理論：

describe()：快速查看每列數(shù)據(jù)的統(tǒng)計信息，以下是可以輸出的統(tǒng)計指標

count，數(shù)據(jù)個數(shù)（非空數(shù)據(jù)）

mean，均值

std，標準差

min，最小值

25%，第1四分位數(shù)，即第25百分位數(shù)

50%，第2四分位數(shù)，即第50百分位數(shù)

75%，第3四分位數(shù)，即第75百分位數(shù)

max，最大值

quantile(q)：

輸出指定位置的百分位數(shù)，默認q=0.5，q的范圍是[0,1]

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常用統(tǒng)計方法：

sum()，求和

mean()，求均值

median()，求中位數(shù)

count()，求非空的個數(shù)

注意：以上統(tǒng)計方法不對缺失數(shù)據(jù)進行統(tǒng)計

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max()，求最大值

min()，求最小值

idxmax()，返回最大值對應的索引

idxmin()，返回最小值對應的索引

注意：argmax()和argmin()在近期的版本中即將停止使用

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mad()，求平均絕對誤差（mean absolute deviation），對表示各個變量值之間差異程度的數(shù)值之一

var()：方差

std()：求標準差

cumsum()，求累加

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第15節(jié) 常用統(tǒng)計方法(1) --describe、quantile

In?[1]:

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import pandas as pd

In?[2]:

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data = pd.read_csv(r'C:\Users\ML Learning\Projects\第四章-數(shù)據(jù)分析預習內(nèi)容\第四章-數(shù)據(jù)分析預習內(nèi)容\第一節(jié)-數(shù)據(jù)分析工具pandas基礎\lesson_05\lesson_05\examples\datasets\2021_happiness.csv') data.head()

Out[2]:

?CountryRegionHappiness RankHappiness ScoreLower Confidence IntervalUpper Confidence IntervalEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual01234

Denmark	Western Europe	1	7.526	7.460	7.592	1.44178	1.16374	0.79504	0.57941	0.44453	0.36171	2.73939
Switzerland	Western Europe	2	7.509	7.428	7.590	1.52733	1.14524	0.86303	0.58557	0.41203	0.28083	2.69463
Iceland	Western Europe	3	7.501	7.333	7.669	1.42666	1.18326	0.86733	0.56624	0.14975	0.47678	2.83137
Norway	Western Europe	4	7.498	7.421	7.575	1.57744	1.12690	0.79579	0.59609	0.35776	0.37895	2.66465
Finland	Western Europe	5	7.413	7.351	7.475	1.40598	1.13464	0.81091	0.57104	0.41004	0.25492	2.82596

In?[3]:

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data.describe()

Out[3]:

?Happiness RankHappiness ScoreLower Confidence IntervalUpper Confidence IntervalEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residualcountmeanstdmin25%50%75%max

157.000000	157.000000	157.000000	157.000000	157.000000	157.000000	157.000000	157.000000	157.000000	157.000000	157.000000
78.980892	5.382185	5.282395	5.481975	0.953880	0.793621	0.557619	0.370994	0.137624	0.242635	2.325807
45.466030	1.141674	1.148043	1.136493	0.412595	0.266706	0.229349	0.145507	0.111038	0.133756	0.542220
1.000000	2.905000	2.732000	3.078000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.817890
40.000000	4.404000	4.327000	4.465000	0.670240	0.641840	0.382910	0.257480	0.061260	0.154570	2.031710
79.000000	5.314000	5.237000	5.419000	1.027800	0.841420	0.596590	0.397470	0.105470	0.222450	2.290740
118.000000	6.269000	6.154000	6.434000	1.279640	1.021520	0.729930	0.484530	0.175540	0.311850	2.664650
157.000000	7.526000	7.460000	7.669000	1.824270	1.183260	0.952770	0.608480	0.505210	0.819710	3.837720

In?[4]:

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data.quantile(q=0.5)

Out[4]:

Happiness Rank 79.00000 Happiness Score 5.31400 Lower Confidence Interval 5.23700 Upper Confidence Interval 5.41900 Economy (GDP per Capita) 1.02780 Family 0.84142 Health (Life Expectancy) 0.59659 Freedom 0.39747 Trust (Government Corruption) 0.10547 Generosity 0.22245 Dystopia Residual 2.29074 Name: 0.5, dtype: float64

