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  • 数据清洗与分析

    数据科学第三章

    1.Panda 的数据结构

    （1）引入panda包

    • part 1
    import pandas as pd
import numpy as np
from pandas import Series, DataFrame

    （2）Series对象

    • part 2
    height = Series([187, 190, 185, 178, 185], index=['13', '14', '7', '2', '9'])
            # index为字符串
height

    13    187
14    190
7     185
2     178
9     185
dtype: int64
    • part 3
    # 可以用字典创建Series 对象
height1 = Series({'13':187, '14':190, '7':185, '2':178, '9':185})
height

    13    187
14    190
7     185
2     178
9     185
dtype: int64

    （3）Series数据访问

    • part 4
    height['13']                # 检索13号球员身高， 同height[0]

    187
    • part 5
    height[['13', '2', '7']]    # 检索13号、2号、7号球员身高， 同height[[0, 3, 2]]

    13    187
2     178
7     185
dtype: int64
    • part 6
    height[1:3]                 # 截断， 选取序号为1~2的球员身高

    14    190
7     185
dtype: int64
    • part 7
    height[height.values >= 186]# 检索高于186的球员

    13    187
14    190
dtype: int64
    • part 8
    height['13'] = 188          # 修改13号球员身高
height['13']

    188
    • part 9
    height[1: 3] = 160          # 修改序号为1，2的数据，标量赋值
height

    13    188
14    160
7     160
2     178
9     185
dtype: int64
    • part 10
    a = Series([190, 187], index=['23', '5'])   # 创建新球员的Series对象a
newheight = height.append(a)                # 取出height对象值添加啊后赋给newheight
newheight

    13    188
14    160
7     160
2     178
9     185
23    190
5     187
dtype: int64
    • part 11
    newheight = height.drop(['13', '9'])       # 删除13号和9号球员数据
newheight

    14    160
7     160
2     178
dtype: int64
    • part 12
    height.index=[1, 2, 3, 4, 5]               # 用新的列表替换即可
height

    1    188
2    160
3    160
4    178
5    185
dtype: int64
    • part 13
    height[ [1, 3] ]              # 使用索引访问，注意：这是数字类型

    1    188
3    160
dtype: int64
    • part 14
    height.iloc[0]               # 基于位置序号的访问

    188

    DataFrame对象

    • part 15
    data = [[19, 170, 68], [20, 165, 65], [18, 175, 65]]
students = DataFrame(data, index=[1, 2, 3], columns=['age', 'height', 'weight'])
students
    age height weight
    1 19 170 68
    2 20 165 65
    3 18 175 65
    DataFrame数据选取方法
    选取类型 选取方法 说明
    基于索引名选取 obj[ col ] 选取某列
    * obj[ colList ] 选取某几列
    * obj.loc[index, col] 选取某行某列
    * obj.loc[indexList, colList] 选取多行多列
    基于位置序号选取 obj.iloc[iloc, cloc] 选取某行某列
    * obj.iloc[ilocList, clocList] 选取多行多列
    * obj.iloc[a:b, c:d] 选取a~(b-1)行，c~(d-1)列
    条件筛选 obj.loc[ condition, colList ] 使用索引构造条件表达式，选取满足条件的行
    * obj.iloc[ condition, clocList ] 使用位置序号构造条件表达式，选取满足条件的行

    （1）查询

    • part 16
    students.loc[1, 'age']         # 查询1号同学的年龄

    19
    • part 17
    students.iloc[[0, 2], [0, 1]]  # 查询第0、2行，0、1列的信息
    age height
    1 19 170
    3 18 175
    * part 18 
    students.loc[:, ['height', 'age']]   # 特定列
    height age
    1 170 19
    2 165 20
    3 175 18
    * part 19 
    students[['height', 'age']]     # 与上同
    height age
    1 170 19
    2 165 20
    3 175 18
    * part 20 
    students.iloc[1:, 0:2]          # 截取
    age height
    2 20 165
    3 18 175
    * part 21 
    students[1:3]                   # 截取行数据
    age height weight
    2 20 165 65
    3 18 175 65
    * part 22 
    students['height'] >= 168

