Pandas基本数据对象及操作

最新推荐文章于 2022-11-14 10:39:44 发布
转载 最新推荐文章于 2022-11-14 10:39:44 发布 · 3.4k 阅读 · 2
文章标签：

#python pandas

本文介绍了Pandas库中Series和DataFrame的基本用法，包括创建、索引、数据处理等核心功能，并通过实例演示如何利用这些功能进行高效的数据操作。

摘要生成于 C知道 ，由 DeepSeek-R1 满血版支持， 前往体验 >

# 基本数据对象及操作 ## 1. Series * 创建Series 
import pandas as pd

countries = ['中国', '美国', '澳大利亚']
countries_s = pd.Series(countries)
print(type(countries_s))
print(countries_s)
numbers = [4, 5, 6]
print(pd.Series(numbers))
0 4 1 5 2 6 dtype: int64 
country_dicts = {'CH': '中国',
                'US': '美国',
                'AU': '澳大利亚'}

country_dict_s = pd.Series(country_dicts)
# 给索引命名
country_dict_s.index.name = 'Code'
# 给数据命名
country_dict_s.name = 'Country'

print(country_dict_s)
print(country_dict_s.values)
print(country_dict_s.index)
Code AU 澳大利亚 CH 中国 US 美国 Name: Country, dtype: object [‘澳大利亚’ ‘中国’ ‘美国’] Index([‘AU’, ‘CH’, ‘US’], dtype=’object’, name=’Code’) * 处理缺失数据 
countries = ['中国', '美国', '澳大利亚', None]
print(pd.Series(countries))
0 中国 1 美国 2 澳大利亚 3 None dtype: object 
numbers = [4, 5, 6, None]
print(pd.Series(numbers))
0 4.0 1 5.0 2 6.0 3 NaN dtype: float64 * Series 索引 
country_dicts = {'CH': '中国',
                'US': '美国',
                'AU': '澳大利亚'}

country_dict_s = pd.Series(country_dicts)
print(country_dict_s)
AU 澳大利亚 CH 中国 US 美国 dtype: object 
# 通过索引判断数据是存在
# Series也可看作定长、有序的字典
print('CH' in country_dict_s)
print('NZ' in country_dict_s)
True False 
print('iloc:', country_dict_s.iloc[1])
print('loc:', country_dict_s.loc['US'])
print('[]:', country_dict_s['US'])
iloc: 中国 loc: 美国 []: 美国 
print('iloc:\n', country_dict_s.iloc[ [0, 2] ])
print()
print('loc:\n', country_dict_s.loc[['US', 'AU']])
iloc: AU 澳大利亚 US 美国 dtype: object loc: US 美国 AU 澳大利亚 dtype: object * 向量化操作 
import numpy as np

s = pd.Series(np.random.randint(0, 1000, 10000))
print(s.head())
print(len(s))
0 494 1 387 2 968 3 458 4 408 dtype: int64 10000 
%%timeit -n 100
total = 0
for item in s:
    total += item
931 µs ± 127 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 
%%timeit -n 100
total = np.sum(s)
147 µs ± 39.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 
%%timeit -n 10
s = pd.Series(np.random.randint(0, 1000, 10000))
for label, value in s.iteritems():
    s.loc[label] = value + 2
4.08 s ± 60.6 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) 
%%timeit -n 10
s = pd.Series(np.random.randint(0, 1000, 10000))
s += 2
The slowest run took 44.70 times longer than the fastest. This could mean that an intermediate result is being cached. 2.12 ms ± 4.45 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) ## 2. DataFrame * 创建Dataframe 
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
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
AreaHappiness RankLanguageName
CH9.597M km279Chinese中国
US9.834M km214English (US)美国
AU7.692M km29English (AU)澳大利亚
# 添加数据
# 如果个数小于要求的个数，会自动进行“广播”操作
# 如果大于要求的个数，会报错
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
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
AreaHappiness RankLanguageNameLocationRegion
CH9.597M km279Chinese中国地球亚洲
US9.834M km214English (US)美国地球北美洲
AU7.692M km29English (AU)澳大利亚地球大洋洲
  • Dataframe索引
# 行索引
print('loc:')
print(df.loc['CH'])
print(type(df.loc['CH']))

print('iloc:')
print(df.iloc[1])

