1.直方图
 
# simple histogram                                                        
hist(mtcars$mpg) #未指定任何选项时的默认图形
 
 
# colored histogram with specified number of bins        
hist(mtcars$mpg, 
     breaks=12, #分组个数
     col="red",  #红色
     xlab="Miles Per Gallon", # x轴标签
     main="Colored histogram with 12 bins")# 标题
 
 
# colored histogram with rug plot, frame, and specified number of bins 
hist(mtcars$mpg, 
     freq=FALSE, 
     breaks=12, 
     col="red", 
     xlab="Miles Per Gallon", 
     main="Histogram, rug plot, density curve")  
rug(jitter(mtcars$mpg)) 
lines(density(mtcars$mpg), col="blue", lwd=2)
 
 
# histogram with superimposed normal curve (Thanks to Peter Dalgaard)  
x <- mtcars$mpg 
h<-hist(x, 
        breaks=12, 
        col="red", 
        xlab="Miles Per Gallon", 
        main="Histogram with normal curve and box") 
xfit<-seq(min(x),max(x),length=40) 
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) 
yfit <- yfit*diff(h$mids[1:2])*length(x) 
lines(xfit, yfit, col="blue", lwd=2)
box()
 
 
2.核密度图
 
d <- density(mtcars$mpg) # returns the density data  
plot(d) # plots the results 
 
 
d <- density(mtcars$mpg)                                  
plot(d, main="Kernel Density of Miles Per Gallon")       
polygon(d, col="red", border="blue")                     
rug(mtcars$mpg, col="brown") 
 
 
参考：《R in action–Data Analysis and Graphics with R》

https://blog.csdn.net/sinat_35406909/article/details/83548694
 
1.复现了上边网址的代码，成功出现密度图
 
链接：https://pan.baidu.com/s/1sqNLNrBsnBC5IdlzgXsl3Q 
 提取码：aki0 
 将自己的弄好的路径文件（windows下的放入百度网盘，在jupyternotebook下直接运行即可）
 
 
2.Linux下路径文件
 
路径改为
 
Path='/home/***/shanghaitech/part_A_final/tarin_data/ground_truth/GT_IMG_'
 
ipath='/home/***/ shanghaitech/part_A_final/tarin_data/images/IMG_1'
 
mp=sio.loadmat('/home/***/map.mat')
 
3.        mat文件分享
 
链接：https://pan.baidu.com/s/1mYUOtWwA2zP_QEE9ys2tdg 
 提取码：78dc 
  

python绘制直方图和密度图的实例
更新时间：2019年07月08日 10:29:13 作者：ColiYin
今天小编就为大家分享一篇python绘制直方图和密度图的实例，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
对于pandas的dataframe，绘制直方图方法如下：
//pdf是pandas的dataframe, delta_time是其中一列
//xlim是x轴的范围，bins是分桶个数
pdf.delta_time.plot(kind='hist', xlim=(-50,300), bins=500)

对于pandas的dataframe，绘制概率密度图方法如下：
//pdf是pandas的dataframe, delta_time是其中一列
pdf.delta_time.dropna().plot(kind='kde', xlim=(-50,300))

以上这篇python绘制直方图和密度图的实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。
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最新评论

关于使用Matlab绘制散点密度图，以前有一些文章，比如博客园文章MATLAB实例：散点密度图，这里面使用了Matlab社区中的scatplot.m文件，代码长度（含注释）为226行。
 
下图为博客园MATLAB实例：散点密度图中的输出结果，其所用数据源于博客园文章MATLAB中“fitgmdist”的用法及其GMM聚类算法。
 
 
在本文中所用数据同样源于MATLAB中“fitgmdist”的用法及其GMM聚类算法，或者参考文中末尾部分，由于数据共有1000条，在示例代码中数据的省略部分用…代替；或者将数据放在Excel中，直接读取Excel文件即可。
 
主要的思路为：
 读入数据（此处只取前两列作为X和Y，第三列未考虑） -> 设置搜索半径 -> 记录目标点所在的半径内的点数 -> 使用scatter函数（scatter函数详细说明）进行显示，参考代码如下：
 
%%
clc;
clear;

