尼采般地抒情

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作者:尼采般地抒情
本站主页面和blog页面暂时一样,目的是为了百度收录,百度收录之后,会将主页换回引导页~

站点信息

文章数目:195
已运行时间:
目录
  1. 一、基本索引
    1. Series 索引
    2. Dataframe 索引
    3. 三种索引方式
  2. 二、Pandas 层级索引
    1. 构造层级索引
    2. 选取子集
    3. 交换分层顺序
    4. 交换并排序分层
  3. 三、透视表和交叉表
    1. 透视表
    2. 交叉表

尼采般地抒情

尼采般地抒情

公告栏

此网站主题为本人手写主题,主题还在开发中……


作者:尼采般地抒情
本站主页面和blog页面暂时一样,目的是为了百度收录,百度收录之后,会将主页换回引导页~

站点信息

文章数目:195
已运行时间:

一、基本索引

import pandas as pd
import numpy as np

Series 索引

ser_obj1 = pd.Series(range(5), index = ['a', 'b', 'c', 'd', 'e'])
print (ser_obj1.head())
a    0
b    1
c    2
d    3
e    4
dtype: int64
'''1. 行索引'''
print('行索引====================================================================')
print (ser_obj1['b'])
print (ser_obj1[0])
print('切片索引====================================================================')
'''2. 切片索引'''
print (ser_obj1[1:3])
print (ser_obj1['b':'d'])
print('不连续索引索引====================================================================')
#注意会不会包含尾巴。。
'''3. 不连续索引'''
print (ser_obj1[[0, 2, 4]])
print (ser_obj1[['a', 'e']])
print('布尔索引====================================================================')
'''4. 布尔索引'''
#是对里面的值进行判断,不是对索引
ser_bool = ser_obj1 > 2
print (ser_bool)
print (ser_obj1[ser_bool])#运用的方式
print (ser_obj1[ser_obj1 > 2])
行索引====================================================================
1
0
切片索引====================================================================
b    1
c    2
dtype: int64
b    1
c    2
d    3
dtype: int64
不连续索引索引====================================================================
a    0
c    2
e    4
dtype: int64
a    0
e    4
dtype: int64
布尔索引====================================================================
a    False
b    False
c    False
d     True
e     True
dtype: bool
d    3
e    4
dtype: int64
d    3
e    4
dtype: int64

Dataframe 索引

df_obj1 = pd.DataFrame(np.random.randn(5,4), columns = ['a', 'b', 'c', 'd'])
print (df_obj1.head())
          a         b         c         d
0 -0.720611 -0.002543 -0.084255 -0.583719
1  0.174715  0.288090 -0.254462 -1.669668
2 -1.872654 -0.466842 -0.481040 -1.735558
3  1.147668  1.205686  0.625582 -2.111863
4  1.177882  0.049581 -0.404098 -0.497125
'''0. 转换成行索引的话===方式一:transpose或者T'''
bb=df_obj1.transpose()
print(df_obj1.T)
print(bb)
'''0. 转换成行索引的话===方式二:重构'''
          0         1         2         3         4
a -0.720611  0.174715 -1.872654  1.147668  1.177882
b -0.002543  0.288090 -0.466842  1.205686  0.049581
c -0.084255 -0.254462 -0.481040  0.625582 -0.404098
d -0.583719 -1.669668 -1.735558 -2.111863 -0.497125
          0         1         2         3         4
a -0.720611  0.174715 -1.872654  1.147668  1.177882
b -0.002543  0.288090 -0.466842  1.205686  0.049581
c -0.084255 -0.254462 -0.481040  0.625582 -0.404098
d -0.583719 -1.669668 -1.735558 -2.111863 -0.497125