    1     True
2    False
3     True
Name: height, dtype: bool
    • part 23
    students.loc[ students['height']>=168, ['height', 'weight'] ]
    height weight
    1 170 68
    3 175 65

    （2）修改操作

    • part 24
    # 增加列
students['expense'] = [1500, 1600, 1200]
students
    age height weight expense
    1 19 170 68 1500
    2 20 165 65 1600
    3 18 175 65 1200
    * part 25 
    # 增加行
students.loc[4] = [5, 5, 5, 5]
students
    age height weight expense
    1 19 170 68 1500
    2 20 165 65 1600
    3 18 175 65 1200
    4 5 5 5 5
    * part 26 
    # 修改列
students['expense'] = 1000
students
    age height weight expense
    1 19 170 68 1000
    2 20 165 65 1000
    3 18 175 65 1000
    4 5 5 5 1000
    * part 27 
    # 修改行
students.loc[1, :] = [21, 188, 70, 20]
students
    age height weight expense
    1 21 188 70 20
    2 20 165 65 1000
    3 18 175 65 1000
    4 5 5 5 1000
    * part 28 
    # 修改点
students.loc[students['expense']<500, 'expense'] = 1200
students
    age height weight expense
    1 21 188 70 1200
    2 20 165 65 1000
    3 18 175 65 1000
    4 5 5 5 1000

    （3）删除

    • part 29
    # 删除行输出
students.drop(1, axis=0)
    age height weight expense
    2 20 165 65 1000
    3 18 175 65 1000
    4 5 5 5 1000
    * part 30 
    # 删除列输出
students.drop('expense', axis=1)
    age height weight
    1 21 188 70
    2 20 165 65
    3 18 175 65
    4 5 5 5
    * part 31 
    # 删除多行，且直接修改students
students.drop([1, 2], axis=0, inplace=True)
students
    age height weight expense
    3 18 175 65 1000
    4 5 5 5 1000

    2.数据文件读写

    （1）csv文件的读写，txt雷同

    • part 32
    # 读取
GINI = pd.read_csv('3th dataGINI.csv')
GINI[-3:]
    Country 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
    155 乌干达 NaN NaN 45.2 NaN NaN 42.9 NaN NaN NaN 44.2 NaN NaN 41.0 NaN NaN
    156 刚果（金） NaN NaN NaN NaN 42.2 NaN NaN NaN NaN NaN NaN NaN 42.1 NaN NaN
    157 津巴布韦 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 43.2 NaN NaN NaN
    * part 33 
    # 保存
students.to_csv('3th dataStudents.csv', mode='w', header=True, index=False)

    （2）读取Excel文件

    • part 34
    # 读取
GINI = pd.read_excel('3th dataGINI.xlsx', 'GINI', index_col=0)

    3.数据清洗

    • part 35
    GINI.dropna(thresh=7, inplace=True)         # 保留有效数据个数大于7的行
GINI
    2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
    Country
    中国 NaN NaN 42.10 NaN NaN 41.0 NaN NaN 42.9 NaN 43.70 42.40 42.20 39.7 39.1
    泰国 42.8 NaN 41.90 NaN 42.50 NaN 41.80 39.80 40.3 39.60 39.40 37.50 39.30 37.8 37.0
    印度尼西亚 28.5 29.0 31.70 31.8 32.70 33.0 34.20 35.70 35.1 35.10 36.40 39.70 39.60 39.9 39.4
    越南 NaN NaN 37.00 NaN 36.80 NaN 35.80 NaN 35.6 NaN 39.30 NaN 35.60 NaN 34.8
    柬埔寨 NaN NaN NaN NaN 35.46 NaN NaN 41.14 35.1 34.65 33.44 31.70 30.76 NaN NaN
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    危地马拉 54.2 NaN 56.94 54.1 50.73 NaN 54.60 NaN NaN NaN NaN 52.35 NaN NaN 48.3
    洪都拉斯 NaN 55.6 55.70 58.1 58.10 59.5 57.50 55.80 55.5 51.30 53.10 56.20 56.10 52.6 50.4
    巴拉圭 NaN 54.6 57.30 54.9 52.30 51.4 53.00 53.00 50.7 49.10 51.00 52.30 47.60 47.9 50.7
    萨尔瓦多 51.5 51.4 51.90 50.4 47.80 48.5 45.70 45.20 46.9 45.80 43.50 42.30 41.80 43.4 41.6
    南非 57.8 NaN NaN NaN NaN 64.8 64.79 NaN 63.0 NaN 63.40 63.38 NaN NaN 63.0

