Lab2
commit
d24a3848a2
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# Default ignored files
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/shelf/
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/workspace.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="CsvFileAttributes">
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<option name="attributeMap">
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<map>
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<entry key="/Diet_R.csv">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="/detected_outliers.csv">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="/zb_6_cleaned_outliers.csv">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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</map>
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</option>
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</component>
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</project>
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<component name="InspectionProjectProfileManager">
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<settings>
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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</settings>
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</component>
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$">
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<excludeFolder url="file://$MODULE_DIR$/venv" />
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</content>
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<orderEntry type="inheritedJdk" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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</module>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="Black">
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<option name="sdkName" value="Python 3.11 (lab2)" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (lab2)" project-jdk-type="Python SDK" />
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/lab2.iml" filepath="$PROJECT_DIR$/.idea/lab2.iml" />
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</modules>
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</component>
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</project>
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Person,gender,Age,Height,pre.weight,Diet,weight6weeks
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25, ,41,171,60,2,60
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26, ,32,174,103,2,103
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1,0,22,159,58,1,54.2
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2,0,46,192,60,1,54
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3,0,55,170,64,1,63.3
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4,0,33,171,64,1,61.1
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5,0,50,170,65,1,62.2
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6,0,50,201,66,1,64
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7,0,37,174,67,1,65
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8,0,28,176,69,1,60.5
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9,0,28,165,70,1,68.1
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10,0,45,165,70,1,66.9
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11,0,60,173,72,1,70.5
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12,0,48,156,72,1,69
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13,0,41,163,72,1,68.4
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14,0,37,167,82,1,81.1
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27,0,44,174,58,2,60.1
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28,0,37,172,58,2,56
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29,0,41,165,59,2,57.3
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30,0,43,171,61,2,56.7
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31,0,20,169,62,2,55
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32,0,51,174,63,2,62.4
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33,0,31,163,63,2,60.3
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34,0,54,173,63,2,59.4
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35,0,50,166,65,2,62
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36,0,48,163,66,2,64
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37,0,16,165,68,2,63.8
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38,0,37,167,68,2,63.3
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39,0,30,161,76,2,72.7
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40,0,29,169,77,2,77.5
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52,0,51,165,60,3,53
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53,0,35,169,62,3,56.4
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54,0,21,159,64,3,60.6
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55,0,22,169,65,3,58.2
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56,0,36,160,66,3,58.2
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57,0,20,169,67,3,61.6
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58,0,35,163,67,3,60.2
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59,0,45,155,69,3,61.8
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60,0,58,141,70,3,63
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61,0,37,170,70,3,62.7
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62,0,31,170,72,3,71.1
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63,0,35,171,72,3,64.4
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64,0,56,171,73,3,68.9
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65,0,48,153,75,3,68.7
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66,0,41,157,76,3,71
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15,1,39,168,71,1,71.6
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16,1,31,158,72,1,70.9
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17,1,40,173,74,1,69.5
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18,1,50,160,78,1,73.9
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19,1,43,162,80,1,71
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20,1,25,165,80,1,77.6
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21,1,52,177,83,1,79.1
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22,1,42,166,85,1,81.5
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23,1,39,166,87,1,81.9
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24,1,40,190,88,1,84.5
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41,1,51,191,71,2,66.8
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42,1,38,199,75,2,72.6
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43,1,54,196,75,2,69.2
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44,1,33,190,76,2,72.5
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45,1,45,160,78,2,72.7
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46,1,37,194,78,2,76.3
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47,1,44,163,79,2,73.6
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48,1,40,171,79,2,72.9
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49,1,37,198,79,2,71.1
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50,1,39,180,80,2,81.4
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51,1,31,182,80,2,75.7
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67,1,36,155,71,3,68.5
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68,1,47,179,73,3,72.1
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69,1,29,166,76,3,72.5
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70,1,37,173,78,3,77.5
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71,1,31,177,78,3,75.2
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72,1,26,179,78,3,69.4
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73,1,40,179,79,3,74.5
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74,1,35,183,83,3,80.2
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75,1,49,177,84,3,79.9
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76,1,28,164,85,3,79.7
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77,1,40,167,87,3,77.8
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78,1,51,175,88,3,81.9
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@ -0,0 +1,44 @@
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index,a_1,a_2,a_3,id
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34,138.485899170175,122.929095022928,126.172417916436,1
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109,120.530449941131,130.750879252726,124.048371043226,1
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110,127.311311345106,127.032667937049,126.663304429044,1
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155,129.368231100468,118.021745958559,125.253403391051,1