'0. 转换成行索引的话===方式二:重构/重塑===还没有完全明白'
'''1. 列索引=====默认为列索引来操作'''
print ('列索引')
print(df_obj1.b)#当成属性来获取值
print('===================')
print (df_obj1['a']) # 返回Series类型
print (type(df_obj1['a']))
print (df_obj1[['a']]) # 返回DataFrame类型
print (type(df_obj1[['a']]))
'''2. 不连续索引'''
print ('不连续索引')
print (df_obj1[['a','c']])
print (df_obj1[['a','c']])
列索引
0   -0.002543
1    0.288090
2   -0.466842
3    1.205686
4    0.049581
Name: b, dtype: float64
===================
0   -0.720611
1    0.174715
2   -1.872654
3    1.147668
4    1.177882
Name: a, dtype: float64
<class 'pandas.core.series.Series'>
          a
0 -0.720611
1  0.174715
2 -1.872654
3  1.147668
4  1.177882
<class 'pandas.core.frame.DataFrame'>
不连续索引
          a         c
0 -0.720611 -0.084255
1  0.174715 -0.254462
2 -1.872654 -0.481040
3  1.147668  0.625582
4  1.177882 -0.404098
          a         c
0 -0.720611 -0.084255
1  0.174715 -0.254462
2 -1.872654 -0.481040
3  1.147668  0.625582
4  1.177882 -0.404098

三种索引方式

print(ser_obj1)
print('============================')
print(df_obj1)
a    0
b    1
c    2
d    3
e    4
dtype: int64
============================
          a         b         c         d
0 -0.720611 -0.002543 -0.084255 -0.583719
1  0.174715  0.288090 -0.254462 -1.669668
2 -1.872654 -0.466842 -0.481040 -1.735558
3  1.147668  1.205686  0.625582 -2.111863
4  1.177882  0.049581 -0.404098 -0.497125
'''1. 标签索引 loc===用的是index和column的值索引【标签索引】'''
# Series
print (ser_obj1['b':'d'])
print (ser_obj1.loc['b':'d'])

# DataFrame
print (df_obj1[['a']])
print (df_obj1.loc[0:2,'a':'c'])
b    1
c    2
d    3
dtype: int64
b    1
c    2
d    3
dtype: int64
          a
0 -0.720611
1  0.174715
2 -1.872654
3  1.147668
4  1.177882
          a         b         c
0 -0.720611 -0.002543 -0.084255
1  0.174715  0.288090 -0.254462
2 -1.872654 -0.466842 -0.481040
'''2. 位置索引 iloc====用的是index和column的默认0,1,2,3···的值索引【位置索引】'''
print (ser_obj1[1:3])
print (ser_obj1.iloc[1:3])

# DataFrame
print (df_obj1.iloc[0:2, 0:2])
# 注意和df_obj.loc[0:2, 'a']的区别 = = = 行和列:都不包括尾巴
b    1
c    2
dtype: int64
b    1
c    2
dtype: int64
          a         b
0 -0.720611 -0.002543
1  0.174715  0.288090
'''3. 混合索引 ix===先按标签索引loc尝试操作,然后再按位置索引iloc尝试操作'''
print (ser_obj1.ix[1:3])
print (ser_obj1.ix['b':'c'])

# DataFrame
print (df_obj1.ix[0:2, 0:3])
b    1
c    2
dtype: int64
b    1
c    2
dtype: int64
          a         b         c
0 -0.720611 -0.002543 -0.084255
1  0.174715  0.288090 -0.254462
2 -1.872654 -0.466842 -0.481040

二、Pandas 层级索引

构造层级索引

ser_obj = pd.Series(np.random.randn(12),
                    index=[['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c', 'd', 'd', 'd'],
                           [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]])
#列表里面的列表
print (ser_obj)
a  0   -0.373441
   1    0.615976
   2    0.959092
b  0    1.743670
   1   -0.791517
   2   -0.774013
c  0    1.271094
   1   -0.723264
   2    0.253038
d  0   -0.767791
   1    0.419253
   2   -0.691644
dtype: float64
  • MultiIndex 索引对象
print (type(ser_obj.index))
print (ser_obj.index)
<class 'pandas.core.indexes.multi.MultiIndex'>
MultiIndex([('a', 0),
            ('a', 1),
            ('a', 2),
            ('b', 0),
            ('b', 1),
            ('b', 2),
            ('c', 0),
            ('c', 1),
            ('c', 2),
            ('d', 0),
            ('d', 1),
            ('d', 2)],
           )