In?[5]:

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data.quantile(q=0.25)

Out[5]:

Happiness Rank 40.00000 Happiness Score 4.40400 Lower Confidence Interval 4.32700 Upper Confidence Interval 4.46500 Economy (GDP per Capita) 0.67024 Family 0.64184 Health (Life Expectancy) 0.38291 Freedom 0.25748 Trust (Government Corruption) 0.06126 Generosity 0.15457 Dystopia Residual 2.03171 Name: 0.25, dtype: float64

In?[6]:

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import pandas as pd

In?[7]:

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data = pd.read_csv(r'C:\Users\ML Learning\Projects\第四章-數(shù)據(jù)分析預習內(nèi)容\第四章-數(shù)據(jù)分析預習內(nèi)容\第一節(jié)-數(shù)據(jù)分析工具pandas基礎\lesson_05\lesson_05\examples\datasets\log.csv') ? data.head()

Out[7]:

?timeuservideoplayback positionpausedvolume01234

1469974424	cheryl	intro.html	5	False	10.0
1469974454	cheryl	intro.html	6	NaN	NaN
1469974544	cheryl	intro.html	9	NaN	NaN
1469974574	cheryl	intro.html	10	NaN	NaN
1469977514	bob	intro.html	1	NaN	NaN

In?[8]:

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data.sum() #求和

Out[8]:

time 48509194942 user cherylcherylcherylcherylbobbobbobbobcherylcher... video intro.htmlintro.htmlintro.htmlintro.htmlintro.... playback position 429 paused 1 volume 35 dtype: object

In?[9]:

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data.mean() # 求均值

Out[9]:

time 1.469976e+09 playback position 1.300000e+01 paused 3.333333e-01 volume 8.750000e+00 dtype: float64

In?[10]:

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data.median() # 求中位數(shù)

Out[10]:

time 1.469975e+09 playback position 1.000000e+01 paused 0.000000e+00 volume 1.000000e+01 dtype: float64

In?[11]:

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data.count() #求非空的個數(shù)

Out[11]:

time 33 user 33 video 33 playback position 33 paused 3 volume 4 dtype: int64

In?[12]:

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import pandas as pd

In?[13]:

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data = pd.read_csv(r'C:\Users\ML Learning\Projects\第四章-數(shù)據(jù)分析預習內(nèi)容\第四章-數(shù)據(jù)分析預習內(nèi)容\第一節(jié)-數(shù)據(jù)分析工具pandas基礎\lesson_05\lesson_05\examples\datasets\2021_happiness.csv') data.head()

Out[13]:

?CountryRegionHappiness RankHappiness ScoreLower Confidence IntervalUpper Confidence IntervalEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual01234

Denmark	Western Europe	1	7.526	7.460	7.592	1.44178	1.16374	0.79504	0.57941	0.44453	0.36171	2.73939
Switzerland	Western Europe	2	7.509	7.428	7.590	1.52733	1.14524	0.86303	0.58557	0.41203	0.28083	2.69463
Iceland	Western Europe	3	7.501	7.333	7.669	1.42666	1.18326	0.86733	0.56624	0.14975	0.47678	2.83137
Norway	Western Europe	4	7.498	7.421	7.575	1.57744	1.12690	0.79579	0.59609	0.35776	0.37895	2.66465
Finland	Western Europe	5	7.413	7.351	7.475	1.40598	1.13464	0.81091	0.57104	0.41004	0.25492	2.82596

In?[14]:

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data.max()

Out[14]:

Country Zimbabwe Region Western Europe Happiness Rank 157 Happiness Score 7.526 Lower Confidence Interval 7.46 Upper Confidence Interval 7.669 Economy (GDP per Capita) 1.82427 Family 1.18326 Health (Life Expectancy) 0.95277 Freedom 0.60848 Trust (Government Corruption) 0.50521 Generosity 0.81971 Dystopia Residual 3.83772 dtype: object

In?[15]:

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data.min()

Out[15]:

Country Afghanistan Region Australia and New Zealand Happiness Rank 1 Happiness Score 2.905 Lower Confidence Interval 2.732 Upper Confidence Interval 3.078 Economy (GDP per Capita) 0 Family 0 Health (Life Expectancy) 0 Freedom 0 Trust (Government Corruption) 0 Generosity 0 Dystopia Residual 0.81789 dtype: object

