2-1
Loading [MathJax]/extensions/Safe.js
lect02_eg01 Last Checkpoint: 11/06/2017 (autosaved) 
Python 3
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 | 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 |
| 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 |
| sue | advanced.html | 23 | False | 10.0 | |
| 1469974454 | cheryl | intro.html | 6 | NaN | NaN |
| 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 |
| 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 |
| sue | advanced.html | 23 | False | 10.0 | |
| 1469974454 | cheryl | intro.html | 6 | 0 | 0.0 |
| 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 |
| 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 |
| 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 |
| sue | advanced.html | 23 | False | 10.0 | |
| 1469974454 | cheryl | intro.html | 6 | False | 10.0 |
| 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 |
| 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 |
| sue | advanced.html | 23 | False | 10.0 | |
| 1469974454 | cheryl | intro.html | 6 | True | 5.0 |
| 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 |
| 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 |
