2-1

Loading [MathJax]/extensions/Safe.js

Pandas进阶及技巧

1. 创建Pandas

 
 
 
 
 

import pandas as pd
​
country1 = pd.Series({'Name': '中国',
                    'Language': 'Chinese',
                    'Area': '9.597M km2',
                     'Happiness Rank': 79})
​
country2 = pd.Series({'Name': '美国',
                    'Language': 'English (US)',
                    'Area': '9.834M km2',
                     'Happiness Rank': 14})
​
country3 = pd.Series({'Name': '澳大利亚',
                    'Language': 'English (AU)',
                    'Area': '7.692M km2',
                     'Happiness Rank': 9})
​
df = pd.DataFrame([country1, country2, country3], index=['CH', 'US', 'AU'])
 

 
# 注意在jupyter中使用print和不使用print的区别
print(df)
df

          Area  Happiness Rank      Language  Name
CH  9.597M km2              79       Chinese    中国
US  9.834M km2              14  English (US)    美国
AU  7.692M km2               9  English (AU)  澳大利亚

	Area	Happiness Rank	Language	Name
CH	9.597M km2	79	Chinese	中国
US	9.834M km2	14	English (US)	美国
AU	7.692M km2	9	English (AU)	澳大利亚

 
 
 
 
 

# 添加数据
# 如果个数为1，会自动进行“广播”操作
# 如果大于要求的个数，会报错
#如果刚好等于要求的个数，会依次填充字段属性值
​
df['Location'] = '地球'
print(df)
​
df['Region'] = ['亚洲', '北美洲', '大洋洲']
print(df)
df
 

          Area  Happiness Rank      Language  Name Location
CH  9.597M km2              79       Chinese    中国       地球
US  9.834M km2              14  English (US)    美国       地球
AU  7.692M km2               9  English (AU)  澳大利亚       地球
          Area  Happiness Rank      Language  Name Location Region
CH  9.597M km2              79       Chinese    中国       地球     亚洲
US  9.834M km2              14  English (US)    美国       地球    北美洲
AU  7.692M km2               9  English (AU)  澳大利亚       地球    大洋洲

	Area	Happiness Rank	Language	Name	Location	Region
CH	9.597M km2	79	Chinese	中国	地球	亚洲
US	9.834M km2	14	English (US)	美国	地球	北美洲
AU	7.692M km2	9	English (AU)	澳大利亚	地球	大洋洲

2. Pandas索引

 
# 行索引
print('loc:')
print(df.loc['CH'])
print(type(df.loc['CH']))
​
print('iloc:')
print(df.iloc[1])

loc:
Area              9.597M km2
Happiness Rank            79
Language             Chinese
Name                      中国
Location                  地球
Region                    亚洲
Name: CH, dtype: object
<class 'pandas.core.series.Series'>
iloc:
Area                9.834M km2
Happiness Rank              14
Language          English (US)
Name                        美国
Location                    地球
Region                     北美洲
Name: US, dtype: object

 
# 列索引
print(df['Area'])
print(type(df['Area']))

CH    9.597M km2
US    9.834M km2
AU    7.692M km2
Name: Area, dtype: object
<class 'pandas.core.series.Series'>

 
# 获取不连续的列数据
print(df[['Name', 'Area']])

    Name        Area
CH    中国  9.597M km2
US    美国  9.834M km2
AU  澳大利亚  7.692M km2

 
# 混合索引
# 注意写法上的区别
print('先取出列，再取行：')
print(df['Area']['CH'])
print(df['Area'].loc['CH'])
print(df['Area'].iloc[0])
​
print('先取出行，再取列：')
print(df.loc['CH']['Area'])
print(df.iloc[0]['Area'])

先取出列，再取行：
9.597M km2
9.597M km2
9.597M km2
先取出行，再取列：
9.597M km2
9.597M km2

 
# 转换行和列
print(df.T)

                        CH            US            AU
Area            9.597M km2    9.834M km2    7.692M km2
Happiness Rank          79            14             9
Language           Chinese  English (US)  English (AU)
Name                    中国            美国          澳大利亚
Location                地球            地球            地球
Region                  亚洲           北美洲           大洋洲

3. 删除数据

 
print(df.drop(['CH']))
# 注意drop操作只是将修改后的数据copy一份，而不会对原始数据进行修改
print(df)

          Area  Happiness Rank      Language  Name Location Region
US  9.834M km2              14  English (US)    美国       地球    北美洲
AU  7.692M km2               9  English (AU)  澳大利亚       地球    大洋洲
          Area  Happiness Rank      Language  Name Location Region
CH  9.597M km2              79       Chinese    中国       地球     亚洲
US  9.834M km2              14  English (US)    美国       地球    北美洲
AU  7.692M km2               9  English (AU)  澳大利亚       地球    大洋洲

 
print(df.drop(['CH'], inplace=True))
# 如果使用了inplace=True，会在原始数据上进行修改，同时不会返回一个copy
print(df)

None
          Area  Happiness Rank      Language  Name Location Region
US  9.834M km2              14  English (US)    美国       地球    北美洲
AU  7.692M km2               9  English (AU)  澳大利亚       地球    大洋洲

 
#  如果需要删除列，需要指定axis=1
print(df.drop(['Area'], axis=1))
print(df)

    Happiness Rank      Language  Name Location Region
CH              79       Chinese    中国       地球     亚洲
US              14  English (US)    美国       地球    北美洲
AU               9  English (AU)  澳大利亚       地球    大洋洲
          Area  Happiness Rank      Language  Name Location Region
CH  9.597M km2              79       Chinese    中国       地球     亚洲
US  9.834M km2              14  English (US)    美国       地球    北美洲
AU  7.692M km2               9  English (AU)  澳大利亚       地球    大洋洲

 
# 也可直接使用del关键字
del df['Name']
print(df)

          Area  Happiness Rank      Language Location Region
US  9.834M km2              14  English (US)       地球    北美洲
AU  7.692M km2               9  English (AU)       地球    大洋洲

4. DataFrame的操作与加载

 
# 注意从DataFrame中取出的数据进行操作后，会对原始数据产生影响
ranks = df['Happiness Rank']
ranks += 2
print(ranks)
print(df)

US    18
AU    13
Name: Happiness Rank, dtype: int64
          Area  Happiness Rank      Language Location Region
US  9.834M km2              18  English (US)       地球    北美洲
AU  7.692M km2              13  English (AU)       地球    大洋洲

