 106 print('===============================') 107 print('右连接') 108 df_right=pd.merge(df,df1,how='right') # 右关联 109 print( df_right ) 110 print('===============================') 111 print('外连接') 112 df_outer=pd.merge(df,df1,how='outer') # 外连接 113 print( df_outer ) 114 print('===============================') 115 print('设置索引列') 116 df_outer.set_index('id') 117 df_outer = df_outer.sort_values('age')　　#按age的值排序 118 #df_outer = df_outer.sort_index()　　#按索引排序 119 print(df_outer) 120 print('===============================') 121 print('如果price列的值>3000，group列显示high，否则显示low') 122 df_outer['group'] = np.where(df_outer['price'] > 3000,'high','low') 123 print(df_outer) 124 print('===============================') 125 print('对复合多个条件的数据进行分组标记') 126 df_inner.loc[(df_inner['city'].map(str.lower) == 'beijing') & (df_inner['price'] >= 4000), 'sign'] = 1 127 print(df_inner) 128 print('===============================') 129 print('对category字段的值依次进行分列，并创建数据表，索引值为df_inner的索引列，列名称为category和size') 130 split = pd.DataFrame( 131 (x.split('-') for x in df_inner['category']), 132 index=df_inner.index, 133 columns=['category','size']) 134 print("split") 135 print(split) 136 print('===============================') 137 print('将完成分裂后的数据表和原df_inner数据表进行匹配') 138 df_inner=pd.merge(df_inner,split,right_index=True, left_index=True) 139 print( df_inner ) 140 print('===============================') 141 print('********************************') 142 print('四、数据提取') 143 print('********************************') 144 print('按索引提取单行的数值') 145 print( df_inner.loc[0] ) 146 print('===============================') 147 print('按索引提取区域行数值') 148 print( df_inner.iloc[0:2] ) 149 print('===============================') 150 print('重设索引') 151  df_inner.reset_index() 152 print('===============================') 153 print('设置日期为索引') 154 df_inner=df_inner.set_index('date') 155 print('===============================') 156 print('提取4日之前的所有数据') 157 print( df_inner[:'2013-01-04'] ) 158 print('===============================') 159 print('使用iloc按位置区域提取数据') 160 print(df_inner.iloc[:2,:2]) #冒号前后的数字不再是索引的标签名称，而是数据所在的位置，从0开始，前三行，前两列。 161 print('===============================') 162 print('适应iloc按位置单独提起数据') 163 print( df_inner.iloc[[0,2,5],[4,5]] )#提取第0、2、5行，4、5列 164 print('===============================') 165 print('使用ix按索引标签和位置混合提取数据') 166 print(df_inner.ix[:'2013-01-03',:4] )#2013-01-03号之前，前四列数据 167 print('===============================') 168 print('判断city列的值是否为北京') 169 print(df_inner['city'].isin(['BEIJING'])) 170 print('===============================') 171 print('判断city列里是否包含beijing和shanghai，然后将符合条件的数据提取出来') 172 print(df_inner.loc[df_inner['city'].isin(['BEIJING','shanghai'])]) 173 print('===============================') 174 print('提取前三个字符，并生成数据表') 175 test = pd.DataFrame(df['category'].str[:3]) 176 print(test) 177 print('===============================') 178 print('********************************') 179 print('五、数据筛选') 180 print('********************************') 181 print('找出年龄大于25并且在beijing的记录') 182 print(df.loc[ 183 (df['age'] > 25) & (df['city'].str.lower() == 'beijing'), 184 ['id','city','age','category','price']]) 185 print('===============================') 186 print('找出年龄大于35或者在beijing的记录') 187 print('===============================') 188 print(df.loc[ 189 (df['age'] > 35) | (df['city'].str.lower() == 'beijing'), 190 ['id','city','age','category','price']]) 191 print('===============================') 192 print('找出不在beijing的记录,对筛选后的数据按city列进行计数') 193 print('===============================') 194 test = df.loc[ 