    67 rows × 15 columns

    * part 36 
    GINI.interpolate(method='linear', axis=1, inplace=True)     # 线性插值
GINI.fillna(method='bfill', axis=1, inplace=True)           # 向前填充
GINI
    2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
    Country
    中国 42.10 42.10 42.10 41.733333 41.366667 41.000000 41.633333 42.266667 42.9 43.30 43.70 42.40 42.200000 39.700000 39.10
    泰国 42.80 42.35 41.90 42.200000 42.500000 42.150000 41.800000 39.800000 40.3 39.60 39.40 37.50 39.300000 37.800000 37.00
    印度尼西亚 28.50 29.00 31.70 31.800000 32.700000 33.000000 34.200000 35.700000 35.1 35.10 36.40 39.70 39.600000 39.900000 39.40
    越南 37.00 37.00 37.00 36.900000 36.800000 36.300000 35.800000 35.700000 35.6 37.45 39.30 37.45 35.600000 35.200000 34.80
    柬埔寨 35.46 35.46 35.46 35.460000 35.460000 37.353333 39.246667 41.140000 35.1 34.65 33.44 31.70 30.760000 30.760000 30.76
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    危地马拉 54.20 55.57 56.94 54.100000 50.730000 52.665000 54.600000 54.150000 53.7 53.25 52.80 52.35 51.000000 49.650000 48.30
    洪都拉斯 55.60 55.60 55.70 58.100000 58.100000 59.500000 57.500000 55.800000 55.5 51.30 53.10 56.20 56.100000 52.600000 50.40
    巴拉圭 54.60 54.60 57.30 54.900000 52.300000 51.400000 53.000000 53.000000 50.7 49.10 51.00 52.30 47.600000 47.900000 50.70
    萨尔瓦多 51.50 51.40 51.90 50.400000 47.800000 48.500000 45.700000 45.200000 46.9 45.80 43.50 42.30 41.800000 43.400000 41.60
    南非 57.80 59.20 60.60 62.000000 63.400000 64.800000 64.790000 63.895000 63.0 63.20 63.40 63.38 63.253333 63.126667 63.00

    67 rows × 15 columns

    * part 37 
    # 去除重复值，即重复行
GINI.drop_duplicates()
    2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
    Country
    中国 42.10 42.10 42.10 41.733333 41.366667 41.000000 41.633333 42.266667 42.9 43.30 43.70 42.40 42.200000 39.700000 39.10
    泰国 42.80 42.35 41.90 42.200000 42.500000 42.150000 41.800000 39.800000 40.3 39.60 39.40 37.50 39.300000 37.800000 37.00
    印度尼西亚 28.50 29.00 31.70 31.800000 32.700000 33.000000 34.200000 35.700000 35.1 35.10 36.40 39.70 39.600000 39.900000 39.40
    越南 37.00 37.00 37.00 36.900000 36.800000 36.300000 35.800000 35.700000 35.6 37.45 39.30 37.45 35.600000 35.200000 34.80
    柬埔寨 35.46 35.46 35.46 35.460000 35.460000 37.353333 39.246667 41.140000 35.1 34.65 33.44 31.70 30.760000 30.760000 30.76
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    危地马拉 54.20 55.57 56.94 54.100000 50.730000 52.665000 54.600000 54.150000 53.7 53.25 52.80 52.35 51.000000 49.650000 48.30
    洪都拉斯 55.60 55.60 55.70 58.100000 58.100000 59.500000 57.500000 55.800000 55.5 51.30 53.10 56.20 56.100000 52.600000 50.40
    巴拉圭 54.60 54.60 57.30 54.900000 52.300000 51.400000 53.000000 53.000000 50.7 49.10 51.00 52.30 47.600000 47.900000 50.70
    萨尔瓦多 51.50 51.40 51.90 50.400000 47.800000 48.500000 45.700000 45.200000 46.9 45.80 43.50 42.30 41.800000 43.400000 41.60
    南非 57.80 59.20 60.60 62.000000 63.400000 64.800000 64.790000 63.895000 63.0 63.20 63.40 63.38 63.253333 63.126667 63.00