print(df['Area'])
loc: Area 9.597M km2 Happiness Rank 79 Language Chinese Name 中国 Location 地球 Region 亚洲 Name: CH, 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 
# 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 * 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 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 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
* DataFrame的操作与加载 
df['Area']
US 9.834M km2 AU 7.692M km2 Name: Area, dtype: object 
# 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)
————————————————————————— KeyError Traceback (most recent call last) ~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance) 2524 try: -> 2525 return self._engine.get_loc(key) 2526 except KeyError: pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: ‘Happiness Rank’ During handling of the above exception, another exception occurred: KeyError Traceback (most recent call last) in () 1 # 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 —-> 2 ranks = df[‘Happiness Rank’] 3 ranks += 2 4 print(ranks) 5 print(df) ~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in __getitem__(self, key) 2137 return self._getitem_multilevel(key) 2138 else: -> 2139 return self._getitem_column(key) 2140 2141 def _getitem_column(self, key): ~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in _getitem_column(self, key) 2144 # get column 2145 if self.columns.is_unique: -> 2146 return self._get_item_cache(key) 2147 2148 # duplicate columns & possible reduce dimensionality ~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py in _get_item_cache(self, item) 1840 res = cache.get(item) 1841 if res is None: -> 1842 values = self._data.get(item) 1843 res = self._box_item_values(item, values) 1844 cache[item] = res ~/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py in get(self, item, fastpath) 3841 3842 if not isna(item): -> 3843 loc = self.items.get_loc(item) 3844 else: 3845 indexer = np.arange(len(self.items))[isna(self.items)] ~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance) 2525 return self._engine.get_loc(key) 2526 except KeyError: -> 2527 return self._engine.get_loc(self._maybe_cast_indexer(key)) 2528 2529 indexer = self.get_indexer([key], method=method, tolerance=tolerance) pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: ‘Happiness Rank’ 
# 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)
# 加载csv文件数据
import pandas as pd
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：
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
CountryRegionHappiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
0SwitzerlandWestern Europe17.5870.034111.396511.349510.941430.665570.419780.296782.51738
1IcelandWestern Europe27.5610.048841.302321.402230.947840.628770.141450.436302.70201
2DenmarkWestern Europe37.5270.033281.325481.360580.874640.649380.483570.341392.49204
3NorwayWestern Europe47.5220.038801.459001.330950.885210.669730.365030.346992.46531
4CanadaNorth America57.4270.035531.326291.322610.905630.632970.329570.458112.45176
print(reprot_2015_df.info())
## 3. 索引 
# 使用index_col指定索引列
# 使用usecols指定需要读取的列
import pandas as pd
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()
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
RegionHappiness RankHappiness Score
Country
DenmarkWestern Europe17.526
SwitzerlandWestern Europe27.509
IcelandWestern Europe37.501
NorwayWestern Europe47.498
FinlandWestern Europe57.413
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) in () 1 # 注意index是不可变的 —-> 2 reprot_2016_df.index[0] = ‘丹麦’ ~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py in __setitem__(self, key, value) 1722 1723 def __setitem__(self, key, value): -> 1724 raise TypeError(“Index does not support mutable operations”) 1725 1726 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(inplace=True)
reprot_2016_df.head()
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
CountryRegionHappiness RankHappiness Score
0DenmarkWestern Europe17.526
1SwitzerlandWestern Europe27.509
2IcelandWestern Europe37.501
3NorwayWestern Europe47.498
4FinlandWestern Europe57.413
# 重命名列名
reprot_2016_df = reprot_2016_df.rename(columns={'Region': '地区', 'Hapiness Rank': '排名', 'Hapiness Score': '幸福指数'})
reprot_2016_df.head()
# 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()
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Country地区排名幸福指数