195 (df['city'].str.lower() != 'beijing'), 196 ['id','city','age','category','price']] 197 print(test) 198 print('总数：'+str(test.sort_values(['age'],ascending = False).city.count())) 199 print('===============================') 200 print('使用query函数进行筛选') 201 print('===============================') 202 test = df.query('city == ["BEIJING", "shanghai"]') 203 #注意这里的单引号与双引号，如果同为双引号或单引号会报错 204 print(test) 205 print('===============================') 206 print('对筛选后的结果按prince进行求和') 207 print('===============================') 208 print( df.query('city == ["BEIJING", "shanghai"]').price.sum() ) 209 print('===============================') 210 print('********************************') 211 print('六、数据汇总') 212 print('********************************') 213 print('对所有的列进行计数汇总') 214 print('===============================') 215 print( df.groupby('city').count() ) 216 print('===============================') 217 print('按城市对id字段进行计数') 218 print('===============================') 219 print( df.groupby('city')['id'].count() ) 220 print('===============================') 221 print('对两个字段进行汇总计数') 222 print('===============================') 223 print( df.groupby(['city','age'])['id'].count() ) 224 print('===============================') 225 print('对city字段进行汇总，并分别计算prince的合计和均值') 226 print('===============================') 227 print( df.groupby('city')['price'].agg([len,np.sum, np.mean]) ) 228 print('===============================') 229 print('********************************') 230 print('七、数据统计') 231 print('********************************') 232 print('简单的数据采样 ') 233 print('===============================') 234 print( df_inner.sample(n=3) ) 235 print('===============================') 236 print('手动设置采样权重 ') 237 print('===============================') 238 weights = [0, 0.1, 0.7, 0, 0.2, 0.1] 239 print( df.sample(n=2, weights=weights) ) 240 print('===============================') 241 print('采样后不放回') 242 print('===============================') 243 print( df.sample(n=6, replace=False) ) 244 print('===============================') 245 print('采样后放回') 246 print('===============================') 247 print( df.sample(n=6, replace=True) ) 248 print('===============================') 249 print('数据表描述性统计') 250 print('===============================') 251 #round函数设置显示小数位，T表示转置 252 print( df.describe().round(2).T ) 253 print('===============================') 254 print('计算列的标准差') 255 print('===============================') 256 print( df['price'].std() ) 257 print('===============================') 258 print('计算两个字段间的协方差') 259 print('===============================') 260 print( df['price'].cov(df_inner['age']) ) 261 print('===============================') 262 print('数据表中所有字段间的协方差') 263 print('===============================') 264 print( df.cov() ) 265 print('===============================') 266 print('两个字段的相关性分析') 267 print('===============================') 268 ##相关系数在-1到1之间，接近1为正相关，接近-1为负相关，0为不相关 269 print( df['price'].corr(df_inner['age']) ) 270 print('===============================') 271 print('数据表的相关性分析') 272 print('===============================') 273 print( df.corr() ) 274 print('===============================') 275 print('********************************') 276 print('八、数据输出') 277 print('********************************') 278 print('数据导出：写入Excel') 279 #此处不知道什么原因，将内容写入到.xlsx文件中一直存在问题 280 df.to_excel('excel_to_python.xls',sheet_name='test1') 281 print('写入成功') 282 print('===============================') 283 print('数据导出：写入到CSV') 284 df.to_csv('excel_to_python.csv') 285 print('写入成功') 286 print('===============================')