    67 rows × 15 columns

    * part 38 
    GINI1 = GINI[:30]
GINI2 = GINI[30:]
# 数据合并
NEW_GINI = pd.concat([GINI1, GINI2], axis=0)     # axis=0表示按行追加
NEW_GINI
    2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
    Country
    中国 42.10 42.10 42.10 41.733333 41.366667 41.000000 41.633333 42.266667 42.9 43.30 43.70 42.40 42.200000 39.700000 39.10
    泰国 42.80 42.35 41.90 42.200000 42.500000 42.150000 41.800000 39.800000 40.3 39.60 39.40 37.50 39.300000 37.800000 37.00
    印度尼西亚 28.50 29.00 31.70 31.800000 32.700000 33.000000 34.200000 35.700000 35.1 35.10 36.40 39.70 39.600000 39.900000 39.40
    越南 37.00 37.00 37.00 36.900000 36.800000 36.300000 35.800000 35.700000 35.6 37.45 39.30 37.45 35.600000 35.200000 34.80
    柬埔寨 35.46 35.46 35.46 35.460000 35.460000 37.353333 39.246667 41.140000 35.1 34.65 33.44 31.70 30.760000 30.760000 30.76
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    危地马拉 54.20 55.57 56.94 54.100000 50.730000 52.665000 54.600000 54.150000 53.7 53.25 52.80 52.35 51.000000 49.650000 48.30
    洪都拉斯 55.60 55.60 55.70 58.100000 58.100000 59.500000 57.500000 55.800000 55.5 51.30 53.10 56.20 56.100000 52.600000 50.40
    巴拉圭 54.60 54.60 57.30 54.900000 52.300000 51.400000 53.000000 53.000000 50.7 49.10 51.00 52.30 47.600000 47.900000 50.70
    萨尔瓦多 51.50 51.40 51.90 50.400000 47.800000 48.500000 45.700000 45.200000 46.9 45.80 43.50 42.30 41.800000 43.400000 41.60
    南非 57.80 59.20 60.60 62.000000 63.400000 64.800000 64.790000 63.895000 63.0 63.20 63.40 63.38 63.253333 63.126667 63.00