0DenmarkWestern Europe17.526
1SwitzerlandWestern Europe27.509
2IcelandWestern Europe37.501
3NorwayWestern Europe47.498
4FinlandWestern Europe57.413
## 4. Boolean Mask 
reprot_2016_df.head()
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Country地区排名幸福指数
0DenmarkWestern Europe17.526
1SwitzerlandWestern Europe27.509
2IcelandWestern Europe37.501
3NorwayWestern Europe47.498
4FinlandWestern Europe57.413
# 过滤 Western Europe 地区的国家
# only_western_europe = reprot_2016_df['地区'] == 'Western Europe'
reprot_2016_df[reprot_2016_df['地区'] == 'Western Europe']
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Country地区排名幸福指数
0DenmarkWestern Europe17.526
1SwitzerlandWestern Europe27.509
2IcelandWestern Europe37.501
3NorwayWestern Europe47.498
4FinlandWestern Europe57.413
6NetherlandsWestern Europe77.339
9SwedenWestern Europe107.291
11AustriaWestern Europe127.119
15GermanyWestern Europe166.994
17BelgiumWestern Europe186.929
18IrelandWestern Europe196.907
19LuxembourgWestern Europe206.871
22United KingdomWestern Europe236.725
29MaltaWestern Europe306.488
31FranceWestern Europe326.478
36SpainWestern Europe376.361
49ItalyWestern Europe505.977
61North CyprusWestern Europe625.771
68CyprusWestern Europe695.546
93PortugalWestern Europe945.123
98GreeceWestern Europe995.033
# 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
0 False 1 False 2 False 3 False 4 False 5 False 6 False 7 False 8 False 9 False 10 False 11 True 12 False 13 False 14 False 15 True 16 False 17 True 18 True 19 True 20 False 21 False 22 True 23 False 24 False 25 False 26 False 27 False 28 False 29 True … 127 False 128 False 129 False 130 False 131 False 132 False 133 False 134 False 135 False 136 False 137 False 138 False 139 False 140 False 141 False 142 False 143 False 144 False 145 False 146 False 147 False 148 False 149 False 150 False 151 False 152 False 153 False 154 False 155 False 156 False Length: 157, dtype: bool 
# 叠加 boolean mask 得到最终结果
reprot_2016_df[only_western_europe_10]
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Country地区排名幸福指数
11AustriaWestern Europe127.119
15GermanyWestern Europe166.994
17BelgiumWestern Europe186.929
18IrelandWestern Europe196.907
19LuxembourgWestern Europe206.871
22United KingdomWestern Europe236.725
29MaltaWestern Europe306.488
31FranceWestern Europe326.478
36SpainWestern Europe376.361
49ItalyWestern Europe505.977
61North CyprusWestern Europe625.771
68CyprusWestern Europe695.546
93PortugalWestern Europe945.123
98GreeceWestern Europe995.033
# 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)]
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Country地区排名幸福指数
11AustriaWestern Europe127.119
15GermanyWestern Europe166.994
17BelgiumWestern Europe186.929
18IrelandWestern Europe196.907
19LuxembourgWestern Europe206.871
22United KingdomWestern Europe236.725
29MaltaWestern Europe306.488
31FranceWestern Europe326.478
36SpainWestern Europe376.361
49ItalyWestern Europe505.977
61North CyprusWestern Europe625.771
68CyprusWestern Europe695.546
93PortugalWestern Europe945.123
98GreeceWestern Europe995.033
## 5. 层级索引 
reprot_2015_df.head()
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
CountryRegionHappiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
0SwitzerlandWestern Europe17.5870.034111.396511.349510.941430.665570.419780.296782.51738
1IcelandWestern Europe27.5610.048841.302321.402230.947840.628770.141450.436302.70201
2DenmarkWestern Europe37.5270.033281.325481.360580.874640.649380.483570.341392.49204
3NorwayWestern Europe47.5220.038801.459001.330950.885210.669730.365030.346992.46531
4CanadaNorth America57.4270.035531.326291.322610.905630.632970.329570.458112.45176
# 设置层级索引
report_2015_df2 = reprot_2015_df.set_index(['Region', 'Country'])
report_2015_df2.head(20)
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Happiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
RegionCountry
Western EuropeSwitzerland17.5870.034111.396511.349510.941430.665570.419780.296782.51738
Iceland27.5610.048841.302321.402230.947840.628770.141450.436302.70201
Denmark37.5270.033281.325481.360580.874640.649380.483570.341392.49204
Norway47.5220.038801.459001.330950.885210.669730.365030.346992.46531
North AmericaCanada57.4270.035531.326291.322610.905630.632970.329570.458112.45176
Western EuropeFinland67.4060.031401.290251.318260.889110.641690.413720.233512.61955