运行结果：

********************************
一、信息表信息查看
********************************
查看前2行
df.head(2)
     id       date      city category  age   price
0  1001 2013-01-02  Beijing     100-A   23  1200.9
1  1002 2013-01-03        SH    100-B   44     NaN
===============================
查看后2行
df.tail(2) id date city category age price 4 1005 2013-01-06 shanghai 210-A 34 NaN 5 1006 2013-01-07 BEIJING 130-F 45 4432.0 =============================== 维度查看 (6, 6) =============================== 数据表基本信息（维度、列名称、数据格式、所占空间等） <class 'pandas.core.frame.DataFrame'> RangeIndex: 6 entries, 0 to 5 Data columns (total 6 columns): id 6 non-null int64 date 6 non-null datetime64[ns] city 6 non-null object category 6 non-null object age 6 non-null int64 price 4 non-null float64 dtypes: datetime64[ns](1), float64(1), int64(2), object(2) memory usage: 276.0+ bytes None =============================== 每一列数据的格式 id int64 date datetime64[ns] city object category object age int64 price float64 dtype: object =============================== category列格式 object =============================== 空值 id date city category age price 0 False False False False False False 1 False False False False False True 2 False False False False False False 3 False False False False False False 4 False False False False False True 5 False False False False False False =============================== 查看price列空值 0 False 1 True 2 False 3 False 4 True 5 False Name: price, dtype: bool =============================== 查看age列的唯一值 [23 44 54 32 34 45] =============================== 查看数据表的值 [[1001L Timestamp('2013-01-02 00:00:00') 'Beijing ' '100-A' 23L 1200.9] [1002L Timestamp('2013-01-03 00:00:00') 'SH' '100-B' 44L nan] [1003L Timestamp('2013-01-04 00:00:00') ' guangzhou ' '110-A' 54L 2133.0] [1004L Timestamp('2013-01-05 00:00:00') 'Shenzhen' '110-C' 32L 5433.2] [1005L Timestamp('2013-01-06 00:00:00') 'shanghai' '210-A' 34L nan] [1006L Timestamp('2013-01-07 00:00:00') 'BEIJING ' '130-F' 45L 4432.0]] =============================== 查看列名称 Index([u'id', u'date', u'city', u'category', u'age', u'price'], dtype='object') =============================== ******************************** 二、数据表清洗 ******************************** 使用列prince的均值对NA进行填充 id date city category age price 0 1001 2013-01-02 Beijing 100-A 23 1200.9 1 1002 2013-01-03 SH 100-B 44 NaN 2 1003 2013-01-04 guangzhou 110-A 54 2133.0 3 1004 2013-01-05 Shenzhen 110-C 32 5433.2 4 1005 2013-01-06 shanghai 210-A 34 NaN 5 1006 2013-01-07 BEIJING 130-F 45 4432.0 id date city category age price 0 1001 2013-01-02 Beijing 100-A 23 1200.9 1 1002 2013-01-03 SH 100-B 44 100.0 2 1003 2013-01-04 guangzhou 110-A 54 2133.0 3 1004 2013-01-05 Shenzhen 110-C 32 5433.2 4 1005 2013-01-06 shanghai 210-A 34 100.0 5 1006 2013-01-07 BEIJING 130-F 45 4432.0 0 1200.900 1 3299.775 2 2133.000 3 5433.200 4 3299.775 5 4432.000 Name: price, dtype: float64 =============================== 清除city字段的字符前后空格,字符的大小写转换 id date city category age price 0 1001 2013-01-02 BEIJING 100-A 23 1200.9 1 1002 2013-01-03 SH 100-B 44 NaN 2 1003 2013-01-04 GUANGZHOU 110-A 54 2133.0 3 1004 2013-01-05 SHENZHEN 110-C 32 5433.2 4 1005 2013-01-06 SHANGHAI 210-A 34 NaN 5 1006 2013-01-07 BEIJING 130-F 45 4432.0 =============================== 更改数据格式 0 1200 1 100 2 2133 3 5433 4 100 5 4432 Name: price, dtype: int32 =============================== 更改列名称 id date city category-size age price 0 1001 2013-01-02 BEIJING 100-A 23 1200.9 1 1002 2013-01-03 SH 100-B 44 NaN 2 1003 