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]

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']

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

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

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 data\GINI.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 data\Students.csv', mode='w', header=True, index=False)

（2）读取Excel文件

part 34

# 读取
GINI = pd.read_excel('3th data\GINI.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 data\GINI_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()

来源：https://www.cnblogs.com/rsmx/p/12195935.html

标签

大数据

科学

数据清洗与分析 - 数据科学 Part 3

数据科学第三章

1.Panda 的数据结构

（1）引入panda包

（2）Series对象

（3）Series数据访问

DataFrame对象

DataFrame数据选取方法

（1）查询

（2）修改操作

（3）删除

2.数据文件读写

（1）csv文件的读写，txt雷同

（2）读取Excel文件

3.数据清洗

数据排序

值排序

4.统计分析