%%
XYZ = [
    -0.752713846442762  2.48140797998545    1
    -0.798625575507672  2.14835001132099    1
    2.82002920206994    1.97084621196340    1
    0.913856576539988   2.24942313999122    1
    1.57243525115195    2.68322568351427    1
    0.241170005783610   1.89791938743627    1
    2.10634746115858    2.20631449410867    1
    1.61173455443266    2.69163655587553    1
    1.39436249281445    1.28104472307183    1
    1.65267727628557    1.85771163664832    1
    ...
    0.580602656916380   -1.38403756935533   2
    -1.27308082491469   -2.20514718975902   2
    -2.92790668922674   -1.98439888443983   2
    -1.62181912776895   -0.851506362212275  2
    ];

datamin = min(min(XYZ(:, 1)), min(XYZ(:, 2)));
datamax = max(max(XYZ(:, 1)), max(XYZ(:, 2)));
datamin = floor(datamin);
datamax = ceil(datamax);

Length = 600;
Width = 600;

axismin = datamin;
axismax = datamax;

% 绘制密度图
XYZ(:, 3) = XYZ(:, 3) - XYZ(:, 3);
sizeXYZ = size(XYZ);
searchR = 1.0;
for i = 1 : sizeXYZ(1)
    index_i = find(XYZ(:, 1) > XYZ(i, 1) - searchR & XYZ(:, 1) < XYZ(i, 1) + searchR ...
        & XYZ(:, 2) > XYZ(i, 2) - searchR & XYZ(:, 2) < XYZ(i, 2) + searchR);
    sizeIndexI = size(index_i);
    XYZ(i, 3) = sizeIndexI(1);
end
[sortXYZ, sortI] = sort(XYZ(:, 3));

scatter(XYZ(sortI, 1), XYZ(sortI, 2), [], XYZ(sortI, 3), 'filled');
hold on;
axis([axismin axismax axismin axismax]);
gc = get(gca);
set(gcf, 'position', [300, 100, Length, Width]);
 