选取子集

# 外层选取
print (ser_obj['c'])
0    1.271094
1   -0.723264
2    0.253038
dtype: float64
# 内层选取
print (ser_obj[:, 2])
a    0.959092
b   -0.774013
c    0.253038
d   -0.691644
dtype: float64

交换分层顺序

df_obj2 = pd.DataFrame(np.random.randn(6,4), columns = ['a', 'b', 'c', 'd'],index=[['a', 'a', 'a', 'b', 'b','b'],[1, 2, 3, 1, 2,3]])
#print (df_obj1.swaplevel())
print (df_obj2)
            a         b         c         d
a 1  0.273776 -0.018659  1.512727 -0.088091
  2 -0.410883 -1.488943  0.917268  1.179941
  3 -0.237532 -0.823717  0.189495  1.060476
b 1  0.727872  0.323352  0.443786  0.780510
  2 -1.407645 -0.059689  1.439843 -1.700740
  3 -0.377628 -0.137348 -0.739980  0.122528

交换并排序分层

print (df_obj2.swaplevel().sortvalues(by='a'))
#Series没有这个内置函数sortlevel===dataframe有

三、透视表和交叉表

dataset_path = './data/starcraft.csv'
df_data = pd.read_csv(dataset_path, usecols=['LeagueIndex', 'Age', 'HoursPerWeek',
                                             'TotalHours', 'APM'])
print(df_data.head())
   LeagueIndex   Age  HoursPerWeek  TotalHours       APM
0            5  27.0          10.0      3000.0  143.7180
1            5  23.0          10.0      5000.0  129.2322
2            4  30.0          10.0       200.0   69.9612
3            3  19.0          20.0       400.0  107.6016
4            3  32.0          10.0       500.0  122.8908

透视表

pd.pivot_table(df_data,
               index=['Age'],
               columns=['LeagueIndex'],
               aggfunc=np.sum)

