1. 导入与预览数据

  1. import numpy as np
  2. import pandas as pd
  3. import seaborn as sns
  4. plants = pd.read_csv('planets.csv')
  5. print(plants.head())
  7.             method  number  orbital_period   mass  distance  year
  8. 0  Radial Velocity       1         269.300   7.10     77.40  2006
  9. 1  Radial Velocity       1         874.774   2.21     56.95  2008
  10. 2  Radial Velocity       1         763.000   2.60     19.84  2011
  11. 3  Radial Velocity       1         326.030  19.40    110.62  2007
  12. 4  Radial Velocity       1         516.220  10.50    119.47  2009

2. 找出存在缺失值列

  1. print(plants.isnull().any())  
  3. method            False
  4. number            False
  5. orbital_period     True
  6. mass               True
  7. distance           True
  8. year              False
  9. dtype: bool

3. 找出多列都为空行数据

  1. print("找出['orbital_period']、['mass']、['distance']三列都为空的行数据:")
  2. plants.loc[plants['orbital_period'].isnull()&plants['mass'].isnull()&plants['distance'].isnull()]
  4. 找出['orbital_period']、['mass']、['distance']三列都为空的行数据:

image.png

4. 去除缺失值所在行数据

  1. print('只保留所有列数据都不为NaN的行数据')
  2. plants.dropna()

image.png

5. 综合统计函数

  1. plants.describe()

image.png

6. 获取所有行星公转周期的中位数

  1. print('不同方法下所有行星公转周期(orbital_period)的中位数')
  2. print(plants.groupby('method'))
  3. print(plants.groupby('method')['orbital_period'])
  4. # groupby对象直到运行累计函数才开始计算
  5. print(plants.groupby('method')['orbital_period'].median())
  7. 不同方法下所有行星公转周期(orbital_period)的中位数
  8. <pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002115A3BBA60>
  9. <pandas.core.groupby.generic.SeriesGroupBy object at 0x000002115A3BBEB0>
  10. method
  11. Astrometry                         631.180000
  12. Eclipse Timing Variations         4343.500000
  13. Imaging                          27500.000000
  14. Microlensing                      3300.000000
  15. Orbital Brightness Modulation        0.342887
  16. Pulsar Timing                       66.541900
  17. Pulsation Timing Variations       1170.000000
  18. Radial Velocity                    360.200000
  19. Transit                              5.714932
  20. Transit Timing Variations           57.011000
  21. Name: orbital_period, dtype: float64

7. 分组使用综合统计函数

  1. print('对每一组数据描述性统计')
  2. plants.groupby('method')['year'].describe()
  3. # 通过count可以看出，大部分行星是通过Radial Velocity和Transit方法发现的，并且后种方法近期比较常用
  5. 对每一组数据描述性统计