    67 rows × 15 columns

    • 列数据处理有：inner内连接-交集；outer外连接-并集；left左连接；right右连接

    数据排序

    值排序
    • part 39
    GINI.sort_values(by=[2014, 2013], ascending=False)
# by-可以多列排序
# ascending-False为降序
    2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
    Country
    南非 57.80 59.20 60.60 62.00 63.4 64.8 64.79 63.895 63.0 63.2 63.4 63.38 63.253333 63.126667 63.0
    哥伦比亚 58.70 57.20 55.80 53.40 54.8 53.7 60.08 59.370 55.5 54.4 54.7 53.50 52.700000 52.800000 52.7
    巴西 58.40 58.40 58.10 57.60 56.5 56.3 55.60 54.900 54.0 53.7 53.3 52.90 52.700000 52.800000 51.5
    巴拉圭 54.60 54.60 57.30 54.90 52.3 51.4 53.00 53.000 50.7 49.1 51.0 52.30 47.600000 47.900000 50.7
    巴拿马 56.80 56.90 56.20 55.80 54.8 53.8 54.60 52.700 52.7 51.8 51.6 51.30 51.700000 51.500000 50.5
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    挪威 27.60 27.60 27.60 27.60 31.6 30.6 26.40 27.100 27.0 26.2 25.7 25.30 25.700000 26.400000 26.8
    斯洛伐克 27.10 27.10 27.10 27.10 27.1 29.3 25.80 24.700 26.0 27.2 27.3 26.50 26.100000 28.100000 26.1
    捷克 27.50 27.50 27.50 27.50 27.5 26.9 26.70 26.000 26.3 26.2 26.6 26.40 26.100000 26.500000 25.9
    斯洛文尼亚 29.12 29.12 29.12 30.87 24.8 24.6 24.40 24.400 23.7 24.8 24.9 24.90 25.600000 26.200000 25.7
    乌克兰 29.00 29.00 29.00 28.70 28.9 29.0 29.80 27.000 26.6 25.3 24.8 24.60 24.700000 24.600000 24.0

    67 rows × 15 columns

    * part 40 
    # 添加排名
GINI['排名'] = GINI[2014].rank(method='min', ascending=False)
GINI
    2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 排名
    Country
    中国 42.10 42.10 42.10 41.733333 41.366667 41.000000 41.633333 42.266667 42.9 43.30 43.70 42.40 42.200000 39.700000 39.10 20.0
    泰国 42.80 42.35 41.90 42.200000 42.500000 42.150000 41.800000 39.800000 40.3 39.60 39.40 37.50 39.300000 37.800000 37.00 24.0
    印度尼西亚 28.50 29.00 31.70 31.800000 32.700000 33.000000 34.200000 35.700000 35.1 35.10 36.40 39.70 39.600000 39.900000 39.40 19.0
    越南 37.00 37.00 37.00 36.900000 36.800000 36.300000 35.800000 35.700000 35.6 37.45 39.30 37.45 35.600000 35.200000 34.80 32.0
    柬埔寨 35.46 35.46 35.46 35.460000 35.460000 37.353333 39.246667 41.140000 35.1 34.65 33.44 31.70 30.760000 30.760000 30.76 46.0
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    危地马拉 54.20 55.57 56.94 54.100000 50.730000 52.665000 54.600000 54.150000 53.7 53.25 52.80 52.35 51.000000 49.650000 48.30 8.0
    洪都拉斯 55.60 55.60 55.70 58.100000 58.100000 59.500000 57.500000 55.800000 55.5 51.30 53.10 56.20 56.100000 52.600000 50.40 6.0
    巴拉圭 54.60 54.60 57.30 54.900000 52.300000 51.400000 53.000000 53.000000 50.7 49.10 51.00 52.30 47.600000 47.900000 50.70 4.0
    萨尔瓦多 51.50 51.40 51.90 50.400000 47.800000 48.500000 45.700000 45.200000 46.9 45.80 43.50 42.30 41.800000 43.400000 41.60 14.0
    南非 57.80 59.20 60.60 62.000000 63.400000 64.800000 64.790000 63.895000 63.0 63.20 63.40 63.38 63.253333 63.126667 63.00 1.0