 
# 注意从DataFrame中取出的数据进行操作后，会对原始数据产生影响
# 安全的操作是使用copy()
ranks = df['Happiness Rank'].copy()
ranks += 2
print(ranks)
print(df)

US    20
AU    15
Name: Happiness Rank, dtype: int64
          Area  Happiness Rank      Language Location Region
US  9.834M km2              18  English (US)       地球    北美洲
AU  7.692M km2              13  English (AU)       地球    大洋洲

 
# 加载csv文件数据
reprot_2015_df = pd.read_csv('./2015.csv')
print('2015年数据预览：')
#print(reprot_2015_df.head())
reprot_2015_df.head()

2015年数据预览：

	Country	Region	Happiness Rank	Happiness Score	Standard Error	Economy (GDP per Capita)	Family	Health (Life Expectancy)	Freedom	Trust (Government Corruption)	Generosity	Dystopia Residual
0	Switzerland	Western Europe	1	7.587	0.03411	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	2.51738
1	Iceland	Western Europe	2	7.561	0.04884	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	2.70201
2	Denmark	Western Europe	3	7.527	0.03328	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	2.49204
3	Norway	Western Europe	4	7.522	0.03880	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	2.46531
4	Canada	North America	5	7.427	0.03553	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	2.45176

 
print(reprot_2015_df.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 158 entries, 0 to 157
Data columns (total 12 columns):
Country                          158 non-null object
Region                           158 non-null object
Happiness Rank                   158 non-null int64
Happiness Score                  158 non-null float64
Standard Error                   158 non-null float64
Economy (GDP per Capita)         158 non-null float64
Family                           158 non-null float64
Health (Life Expectancy)         158 non-null float64
Freedom                          158 non-null float64
Trust (Government Corruption)    158 non-null float64
Generosity                       158 non-null float64
Dystopia Residual                158 non-null float64
dtypes: float64(9), int64(1), object(2)
memory usage: 14.9+ KB
None

 
# 使用index_col指定索引列
# 使用usecols指定需要读取的列
reprot_2016_df = pd.read_csv('./2016.csv', 
                             index_col='Country',
                             usecols=['Country', 'Happiness Rank', 'Happiness Score', 'Region'])
# 数据预览
reprot_2016_df.head()

	Region	Happiness Rank	Happiness Score
Country
Denmark	Western Europe	1	7.526
Switzerland	Western Europe	2	7.509
Iceland	Western Europe	3	7.501
Norway	Western Europe	4	7.498
Finland	Western Europe	5	7.413

 
print('列名(column)：', reprot_2016_df.columns)
print('行名(index)：', reprot_2016_df.index)

列名(column)： Index(['Region', 'Happiness Rank', 'Happiness Score'], dtype='object')
行名(index)： Index(['Denmark', 'Switzerland', 'Iceland', 'Norway', 'Finland', 'Canada',
       'Netherlands', 'New Zealand', 'Australia', 'Sweden',
       ...
       'Madagascar', 'Tanzania', 'Liberia', 'Guinea', 'Rwanda', 'Benin',
       'Afghanistan', 'Togo', 'Syria', 'Burundi'],
      dtype='object', name='Country', length=157)

 
# 注意index是不可变的
reprot_2016_df.index[0] = '丹麦'

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-104-9214cb35e379> in <module>()
----> 1 reprot_2016_df.index[0] = '丹麦'

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in __setitem__(self, key, value)
   1618 
   1619     def __setitem__(self, key, value):
-> 1620         raise TypeError("Index does not support mutable operations")
   1621 
   1622     def __getitem__(self, key):

TypeError: Index does not support mutable operations

 
# 重置index
# 注意inplace加与不加的区别
reprot_2016_df.reset_index().head()

	Country	地区	排名	幸福指数
0	Denmark	Western Europe	1	7.526
1	Switzerland	Western Europe	2	7.509
2	Iceland	Western Europe	3	7.501
3	Norway	Western Europe	4	7.498
4	Finland	Western Europe	5	7.413

 
# 重命名列名
reprot_2016_df.rename(columns={'Region': '地区', 'Hapiness Rank': '排名', 'Hapiness Score': '幸福指数'})
reprot_2016_df.head()

	地区	排名	幸福指数
Country
Denmark	Western Europe	1	7.526
Switzerland	Western Europe	2	7.509
Iceland	Western Europe	3	7.501
Norway	Western Europe	4	7.498
Finland	Western Europe	5	7.413

 
# 重命名列名，注意inplace的使用
reprot_2016_df.rename(columns={'Region': '地区', 'Happiness Rank': '排名', 'Happiness Score': '幸福指数'},
                     inplace=True)
reprot_2016_df.head()

	地区	排名	幸福指数
Country
Denmark	Western Europe	1	7.526
Switzerland	Western Europe	2	7.509
Iceland	Western Europe	3	7.501
Norway	Western Europe	4	7.498
Finland	Western Europe	5	7.413

5. Boolean Mask

 
# 过滤 Western Europe 地区的国家
only_western_europe = reprot_2016_df['地区'] == 'Western Europe'
only_western_europe

Country
Denmark                  True
Switzerland              True
Iceland                  True
Norway                   True
Finland                  True
Canada                  False
Netherlands              True
New Zealand             False
Australia               False
Sweden                   True
Israel                  False
Austria                  True
United States           False
Costa Rica              False
Puerto Rico             False
Germany                  True
Brazil                  False
Belgium                  True
Ireland                  True
Luxembourg               True
Mexico                  False
Singapore               False
United Kingdom           True
Chile                   False
Panama                  False
Argentina               False
Czech Republic          False
United Arab Emirates    False
Uruguay                 False
Malta                    True
                        ...  
Senegal                 False
Bulgaria                False
Mauritania              False
Zimbabwe                False
Malawi                  False
Sudan                   False
Gabon                   False
Mali                    False
Haiti                   False
Botswana                False
Comoros                 False
Ivory Coast             False
Cambodia                False
Angola                  False
Niger                   False
South Sudan             False
Chad                    False
Burkina Faso            False
Uganda                  False
Yemen                   False
Madagascar              False
Tanzania                False
Liberia                 False
Guinea                  False
Rwanda                  False
Benin                   False
Afghanistan             False
Togo                    False
Syria                   False
Burundi                 False
Name: 地区, Length: 157, dtype: bool

