数据分析（python或Matlab均可）

[powerlwj]

在做数据拟合的过程中，得到300个数据，但是数据之间差异性太大，我最后想得到30个左右的值，有什么好的方法？下面是对这300数据的简单分析：

mean 159.4604181
std 14.08847471
min 125.115372
25% 149.8761805
50% 159.13718
75% 168.4387325
max 206.164806

附上300数据：

179.810669	175.676689	184.907814	185.026245	176.046089	180.488594	162.373795	142.625153156.730606	171.185516182.506538	171.279892	157.094376	175.523239	138.663803	154.037315	146.47934	166.05497	154.187614	160.262428165.520088	159.510735	168.477242	155.714188	159.907532	154.23027	148.736519	168.688332	160.950378	150.446991154.586983	169.485733	179.650826177.456154	164.688042	158.481346	157.529823	177.155075	195.534782	185.3591	166.674973	169.619331	163.814583	171.758568	178.962212	166.57563	168.160606	175.033401	166.368645	155.475143	164.136368	129.815918	171.380379	139.883484	155.503197	144.567993	148.340202	146.3424	159.094955	138.338203	140.137421	171.319687	153.106209	165.617142	165.526573	162.551674	149.410728	169.361961	169.951523	167.23304	154.108025	147.144096	146.922226	143.736519	174.33152	178.938164	174.354897	168.984444	169.659035	159.179405	164.230553	174.717148	168.7015	168.840828	165.545853	149.285713	149.012978	156.370644	171.694534	160.497284	152.712379	162.705696	150.347458	173.261192	147.494514	175.424751	178.045708	171.828514	165.673157	160.281517	159.184944	149.384384	156.638893	173.703499	162.53994	154.150589	161.535164	162.097717	166.458252	152.737953	152.43277	164.732224	161.109459	161.410538	151.811913	144.878899	151.292107	174.201546	179.860199	156.777939	157.412254	128.193626	134.273026	165.721443	151.169197	146.080826	158.473038	156.739098	164.361488	165.708656	168.370171	131.646339	138.349174	140.834816	140.425423	141.339821	153.862488	132.983589	143.058212	157.537552	140.514938	165.444321	164.64386	152.068802	164.700546	157.977196	161.475334	152.682129	159.353088	181.302414	177.709511	174.072334	161.575821	153.542702	167.670769	177.191048	161.461784	163.927948	166.825462	147.836052	155.826508	165.665619	147.922226	150.064796	142.898941	154.046188	176.772972	162.681747	133.64357	153.721153	183.379425	170.639091	174.363014	152.349274	171.839104	154.853706	160.088364	152.339348	147.243919	167.363869	163.696533	169.445465	149.822212	188.664185	149.297455	149.38324	162.856377	167.585457	149.559311	142.964882	129.458916	160.373032	162.140373	155.924423	155.679741	154.898079	139.470657	155.763046	156.382004	149.876038	134.911156	134.708656	157.063934	143.389061	138.854088	146.451187	143.490692	170.697395	165.124054	171.297455	148.049622	156.399467	149.876228	162.300697	149.619713	143.149063	148.450333	155.154694	168.817543	169.427711	161.739861	149.329041	137.551292	162.045517	155.713234	167.576202	147.337914	156.440407	169.3153	170.732796	158.153931	148.641853	160.371506	152.678215	151.62735	144.289818	150.412636	135.979866	158.555397	144.935394	152.542038	131.922989	125.718201	160.387344	150.361771	159.63842	150.993797	141.396507	161.954765	125.115372	138.610939	156.862579	155.175972	146.390205	185.087791	164.873558	160.305084	194.200592	168.425896	168.09037	147.101822	161.535454	151.740341	162.898369	169.418549	143.773643	164.353752	175.510162	206.164806	198.906193	204.26844	173.567131	177.433823	187.072243	187.453857	180.494034	202.663326	198.192097	145.617523	148.858864	174.640137	159.404617	153.754082	150.244583	133.0271	156.841972	152.707985	172.945221	155.878998	150.713615	162.749596	151.863152	153.257278	133.326752	147.612083	151.167763	158.199638	161.290581	149.243629	155.955055

[欢乐的小猪]