    67 rows × 16 columns

    4.统计分析

    • part 41
    GINI.T.describe()           # 给出常用统计量
    Country 中国 泰国 印度尼西亚 越南 柬埔寨 奥地利 比利时 瑞士 捷克 德国 ... 厄瓜多尔 墨西哥 巴拿马 秘鲁 玻利维亚 危地马拉 洪都拉斯 巴拉圭 萨尔瓦多 南非
    count 16.000000 16.000000 16.000000 16.000000 16.000000 16.00000 16.000000 16.000000 16.000000 16.000000 ... 16.00000 16.000000 16.000000 16.000000 16.000000 16.000000 16.000000 16.000000 16.000000 16.000000
    mean 40.475000 39.400000 33.800000 36.243750 35.513125 31.28125 30.075000 33.450000 29.131250 31.506250 ... 48.36250 44.831250 50.481250 46.056250 50.306250 50.125312 52.318750 49.025000 44.481250 58.740313
    std 5.584429 4.526919 5.362586 1.575106 4.053855 4.77678 6.685656 1.097877 9.583299 3.417498 ... 10.61652 9.515301 12.310766 9.394606 12.146315 11.445617 12.598054 12.285249 8.852285 15.512899
    min 20.000000 24.000000 19.000000 32.000000 30.760000 28.70000 27.500000 31.600000 25.900000 28.800000 ... 11.00000 10.000000 5.000000 13.000000 9.000000 8.000000 6.000000 4.000000 14.000000 1.000000
    25% 41.275000 38.925000 31.775000 35.600000 33.005000 29.50000 28.100000 32.575000 26.275000 30.300000 ... 46.70000 45.337500 51.575000 44.625000 47.637500 50.932500 52.975000 50.300000 43.125000 61.650000
    50% 42.100000 40.050000 34.650000 36.550000 35.460000 30.35000 28.100000 33.900000 26.650000 30.650000 ... 50.95000 45.975000 52.700000 48.300000 52.650000 53.025000 55.650000 51.850000 45.750000 63.163333
    75% 42.300000 42.162500 37.150000 37.000000 35.933333 30.65000 28.675000 33.900000 27.500000 31.225000 ... 53.52500 48.712500 55.050000 50.325000 57.212500 54.162500 56.525000 53.400000 48.975000 63.400000
    max 43.700000 42.800000 39.900000 39.300000 46.000000 49.00000 55.000000 36.000000 65.000000 44.000000 ... 56.40000 51.400000 56.900000 53.600000 61.600000 56.940000 59.500000 57.300000 51.900000 64.800000

    8 rows × 67 columns

    * part 42 
    result = GINI.T[0:15].cov()          # 寻找协方差矩阵
result
    Country 中国 泰国 印度尼西亚 越南 柬埔寨 奥地利 比利时 瑞士 捷克 德国 ... 厄瓜多尔 墨西哥 巴拿马 秘鲁 玻利维亚 危地马拉 洪都拉斯 巴拉圭 萨尔瓦多 南非
    Country
    中国 1.472413 0.574690 -1.138000 0.961833 0.990535 0.136190 0.030381 0.068476 0.042095 -0.323381 ... 0.851048 -0.308119 0.322762 0.832905 0.049214 1.650081 0.367333 0.460762 0.765381 -0.248414
    泰国 0.574690 3.887452 -6.833905 0.524238 3.745933 -1.119286 0.528905 1.387714 0.990286 -0.412095 ... 6.916690 3.435619 3.936405 5.758643 9.400071 2.907842 3.463024 3.382095 6.296405 -1.531205
    印度尼西亚 -1.138000 -6.833905 14.122667 -1.356048 -6.489586 1.886429 -0.741238 -2.683857 -1.907286 1.303905 ... -13.407095 -6.943881 -7.688381 -10.532714 -18.070571 -6.005233 -4.305619 -7.516762 -12.982667 4.659531
    越南 0.961833 0.524238 -1.356048 1.286024 0.095826 -0.047500 0.106048 -0.028000 0.267429 -0.462810 ... 0.910262 0.341690 0.487833 0.707929 0.389357 1.017324 0.105167 0.893524 0.997833 -0.490467
    柬埔寨 0.990535 3.745933 -6.489586 0.095826 9.228565 -0.930857 1.087014 2.284157 0.442090 0.426067 ... 8.361343 2.486307 3.289538 7.452195 11.003314 4.353009 4.024590 4.168433 5.390538 0.507772
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    危地马拉 1.650081 2.907842 -6.005233 1.017324 4.353009 -0.665786 -0.105945 1.220221 0.702843 -0.544824 ... 6.064755 2.332612 3.492340 6.009764 7.909443 5.156259 2.263938 4.513252 6.001876 -1.842574
    洪都拉斯 0.367333 3.463024 -4.305619 0.105167 4.024590 -1.337857 0.729190 1.060857 0.825429 0.308048 ... 5.653190 2.753190 3.137762 5.501714 7.913857 2.263938 6.586381 2.893381 4.284190 0.413193
    巴拉圭 0.460762 3.382095 -7.516762 0.893524 4.168433 -1.205714 0.243190 1.571286 1.186000 -0.922810 ... 7.842762 4.199548 4.723905 7.370429 11.137214 4.513252 2.893381 7.250667 7.554619 -2.830776
    萨尔瓦多 0.765381 6.296405 -12.982667 0.997833 5.390538 -1.812143 0.501952 2.529571 1.915143 -1.159619 ... 12.506381 6.511738 7.451952 10.684857 17.325214 6.001876 4.284190 7.554619 13.171238 -4.599483
    南非 -0.248414 -1.531205 4.659531 -0.490467 0.507772 0.369179 0.430743 -0.412436 -0.717974 1.246205 ... -3.732493 -2.743832 -2.793590 -2.007002 -4.964467 -1.842574 0.413193 -2.830776 -4.599483 3.824582