| | APM | HoursPerWeek | TotalHours |
| ———– | ———- | ———— | ———- | ———- | ———– | ———- | ——— | —– | —– | —– | — | —— | —— | —– | —— | ——- | ——- | ——- | ——— | ——– | ——- |
| LeagueIndex | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 1 | 2 | 3 | … | 5 | 6 | 7 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Age | | | | | | | | | | | | | | | | | | | | | |
| 16.0 | 1062.44754 | 2919.70434 | 4851.9222 | 5149.7310 | 7787.37780 | 9042.1722 | 386.7774 | 324.0 | 720.0 | 778.0 | … | 1220.0 | 1280.0 | 56.0 | 4307.0 | 13143.0 | 29211.0 | 23581.0 | 49233.0 | 51320.0 | 3000.0 |
| 17.0 | 655.67280 | 1661.01540 | 4181.8920 | 5525.3586 | 10052.72100 | 8310.0858 | 573.8286 | 184.0 | 378.0 | 664.0 | … | 1460.0 | 1116.0 | 104.0 | 2044.0 | 7423.0 | 16602.0 | 24005.0 | 53375.0 | 45421.0 | 12700.0 |
| 18.0 | 704.47680 | 3300.41040 | 4847.2152 | 8763.0783 | 10988.66100 | 9134.7240 | 618.5790 | 204.0 | 548.0 | 886.0 | … | 1644.0 | 1194.0 | 164.0 | 3570.0 | 11471.0 | 21037.0 | 46034.0 | 1056486.0 | 50378.0 | 3200.0 |
| 19.0 | 734.55600 | 2216.81880 | 5183.7888 | 8030.1960 | 9271.09260 | 11955.6030 | 696.7770 | 126.0 | 458.0 | 950.0 | … | 962.0 | 1642.0 | 168.0 | 2355.0 | 8467.0 | 31861.0 | 39705.0 | 44697.0 | 70331.0 | 4166.0 |
| 20.0 | 1624.89660 | 2147.23200 | 4211.5686 | 10596.2070 | 10871.65440 | 14291.8692 | NaN | 328.0 | 288.0 | 654.0 | … | 1290.0 | 1816.0 | NaN | 7212.0 | 6325.0 | 20174.0 | 55083.0 | 64170.0 | 105131.0 | NaN |
| 21.0 | 780.67950 | 1578.02880 | 3949.3062 | 8689.8804 | 11954.91660 | 13165.7649 | 867.3474 | 162.0 | 270.0 | 580.0 | … | 1446.0 | 1858.0 | 62.0 | 3377.0 | 7673.0 | 19095.0 | 42296.0 | 68739.0 | 82061.0 | 3180.0 |
| 22.0 | 674.59860 | 2147.50980 | 4379.3424 | 7818.7302 | 10473.28380 | 10165.8672 | 493.1586 | 146.0 | 372.0 | 680.0 | … | 1304.0 | 1448.0 | 112.0 | 4225.0 | 10861.0 | 23030.0 | 57996.0 | 84330.0 | 67069.0 | 6950.0 |
| 23.0 | 359.65980 | 1575.06120 | 4602.7416 | 7616.9298 | 8292.86160 | 6131.1936 | 1799.6520 | 46.0 | 320.0 | 598.0 | … | 998.0 | 726.0 | 296.0 | 896.0 | 12350.0 | 23081.0 | 40025.0 | 56097.0 | 43176.0 | 14290.0 |
| 24.0 | 439.43040 | 1717.55340 | 2876.8572 | 5503.7736 | 7292.32740 | 7240.4076 | 428.6538 | 116.0 | 344.0 | 406.0 | … | 668.0 | 1048.0 | 36.0 | 2070.0 | 9543.0 | 25421.0 | 35384.0 | 36147.0 | 43114.0 | 2250.0 |
| 25.0 | 572.61420 | 1178.02440 | 2201.6388 | 4710.9924 | 6168.19260 | 2200.6362 | 361.4550 | 124.0 | 166.0 | 268.0 | … | 682.0 | 256.0 | 52.0 | 2440.0 | 5846.0 | 11270.0 | 26610.0 | 40681.0 | 14890.0 | 3300.0 |
| 26.0 | 418.70874 | 1165.96680 | 1794.1890 | 3139.2852 | 4016.67060 | 3301.8498 | 408.2202 | 96.0 | 148.0 | 272.0 | … | 418.0 | 354.0 | 60.0 | 1608.0 | 3417.0 | 10548.0 | 16839.0 | 20100.0 | 17663.0 | 2300.0 |