 
# 过滤 Western Europe 地区的国家
# 并且排名在10之外
only_western_europe_10 = (reprot_2016_df['地区'] == 'Western Europe') & (reprot_2016_df['排名'] > 10)
only_western_europe_10

Country
Denmark                 False
Switzerland             False
Iceland                 False
Norway                  False
Finland                 False
Canada                  False
Netherlands             False
New Zealand             False
Australia               False
Sweden                  False
Israel                  False
Austria                  True
United States           False
Costa Rica              False
Puerto Rico             False
Germany                  True
Brazil                  False
Belgium                  True
Ireland                  True
Luxembourg               True
Mexico                  False
Singapore               False
United Kingdom           True
Chile                   False
Panama                  False
Argentina               False
Czech Republic          False
United Arab Emirates    False
Uruguay                 False
Malta                    True
                        ...  
Senegal                 False
Bulgaria                False
Mauritania              False
Zimbabwe                False
Malawi                  False
Sudan                   False
Gabon                   False
Mali                    False
Haiti                   False
Botswana                False
Comoros                 False
Ivory Coast             False
Cambodia                False
Angola                  False
Niger                   False
South Sudan             False
Chad                    False
Burkina Faso            False
Uganda                  False
Yemen                   False
Madagascar              False
Tanzania                False
Liberia                 False
Guinea                  False
Rwanda                  False
Benin                   False
Afghanistan             False
Togo                    False
Syria                   False
Burundi                 False
Length: 157, dtype: bool

 
# 叠加 boolean mask 得到最终结果
reprot_2016_df[only_western_europe_10]

	地区	排名	幸福指数
Country
Austria	Western Europe	12	7.119
Germany	Western Europe	16	6.994
Belgium	Western Europe	18	6.929
Ireland	Western Europe	19	6.907
Luxembourg	Western Europe	20	6.871
United Kingdom	Western Europe	23	6.725
Malta	Western Europe	30	6.488
France	Western Europe	32	6.478
Spain	Western Europe	37	6.361
Italy	Western Europe	50	5.977
North Cyprus	Western Europe	62	5.771
Cyprus	Western Europe	69	5.546
Portugal	Western Europe	94	5.123
Greece	Western Europe	99	5.033

 
# 熟练以后可以写在一行中
reprot_2016_df[(reprot_2016_df['地区'] == 'Western Europe') & (reprot_2016_df['排名'] > 10)]

	地区	排名	幸福指数
Country
Austria	Western Europe	12	7.119
Germany	Western Europe	16	6.994
Belgium	Western Europe	18	6.929
Ireland	Western Europe	19	6.907
Luxembourg	Western Europe	20	6.871
United Kingdom	Western Europe	23	6.725
Malta	Western Europe	30	6.488
France	Western Europe	32	6.478
Spain	Western Europe	37	6.361
Italy	Western Europe	50	5.977
North Cyprus	Western Europe	62	5.771
Cyprus	Western Europe	69	5.546
Portugal	Western Europe	94	5.123
Greece	Western Europe	99	5.033

6. 层级索引

reprot_2015_df.head()

	Country	Region	Happiness Rank	Happiness Score	Standard Error	Economy (GDP per Capita)	Family	Health (Life Expectancy)	Freedom	Trust (Government Corruption)	Generosity	Dystopia Residual
0	Switzerland	Western Europe	1	7.587	0.03411	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	2.51738
1	Iceland	Western Europe	2	7.561	0.04884	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	2.70201
2	Denmark	Western Europe	3	7.527	0.03328	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	2.49204
3	Norway	Western Europe	4	7.522	0.03880	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	2.46531
4	Canada	North America	5	7.427	0.03553	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	2.45176

 
# 设置层级索引
report_2015_df2 = reprot_2015_df.set_index(['Region', 'Country'])
report_2015_df2.head(20)

		Happiness Rank	Happiness Score	Standard Error	Economy (GDP per Capita)	Family	Health (Life Expectancy)	Freedom	Trust (Government Corruption)	Generosity	Dystopia Residual
Region	Country
Western Europe	Switzerland	1	7.587	0.03411	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	2.51738
	Iceland	2	7.561	0.04884	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	2.70201
	Denmark	3	7.527	0.03328	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	2.49204
	Norway	4	7.522	0.03880	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	2.46531
North America	Canada	5	7.427	0.03553	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	2.45176
Western Europe	Finland	6	7.406	0.03140	1.29025	1.31826	0.88911	0.64169	0.41372	0.23351	2.61955
	Netherlands	7	7.378	0.02799	1.32944	1.28017	0.89284	0.61576	0.31814	0.47610	2.46570
	Sweden	8	7.364	0.03157	1.33171	1.28907	0.91087	0.65980	0.43844	0.36262	2.37119
Australia and New Zealand	New Zealand	9	7.286	0.03371	1.25018	1.31967	0.90837	0.63938	0.42922	0.47501	2.26425
Australia and New Zealand	Australia	10	7.284	0.04083	1.33358	1.30923	0.93156	0.65124	0.35637	0.43562	2.26646
Middle East and Northern Africa	Israel	11	7.278	0.03470	1.22857	1.22393	0.91387	0.41319	0.07785	0.33172	3.08854
Latin America and Caribbean	Costa Rica	12	7.226	0.04454	0.95578	1.23788	0.86027	0.63376	0.10583	0.25497	3.17728
Western Europe	Austria	13	7.200	0.03751	1.33723	1.29704	0.89042	0.62433	0.18676	0.33088	2.53320
Latin America and Caribbean	Mexico	14	7.187	0.04176	1.02054	0.91451	0.81444	0.48181	0.21312	0.14074	3.60214
North America	United States	15	7.119	0.03839	1.39451	1.24711	0.86179	0.54604	0.15890	0.40105	2.51011
Latin America and Caribbean	Brazil	16	6.983	0.04076	0.98124	1.23287	0.69702	0.49049	0.17521	0.14574	3.26001
Western Europe	Luxembourg	17	6.946	0.03499	1.56391	1.21963	0.91894	0.61583	0.37798	0.28034	1.96961
	Ireland	18	6.940	0.03676	1.33596	1.36948	0.89533	0.61777	0.28703	0.45901	1.97570
	Belgium	19	6.937	0.03595	1.30782	1.28566	0.89667	0.58450	0.22540	0.22250	2.41484
Middle East and Northern Africa	United Arab Emirates	20	6.901	0.03729	1.42727	1.12575	0.80925	0.64157	0.38583	0.26428	2.24743