Netherlands77.3780.027991.329441.280170.892840.615760.318140.476102.46570
Sweden87.3640.031571.331711.289070.910870.659800.438440.362622.37119
Australia and New ZealandNew Zealand97.2860.033711.250181.319670.908370.639380.429220.475012.26425
Australia107.2840.040831.333581.309230.931560.651240.356370.435622.26646
Middle East and Northern AfricaIsrael117.2780.034701.228571.223930.913870.413190.077850.331723.08854
Latin America and CaribbeanCosta Rica127.2260.044540.955781.237880.860270.633760.105830.254973.17728
Western EuropeAustria137.2000.037511.337231.297040.890420.624330.186760.330882.53320
Latin America and CaribbeanMexico147.1870.041761.020540.914510.814440.481810.213120.140743.60214
North AmericaUnited States157.1190.038391.394511.247110.861790.546040.158900.401052.51011
Latin America and CaribbeanBrazil166.9830.040760.981241.232870.697020.490490.175210.145743.26001
Western EuropeLuxembourg176.9460.034991.563911.219630.918940.615830.377980.280341.96961
Ireland186.9400.036761.335961.369480.895330.617770.287030.459011.97570
Belgium196.9370.035951.307821.285660.896670.584500.225400.222502.41484
Middle East and Northern AfricaUnited Arab Emirates206.9010.037291.427271.125750.809250.641570.385830.264282.24743
# level0 索引
report_2015_df2.loc['Western Europe']
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Happiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
Country
Switzerland17.5870.034111.396511.349510.941430.665570.419780.296782.51738
Iceland27.5610.048841.302321.402230.947840.628770.141450.436302.70201
Denmark37.5270.033281.325481.360580.874640.649380.483570.341392.49204
Norway47.5220.038801.459001.330950.885210.669730.365030.346992.46531
Finland67.4060.031401.290251.318260.889110.641690.413720.233512.61955
Netherlands77.3780.027991.329441.280170.892840.615760.318140.476102.46570
Sweden87.3640.031571.331711.289070.910870.659800.438440.362622.37119
Austria137.2000.037511.337231.297040.890420.624330.186760.330882.53320
Luxembourg176.9460.034991.563911.219630.918940.615830.377980.280341.96961
Ireland186.9400.036761.335961.369480.895330.617770.287030.459011.97570
Belgium196.9370.035951.307821.285660.896670.584500.225400.222502.41484
United Kingdom216.8670.018661.266371.285480.909430.596250.320670.519121.96994
Germany266.7500.018481.327921.299370.891860.614770.218430.282142.11569
France296.5750.035121.277781.260380.945790.550110.206460.123322.21126
Spain366.3290.034681.230111.313790.955620.459510.063980.182272.12367
Malta376.3020.042061.207401.302030.887210.603650.135860.517521.64880
Italy505.9480.039141.251141.197770.954460.262360.029010.228232.02518
North Cyprus665.6950.056351.208061.070080.923560.490270.142800.261691.59888
Cyprus675.6890.055801.208130.893180.923560.406720.061460.306381.88931
Portugal885.1020.048021.159911.139350.875190.514690.010780.137191.26462
Greece1024.8570.050621.154060.929330.882130.076990.013970.000001.80101
# 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()
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Happiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
CountryRegion
SwitzerlandWestern Europe17.5870.034111.396511.349510.941430.665570.419780.296782.51738
IcelandWestern Europe27.5610.048841.302321.402230.947840.628770.141450.436302.70201
DenmarkWestern Europe37.5270.033281.325481.360580.874640.649380.483570.341392.49204
NorwayWestern Europe47.5220.038801.459001.330950.885210.669730.365030.346992.46531
CanadaNorth America57.4270.035531.326291.322610.905630.632970.329570.458112.45176
FinlandWestern Europe67.4060.031401.290251.318260.889110.641690.413720.233512.61955
NetherlandsWestern Europe77.3780.027991.329441.280170.892840.615760.318140.476102.46570
SwedenWestern Europe87.3640.031571.331711.289070.910870.659800.438440.362622.37119
New ZealandAustralia and New Zealand97.2860.033711.250181.319670.908370.639380.429220.475012.26425
AustraliaAustralia and New Zealand107.2840.040831.333581.309230.931560.651240.356370.435622.26646
IsraelMiddle East and Northern Africa117.2780.034701.228571.223930.913870.413190.077850.331723.08854
Costa RicaLatin America and Caribbean127.2260.044540.955781.237880.860270.633760.105830.254973.17728
AustriaWestern Europe137.2000.037511.337231.297040.890420.624330.186760.330882.53320
MexicoLatin America and Caribbean147.1870.041761.020540.914510.814440.481810.213120.140743.60214