| 27.0 | 359.17320 | 1164.15960 | 1426.4550 | 2850.1320 | 3498.30300 | 2040.8454 | NaN | 40.0 | 152.0 | 226.0 | … | 340.0 | 164.0 | NaN | 1100.0 | 3615.0 | 7525.0 | 15935.0 | 19770.0 | 11796.0 | NaN |
| 28.0 | 333.84240 | 479.34000 | 1152.5958 | 2205.8778 | 1992.60540 | 521.7666 | NaN | 28.0 | 90.0 | 220.0 | … | 186.0 | 44.0 | NaN | 466.0 | 1860.0 | 7901.0 | 15370.0 | 10872.0 | 2500.0 | NaN |
| 29.0 | 236.74020 | 695.88480 | 568.2594 | 1447.5906 | 1398.78540 | 715.9404 | NaN | 54.0 | 56.0 | 80.0 | … | 180.0 | 106.0 | NaN | 2490.0 | 2000.0 | 3816.0 | 8220.0 | 10292.0 | 5950.0 | NaN |
| 30.0 | 125.53740 | 441.14160 | 733.6416 | 743.4468 | 578.32020 | 123.3774 | NaN | 14.0 | 54.0 | 76.0 | … | 90.0 | 28.0 | NaN | 210.0 | 2440.0 | 4370.0 | 6310.0 | 3440.0 | 1500.0 | NaN |
| 31.0 | 41.58600 | 314.92980 | 659.2626 | 1166.7606 | 315.53460 | 200.1708 | NaN | 12.0 | 36.0 | 68.0 | … | 12.0 | 16.0 | NaN | 200.0 | 1300.0 | 3500.0 | 8710.0 | 1050.0 | 1500.0 | NaN |
| 32.0 | 179.14380 | 315.54180 | 457.5174 | 541.8996 | 66.19740 | NaN | NaN | 40.0 | 56.0 | 68.0 | … | 10.0 | NaN | NaN | 1600.0 | 860.0 | 2300.0 | 6040.0 | 800.0 | NaN | NaN |
| 33.0 | 198.77880 | 153.34680 | 284.8218 | 116.7516 | 245.45166 | NaN | NaN | 32.0 | 12.0 | 42.0 | … | 60.0 | NaN | NaN | 1200.0 | 220.0 | 2065.0 | 1130.0 | 2050.0 | NaN | NaN |
| 34.0 | 49.11360 | 276.88260 | 268.4100 | 340.7124 | 174.54540 | NaN | NaN | 12.0 | 56.0 | 28.0 | … | 14.0 | NaN | NaN | 150.0 | 3380.0 | 1150.0 | 2400.0 | 2764.0 | NaN | NaN |
| 35.0 | 229.31280 | 54.04680 | 170.4930 | 634.7688 | 115.06440 | NaN | NaN | 54.0 | 8.0 | 26.0 | … | 12.0 | NaN | NaN | 1350.0 | 500.0 | 1950.0 | 3800.0 | 800.0 | NaN | NaN |
| 36.0 | NaN | 150.13140 | 333.6474 | 73.6980 | NaN | NaN | NaN | NaN | 16.0 | 40.0 | … | NaN | NaN | NaN | NaN | 500.0 | 1950.0 | 400.0 | NaN | NaN | NaN |
| 37.0 | 22.05960 | 49.89600 | 44.9682 | NaN | 451.13160 | NaN | NaN | 12.0 | 30.0 | 6.0 | … | 32.0 | NaN | NaN | 300.0 | 125.0 | 600.0 | NaN | 1800.0 | NaN | NaN |
| 38.0 | 71.59500 | NaN | 334.6878 | NaN | NaN | NaN | NaN | 16.0 | NaN | 46.0 | … | NaN | NaN | NaN | 300.0 | NaN | 2280.0 | NaN | NaN | NaN | NaN |
| 39.0 | 29.87640 | NaN | 53.7690 | 86.7246 | NaN | NaN | NaN | 10.0 | NaN | 12.0 | … | NaN | NaN | NaN | 500.0 | NaN | 450.0 | 500.0 | NaN | NaN | NaN |
| 40.0 | 38.55900 | 51.83580 | 107.4684 | NaN | NaN | NaN | NaN | 12.0 | 14.0 | 26.0 | … | NaN | NaN | NaN | 150.0 | 500.0 | 1080.0 | NaN | NaN | NaN | NaN |
| 41.0 | NaN | 108.45720 | 77.6472 | NaN | NaN | NaN | NaN | NaN | 12.0 | 14.0 | … | NaN | NaN | NaN | NaN | 450.0 | 800.0 | NaN | NaN | NaN | NaN |
| 43.0 | NaN | 86.05860 | NaN | NaN | NaN | NaN | NaN | NaN | 10.0 | NaN | … | NaN | NaN | NaN | NaN | 730.0 | NaN | NaN | NaN | NaN | NaN |
| 44.0 | NaN | NaN | NaN | 89.5266 | NaN | NaN | NaN | NaN | NaN | NaN | … | NaN | NaN | NaN | NaN | NaN | NaN | 500.0 | NaN | NaN | NaN |