 
# level0 索引
report_2015_df2.loc['Western Europe']

	Happiness Rank	Happiness Score	Standard Error	Economy (GDP per Capita)	Family	Health (Life Expectancy)	Freedom	Trust (Government Corruption)	Generosity	Dystopia Residual
Country
Switzerland	1	7.587	0.03411	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	2.51738
Iceland	2	7.561	0.04884	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	2.70201
Denmark	3	7.527	0.03328	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	2.49204
Norway	4	7.522	0.03880	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	2.46531
Finland	6	7.406	0.03140	1.29025	1.31826	0.88911	0.64169	0.41372	0.23351	2.61955
Netherlands	7	7.378	0.02799	1.32944	1.28017	0.89284	0.61576	0.31814	0.47610	2.46570
Sweden	8	7.364	0.03157	1.33171	1.28907	0.91087	0.65980	0.43844	0.36262	2.37119
Austria	13	7.200	0.03751	1.33723	1.29704	0.89042	0.62433	0.18676	0.33088	2.53320
Luxembourg	17	6.946	0.03499	1.56391	1.21963	0.91894	0.61583	0.37798	0.28034	1.96961
Ireland	18	6.940	0.03676	1.33596	1.36948	0.89533	0.61777	0.28703	0.45901	1.97570
Belgium	19	6.937	0.03595	1.30782	1.28566	0.89667	0.58450	0.22540	0.22250	2.41484
United Kingdom	21	6.867	0.01866	1.26637	1.28548	0.90943	0.59625	0.32067	0.51912	1.96994
Germany	26	6.750	0.01848	1.32792	1.29937	0.89186	0.61477	0.21843	0.28214	2.11569
France	29	6.575	0.03512	1.27778	1.26038	0.94579	0.55011	0.20646	0.12332	2.21126
Spain	36	6.329	0.03468	1.23011	1.31379	0.95562	0.45951	0.06398	0.18227	2.12367
Malta	37	6.302	0.04206	1.20740	1.30203	0.88721	0.60365	0.13586	0.51752	1.64880
Italy	50	5.948	0.03914	1.25114	1.19777	0.95446	0.26236	0.02901	0.22823	2.02518
North Cyprus	66	5.695	0.05635	1.20806	1.07008	0.92356	0.49027	0.14280	0.26169	1.59888
Cyprus	67	5.689	0.05580	1.20813	0.89318	0.92356	0.40672	0.06146	0.30638	1.88931
Portugal	88	5.102	0.04802	1.15991	1.13935	0.87519	0.51469	0.01078	0.13719	1.26462
Greece	102	4.857	0.05062	1.15406	0.92933	0.88213	0.07699	0.01397	0.00000	1.80101

 
# 两层索引
report_2015_df2.loc['Western Europe', 'Switzerland']

Happiness Rank                   1.00000
Happiness Score                  7.58700
Standard Error                   0.03411
Economy (GDP per Capita)         1.39651
Family                           1.34951
Health (Life Expectancy)         0.94143
Freedom                          0.66557
Trust (Government Corruption)    0.41978
Generosity                       0.29678
Dystopia Residual                2.51738
Name: (Western Europe, Switzerland), dtype: float64

