1. 行标签与列标签获取

  1. print(cities)
  2. print(cities.index)
  3. print(cities.columns)
  5.    area  bb
  6. a    12  12
  7. b    25  25
  8. c    56  56
  9. d    67  67
  10. e    42  42
  11. Index(['a', 'b', 'c', 'd', 'e'], dtype='object')
  12. Index(['area', 'bb'], dtype='object')

2. 使用类名取值

  1. population = {'a': 12, 'b': 25, 'c': 56, 'd': 67, 'e': 42}
  2. area = pd.Series({'a': 11, 'b': 22, 'c': 55, 'd': 66, 'e': 44})
  3. data = pd.DataFrame({'population': population, 'area': area})
  4. # data = pd.DataFrame({1.1: population_dict, 2.2: area})
  5. print(data)
  6. print('用area的类名取值')
  7. print(data['area'])
  9.    population  area
  10. a          12    11
  11. b          25    22
  12. c          56    55
  13. d          67    66
  14. e          42    44
  15. 用类名取值
  16. a    11
  17. b    22
  18. c    55
  19. d    66
  20. e    44
  21. Name: area, dtype: int64

3. 使用属性形式取值

  1. print('使用属性形式取值')
  2. print(data.area)
  4. a    11
  5. b    22
  6. c    55
  7. d    66
  8. e    44
  9. Name: area, dtype: int64

4. 增加一列

  1. data['density'] = data['population'] / data['area']
  2. print(data)
  4.    population  area   density
  5. a          12    11  1.090909
  6. b          25    22  1.136364
  7. c          56    55  1.018182
  8. d          67    66  1.015152
  9. e          42    44  0.954545

5. 使用values属性按行查看数据

  1. print(data.values)
  3. [[12.         11.          1.09090909]
  4.  [25.         22.          1.13636364]
  5.  [56.         55.          1.01818182]
  6.  [67.         66.          1.01515152]
  7.  [42.         44.          0.95454545]]

6. 数据转置

转置时并不会改变原值。

  1. print(data.T)
  2. print(data)
  4.                     a          b          c          d          e
  5. population  12.000000  25.000000  56.000000  67.000000  42.000000
  6. area        11.000000  22.000000  55.000000  66.000000  44.000000
  7. density      1.090909   1.136364   1.018182   1.015152   0.954545
  8.    population  area   density
  9. a          12    11  1.090909
  10. b          25    22  1.136364
  11. c          56    55  1.018182
  12. d          67    66  1.015152
  13. e          42    44  0.954545

7. 切片时隐式与显式

  1. print('进行切片，选择一定范围内，显式索引')
  2. print(data.loc[:'b', :'area'])
  3. print(data.loc['b']['area'])
  4. print('隐式索引')
  5. print(data.iloc[:3, :2])
  7.    population  area
  8. a          12    11
  9. b          25    22
  10. 22.0
  11. 隐式索引
  12.    population  area
  13. a          12    11
  14. b          25    22
  15. c          56    55
  1. print(data['a': 'd'])
  2. print('上式相当与用了')
  3. print(data.loc['a': 'd'])
  5.    population  area   density
  6. a          12    11  1.090909
  7. b          25    22  1.136364
  8. c          56    55  1.018182
  9. d          67    66  1.015152
  10. 上式相当与用了
  11.    population  area   density
  12. a          12    11  1.090909
  13. b          25    22  1.136364
  14. c          56    55  1.018182
  15. d          67    66  1.015152
  1. print(data.loc['a': 'd', :])
  2. print('上式相当与用了')
  3. print(data[:4])  # 隐式索引
  5.    population  area   density
  6. a          12    11  1.090909
  7. b          25    22  1.136364
  8. c          56    55  1.018182
  9. d          67    66  1.015152
  10. 上式相当与用了
  11.    population  area   density
  12. a          12    11  1.090909
  13. b          25    22  1.136364
  14. c          56    55  1.018182
  15. d          67    66  1.015152

8. 使用numpy的方法取值

  1. print(data.loc[data.density > 1, ['population', 'density']])
  2. print(data.iloc[0, 2])
  3. print(data.iloc[0][2])
  5.    population   density
  6. a          12  1.090909
  7. b          25  1.136364
  8. c          56  1.018182
  9. d          67  1.015152
  10. 1.0909090909090908
  11. 1.0909090909090908

9. 单个或多个标签取值

  1. print('单个标签取值')
  2. print(data['area'])
  3. print('多个标签取值')
  4. print(data.loc[:, 'population':'area'])
  6. 单个标签取值
  7. a    11
  8. b    22
  9. c    55
  10. d    66
  11. e    44
  12. Name: area, dtype: int64
  13. 多个标签取值
  14.    population  area
  15. a          12    11
  16. b          25    22
  17. c          56    55
  18. d          67    66
  19. e          42    44

10. 特定列与某个值比大小

判断data中每一行density是否大于1

  1. print(data.density > 1)
  3. a     True
  4. b     True
  5. c     True
  6. d     True
  7. e    False
  8. Name: density, dtype: bool