image.png

8. 对所有列进行指定累计操作

  1. aa = plants.groupby('method').aggregate([min, max, np.median])
  2. print(aa)
  4.                               number            orbital_period                 \
  5.                                  min max median            min            max   
  6. method                                                                          
  7. Astrometry                         1   1      1     246.360000    1016.000000   
  8. Eclipse Timing Variations          1   2      2    1916.250000   10220.000000   
  9. Imaging                            1   4      1    4639.150000  730000.000000   
  10. Microlensing                       1   2      1    1825.000000    5100.000000   
  11. Orbital Brightness Modulation      1   2      2       0.240104       1.544929   
  12. Pulsar Timing                      1   3      3       0.090706   36525.000000   
  13. Pulsation Timing Variations        1   1      1    1170.000000    1170.000000   
  14. Radial Velocity                    1   6      1       0.736540   17337.500000   
  15. Transit                            1   7      1       0.355000     331.600590   
  16. Transit Timing Variations          2   3      2      22.339500     160.000000   
  18.                                                mass               distance  \
  19.                                      median     min    max median      min   
  20. method                                                                       
  21. Astrometry                       631.180000     NaN    NaN    NaN    14.98   
  22. Eclipse Timing Variations       4343.500000  4.2000   6.05  5.125   130.72   
  23. Imaging                        27500.000000     NaN    NaN    NaN     7.69   
  24. Microlensing                    3300.000000     NaN    NaN    NaN  1760.00   
  25. Orbital Brightness Modulation      0.342887     NaN    NaN    NaN  1180.00   
  26. Pulsar Timing                     66.541900     NaN    NaN    NaN  1200.00   
  27. Pulsation Timing Variations     1170.000000     NaN    NaN    NaN      NaN   
  28. Radial Velocity                  360.200000  0.0036  25.00  1.260     1.35   
  29. Transit                            5.714932  1.4700   1.47  1.470    38.00   
  30. Transit Timing Variations         57.011000     NaN    NaN    NaN   339.00   
  32.                                                   year                
  33.                                    max    median   min   max  median  
  34. method                                                                
  35. Astrometry                       20.77    17.875  2010  2013  2011.5  
  36. Eclipse Timing Variations       500.00   315.360  2008  2012  2010.0  
  37. Imaging                         165.00    40.395  2004  2013  2009.0  
  38. Microlensing                   7720.00  3840.000  2004  2013  2010.0  
  39. Orbital Brightness Modulation  1180.00  1180.000  2011  2013  2011.0  
  40. Pulsar Timing                  1200.00  1200.000  1992  2011  1994.0  
  41. Pulsation Timing Variations        NaN       NaN  2007  2007  2007.0  
  42. Radial Velocity                 354.00    40.445  1989  2014  2009.0  
  43. Transit                        8500.00   341.000  2002  2014  2012.0  
  44. Transit Timing Variations      2119.00   855.000  2011  2014  2012.5

9. 对指定列进行指定操作

  1. print('各方法每年发现行星最大的是多少；各方法在哪年最早有发现行星')
  2. bb = plants.groupby('method').aggregate({'number': max, 'year':min})
  3. print(bb)
  5. 各方法每年发现行星最大的是多少；各方法在哪年最早有发现行星
  6.                                number  year
  7. method                                     
  8. Astrometry                          1  2010
  9. Eclipse Timing Variations           2  2008
  10. Imaging                             4  2004
  11. Microlensing                        2  2004
  12. Orbital Brightness Modulation       2  2011
  13. Pulsar Timing                       3  1992
  14. Pulsation Timing Variations         1  2007
  15. Radial Velocity                     6  1989
  16. Transit                             7  2002
  17. Transit Timing Variations           3  2011
  1. bb = plants.groupby('method').aggregate({'number': min, 'year':min})
  2. print('各方法每年发现行星最少的是多少；各方法在哪年最早有发现行星')
  3. print(bb)
  5. 各方法每年发现行星最少的是多少；各方法在哪年最早有发现行星
  6.                                number  year
  7. method                                     
  8. Astrometry                          1  2010
  9. Eclipse Timing Variations           1  2008
  10. Imaging                             1  2004
  11. Microlensing                        1  2004
  12. Orbital Brightness Modulation       1  2011
  13. Pulsar Timing                       1  1992
  14. Pulsation Timing Variations         1  2007
  15. Radial Velocity                     1  1989
  16. Transit                             1  2002
  17. Transit Timing Variations           2  2011
  1. bb = plants.groupby('method').aggregate({'number': min, 'year':max})
  2. print('各方法每年发现行星最少的是多少；各方法在哪年最近有发现行星')
  3. print(bb)
  5. 各方法每年发现行星最少的是多少；各方法在哪年最近有发现行星
  6.                                number  year
  7. method                                     
  8. Astrometry                          1  2013
  9. Eclipse Timing Variations           1  2012
  10. Imaging                             1  2013
  11. Microlensing                        1  2013
  12. Orbital Brightness Modulation       1  2013
  13. Pulsar Timing                       1  2011
  14. Pulsation Timing Variations         1  2007
  15. Radial Velocity                     1  2014
  16. Transit                             1  2014
  17. Transit Timing Variations           2  2014
  1. bb = plants.groupby('method').aggregate({'number': max, 'year':max})
  2. print('各方法每年发现行星最大的是多少；各方法在哪年最近有发现行星')
  3. print(bb)
  5. 各方法每年发现行星最大的是多少；各方法在哪年最近有发现行星
  6.                                number  year
  7. method                                     
  8. Astrometry                          1  2013
  9. Eclipse Timing Variations           2  2012
  10. Imaging                             4  2013
  11. Microlensing                        2  2013
  12. Orbital Brightness Modulation       2  2013
  13. Pulsar Timing                       3  2011
  14. Pulsation Timing Variations         1  2007
  15. Radial Velocity                     6  2014
  16. Transit                             7  2014
  17. Transit Timing Variations           3  2014
  1. print('各个方法发现行星年份的中位数：')
  2. cc = plants.groupby('method')['year'].median()
  3. print(cc)
  5. 各个方法发现行星年份的中位数：
  6. method
  7. Astrometry                       2011.5
  8. Eclipse Timing Variations        2010.0
  9. Imaging                          2009.0
  10. Microlensing                     2010.0
  11. Orbital Brightness Modulation    2011.0
  12. Pulsar Timing                    1994.0
  13. Pulsation Timing Variations      2007.0
  14. Radial Velocity                  2009.0
  15. Transit                          2012.0
  16. Transit Timing Variations        2012.5
  17. Name: year, dtype: float64