United StatesNorth America157.1190.038391.394511.247110.861790.546040.158900.401052.51011
BrazilLatin America and Caribbean166.9830.040760.981241.232870.697020.490490.175210.145743.26001
LuxembourgWestern Europe176.9460.034991.563911.219630.918940.615830.377980.280341.96961
IrelandWestern Europe186.9400.036761.335961.369480.895330.617770.287030.459011.97570
BelgiumWestern Europe196.9370.035951.307821.285660.896670.584500.225400.222502.41484
United Arab EmiratesMiddle East and Northern Africa206.9010.037291.427271.125750.809250.641570.385830.264282.24743
United KingdomWestern Europe216.8670.018661.266371.285480.909430.596250.320670.519121.96994
OmanMiddle East and Northern Africa226.8530.053351.360111.081820.762760.632740.325240.215422.47489
VenezuelaLatin America and Caribbean236.8100.064761.044241.255960.720520.429080.110690.058413.19131
SingaporeSoutheastern Asia246.7980.037801.521861.020001.025250.542520.492100.311051.88501
PanamaLatin America and Caribbean256.7860.049101.063531.198500.796610.542100.092700.244342.84848
GermanyWestern Europe266.7500.018481.327921.299370.891860.614770.218430.282142.11569
ChileLatin America and Caribbean276.6700.058001.107151.124470.858570.441320.128690.333632.67585
QatarMiddle East and Northern Africa286.6110.062571.690421.078600.797330.640400.522080.325731.55674
FranceWestern Europe296.5750.035121.277781.260380.945790.550110.206460.123322.21126
ArgentinaLatin America and Caribbean306.5740.046121.053511.248230.787230.449740.084840.114512.83600
………………………………
MyanmarSoutheastern Asia1294.3070.043510.271080.709050.482460.440170.190340.795881.41805
GeorgiaCentral and Eastern Europe1304.2970.042210.741900.385620.729260.405770.383310.055471.59541
MalawiSub-Saharan Africa1314.2920.061300.016040.411340.225620.430540.069770.331282.80791
Sri LankaSouthern Asia1324.2710.037510.835241.019050.708060.537260.091790.408280.67108
CameroonSub-Saharan Africa1334.2520.046780.422500.887670.234020.493090.057860.206181.95071
BulgariaCentral and Eastern Europe1344.2180.048281.012161.106140.766490.305870.008720.119210.89991
EgyptMiddle East and Northern Africa1354.1940.032600.881800.747000.617120.172880.063240.112911.59927
YemenMiddle East and Northern Africa1364.0770.043670.546490.680930.400640.355710.078540.091311.92313
AngolaSub-Saharan Africa1374.0330.047580.757780.860400.166830.103840.071220.123441.94939
MaliSub-Saharan Africa1383.9950.056020.260741.035260.205830.388570.123520.187981.79293
Congo (Brazzaville)Sub-Saharan Africa1393.9890.066820.678660.662900.310510.414660.116860.123881.68135
ComorosSub-Saharan Africa1403.9560.047970.239060.792730.363150.229170.199000.174411.95812
UgandaSub-Saharan Africa1413.9310.043170.211021.132990.338610.457270.072670.290661.42766
SenegalSub-Saharan Africa1423.9040.036080.364980.976190.435400.367720.107130.208431.44395
GabonSub-Saharan Africa1433.8960.045471.060240.905280.433720.319140.110910.068220.99895
NigerSub-Saharan Africa1443.8450.036020.069400.772650.297070.476920.156390.193871.87877
CambodiaSoutheastern Asia1453.8190.050690.460380.627360.611140.662460.072470.403590.98195
TanzaniaSub-Saharan Africa1463.7810.050610.285201.002680.382150.328780.057470.343771.38079
MadagascarSub-Saharan Africa1473.6810.036330.208240.668010.467210.191840.081240.213331.85100
Central African RepublicSub-Saharan Africa1483.6780.061120.078500.000000.066990.488790.082890.238352.72230
ChadSub-Saharan Africa1493.6670.038300.341930.760620.150100.235010.052690.183861.94296
GuineaSub-Saharan Africa1503.6560.035900.174170.464750.240090.377250.121390.286571.99172
Ivory CoastSub-Saharan Africa1513.6550.051410.465340.771150.151850.468660.179220.201651.41723
Burkina FasoSub-Saharan Africa1523.5870.043240.258120.851880.271250.394930.128320.217471.46494
AfghanistanSouthern Asia1533.5750.030840.319820.302850.303350.234140.097190.365101.95210
RwandaSub-Saharan Africa1543.4650.034640.222080.773700.428640.592010.551910.226280.67042
BeninSub-Saharan Africa1553.3400.036560.286650.353860.319100.484500.080100.182601.63328
SyriaMiddle East and Northern Africa1563.0060.050150.663200.474890.721930.156840.189060.471790.32858
BurundiSub-Saharan Africa1572.9050.086580.015300.415870.223960.118500.100620.197271.83302
TogoSub-Saharan Africa1582.8390.067270.208680.139950.284430.364530.107310.166811.56726