 
# 交换分层顺序
report_2015_df2.swaplevel()

		Happiness Rank	Happiness Score	Standard Error	Economy (GDP per Capita)	Family	Health (Life Expectancy)	Freedom	Trust (Government Corruption)	Generosity	Dystopia Residual
Country	Region
Switzerland	Western Europe	1	7.587	0.03411	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	2.51738
Iceland	Western Europe	2	7.561	0.04884	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	2.70201
Denmark	Western Europe	3	7.527	0.03328	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	2.49204
Norway	Western Europe	4	7.522	0.03880	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	2.46531
Canada	North America	5	7.427	0.03553	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	2.45176
Finland	Western Europe	6	7.406	0.03140	1.29025	1.31826	0.88911	0.64169	0.41372	0.23351	2.61955
Netherlands	Western Europe	7	7.378	0.02799	1.32944	1.28017	0.89284	0.61576	0.31814	0.47610	2.46570
Sweden	Western Europe	8	7.364	0.03157	1.33171	1.28907	0.91087	0.65980	0.43844	0.36262	2.37119
New Zealand	Australia and New Zealand	9	7.286	0.03371	1.25018	1.31967	0.90837	0.63938	0.42922	0.47501	2.26425
Australia	Australia and New Zealand	10	7.284	0.04083	1.33358	1.30923	0.93156	0.65124	0.35637	0.43562	2.26646
Israel	Middle East and Northern Africa	11	7.278	0.03470	1.22857	1.22393	0.91387	0.41319	0.07785	0.33172	3.08854
Costa Rica	Latin America and Caribbean	12	7.226	0.04454	0.95578	1.23788	0.86027	0.63376	0.10583	0.25497	3.17728
Austria	Western Europe	13	7.200	0.03751	1.33723	1.29704	0.89042	0.62433	0.18676	0.33088	2.53320
Mexico	Latin America and Caribbean	14	7.187	0.04176	1.02054	0.91451	0.81444	0.48181	0.21312	0.14074	3.60214
United States	North America	15	7.119	0.03839	1.39451	1.24711	0.86179	0.54604	0.15890	0.40105	2.51011
Brazil	Latin America and Caribbean	16	6.983	0.04076	0.98124	1.23287	0.69702	0.49049	0.17521	0.14574	3.26001
Luxembourg	Western Europe	17	6.946	0.03499	1.56391	1.21963	0.91894	0.61583	0.37798	0.28034	1.96961
Ireland	Western Europe	18	6.940	0.03676	1.33596	1.36948	0.89533	0.61777	0.28703	0.45901	1.97570
Belgium	Western Europe	19	6.937	0.03595	1.30782	1.28566	0.89667	0.58450	0.22540	0.22250	2.41484
United Arab Emirates	Middle East and Northern Africa	20	6.901	0.03729	1.42727	1.12575	0.80925	0.64157	0.38583	0.26428	2.24743
United Kingdom	Western Europe	21	6.867	0.01866	1.26637	1.28548	0.90943	0.59625	0.32067	0.51912	1.96994
Oman	Middle East and Northern Africa	22	6.853	0.05335	1.36011	1.08182	0.76276	0.63274	0.32524	0.21542	2.47489
Venezuela	Latin America and Caribbean	23	6.810	0.06476	1.04424	1.25596	0.72052	0.42908	0.11069	0.05841	3.19131
Singapore	Southeastern Asia	24	6.798	0.03780	1.52186	1.02000	1.02525	0.54252	0.49210	0.31105	1.88501
Panama	Latin America and Caribbean	25	6.786	0.04910	1.06353	1.19850	0.79661	0.54210	0.09270	0.24434	2.84848
Germany	Western Europe	26	6.750	0.01848	1.32792	1.29937	0.89186	0.61477	0.21843	0.28214	2.11569
Chile	Latin America and Caribbean	27	6.670	0.05800	1.10715	1.12447	0.85857	0.44132	0.12869	0.33363	2.67585
Qatar	Middle East and Northern Africa	28	6.611	0.06257	1.69042	1.07860	0.79733	0.64040	0.52208	0.32573	1.55674
France	Western Europe	29	6.575	0.03512	1.27778	1.26038	0.94579	0.55011	0.20646	0.12332	2.21126
Argentina	Latin America and Caribbean	30	6.574	0.04612	1.05351	1.24823	0.78723	0.44974	0.08484	0.11451	2.83600
...	...	...	...	...	...	...	...	...	...	...	...
Myanmar	Southeastern Asia	129	4.307	0.04351	0.27108	0.70905	0.48246	0.44017	0.19034	0.79588	1.41805
Georgia	Central and Eastern Europe	130	4.297	0.04221	0.74190	0.38562	0.72926	0.40577	0.38331	0.05547	1.59541
Malawi	Sub-Saharan Africa	131	4.292	0.06130	0.01604	0.41134	0.22562	0.43054	0.06977	0.33128	2.80791
Sri Lanka	Southern Asia	132	4.271	0.03751	0.83524	1.01905	0.70806	0.53726	0.09179	0.40828	0.67108
Cameroon	Sub-Saharan Africa	133	4.252	0.04678	0.42250	0.88767	0.23402	0.49309	0.05786	0.20618	1.95071
Bulgaria	Central and Eastern Europe	134	4.218	0.04828	1.01216	1.10614	0.76649	0.30587	0.00872	0.11921	0.89991
Egypt	Middle East and Northern Africa	135	4.194	0.03260	0.88180	0.74700	0.61712	0.17288	0.06324	0.11291	1.59927
Yemen	Middle East and Northern Africa	136	4.077	0.04367	0.54649	0.68093	0.40064	0.35571	0.07854	0.09131	1.92313
Angola	Sub-Saharan Africa	137	4.033	0.04758	0.75778	0.86040	0.16683	0.10384	0.07122	0.12344	1.94939
Mali	Sub-Saharan Africa	138	3.995	0.05602	0.26074	1.03526	0.20583	0.38857	0.12352	0.18798	1.79293
Congo (Brazzaville)	Sub-Saharan Africa	139	3.989	0.06682	0.67866	0.66290	0.31051	0.41466	0.11686	0.12388	1.68135
Comoros	Sub-Saharan Africa	140	3.956	0.04797	0.23906	0.79273	0.36315	0.22917	0.19900	0.17441	1.95812
Uganda	Sub-Saharan Africa	141	3.931	0.04317	0.21102	1.13299	0.33861	0.45727	0.07267	0.29066	1.42766
Senegal	Sub-Saharan Africa	142	3.904	0.03608	0.36498	0.97619	0.43540	0.36772	0.10713	0.20843	1.44395
Gabon	Sub-Saharan Africa	143	3.896	0.04547	1.06024	0.90528	0.43372	0.31914	0.11091	0.06822	0.99895
Niger	Sub-Saharan Africa	144	3.845	0.03602	0.06940	0.77265	0.29707	0.47692	0.15639	0.19387	1.87877
Cambodia	Southeastern Asia	145	3.819	0.05069	0.46038	0.62736	0.61114	0.66246	0.07247	0.40359	0.98195
Tanzania	Sub-Saharan Africa	146	3.781	0.05061	0.28520	1.00268	0.38215	0.32878	0.05747	0.34377	1.38079
Madagascar	Sub-Saharan Africa	147	3.681	0.03633	0.20824	0.66801	0.46721	0.19184	0.08124	0.21333	1.85100
Central African Republic	Sub-Saharan Africa	148	3.678	0.06112	0.07850	0.00000	0.06699	0.48879	0.08289	0.23835	2.72230
Chad	Sub-Saharan Africa	149	3.667	0.03830	0.34193	0.76062	0.15010	0.23501	0.05269	0.18386	1.94296
Guinea	Sub-Saharan Africa	150	3.656	0.03590	0.17417	0.46475	0.24009	0.37725	0.12139	0.28657	1.99172
Ivory Coast	Sub-Saharan Africa	151	3.655	0.05141	0.46534	0.77115	0.15185	0.46866	0.17922	0.20165	1.41723
Burkina Faso	Sub-Saharan Africa	152	3.587	0.04324	0.25812	0.85188	0.27125	0.39493	0.12832	0.21747	1.46494
Afghanistan	Southern Asia	153	3.575	0.03084	0.31982	0.30285	0.30335	0.23414	0.09719	0.36510	1.95210
Rwanda	Sub-Saharan Africa	154	3.465	0.03464	0.22208	0.77370	0.42864	0.59201	0.55191	0.22628	0.67042
Benin	Sub-Saharan Africa	155	3.340	0.03656	0.28665	0.35386	0.31910	0.48450	0.08010	0.18260	1.63328
Syria	Middle East and Northern Africa	156	3.006	0.05015	0.66320	0.47489	0.72193	0.15684	0.18906	0.47179	0.32858
Burundi	Sub-Saharan Africa	157	2.905	0.08658	0.01530	0.41587	0.22396	0.11850	0.10062	0.19727	1.83302
Togo	Sub-Saharan Africa	158	2.839	0.06727	0.20868	0.13995	0.28443	0.36453	0.10731	0.16681	1.56726