28 rows × 21 columns

pd.pivot_table(df_data,
               index=['Age'],
               columns=['LeagueIndex'],
               aggfunc=np.sum,
               fill_value=-100.)

| | APM | HoursPerWeek | TotalHours |
| ———– | ———- | ———— | ———- | ———- | ———– | ———- | ——— | —- | —- | —- | — | —- | —- | —- | —- | —– | —– | —– | ——- | —— | —– |
| LeagueIndex | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 1 | 2 | 3 | … | 5 | 6 | 7 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Age | | | | | | | | | | | | | | | | | | | | | |
| 16.0 | 1062.44754 | 2919.70434 | 4851.9222 | 5149.7310 | 7787.37780 | 9042.1722 | 386.7774 | 324 | 720 | 778 | … | 1220 | 1280 | 56 | 4307 | 13143 | 29211 | 23581 | 49233 | 51320 | 3000 |
| 17.0 | 655.67280 | 1661.01540 | 4181.8920 | 5525.3586 | 10052.72100 | 8310.0858 | 573.8286 | 184 | 378 | 664 | … | 1460 | 1116 | 104 | 2044 | 7423 | 16602 | 24005 | 53375 | 45421 | 12700 |
| 18.0 | 704.47680 | 3300.41040 | 4847.2152 | 8763.0783 | 10988.66100 | 9134.7240 | 618.5790 | 204 | 548 | 886 | … | 1644 | 1194 | 164 | 3570 | 11471 | 21037 | 46034 | 1056486 | 50378 | 3200 |
| 19.0 | 734.55600 | 2216.81880 | 5183.7888 | 8030.1960 | 9271.09260 | 11955.6030 | 696.7770 | 126 | 458 | 950 | … | 962 | 1642 | 168 | 2355 | 8467 | 31861 | 39705 | 44697 | 70331 | 4166 |
| 20.0 | 1624.89660 | 2147.23200 | 4211.5686 | 10596.2070 | 10871.65440 | 14291.8692 | -100.0000 | 328 | 288 | 654 | … | 1290 | 1816 | -100 | 7212 | 6325 | 20174 | 55083 | 64170 | 105131 | -100 |
| 21.0 | 780.67950 | 1578.02880 | 3949.3062 | 8689.8804 | 11954.91660 | 13165.7649 | 867.3474 | 162 | 270 | 580 | … | 1446 | 1858 | 62 | 3377 | 7673 | 19095 | 42296 | 68739 | 82061 | 3180 |
| 22.0 | 674.59860 | 2147.50980 | 4379.3424 | 7818.7302 | 10473.28380 | 10165.8672 | 493.1586 | 146 | 372 | 680 | … | 1304 | 1448 | 112 | 4225 | 10861 | 23030 | 57996 | 84330 | 67069 | 6950 |
| 23.0 | 359.65980 | 1575.06120 | 4602.7416 | 7616.9298 | 8292.86160 | 6131.1936 | 1799.6520 | 46 | 320 | 598 | … | 998 | 726 | 296 | 896 | 12350 | 23081 | 40025 | 56097 | 43176 | 14290 |
| 24.0 | 439.43040 | 1717.55340 | 2876.8572 | 5503.7736 | 7292.32740 | 7240.4076 | 428.6538 | 116 | 344 | 406 | … | 668 | 1048 | 36 | 2070 | 9543 | 25421 | 35384 | 36147 | 43114 | 2250 |
| 25.0 | 572.61420 | 1178.02440 | 2201.6388 | 4710.9924 | 6168.19260 | 2200.6362 | 361.4550 | 124 | 166 | 268 | … | 682 | 256 | 52 | 2440 | 5846 | 11270 | 26610 | 40681 | 14890 | 3300 |
| 26.0 | 418.70874 | 1165.96680 | 1794.1890 | 3139.2852 | 4016.67060 | 3301.8498 | 408.2202 | 96 | 148 | 272 | … | 418 | 354 | 60 | 1608 | 3417 | 10548 | 16839 | 20100 | 17663 | 2300 |
| 27.0 | 359.17320 | 1164.15960 | 1426.4550 | 2850.1320 | 3498.30300 | 2040.8454 | -100.0000 | 40 | 152 | 226 | … | 340 | 164 | -100 | 1100 | 3615 | 7525 | 15935 | 19770 | 11796 | -100 |