300个数想的到30 个数，那就从每10个数据中产生一个数。 这里用python给出 均值，中位数：

import re
import math
str='179.810669    175.676689    184.907814    185.026245    176.046089    180.488594    162.373795    142.625153 156.730606    171.185516 182.506538    171.279892    157.094376    175.523239    138.663803    154.037315    146.47934    166.05497    154.187614    160.262428 165.520088    159.510735    168.477242    155.714188    159.907532    154.23027    148.736519    168.688332    160.950378    150.446991 154.586983    169.485733    179.650826 177.456154    164.688042    158.481346    157.529823    177.155075    195.534782    185.3591    166.674973    169.619331    163.814583    171.758568    178.962212    166.57563    168.160606    175.033401    166.368645    155.475143    164.136368    129.815918    171.380379    139.883484    155.503197    144.567993    148.340202    146.3424    159.094955    138.338203    140.137421    171.319687    153.106209    165.617142    165.526573    162.551674    149.410728    169.361961    169.951523    167.23304    154.108025    147.144096    146.922226    143.736519    174.33152    178.938164    174.354897    168.984444    169.659035    159.179405    164.230553    174.717148    168.7015    168.840828    165.545853    149.285713    149.012978    156.370644    171.694534    160.497284    152.712379    162.705696    150.347458    173.261192    147.494514    175.424751    178.045708    171.828514    165.673157    160.281517    159.184944    149.384384    156.638893    173.703499    162.53994    154.150589    161.535164    162.097717    166.458252    152.737953    152.43277    164.732224    161.109459    161.410538    151.811913    144.878899    151.292107    174.201546    179.860199    156.777939    157.412254    128.193626    134.273026    165.721443    151.169197    146.080826    158.473038    156.739098    164.361488    165.708656    168.370171    131.646339    138.349174    140.834816    140.425423    141.339821    153.862488    132.983589    143.058212    157.537552    140.514938    165.444321    164.64386    152.068802    164.700546    157.977196    161.475334    152.682129    159.353088    181.302414    177.709511    174.072334    161.575821    153.542702    167.670769    177.191048    161.461784    163.927948    166.825462    147.836052    155.826508    165.665619    147.922226    150.064796    142.898941    154.046188    176.772972    162.681747    133.64357    153.721153    183.379425    170.639091    174.363014    152.349274    171.839104    154.853706    160.088364    152.339348    147.243919    167.363869    163.696533    169.445465    149.822212    188.664185    149.297455    149.38324    162.856377    167.585457    149.559311    142.964882    129.458916    160.373032    162.140373    155.924423    155.679741    154.898079    139.470657    155.763046    156.382004    149.876038    134.911156    134.708656    157.063934    143.389061    138.854088    146.451187    143.490692    170.697395    165.124054    171.297455    148.049622    156.399467    149.876228    162.300697    149.619713    143.149063    148.450333    155.154694    168.817543    169.427711    161.739861    149.329041    137.551292    162.045517    155.713234    167.576202    147.337914    156.440407    169.3153    170.732796    158.153931    148.641853    160.371506    152.678215    151.62735    144.289818    150.412636    135.979866    158.555397    144.935394    152.542038    131.922989    125.718201    160.387344    150.361771    159.63842    150.993797    141.396507    161.954765    125.115372    138.610939    156.862579    155.175972    146.390205    185.087791    164.873558    160.305084    194.200592    168.425896    168.09037    147.101822    161.535454    151.740341    162.898369    169.418549    143.773643    164.353752    175.510162    206.164806    198.906193    204.26844    173.567131    177.433823    187.072243    187.453857    180.494034    202.663326    198.192097    145.617523    148.858864    174.640137    159.404617    153.754082    150.244583    133.0271    156.841972    152.707985    172.945221    155.878998    150.713615    162.749596    151.863152    153.257278    133.326752    147.612083    151.167763    158.199638    161.290581    149.243629    155.955055'
ls=[float(s.strip()) for s in re.findall('[\d.]+',str)]

def get_avg(ls):
    return math.fsum(ls)/len(ls)

def get_median(ls):
    return math.fsum(sorted(ls)[4:6])/2

result=[]
for i in range(30):
    result.append(get_avg(ls[0+i*10:10+i*10]))

print(result)