此处的搜索为矩形搜索，大家可以根据自己的需求自行修改，绘制的结果如下：
 

 参考的数据如下：
 
-0.752713846442762  2.48140797998545    1
-0.798625575507672  2.14835001132099    1
2.82002920206994    1.97084621196340    1
0.913856576539988   2.24942313999122    1
1.57243525115195    2.68322568351427    1
0.241170005783610   1.89791938743627    1
2.10634746115858    2.20631449410867    1
1.61173455443266    2.69163655587553    1
1.39436249281445    1.28104472307183    1
1.65267727628557    1.85771163664832    1
0.0927741368750946  2.00698799954306    1
2.79887910552062    1.70868183551872    1
0.652907219449091   1.88702134773695    1
3.43327629572431    2.17571612839302    1
1.96527202098605    2.34069549818768    1
0.867813825335363   1.68534433959204    1
2.08252259376894    1.02114736190308    1
0.613863159235077   2.17081564512242    1
0.399552396654452   2.11342763957560    1
2.91186118166440    1.82639334702902    1
0.377524852838774   3.08573945644000    1
1.98881806018168    1.98460069084178    1
2.68223790603780    1.96134300697451    1
-0.622058850926772  3.30007822155342    1
0.275231442237888   1.59279139683080    1
3.34643957013717    1.68055674830698    1
1.03601109129843    2.70541249651070    1
0.752332013138056   2.49601620599903    1
0.508824726005269   2.57436514443626    1
1.73027562632102    2.19502286037081    1
-0.713230300478002  3.73262960861479    1
0.00814731258125789 1.52233440802986    1
4.86084534117603    2.64339300870922    1
2.82851799598685    1.79094004967764    1
0.725784406602703   1.32609248276320    1
0.147485590657029   1.49033710171378    1
0.601129894478657   3.58795933263211    1
1.32767044246750    1.73683867424831    1
1.27723837775742    2.62061783710240    1
0.240726647338080   1.43127112737987    1
0.387110343064517   2.39576239976627    1
-0.735171242460893  2.21884994425795    1
0.821208540380224   2.28230651108960    1
2.84910757299470    2.40958045875721    1
-0.00305938218238255    1.36758622169689    1
0.664256129846708   1.22550082896155    1
-0.178114240634728  2.30408704591301    1
2.00786623874121    2.03129722636611    1
1.20272622506596    2.22475373629028    1
2.50250207292945    2.98770457384530    1
-0.874927698714206  1.71654280942273    1
2.39750117925196    1.84040401876970    1
0.369966892118764   2.74170598529279    1
2.15279953669453    1.58546917422906    1
0.591444584760838   2.29854661364821    1
2.57828408797099    2.37460697014727    1
0.816618335677021   1.11574196519408    1
1.86043373148875    2.20777177264008    1
0.100606202686330   3.21045350847405    1
2.53445397100630    0.854302259627375   1
1.39168420124415    1.49609940831950    1
1.93929865340817    1.15181790512326    1
-0.727792104505467  1.79285231965211    1
0.646252982052485   1.03986431564848    1
1.19673169055003    1.93448155511090    1
1.12984878643876    2.22883744483371    1
4.02666426647567    1.86649614448613    1
1.27117265547696    2.03774514826000    1
1.00533765244964    1.17061188123361    1
-0.414741919400961  2.28828102447677    1
0.495317556413179   1.83288455064957    1
2.76823223857300    2.97934893222715    1
1.08058855707478    2.26487370266768    1
1.38546325331279    1.40278788558597    1
1.52167644900484    2.69183023331115    1
1.67505702182493    2.85222287490608    1
4.10199034200796    2.18231282334728    1
2.40054022033848    2.07878116194682    1
1.49836399169312    1.92754270979332    1
1.52768214037169    0.604144369658773   1
1.48106369736206    1.89716183115311    1
1.88582351601666    2.33794971444330    1
2.26705524676369    1.71381451846972    1
1.60866449353269    1.49135394247519    1
-0.115055715038331  1.12849347288791    1
3.57622395160265    1.10664344270162    1
2.26630255633806    1.22083515972890    1
1.33163191466841    1.44619768873473    1
-1.45842213709207   2.22949005602252    1
1.70411565663116    3.47971193248190    1
1.48213175474953    2.96603725956335    1
1.72101076041395    3.01672973472638    1
-1.35146166439553   2.85558221164039    1
0.380649241141038   2.56729542762187    1
1.88667156332277    2.85630884112937    1
1.97758943297860    1.70558017430484    1
3.24821546740318    1.82804963183289    1
0.843823793198429   2.64758378735128    1
0.0405183480549609  1.71670699774521    1
0.976813370248264   2.49595625636074    1
-0.962694637418230  2.58074576675867    1
1.82963248497625    1.90436660429995    1
-0.0311001170696965 2.55990131230256    1
1.78671558216327    1.30722487774421    1
-0.0804210798437657 1.50783793009072    1
0.592128283430208   0.755659320204709   1
1.95365332837982    2.16200348956491    1
1.36682968081700    1.73744055959892    1
0.979475411390431   2.42632494823554    1
1.39902046293800    1.73826197272758    1
-0.765939069959433  1.80573420327555    1
-0.635816070635523  2.54485465981086    1
0.430871671985252   2.95078063841104    1
1.07190555054187    2.60824897906429    1