158 rows × 10 columns

 
# 排序分层
report_2015_df2.sort_index(level=0)

		Happiness Rank	Happiness Score	Standard Error	Economy (GDP per Capita)	Family	Health (Life Expectancy)	Freedom	Trust (Government Corruption)	Generosity	Dystopia Residual
Region	Country
Australia and New Zealand	Australia	10	7.284	0.04083	1.33358	1.30923	0.93156	0.65124	0.35637	0.43562	2.26646
Australia and New Zealand	New Zealand	9	7.286	0.03371	1.25018	1.31967	0.90837	0.63938	0.42922	0.47501	2.26425
Central and Eastern Europe	Albania	95	4.959	0.05013	0.87867	0.80434	0.81325	0.35733	0.06413	0.14272	1.89894
	Armenia	127	4.350	0.04763	0.76821	0.77711	0.72990	0.19847	0.03900	0.07855	1.75873
	Azerbaijan	80	5.212	0.03363	1.02389	0.93793	0.64045	0.37030	0.16065	0.07799	2.00073
	Belarus	59	5.813	0.03938	1.03192	1.23289	0.73608	0.37938	0.19090	0.11046	2.13090
	Bosnia and Herzegovina	96	4.949	0.06913	0.83223	0.91916	0.79081	0.09245	0.00227	0.24808	2.06367
	Bulgaria	134	4.218	0.04828	1.01216	1.10614	0.76649	0.30587	0.00872	0.11921	0.89991
	Croatia	62	5.759	0.04394	1.08254	0.79624	0.78805	0.25883	0.02430	0.05444	2.75414
	Czech Republic	31	6.505	0.04168	1.17898	1.20643	0.84483	0.46364	0.02652	0.10686	2.67782
	Estonia	73	5.429	0.04013	1.15174	1.22791	0.77361	0.44888	0.15184	0.08680	1.58782
	Georgia	130	4.297	0.04221	0.74190	0.38562	0.72926	0.40577	0.38331	0.05547	1.59541
	Hungary	104	4.800	0.06107	1.12094	1.20215	0.75905	0.32112	0.02758	0.12800	1.24074
	Kazakhstan	54	5.855	0.04114	1.12254	1.12241	0.64368	0.51649	0.08454	0.11827	2.24729
	Kosovo	69	5.589	0.05018	0.80148	0.81198	0.63132	0.24749	0.04741	0.28310	2.76579
	Kyrgyzstan	77	5.286	0.03823	0.47428	1.15115	0.65088	0.43477	0.04232	0.30030	2.23270
	Latvia	89	5.098	0.04640	1.11312	1.09562	0.72437	0.29671	0.06332	0.18226	1.62215
	Lithuania	56	5.833	0.03843	1.14723	1.25745	0.73128	0.21342	0.01031	0.02641	2.44649
	Macedonia	93	5.007	0.05376	0.91851	1.00232	0.73545	0.33457	0.05327	0.22359	1.73933
	Moldova	52	5.889	0.03799	0.59448	1.01528	0.61826	0.32818	0.01615	0.20951	3.10712
	Montenegro	82	5.192	0.05235	0.97438	0.90557	0.72521	0.18260	0.14296	0.16140	2.10017
	Poland	60	5.791	0.04263	1.12555	1.27948	0.77903	0.53122	0.04212	0.16759	1.86565
	Romania	86	5.124	0.06607	1.04345	0.88588	0.76890	0.35068	0.00649	0.13748	1.93129
	Russia	64	5.716	0.03135	1.13764	1.23617	0.66926	0.36679	0.03005	0.00199	2.27394
	Serbia	87	5.123	0.04864	0.92053	1.00964	0.74836	0.20107	0.02617	0.19231	2.02500
	Slovakia	45	5.995	0.04267	1.16891	1.26999	0.78902	0.31751	0.03431	0.16893	2.24639
	Slovenia	55	5.848	0.04251	1.18498	1.27385	0.87337	0.60855	0.03787	0.25328	1.61583
	Tajikistan	106	4.786	0.03198	0.39047	0.85563	0.57379	0.47216	0.15072	0.22974	2.11399
	Turkmenistan	70	5.548	0.04175	0.95847	1.22668	0.53886	0.47610	0.30844	0.16979	1.86984
	Ukraine	111	4.681	0.04412	0.79907	1.20278	0.67390	0.25123	0.02961	0.15275	1.57140
...	...	...	...	...	...	...	...	...	...	...	...
Sub-Saharan Africa	Somaliland region	91	5.057	0.06161	0.18847	0.95152	0.43873	0.46582	0.39928	0.50318	2.11032
	South Africa	113	4.642	0.04585	0.92049	1.18468	0.27688	0.33207	0.08884	0.11973	1.71956
	Sudan	118	4.550	0.06740	0.52107	1.01404	0.36878	0.10081	0.14660	0.19062	2.20857
	Swaziland	101	4.867	0.08742	0.71206	1.07284	0.07566	0.30658	0.03060	0.18259	2.48676
	Tanzania	146	3.781	0.05061	0.28520	1.00268	0.38215	0.32878	0.05747	0.34377	1.38079
	Togo	158	2.839	0.06727	0.20868	0.13995	0.28443	0.36453	0.10731	0.16681	1.56726
	Uganda	141	3.931	0.04317	0.21102	1.13299	0.33861	0.45727	0.07267	0.29066	1.42766
	Zambia	85	5.129	0.06988	0.47038	0.91612	0.29924	0.48827	0.12468	0.19591	2.63430
	Zimbabwe	115	4.610	0.04290	0.27100	1.03276	0.33475	0.25861	0.08079	0.18987	2.44191
Western Europe	Austria	13	7.200	0.03751	1.33723	1.29704	0.89042	0.62433	0.18676	0.33088	2.53320
	Belgium	19	6.937	0.03595	1.30782	1.28566	0.89667	0.58450	0.22540	0.22250	2.41484
	Cyprus	67	5.689	0.05580	1.20813	0.89318	0.92356	0.40672	0.06146	0.30638	1.88931
	Denmark	3	7.527	0.03328	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	2.49204
	Finland	6	7.406	0.03140	1.29025	1.31826	0.88911	0.64169	0.41372	0.23351	2.61955
	France	29	6.575	0.03512	1.27778	1.26038	0.94579	0.55011	0.20646	0.12332	2.21126
	Germany	26	6.750	0.01848	1.32792	1.29937	0.89186	0.61477	0.21843	0.28214	2.11569
	Greece	102	4.857	0.05062	1.15406	0.92933	0.88213	0.07699	0.01397	0.00000	1.80101
	Iceland	2	7.561	0.04884	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	2.70201
	Ireland	18	6.940	0.03676	1.33596	1.36948	0.89533	0.61777	0.28703	0.45901	1.97570
	Italy	50	5.948	0.03914	1.25114	1.19777	0.95446	0.26236	0.02901	0.22823	2.02518
	Luxembourg	17	6.946	0.03499	1.56391	1.21963	0.91894	0.61583	0.37798	0.28034	1.96961
	Malta	37	6.302	0.04206	1.20740	1.30203	0.88721	0.60365	0.13586	0.51752	1.64880
	Netherlands	7	7.378	0.02799	1.32944	1.28017	0.89284	0.61576	0.31814	0.47610	2.46570
	North Cyprus	66	5.695	0.05635	1.20806	1.07008	0.92356	0.49027	0.14280	0.26169	1.59888
	Norway	4	7.522	0.03880	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	2.46531
	Portugal	88	5.102	0.04802	1.15991	1.13935	0.87519	0.51469	0.01078	0.13719	1.26462
	Spain	36	6.329	0.03468	1.23011	1.31379	0.95562	0.45951	0.06398	0.18227	2.12367
	Sweden	8	7.364	0.03157	1.33171	1.28907	0.91087	0.65980	0.43844	0.36262	2.37119
	Switzerland	1	7.587	0.03411	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	2.51738
	United Kingdom	21	6.867	0.01866	1.26637	1.28548	0.90943	0.59625	0.32067	0.51912	1.96994