In?[17]:

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data['Happiness Score'].idxmax()

Out[17]:

0

In?[18]:

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data['Happiness Score'].idxmin()

Out[18]:

156

In?[21]:

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data.mad() # 求絕對值誤差

Out[21]:

Happiness Rank 39.254899 Happiness Score 0.955256 Lower Confidence Interval 0.957480 Upper Confidence Interval 0.953032 Economy (GDP per Capita) 0.342828 Family 0.211727 Health (Life Expectancy) 0.188426 Freedom 0.119887 Trust (Government Corruption) 0.084441 Generosity 0.102143 Dystopia Residual 0.413041 dtype: float64

In?[22]:

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data.var() #求方差

Out[22]:

Happiness Rank 2067.159889 Happiness Score 1.303418 Lower Confidence Interval 1.318002 Upper Confidence Interval 1.291617 Economy (GDP per Capita) 0.170235 Family 0.071132 Health (Life Expectancy) 0.052601 Freedom 0.021172 Trust (Government Corruption) 0.012329 Generosity 0.017891 Dystopia Residual 0.294003 dtype: float64

In?[23]:

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data.std() #求標準差

Out[23]:

Happiness Rank 45.466030 Happiness Score 1.141674 Lower Confidence Interval 1.148043 Upper Confidence Interval 1.136493 Economy (GDP per Capita) 0.412595 Family 0.266706 Health (Life Expectancy) 0.229349 Freedom 0.145507 Trust (Government Corruption) 0.111038 Generosity 0.133756 Dystopia Residual 0.542220 dtype: float64

In?[24]:

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data.cumsum() #求累加

Out[24]:

?CountryRegionHappiness RankHappiness ScoreLower Confidence IntervalUpper Confidence IntervalEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual01234...152153154155156

Denmark	Western Europe	1	7.526	7.460	7.592	1.44178	1.16374	0.79504	0.57941	0.44453	0.36171	2.73939
DenmarkSwitzerland	Western EuropeWestern Europe	3	15.035	14.888	15.182	2.96911	2.30898	1.65807	1.16498	0.85656	0.64254	5.43402
DenmarkSwitzerlandIceland	Western EuropeWestern EuropeWestern Europe	6	22.536	22.221	22.851	4.39577	3.49224	2.52540	1.73122	1.00631	1.11932	8.26539
DenmarkSwitzerlandIcelandNorway	Western EuropeWestern EuropeWestern EuropeWest...	10	30.034	29.642	30.426	5.97321	4.61914	3.32119	2.32731	1.36407	1.49827	10.93004
DenmarkSwitzerlandIcelandNorwayFinland	Western EuropeWestern EuropeWestern EuropeWest...	15	37.447	36.993	37.901	7.37919	5.75378	4.13210	2.89835	1.77411	1.75319	13.75600
...	...	...	...	...	...	...	...	...	...	...	...	...
DenmarkSwitzerlandIcelandNorwayFinlandCanadaNe...	Western EuropeWestern EuropeWestern EuropeWest...	11778	832.366	817.188	847.544	148.28013	124.10506	86.33722	57.62264	21.15342	36.91896	357.94872
DenmarkSwitzerlandIcelandNorwayFinlandCanadaNe...	Western EuropeWestern EuropeWestern EuropeWest...	11932	835.726	820.476	850.976	148.66240	124.21543	86.51066	57.78694	21.22454	37.23164	360.09430
DenmarkSwitzerlandIcelandNorwayFinlandCanadaNe...	Western EuropeWestern EuropeWestern EuropeWest...	12087	839.029	823.668	854.390	148.94363	124.21543	86.75877	58.13372	21.34041	37.40681	362.22970
DenmarkSwitzerlandIcelandNorwayFinlandCanadaNe...	Western EuropeWestern EuropeWestern EuropeWest...	12243	842.098	826.604	857.592	149.69082	124.36409	87.38871	58.20284	21.51274	37.89078	363.04759
DenmarkSwitzerlandIcelandNorwayFinlandCanadaNe...	Western EuropeWestern EuropeWestern EuropeWest...	12400	845.003	829.336	860.670	149.75913	124.59851	87.54618	58.24604	21.60693	38.09368	365.15163

157 rows × 13 columns

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總結(jié)

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