158 rows × 10 columns

# 排序分层
report_2015_df2.sort_index(level=0)
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Happiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
RegionCountry
Australia and New ZealandAustralia107.2840.040831.333581.309230.931560.651240.356370.435622.26646
New Zealand97.2860.033711.250181.319670.908370.639380.429220.475012.26425
Central and Eastern EuropeAlbania954.9590.050130.878670.804340.813250.357330.064130.142721.89894
Armenia1274.3500.047630.768210.777110.729900.198470.039000.078551.75873
Azerbaijan805.2120.033631.023890.937930.640450.370300.160650.077992.00073
Belarus595.8130.039381.031921.232890.736080.379380.190900.110462.13090
Bosnia and Herzegovina964.9490.069130.832230.919160.790810.092450.002270.248082.06367
Bulgaria1344.2180.048281.012161.106140.766490.305870.008720.119210.89991
Croatia625.7590.043941.082540.796240.788050.258830.024300.054442.75414
Czech Republic316.5050.041681.178981.206430.844830.463640.026520.106862.67782
Estonia735.4290.040131.151741.227910.773610.448880.151840.086801.58782
Georgia1304.2970.042210.741900.385620.729260.405770.383310.055471.59541
Hungary1044.8000.061071.120941.202150.759050.321120.027580.128001.24074
Kazakhstan545.8550.041141.122541.122410.643680.516490.084540.118272.24729
Kosovo695.5890.050180.801480.811980.631320.247490.047410.283102.76579
Kyrgyzstan775.2860.038230.474281.151150.650880.434770.042320.300302.23270
Latvia895.0980.046401.113121.095620.724370.296710.063320.182261.62215
Lithuania565.8330.038431.147231.257450.731280.213420.010310.026412.44649
Macedonia935.0070.053760.918511.002320.735450.334570.053270.223591.73933
Moldova525.8890.037990.594481.015280.618260.328180.016150.209513.10712
Montenegro825.1920.052350.974380.905570.725210.182600.142960.161402.10017
Poland605.7910.042631.125551.279480.779030.531220.042120.167591.86565
Romania865.1240.066071.043450.885880.768900.350680.006490.137481.93129
Russia645.7160.031351.137641.236170.669260.366790.030050.001992.27394
Serbia875.1230.048640.920531.009640.748360.201070.026170.192312.02500
Slovakia455.9950.042671.168911.269990.789020.317510.034310.168932.24639
Slovenia555.8480.042511.184981.273850.873370.608550.037870.253281.61583
Tajikistan1064.7860.031980.390470.855630.573790.472160.150720.229742.11399
Turkmenistan705.5480.041750.958471.226680.538860.476100.308440.169791.86984
Ukraine1114.6810.044120.799071.202780.673900.251230.029610.152751.57140
………………………………
Sub-Saharan AfricaSomaliland region915.0570.061610.188470.951520.438730.465820.399280.503182.11032
South Africa1134.6420.045850.920491.184680.276880.332070.088840.119731.71956
Sudan1184.5500.067400.521071.014040.368780.100810.146600.190622.20857
Swaziland1014.8670.087420.712061.072840.075660.306580.030600.182592.48676
Tanzania1463.7810.050610.285201.002680.382150.328780.057470.343771.38079
Togo1582.8390.067270.208680.139950.284430.364530.107310.166811.56726
Uganda1413.9310.043170.211021.132990.338610.457270.072670.290661.42766
Zambia855.1290.069880.470380.916120.299240.488270.124680.195912.63430
Zimbabwe1154.6100.042900.271001.032760.334750.258610.080790.189872.44191
Western EuropeAustria137.2000.037511.337231.297040.890420.624330.186760.330882.53320
Belgium196.9370.035951.307821.285660.896670.584500.225400.222502.41484
Cyprus675.6890.055801.208130.893180.923560.406720.061460.306381.88931
Denmark37.5270.033281.325481.360580.874640.649380.483570.341392.49204
Finland67.4060.031401.290251.318260.889110.641690.413720.233512.61955
France296.5750.035121.277781.260380.945790.550110.206460.123322.21126
Germany266.7500.018481.327921.299370.891860.614770.218430.282142.11569
Greece1024.8570.050621.154060.929330.882130.076990.013970.000001.80101
Iceland27.5610.048841.302321.402230.947840.628770.141450.436302.70201
Ireland186.9400.036761.335961.369480.895330.617770.287030.459011.97570
Italy505.9480.039141.251141.197770.954460.262360.029010.228232.02518
Luxembourg176.9460.034991.563911.219630.918940.615830.377980.280341.96961
Malta376.3020.042061.207401.302030.887210.603650.135860.517521.64880
Netherlands77.3780.027991.329441.280170.892840.615760.318140.476102.46570
North Cyprus665.6950.056351.208061.070080.923560.490270.142800.261691.59888
Norway47.5220.038801.459001.330950.885210.669730.365030.346992.46531
Portugal885.1020.048021.159911.139350.875190.514690.010780.137191.26462
Spain366.3290.034681.230111.313790.955620.459510.063980.182272.12367
Sweden87.3640.031571.331711.289070.910870.659800.438440.362622.37119
Switzerland17.5870.034111.396511.349510.941430.665570.419780.296782.51738
United Kingdom216.8670.018661.266371.285480.909430.596250.320670.519121.96994