158 rows × 10 columns

7. 数据清洗

 
log_data = pd.read_csv('log.csv')
log_data

	time	user	video	playback position	paused	volume
0	1469974424	cheryl	intro.html	5	False	10.0
1	1469974454	cheryl	intro.html	6	NaN	NaN
2	1469974544	cheryl	intro.html	9	NaN	NaN
3	1469974574	cheryl	intro.html	10	NaN	NaN
4	1469977514	bob	intro.html	1	NaN	NaN
5	1469977544	bob	intro.html	1	NaN	NaN
6	1469977574	bob	intro.html	1	NaN	NaN
7	1469977604	bob	intro.html	1	NaN	NaN
8	1469974604	cheryl	intro.html	11	NaN	NaN
9	1469974694	cheryl	intro.html	14	NaN	NaN
10	1469974724	cheryl	intro.html	15	NaN	NaN
11	1469974454	sue	advanced.html	24	NaN	NaN
12	1469974524	sue	advanced.html	25	NaN	NaN
13	1469974424	sue	advanced.html	23	False	10.0
14	1469974554	sue	advanced.html	26	NaN	NaN
15	1469974624	sue	advanced.html	27	NaN	NaN
16	1469974654	sue	advanced.html	28	NaN	5.0
17	1469974724	sue	advanced.html	29	NaN	NaN
18	1469974484	cheryl	intro.html	7	NaN	NaN
19	1469974514	cheryl	intro.html	8	NaN	NaN
20	1469974754	sue	advanced.html	30	NaN	NaN
21	1469974824	sue	advanced.html	31	NaN	NaN
22	1469974854	sue	advanced.html	32	NaN	NaN
23	1469974924	sue	advanced.html	33	NaN	NaN
24	1469977424	bob	intro.html	1	True	10.0
25	1469977454	bob	intro.html	1	NaN	NaN
26	1469977484	bob	intro.html	1	NaN	NaN
27	1469977634	bob	intro.html	1	NaN	NaN
28	1469977664	bob	intro.html	1	NaN	NaN
29	1469974634	cheryl	intro.html	12	NaN	NaN
30	1469974664	cheryl	intro.html	13	NaN	NaN
31	1469977694	bob	intro.html	1	NaN	NaN
32	1469977724	bob	intro.html	1	NaN	NaN

 
log_data.set_index(['time', 'user'], inplace=True)
log_data.sort_index(inplace=True)
log_data

		video	playback position	paused	volume
time	user
1469974424	cheryl	intro.html	5	False	10.0
1469974424	sue	advanced.html	23	False	10.0
1469974454	cheryl	intro.html	6	NaN	NaN
1469974454	sue	advanced.html	24	NaN	NaN
1469974484	cheryl	intro.html	7	NaN	NaN
1469974514	cheryl	intro.html	8	NaN	NaN
1469974524	sue	advanced.html	25	NaN	NaN
1469974544	cheryl	intro.html	9	NaN	NaN
1469974554	sue	advanced.html	26	NaN	NaN
1469974574	cheryl	intro.html	10	NaN	NaN
1469974604	cheryl	intro.html	11	NaN	NaN
1469974624	sue	advanced.html	27	NaN	NaN
1469974634	cheryl	intro.html	12	NaN	NaN
1469974654	sue	advanced.html	28	NaN	5.0
1469974664	cheryl	intro.html	13	NaN	NaN
1469974694	cheryl	intro.html	14	NaN	NaN
1469974724	cheryl	intro.html	15	NaN	NaN
1469974724	sue	advanced.html	29	NaN	NaN
1469974754	sue	advanced.html	30	NaN	NaN
1469974824	sue	advanced.html	31	NaN	NaN
1469974854	sue	advanced.html	32	NaN	NaN
1469974924	sue	advanced.html	33	NaN	NaN
1469977424	bob	intro.html	1	True	10.0
1469977454	bob	intro.html	1	NaN	NaN
1469977484	bob	intro.html	1	NaN	NaN
1469977514	bob	intro.html	1	NaN	NaN
1469977544	bob	intro.html	1	NaN	NaN
1469977574	bob	intro.html	1	NaN	NaN
1469977604	bob	intro.html	1	NaN	NaN
1469977634	bob	intro.html	1	NaN	NaN
1469977664	bob	intro.html	1	NaN	NaN
1469977694	bob	intro.html	1	NaN	NaN
1469977724	bob	intro.html	1	NaN	NaN

 
log_data.fillna(0)