10. 不同方法和年份发现的行星数量

  1. dd = plants.pivot_table('number', index='method', columns='year', aggfunc='sum')
  2. print(dd)
  4. 不同方法和年份发现的行星数量
  5. year                           1989  1992  1994  1995  1996  1997  1998  1999  \
  6. method                                                                          
  7. Astrometry                      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
  8. Eclipse Timing Variations       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
  9. Imaging                         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
  10. Microlensing                    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
  11. Orbital Brightness Modulation   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
  12. Pulsar Timing                   NaN   6.0   3.0   NaN   NaN   NaN   NaN   NaN   
  13. Pulsation Timing Variations     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
  14. Radial Velocity                 1.0   NaN   NaN   1.0  15.0   1.0  11.0  24.0   
  15. Transit                         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
  16. Transit Timing Variations       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
  18. year                           2000  2001  ...  2005  2006  2007  2008   2009  \
  19. method                                     ...                                  
  20. Astrometry                      NaN   NaN  ...   NaN   NaN   NaN   NaN    NaN   
  21. Eclipse Timing Variations       NaN   NaN  ...   NaN   NaN   NaN   4.0    1.0   
  22. Imaging                         NaN   NaN  ...   1.0   4.0   1.0  17.0    3.0   
  23. Microlensing                    NaN   NaN  ...   2.0   1.0   NaN   6.0    2.0   
  24. Orbital Brightness Modulation   NaN   NaN  ...   NaN   NaN   NaN   NaN    NaN   
  25. Pulsar Timing                   NaN   NaN  ...   NaN   NaN   NaN   NaN    NaN   
  26. Pulsation Timing Variations     NaN   NaN  ...   NaN   NaN   1.0   NaN    NaN   
  27. Radial Velocity                27.0  15.0  ...  61.0  33.0  47.0  76.0  105.0   
  28. Transit                         NaN   NaN  ...   NaN   5.0  16.0  17.0   20.0   
  29. Transit Timing Variations       NaN   NaN  ...   NaN   NaN   NaN   NaN    NaN   
  31. year                           2010   2011   2012   2013  2014  
  32. method                                                          
  33. Astrometry                      1.0    NaN    NaN    1.0   NaN  
  34. Eclipse Timing Variations       4.0    5.0    1.0    NaN   NaN  
  35. Imaging                         9.0    3.0    2.0    7.0   NaN  
  36. Microlensing                    2.0    1.0    8.0    4.0   NaN  
  37. Orbital Brightness Modulation   NaN    4.0    NaN    1.0   NaN  
  38. Pulsar Timing                   NaN    1.0    NaN    NaN   NaN  
  39. Pulsation Timing Variations     NaN    NaN    NaN    NaN   NaN  
  40. Radial Velocity                92.0  176.0   70.0   65.0  21.0  
  41. Transit                        85.0  162.0  175.0  197.0  93.0  
  42. Transit Timing Variations       NaN    2.0    2.0    2.0   3.0  
  44. [10 rows x 23 columns]