| 28.0 | 333.84240 | 479.34000 | 1152.5958 | 2205.8778 | 1992.60540 | 521.7666 | -100.0000 | 28 | 90 | 220 | … | 186 | 44 | -100 | 466 | 1860 | 7901 | 15370 | 10872 | 2500 | -100 |
| 29.0 | 236.74020 | 695.88480 | 568.2594 | 1447.5906 | 1398.78540 | 715.9404 | -100.0000 | 54 | 56 | 80 | … | 180 | 106 | -100 | 2490 | 2000 | 3816 | 8220 | 10292 | 5950 | -100 |
| 30.0 | 125.53740 | 441.14160 | 733.6416 | 743.4468 | 578.32020 | 123.3774 | -100.0000 | 14 | 54 | 76 | … | 90 | 28 | -100 | 210 | 2440 | 4370 | 6310 | 3440 | 1500 | -100 |
| 31.0 | 41.58600 | 314.92980 | 659.2626 | 1166.7606 | 315.53460 | 200.1708 | -100.0000 | 12 | 36 | 68 | … | 12 | 16 | -100 | 200 | 1300 | 3500 | 8710 | 1050 | 1500 | -100 |
| 32.0 | 179.14380 | 315.54180 | 457.5174 | 541.8996 | 66.19740 | -100.0000 | -100.0000 | 40 | 56 | 68 | … | 10 | -100 | -100 | 1600 | 860 | 2300 | 6040 | 800 | -100 | -100 |
| 33.0 | 198.77880 | 153.34680 | 284.8218 | 116.7516 | 245.45166 | -100.0000 | -100.0000 | 32 | 12 | 42 | … | 60 | -100 | -100 | 1200 | 220 | 2065 | 1130 | 2050 | -100 | -100 |
| 34.0 | 49.11360 | 276.88260 | 268.4100 | 340.7124 | 174.54540 | -100.0000 | -100.0000 | 12 | 56 | 28 | … | 14 | -100 | -100 | 150 | 3380 | 1150 | 2400 | 2764 | -100 | -100 |
| 35.0 | 229.31280 | 54.04680 | 170.4930 | 634.7688 | 115.06440 | -100.0000 | -100.0000 | 54 | 8 | 26 | … | 12 | -100 | -100 | 1350 | 500 | 1950 | 3800 | 800 | -100 | -100 |
| 36.0 | -100.00000 | 150.13140 | 333.6474 | 73.6980 | -100.00000 | -100.0000 | -100.0000 | -100 | 16 | 40 | … | -100 | -100 | -100 | -100 | 500 | 1950 | 400 | -100 | -100 | -100 |
| 37.0 | 22.05960 | 49.89600 | 44.9682 | -100.0000 | 451.13160 | -100.0000 | -100.0000 | 12 | 30 | 6 | … | 32 | -100 | -100 | 300 | 125 | 600 | -100 | 1800 | -100 | -100 |
| 38.0 | 71.59500 | -100.00000 | 334.6878 | -100.0000 | -100.00000 | -100.0000 | -100.0000 | 16 | -100 | 46 | … | -100 | -100 | -100 | 300 | -100 | 2280 | -100 | -100 | -100 | -100 |
| 39.0 | 29.87640 | -100.00000 | 53.7690 | 86.7246 | -100.00000 | -100.0000 | -100.0000 | 10 | -100 | 12 | … | -100 | -100 | -100 | 500 | -100 | 450 | 500 | -100 | -100 | -100 |
| 40.0 | 38.55900 | 51.83580 | 107.4684 | -100.0000 | -100.00000 | -100.0000 | -100.0000 | 12 | 14 | 26 | … | -100 | -100 | -100 | 150 | 500 | 1080 | -100 | -100 | -100 | -100 |
| 41.0 | -100.00000 | 108.45720 | 77.6472 | -100.0000 | -100.00000 | -100.0000 | -100.0000 | -100 | 12 | 14 | … | -100 | -100 | -100 | -100 | 450 | 800 | -100 | -100 | -100 | -100 |
| 43.0 | -100.00000 | 86.05860 | -100.0000 | -100.0000 | -100.00000 | -100.0000 | -100.0000 | -100 | 10 | -100 | … | -100 | -100 | -100 | -100 | 730 | -100 | -100 | -100 | -100 | -100 |
| 44.0 | -100.00000 | -100.00000 | -100.0000 | 89.5266 | -100.00000 | -100.0000 | -100.0000 | -100 | -100 | -100 | … | -100 | -100 | -100 | -100 | -100 | -100 | 500 | -100 | -100 | -100 |