0.190820867383636   1.56628799263572    1
-0.877375755503489  2.32098851030493    1
2.18596583418406    1.73505362502330    1
-0.858977422721553  1.67583237334513    1
-0.209010836036135  1.61369208932597    1
0.320019483215927   1.82234886961005    1
2.22606598804851    2.92553777460368    1
-1.34847806650935   1.73833614701202    1
-1.10055386975634   1.73836801433344    1
-0.742975839183661  2.56906224418957    1
0.417690253375689   2.64425612559673    1
2.21840601718359    1.86213274353155    1
-1.80189093986379   1.88663190741782    1
2.18214937211589    2.81206394431873    1
0.0239657716321194  2.37810261200744    1
1.48366333882258    1.88062228076692    1
-1.50485783728876   1.75467615653968    1
-0.156597929943039  2.92699154393160    1
1.00641966523359    1.82300801832677    1
0.229462398563604   0.218216163076399   1
3.08788315084484    2.44977421952063    1
-2.75317240661940   1.47607778387816    1
1.57689796637766    1.14148728120332    1
0.289637818858192   1.37410983329226    1
1.80546824398122    1.11123227831127    1
2.05342064550405    2.59331992761210    1
-0.908793900499392  1.78719275353105    1
0.366987870101896   0.201547399427021   1
0.449481220306850   3.37425663694842    1
1.77118120808653    1.75664778049838    1
-0.418491981240371  1.67286506452260    1
3.52677560506185    2.36946900535883    1
1.02163690286654    2.47962254560261    1
1.41689244102377    2.38217492483121    1
1.13691599064491    0.957013586442155   1
0.671020944057007   2.56279320248092    1
0.416289186862122   1.39483608724636    1
-0.0282160104049050 2.45786458707630    1
0.0521611900408511  2.82924447860124    1
1.24447082399573    3.12144145109146    1
2.55665751225502    2.59582682074224    1
-1.72738716416235   2.87567939582205    1
0.818034818369206   2.08879327658097    1
0.665710432632597   3.02252628314314    1
-0.830517107648893  1.19084685791838    1
-0.738848145854706  1.25759468066556    1
0.884558977595650   2.40820510111672    1
0.588696730035120   0.647436434761032   1
0.458454700841834   2.31765414523682    1
1.64997736187883    3.05662030735942    1
1.39057520552050    1.64617201786334    1
0.694081545245352   2.15199590331699    1
0.0806501948918665  1.80117182589078    1
-0.184406277418025  1.67502632215727    1
0.775990550072731   2.43159523244796    1
0.442197809595544   2.42563275519744    1
0.408665021617116   1.66059134779610    1
0.587376671955379   1.86069409237464    1
-0.342114019627880  1.33691809907463    1
-0.142841819015369  1.71852333719017    1
-0.260651941264746  1.99425566102537    1
1.09807519449644    2.47217799103807    1
2.62993953926983    0.646836252256355   1
3.36278068810442    3.41074385787854    1
1.80913370829291    3.17557239985633    1
-1.59335469603891   2.64454888986985    1
-0.735293470649240  1.69761457507026    1
1.46149445409731    2.51621553558738    1
0.0529118189982672  2.66819865723460    1
1.98005993371040    2.93220202949268    1
1.33800754576975    1.75196414069653    1
2.15416642093573    1.87958480581846    1
1.28247803384193    3.41980786237121    1
2.92917443686860    2.35403149513118    1
-0.470738428706770  0.823541040173880   1
2.78697845539703    2.47701324514116    1
0.518320239654389   2.40424725623016    1
3.02250396484622    2.55097729604176    1
-0.839762255830834  2.00164329123226    1
1.97089798972180    2.55221447381585    1
-0.289887426353096  2.07610884850028    1
-0.718531739571935  2.78476179702835    1
0.159548738233182   2.22484818846287    1
0.0714188829572822  2.17574623522435    1
0.464260706095287   2.07341615166054    1
2.02556814345805    1.45062673448325    1
0.737020824815466   3.63975837407915    1
2.14750623700563    2.23282234495443    1
2.53296991781638    2.22406578277892    1
0.240179086504137   1.76265939266772    1
2.36877906808012    1.99152631129811    1
-0.893624073060507  2.42679500507945    1
-0.723268011951694  2.32859127688179    1
-2.59545056360228   1.52181861224035    1
0.657633251364557   2.01724805594542    1
2.85299832181112    0.880567071545951   1
1.95540343663813    1.24583714921799    1
0.960390843211572   1.77723068955129    1
0.407588468887096   2.22032850805760    1
2.04391159921017    2.62263432946166    1
0.139422860698296   3.46606702350245    1
0.183723949354679   1.53015817186665    1
3.39797551359993    0.230231575623043   1
-0.497286845513600  3.06777504068115    1
2.70349699479679    2.60386143158266    1
0.185856815839956   2.77421368229388    1
-1.25352566567544   2.61455356095118    1
2.97698189327392    2.79489518362215    1
2.96340883068021    1.70435402189933    1
-2.93795294934390   1.69031063599255    1
3.20630707170919    1.90544165791216    1
-0.781101952149974  1.34505757509867    1
-0.110254964395879  1.88887924037098    1
-0.148493182169401  2.78931982306986    1
1.57894475487651    1.95012113995437    1
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