		video	playback position	paused	volume
time	user
1469974424	cheryl	intro.html	5	False	10.0
1469974424	sue	advanced.html	23	False	10.0
1469974454	cheryl	intro.html	6	0	0.0
1469974454	sue	advanced.html	24	0	0.0
1469974484	cheryl	intro.html	7	0	0.0
1469974514	cheryl	intro.html	8	0	0.0
1469974524	sue	advanced.html	25	0	0.0
1469974544	cheryl	intro.html	9	0	0.0
1469974554	sue	advanced.html	26	0	0.0
1469974574	cheryl	intro.html	10	0	0.0
1469974604	cheryl	intro.html	11	0	0.0
1469974624	sue	advanced.html	27	0	0.0
1469974634	cheryl	intro.html	12	0	0.0
1469974654	sue	advanced.html	28	0	5.0
1469974664	cheryl	intro.html	13	0	0.0
1469974694	cheryl	intro.html	14	0	0.0
1469974724	cheryl	intro.html	15	0	0.0
1469974724	sue	advanced.html	29	0	0.0
1469974754	sue	advanced.html	30	0	0.0
1469974824	sue	advanced.html	31	0	0.0
1469974854	sue	advanced.html	32	0	0.0
1469974924	sue	advanced.html	33	0	0.0
1469977424	bob	intro.html	1	True	10.0
1469977454	bob	intro.html	1	0	0.0
1469977484	bob	intro.html	1	0	0.0
1469977514	bob	intro.html	1	0	0.0
1469977544	bob	intro.html	1	0	0.0
1469977574	bob	intro.html	1	0	0.0
1469977604	bob	intro.html	1	0	0.0
1469977634	bob	intro.html	1	0	0.0
1469977664	bob	intro.html	1	0	0.0
1469977694	bob	intro.html	1	0	0.0
1469977724	bob	intro.html	1	0	0.0

log_data.dropna()

		video	playback position	paused	volume
time	user
1469974424	cheryl	intro.html	5	False	10.0
1469974424	sue	advanced.html	23	False	10.0
1469977424	bob	intro.html	1	True	10.0

log_data.ffill()

		video	playback position	paused	volume
time	user
1469974424	cheryl	intro.html	5	False	10.0
1469974424	sue	advanced.html	23	False	10.0
1469974454	cheryl	intro.html	6	False	10.0
1469974454	sue	advanced.html	24	False	10.0
1469974484	cheryl	intro.html	7	False	10.0
1469974514	cheryl	intro.html	8	False	10.0
1469974524	sue	advanced.html	25	False	10.0
1469974544	cheryl	intro.html	9	False	10.0
1469974554	sue	advanced.html	26	False	10.0
1469974574	cheryl	intro.html	10	False	10.0
1469974604	cheryl	intro.html	11	False	10.0
1469974624	sue	advanced.html	27	False	10.0
1469974634	cheryl	intro.html	12	False	10.0
1469974654	sue	advanced.html	28	False	5.0
1469974664	cheryl	intro.html	13	False	5.0
1469974694	cheryl	intro.html	14	False	5.0
1469974724	cheryl	intro.html	15	False	5.0
1469974724	sue	advanced.html	29	False	5.0
1469974754	sue	advanced.html	30	False	5.0
1469974824	sue	advanced.html	31	False	5.0
1469974854	sue	advanced.html	32	False	5.0
1469974924	sue	advanced.html	33	False	5.0
1469977424	bob	intro.html	1	True	10.0
1469977454	bob	intro.html	1	True	10.0
1469977484	bob	intro.html	1	True	10.0
1469977514	bob	intro.html	1	True	10.0
1469977544	bob	intro.html	1	True	10.0
1469977574	bob	intro.html	1	True	10.0
1469977604	bob	intro.html	1	True	10.0
1469977634	bob	intro.html	1	True	10.0
1469977664	bob	intro.html	1	True	10.0
1469977694	bob	intro.html	1	True	10.0
1469977724	bob	intro.html	1	True	10.0

log_data.bfill()

		video	playback position	paused	volume
time	user
1469974424	cheryl	intro.html	5	False	10.0
1469974424	sue	advanced.html	23	False	10.0
1469974454	cheryl	intro.html	6	True	5.0
1469974454	sue	advanced.html	24	True	5.0
1469974484	cheryl	intro.html	7	True	5.0
1469974514	cheryl	intro.html	8	True	5.0
1469974524	sue	advanced.html	25	True	5.0
1469974544	cheryl	intro.html	9	True	5.0
1469974554	sue	advanced.html	26	True	5.0
1469974574	cheryl	intro.html	10	True	5.0
1469974604	cheryl	intro.html	11	True	5.0
1469974624	sue	advanced.html	27	True	5.0
1469974634	cheryl	intro.html	12	True	5.0
1469974654	sue	advanced.html	28	True	5.0
1469974664	cheryl	intro.html	13	True	10.0
1469974694	cheryl	intro.html	14	True	10.0
1469974724	cheryl	intro.html	15	True	10.0
1469974724	sue	advanced.html	29	True	10.0
1469974754	sue	advanced.html	30	True	10.0
1469974824	sue	advanced.html	31	True	10.0
1469974854	sue	advanced.html	32	True	10.0
1469974924	sue	advanced.html	33	True	10.0
1469977424	bob	intro.html	1	True	10.0
1469977454	bob	intro.html	1	NaN	NaN
1469977484	bob	intro.html	1	NaN	NaN
1469977514	bob	intro.html	1	NaN	NaN
1469977544	bob	intro.html	1	NaN	NaN
1469977574	bob	intro.html	1	NaN	NaN
1469977604	bob	intro.html	1	NaN	NaN
1469977634	bob	intro.html	1	NaN	NaN
1469977664	bob	intro.html	1	NaN	NaN
1469977694	bob	intro.html	1	NaN	NaN
1469977724	bob	intro.html	1	NaN	NaN

posted @ 2017-11-15 08:45 在路上-UP 阅读(596) 评论(0) 编辑收藏举报

会员力量，点亮园子希望

刷新页面返回顶部

在路上-UP

唯有前行，不负年华！

2-1

Pandas进阶及技巧

1. 创建Pandas

2. Pandas索引

3. 删除数据

4. DataFrame的操作与加载

5. Boolean Mask

6. 层级索引

7. 数据清洗

公告