2013-01-04 GUANGZHOU 110-A 54 2133.0 3 1004 2013-01-05 SHENZHEN 110-C 32 5433.2 4 1005 2013-01-06 SHANGHAI 210-A 34 NaN 5 1006 2013-01-07 BEIJING 130-F 45 4432.0 =============================== 删除后出现的重复值 0 BEIJING 1 SH 2 GUANGZHOU 3 SHENZHEN 4 SHANGHAI Name: city, dtype: object =============================== 数据替换 id date city category age price 0 1001 2013-01-02 BEIJING 100-A 23 1200.9 1 1002 2013-01-03 shanghai 100-B 44 NaN 2 1003 2013-01-04 GUANGZHOU 110-A 54 2133.0 3 1004 2013-01-05 SHENZHEN 110-C 32 5433.2 4 1005 2013-01-06 SHANGHAI 210-A 34 NaN 5 1006 2013-01-07 BEIJING 130-F 45 4432.0 =============================== ******************************** 三、数据预处理 ******************************** 内连接 id date city category age price gender m-point pay 0 1001 2013-01-02 BEIJING 100-A 23 1200.9 male 10 Y 1 1002 2013-01-03 shanghai 100-B 44 NaN female 12 N 2 1003 2013-01-04 GUANGZHOU 110-A 54 2133.0 male 20 Y 3 1004 2013-01-05 SHENZHEN 110-C 32 5433.2 female 40 Y 4 1005 2013-01-06 SHANGHAI 210-A 34 NaN male 40 N 5 1006 2013-01-07 BEIJING 130-F 45 4432.0 female 40 Y =============================== 左连接 id date city category age price gender m-point pay 0 1001 2013-01-02 BEIJING 100-A 23 1200.9 male 10 Y 1 1002 2013-01-03 shanghai 100-B 44 NaN female 12 N 2 1003 2013-01-04 GUANGZHOU 110-A 54 2133.0 male 20 Y 3 1004 2013-01-05 SHENZHEN 110-C 32 5433.2 female 40 Y 4 1005 2013-01-06 SHANGHAI 210-A 34 NaN male 40 N 5 1006 2013-01-07 BEIJING 130-F 45 4432.0 female 40 Y =============================== 右连接 id date city category age price gender m-point pay 0 1001 2013-01-02 BEIJING 100-A 23.0 1200.9 male 10 Y 1 1002 2013-01-03 shanghai 100-B 44.0 NaN female 12 N 2 1003 2013-01-04 GUANGZHOU 110-A 54.0 2133.0 male 20 Y 3 1004 2013-01-05 SHENZHEN 110-C 32.0 5433.2 female 40 Y 4 1005 2013-01-06 SHANGHAI 210-A 34.0 NaN male 40 N 5 1006 2013-01-07 BEIJING 130-F 45.0 4432.0 female 40 Y 6 1007 NaT NaN NaN NaN NaN male 30 N 7 108 NaT NaN NaN NaN NaN female 20 Y =============================== 外连接 id date city category age price gender m-point pay 0 1001 2013-01-02 BEIJING 100-A 23.0 1200.9 male 10 Y 1 1002 2013-01-03 shanghai 100-B 44.0 NaN female 12 N 2 1003 2013-01-04 GUANGZHOU 110-A 54.0 2133.0 male 20 Y 3 1004 2013-01-05 SHENZHEN 110-C 32.0 5433.2 female 40 Y 4 1005 2013-01-06 SHANGHAI 210-A 34.0 NaN male 40 N 5 1006 2013-01-07 BEIJING 130-F 45.0 4432.0 female 40 Y 6 1007 NaT NaN NaN NaN NaN male 30 N 7 108 NaT NaN NaN NaN NaN female 20 Y =============================== 设置索引列 id date city category age price gender m-point pay 0 1001 2013-01-02 BEIJING 100-A 23.0 1200.9 male 10 Y 3 1004 2013-01-05 SHENZHEN 110-C 32.0 5433.2 female 40 Y 4 1005 2013-01-06 SHANGHAI 210-A 34.0 NaN male 40 N 1 1002 2013-01-03 shanghai 100-B 44.0 NaN female 12 N 5 1006 2013-01-07 BEIJING 130-F 45.0 4432.0 female 40 Y 2 1003 2013-01-04 GUANGZHOU 110-A 54.0 2133.0 male 20 Y 6 1007 NaT NaN NaN NaN NaN male 30 N 7 108 NaT NaN NaN NaN NaN female 20 Y =============================== 如果price列的值>3000，group列显示high，否则显示low id date city category ... gender m-point pay group 0 1001 2013-01-02 BEIJING 100-A ... male 10 Y low 3 1004 2013-01-05 SHENZHEN 110-C ... female 40 Y high 4 1005 2013-01-06 SHANGHAI 210-A ... male 40 N low 1 1002 2013-01-03 shanghai 100-B ... female 12 N low 5 1006 2013-01-07 BEIJING 130-F ... female 40 Y high 2 1003 2013-01-04 GUANGZHOU 110-A ... male 20 Y low 6 1007 NaT NaN NaN ... male 30 N low 7 108 NaT NaN NaN ... female 20 Y low [8 rows x 10 columns] =============================== 