    67 rows × 67 columns

    * part 43 
    # 保存
result.to_csv('3th dataGINI_COV.csv', mode='w', header=True, index=True)

    # 清洗出协方差绝对值大于1的两个国家
RES = DataFrame(GINI.index, index=GINI.index, columns=['Name'])
for il in list(GINI.index):
    mask = np.abs(result[il])>=1
    mask = result.loc[mask, il]
    mask = DataFrame(mask, index=mask.index, columns=[il])
    RES = pd.concat([RES, mask], axis=1, sort=False)
RES = RES.iloc[:, 1:]
RES.fillna(0, inplace=True)
for num in range(RES.index.size):
    RES.iloc[num, num] = 0
    for mun in range(RES.index.size):
        RES.iloc[num, mun] = np.sign(RES.iloc[num, mun])
RES
    中国 泰国 印度尼西亚 越南 柬埔寨 奥地利 比利时 瑞士 捷克 德国 ... 厄瓜多尔 墨西哥 巴拿马 秘鲁 玻利维亚 危地马拉 洪都拉斯 巴拉圭 萨尔瓦多 南非
    中国 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
    泰国 0.0 0.0 -1.0 0.0 1.0 -1.0 0.0 1.0 0.0 0.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1.0
    印度尼西亚 -1.0 -1.0 0.0 -1.0 -1.0 1.0 0.0 -1.0 -1.0 1.0 ... -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 1.0
    越南 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
    柬埔寨 0.0 1.0 -1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    危地马拉 1.0 1.0 -1.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 ... 1.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 -1.0
    洪都拉斯 0.0 1.0 -1.0 0.0 1.0 -1.0 0.0 1.0 0.0 0.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0
    巴拉圭 0.0 1.0 -1.0 0.0 1.0 -1.0 0.0 1.0 1.0 0.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 -1.0
    萨尔瓦多 0.0 1.0 -1.0 0.0 1.0 -1.0 0.0 1.0 1.0 -1.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -1.0
    南非 0.0 -1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.0 -1.0 -1.0 0.0

    67 rows × 67 columns

    * part 44 
    # 原本想做一个有向连通图的，比较懒，没有写完

# import networkx as nx
# import matplotlib.pyplot as plt

# G = nx.DiGraph()
# G.add_node(tuple(RES.index))

# nx.draw(G)
# plt.show()

# G = nx.DiGraph()
# G.add_node(1)
# G.add_node(2)
# G.add_nodes_from([3,4,5,6])
# G.add_edge(1,3)
# G.add_edges_from([(3,5),(3,6),(6,7)])
# G = G.to_undirected()
# nx.draw(G)
# plt.show()
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  • 原文地址：https://www.cnblogs.com/rsmx/p/12195935.html
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