28 rows × 21 columns

交叉表

pd.crosstab(df_data['LeagueIndex'],
            df_data['Age'])
Age 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 34.0 35.0 36.0 37.0 38.0 39.0 40.0 41.0 43.0 44.0
LeagueIndex
1 18 9 13 12 22 12 12 6 9 11 1 5 0 1 1 1 1 0 0 0
2 38 22 38 32 25 23 28 24 24 18 5 1 2 1 0 0 1 2 1 0
3 48 43 56 53 47 44 47 47 32 26 3 3 5 1 4 1 2 1 0 0
4 45 49 78 71 97 81 75 72 52 45 4 7 1 0 0 1 0 0 0 1
5 55 71 81 68 80 96 81 59 58 51 2 1 0 2 0 0 0 0 0 0
6 50 51 56 73 86 83 68 42 48 15 0 0 0 0 0 0 0 0 0 0
7 2 3 3 4 0 5 3 9 2 2 0 0 0 0 0 0 0 0 0 0

7 rows × 28 columns

pd.crosstab(df_data['LeagueIndex'],
            [df_data['Age'], df_data['HoursPerWeek']])

| Age | 16.0 | … | 39.0 | 40.0 | 41.0 | 43.0 | 44.0 |
| ———— | —- | — | —- | —- | —- | —- | —- | —- | —- | —- | — | —- | —- | —- | —- | —- | — | — | —- | —- | — |
| HoursPerWeek | 0.0 | 2.0 | 4.0 | 6.0 | 8.0 | 10.0 | 12.0 | 14.0 | 16.0 | 20.0 | … | 12.0 | 10.0 | 12.0 | 14.0 | 16.0 | 4.0 | 8.0 | 14.0 | 10.0 | 6.0 |
| LeagueIndex | | | | | | | | | | | | | | | | | | | | | |
| 1 | 0 | 0 | 0 | 1 | 3 | 1 | 1 | 2 | 3 | 3 | … | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 2 | 1 | 0 | 9 | 4 | 4 | 3 | 3 | … | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 |
| 3 | 0 | 0 | 1 | 6 | 7 | 6 | 6 | 7 | 2 | 1 | … | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| 4 | 0 | 2 | 5 | 1 | 6 | 4 | 3 | 6 | 3 | 1 | … | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 5 | 0 | 1 | 2 | 1 | 3 | 8 | 3 | 6 | 3 | 6 | … | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 1 | 1 | 4 | 2 | 1 | 1 | 4 | 3 | 1 | 7 | … | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | … | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

7 rows × 325 columns

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