对复合多个条件的数据进行分组标记 id date city category ... gender m-point pay sign 0 1001 2013-01-02 BEIJING 100-A ... male 10 Y NaN 1 1002 2013-01-03 shanghai 100-B ... female 12 N NaN 2 1003 2013-01-04 GUANGZHOU 110-A ... male 20 Y NaN 3 1004 2013-01-05 SHENZHEN 110-C ... female 40 Y NaN 4 1005 2013-01-06 SHANGHAI 210-A ... male 40 N NaN 5 1006 2013-01-07 BEIJING 130-F ... female 40 Y 1.0 [6 rows x 10 columns] =============================== 对category字段的值依次进行分列，并创建数据表，索引值为df_inner的索引列，列名称为category和size split category size 0 100 A 1 100 B 2 110 A 3 110 C 4 210 A 5 130 F =============================== 将完成分裂后的数据表和原df_inner数据表进行匹配 id date city category_x ... pay sign category_y size 0 1001 2013-01-02 BEIJING 100-A ... Y NaN 100 A 1 1002 2013-01-03 shanghai 100-B ... N NaN 100 B 2 1003 2013-01-04 GUANGZHOU 110-A ... Y NaN 110 A 3 1004 2013-01-05 SHENZHEN 110-C ... Y NaN 110 C 4 1005 2013-01-06 SHANGHAI 210-A ... N NaN 210 A 5 1006 2013-01-07 BEIJING 130-F ... Y 1.0 130 F [6 rows x 12 columns] =============================== ******************************** 四、数据提取 ******************************** 按索引提取单行的数值 id 1001 date 2013-01-02 00:00:00 city BEIJING category_x 100-A age 23 price 1200.9 gender male m-point 10 pay Y sign NaN category_y 100 size A Name: 0, dtype: object =============================== 按索引提取区域行数值 id date city category_x ... pay sign category_y size 0 1001 2013-01-02 BEIJING 100-A ... Y NaN 100 A 1 1002 2013-01-03 shanghai 100-B ... N NaN 100 B [2 rows x 12 columns] =============================== 重设索引 =============================== 设置日期为索引 =============================== 提取4日之前的所有数据 id city category_x age ... pay sign category_y size date ... 2013-01-02 1001 BEIJING 100-A 23 ... Y NaN 100 A 2013-01-03 1002 shanghai 100-B 44 ... N NaN 100 B 2013-01-04 1003 GUANGZHOU 110-A 54 ... Y NaN 110 A [3 rows x 11 columns] =============================== 使用iloc按位置区域提取数据 id city date 2013-01-02 1001 BEIJING 2013-01-03 1002 shanghai =============================== 适应iloc按位置单独提起数据 price gender date 2013-01-02 1200.9 male 2013-01-04 2133.0 male 2013-01-07 4432.0 female =============================== 使用ix按索引标签和位置混合提取数据 id city category_x age date 2013-01-02 1001 BEIJING 100-A 23 2013-01-03 1002 shanghai 100-B 44 =============================== 判断city列的值是否为北京 date 2013-01-02 True 2013-01-03 False 2013-01-04 False 2013-01-05 False 2013-01-06 False 2013-01-07 True Name: city, dtype: bool =============================== 判断city列里是否包含beijing和shanghai，然后将符合条件的数据提取出来 id city category_x age ... pay sign category_y size date ... 2013-01-02 1001 BEIJING 100-A 23 ... Y NaN 100 A 2013-01-03 1002 shanghai 100-B 44 ... N NaN 100 B 2013-01-07 1006 BEIJING 130-F 45 ... Y 1.0 130 F [3 rows x 11 columns] =============================== 提取前三个字符，并生成数据表 category 0 100 1 100 2 110 3 110 4 210 5 130 =============================== ******************************** 五、数据筛选 ******************************** 找出年龄大于25并且在beijing的记录 id city age category price 5 1006 BEIJING 45 130-F 4432.0 =============================== 找出年龄大于35或者在beijing的记录 =============================== id city age category price 0 1001 BEIJING 23 100-A 1200.9 1 1002 shanghai 44 100-B NaN 2 1003 GUANGZHOU 54 110-A 2133.0 5 1006 BEIJING 45 130-F 4432.0 =============================== 找出不在beijing的记录,对筛选后的数据按city列进行计数 =============================== id city age category price 1 1002 shanghai 44 100-B NaN 2 1003 GUANGZHOU 54 110-A 2133.0 3 1004 SHENZHEN 32 110-C 5433.2 4 1005 SHANGHAI 34 210-A NaN 总数：4 =============================== 使用query函数进行筛选 =============================== id date city category age price 0 1001 2013-01-02 BEIJING 100-A 23 1200.9 1 1002 2013-01-03 shanghai 100-B 44 NaN 5 1006 2013-01-07 BEIJING 130-F 45 4432.0 =============================== 对筛选后的结果按prince进行求和 =============================== 5632.9 =============================== ******************************** 六、数据汇总 ******************************** 对所有的列进行计数汇总 =============================== id date category age price city BEIJING 2 2 2 2 2 GUANGZHOU 1 1 1 1 1 SHANGHAI 1 1 1 1 0 SHENZHEN 1 1 1 1 1 shanghai 1 1 1 1 0 =============================== 按城市对id字段进行计数 =============================== city BEIJING 2 GUANGZHOU 1 SHANGHAI 1 SHENZHEN 1 shanghai 1 Name: id, dtype: int64 =============================== 对两个字段进行汇总计数 =============================== city age BEIJING 23 1 45 1 GUANGZHOU 54 1 SHANGHAI 34 1 SHENZHEN 32 1 shanghai 44 1 Name: id, dtype: int64 =============================== 对city字段进行汇总，并分别计算prince的合计和均值 =============================== len sum mean city BEIJING 2.0 5632.9 2816.45 GUANGZHOU 1.0 2133.0 2133.00 SHANGHAI 1.0 0.0 NaN SHENZHEN 1.0 5433.2 5433.20 shanghai 1.0 0.0 NaN =============================== ******************************** 七、数据统计 ******************************** 简单的数据采样 =============================== id city category_x age ... pay sign category_y size date ... 2013-01-07 1006 BEIJING 130-F 45 ... Y 1.0 130 F 2013-01-02 1001 BEIJING 100-A 23 ... Y NaN 100 A 2013-01-04 1003 GUANGZHOU 110-A 54 ... Y NaN 110 A [3 rows x 11 columns] =============================== 手动设置采样权重 =============================== id date city category age price 2 1003 2013-01-04 GUANGZHOU 110-A 54 2133.0 5 1006 2013-01-07 BEIJING 130-F 45 4432.0 =============================== 采样后不放回 =============================== id date city category age price 0 1001 2013-01-02 BEIJING 100-A 23 1200.9 4 1005 2013-01-06 SHANGHAI 210-A 34 NaN 3 1004 2013-01-05 SHENZHEN 110-C 32 5433.2 2 1003 2013-01-04 GUANGZHOU 110-A 54 2133.0 1 1002 2013-01-03 shanghai 100-B 44 NaN 5 1006 2013-01-07 BEIJING 130-F 45 4432.0 =============================== 采样后放回 =============================== id date city category age price 1 1002 2013-01-03 shanghai 100-B 44 NaN 2 1003 2013-01-04 GUANGZHOU 110-A 54 2133.0 3 1004 2013-01-05 SHENZHEN 110-C 32 5433.2 2 1003 2013-01-04 GUANGZHOU 110-A 54 2133.0 4 1005 2013-01-06 SHANGHAI 210-A 34 NaN 0 1001 2013-01-02 BEIJING 100-A 23 1200.9 =============================== 数据表描述性统计 =============================== count mean std min 25% 50% 75% max id 6.0 1003.50 1.87 1001.0 1002.25 1003.5 1004.75 1006.0 age 6.0 38.67 11.09 23.0 32.50 39.0 44.75 54.0 price 4.0 3299.78 1966.39 1200.9 1899.98 3282.5 4682.30 5433.2 =============================== 计算列的标准差 =============================== 1966.39058917 =============================== 计算两个字段间的协方差 =============================== nan =============================== 数据表中所有字段间的协方差 =============================== id age price id 3.500000 5.800000 3.242617e+03 age 5.800000 123.066667 2.646583e+03 price 3242.616667 2646.583333 3.866692e+06 =============================== 两个字段的相关性分析 =============================== nan =============================== 数据表的相关性分析 =============================== id age price id 1.000000 0.279463 0.792163 age 0.279463 1.000000 0.098074 price 0.792163 0.098074 1.000000 =============================== ******************************** 八、数据输出 ******************************** 数据导出：写入Excel 写入成功 =============================== 数据导出：写入到CSV 写入成功 ===============================