158 rows × 10 columns

Pandas处理数据基本操作汇总
一梦南柯
04-02 2924
本文主要介绍pandas的一些基本操作，也是用的比较频繁是操作。主要分为以下几点： 1. pandas查看数据类型等 2. pandas构建一个完整的dataframe 3. pandas取行列的3种方法 4.Pandas实现where filter以及逻辑语句 5. pandas对where条件筛选后只有一行的dataframe取其中某一列的值 6. pandas数据去重 7.p...
pandas用法大全
热门推荐
liufang0001的博客
09-05 22万+
一、生成数据表 1、首先导入pandas库，一般都会用到numpy库，所以我们先导入备用： import numpy as np import pandas as pd 2、导入CSV或者xlsx文件： df = pd.DataFrame(pd.read_csv(‘name.csv’,header=1)) df = pd.DataFrame
pandas中的常用索引方式总结(一)
weixin_40321125的博客
01-09 8150
pandas中的常用索引方式总结(一)–df[]方式 import pandas as pd import numpy as np 1.Series对象 s1 = pd.Series(np.array([1,2,3,4])) print(s1) print("---------------------------") s2 = pd.Series(np.array([1,2,3,4]),inde...
划分数据集索引不连续导致pandas读取不了报错pandas.Index.IndexEngine.get_loc
weixin_43680575的博客
11-14 617
pandas数据索引不连续导致报错
pandas错误之: in pandas._libs.hashtable.PyObjectHashTable.get_item
CodeLuweir 的博客
08-03 4283
目的：取data的第41行第6个数据 运行data[40][5]返回一下错误： File "pandas\_libs\hashtable_class_helper.pxi", line 1619, in pandas._libs.hashtable. PyObjectHashTable.get_item File "pandas\_libs\hashtable_class_helper.pxi", line 1627, in pandas._libs.hashtable. PyObjectHashTable
Pandas数据选取
weixin_41659286的博客
11-27 7427
In [1]:   import pandas as pd In [2]:   xxxxxxxxxx data = {     'name':['张三','李四','王五','赵六'],     'age':[19,19,20,21],     'height':[1.65,1.70,1.67,1.73], } df =pd.DataFrame(data) df Out[2]: name    a...
数据分析Pandas库基本操作详解：涵盖数据结构、导入导出、查看清洗及统计分析方法
04-07
本文详细地介绍了Pandas库的基本操作，包括如何创建Series和DataFrame对象，以及如何进行数据导入导出、查看清洗、排序和统计分析等。掌握了这些基本操作，对于进行Python数据分析和处理将会非常有帮助。
数据分析Pandas基本操作详解：数据导入导出、清洗、筛选、排序与统计分析入门指南
最新发布
04-08
整体而言，Pandas库具有强大的数据处理能力，其基本操作包括数据结构的创建、数据的导入导出、数据查看与选取、数据的清洗以及排序和统计分析等。掌握这些操作对于进行数据分析和处理是至关重要的。本文旨在为初学者...
pandas将数据保存到csv或者xlsx 中的最基本操作
01-20
在Python的数据分析领域，Pandas库是一个不可或缺的...但以上内容已经涵盖了保存数据到CSV和XLSX文件的基本操作。通过熟练掌握这些基础，我们可以轻松地在Pandas中进行数据的导入和导出，从而实现数据的存储和传输。
Pandas:DataFrame对象的基础操作方法
12-23
在Python的数据分析领域，Pandas库是不可或缺的工具，它提供了DataFrame对象，这是一个二维表格型数据结构，可以存储各种类型的数据，并且具有丰富的操作方法。本文将深入探讨DataFrame对象的基础操作，包括创建、...
KeyError: ‘jieba‘ During handling of the above exception, another exc
weiyingme的博客
03-08 1311
@KeyError: ‘jieba’ During handling of the above exception, another exc cmd 下的 pip install 时可能会遇到各种各样的问题，Traceback (most recent call last): File “c:\users\asus\appdata\local\programs\python\python36-32\lib\site-packages\pip_vendor\resolvelib\resolvers.py”,
utf-8和utf-8-sig
u011854875的博客
11-07 5905
As UTF-8 is an 8-bit encoding no BOM is required and anyU+FEFF character in the decoded Unicode string (even if it’s the firstcharacter) is treated as a ZERO WIDTH NO-BREAK SPACE. UTF-8以字节为编码单元，它的字
python pandas csv getitem,python – Pandas DataFrame：__ getitem __()的完整规范？
weixin_32778881的博客
03-25 346
我怀疑这个功能缺少doc的部分原因是由于源代码中缺少doc注释,现在我看一下.如果没有人提出任何更加用户友好的东西,这里是实际的DataFrame .__ getitem __()方法：def __getitem__(self, key):# shortcut if we are an actual columnis_mi_columns = isinstance(self.columns, Mu...
python中的get()和item()函数
fly_Xiaoma的博客
06-04 5万+
1.get() 当我们获取字典里的值的时候，一个是通过键值对，即dict['key'],另一个就是dict.get()方法。 例如： >>> dict = {'a':'AA', 'b':'BB', 'c':'CC'} >>> dict['a'] 'AA' >>> dict.get('a') 'AA' get()方法语法： dict.g...
Pandas-进阶应用
Plus Ultra !!!
08-07 824
1 pandas中的数据运算与算术对齐 2 iloc与loc的切片与索引 3 DataFrame与Series之间的运算 4 函数应用和映射 4.1 用apply将一个规则应用到DataFrame的行或者列上 4.2 applymap 将一个规则应用到DataFrame中的每一个元素 5 Series和DataFrame的排序 6 处理Series的重复索引 7 汇总计算描述统计 8...
【解决方案】During handling of the above exception, another exception occurred:
云雀叫了一整天
07-16 3万+
问题 在大量访问网页时，有可能出现这种报错： 14-09-2019 12:1138 root: ERROR: HTTPSConnectionPool(host='www.mzitu.com', port=443): Max retries exceeded with url: /187465/31 (Caused by NewConnectionError('<requests.packag...
评论
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付：10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明：

1.余额是钱包充值的虚拟货币，按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载，可以购买VIP、付费专栏及课程。

余额充值