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201,121.425022474716,131.859816486622,129.904568828899,1
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205,110.102275454427,132.655114719978,127.775782042087,1
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259,131.435016375916,115.572010552953,115.944265693,1
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273,128.44891783545,125.560167069906,126.983268672444,1
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306,127.654182919591,124.13389221102,134.420434743023,1
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341,122.598465207028,121.078619208422,127.882567550894,1
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356,121.663994537573,130.691895715687,118.183688992492,1
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396,131.196588840929,115.495838124466,125.804556729539,1
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436,117.347471617317,126.427664977539,129.097661740584,1
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466,131.541919997184,121.394265742161,116.734247753745,1
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472,120.583043526426,127.066196882151,110.922208446728,1
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478,127.969482196787,121.999166952041,118.756977485369,1
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479,113.977630762684,129.563917897202,125.23699868556,1
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541,120.84664843893,118.400886985925,129.174917770816,1
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562,132.485442804093,122.712546708689,122.083320312769,1
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569,118.55534283095,127.254749784513,120.393102285104,1
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630,124.91219273033,127.27165803814,130.218763471469,1
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701,129.902362319478,130.256075853809,120.196016971286,1
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770,114.598223298961,118.93540130129,128.488285033541,1
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821,116.951391359065,129.571989973166,125.814814692545,1
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931,112.985171033967,130.427336194935,125.512936296744,1
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1053,124.338390603879,122.151317355276,130.616547836666,1
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1065,130.539775478334,119.028932834111,117.282837634037,1
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1099,129.037419669311,127.107507791353,124.180827870454,1
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1196,115.599094881676,129.136779334136,126.332356371468,1
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1205,134.778364721566,118.962106577339,121.889179688135,1
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1333,130.712202709264,109.484613386081,123.551624037055,1
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1384,124.707512593542,132.008238660605,111.096035260158,1
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1397,115.380552840751,129.059473129573,122.765601709126,1
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1453,122.777272707575,126.947398223481,128.895048251861,1
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1492,119.526488992333,127.734375888783,127.742296671876,1
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1532,123.577214053843,130.265344604706,121.513138303347,1
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1557,124.097805702169,130.251148713943,119.098551931573,1
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1559,120.627976386864,126.519214114161,126.228370549419,1
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1812,132.037558446089,113.806457986605,111.833483565089,1
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1827,122.441410727444,124.761007127792,128.370446549678,1
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1885,123.52845529026,126.684983276764,126.669140290452,1
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1923,128.581623737032,129.397198754531,123.452343679533,1
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1959,120.664772641529,137.454306930058,118.606096825007,1
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@ -0,0 +1,79 @@
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"""
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author: Wojciech Janota
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Laboratory: Lab 2, ex. 3
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"""
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import pandas as pd
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import numpy as np
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input_data = pd.read_csv('Diet_R.csv', sep=',')
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input_data["gender"].replace(" ", np.NaN, inplace=True)
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input_data["gender"].fillna(input_data["gender"].mode()[0], inplace=True)
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summary_stats = input_data.describe()
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median = summary_stats.loc['50%', :]
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first_quartile = summary_stats.loc['25%', :]
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third_quartile = summary_stats.loc['75%', :]
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mean = summary_stats.loc['mean', :]
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std_dev = summary_stats.loc['std', :]
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print("Statistics")
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# Print the results
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print("Median:\n", median)
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print("\nFirst Quartile:\n", first_quartile)
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print("\nThird Quartile:\n", third_quartile)
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print("\nMean:\n", mean)
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print("\nStandard Deviation:\n", std_dev)
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with open("result.txt", "w") as output_file:
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result = f"""
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\t\tGeneral statistics:
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\tMedian:
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{median}
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\tFirst Quartile:
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{first_quartile}
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\tThird Quartile:
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{third_quartile}
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\tMean:
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{mean}
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\tStd. deviation:
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{std_dev}
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"""
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output_file.write(result)
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grouped_stats = input_data.groupby('gender').describe()
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print("Statistics grouped by gender")
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# Extract specific values
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median = grouped_stats.xs(key='50%', level=1, axis=1)
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first_quartile = grouped_stats.xs(key='25%', level=1, axis=1)
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third_quartile = grouped_stats.xs(key='75%', level=1, axis=1)
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mean = grouped_stats.xs(key='mean', level=1, axis=1)
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std_dev = grouped_stats.xs(key='std', level=1, axis=1)
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# Print the results
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print("Median:\n", median)
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print("\nFirst Quartile:\n", first_quartile)
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print("\nThird Quartile:\n", third_quartile)
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print("\nMean:\n", mean)
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print("\nStandard Deviation:\n", std_dev)
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with open("result.txt", "a") as output_file:
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result = f"""
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\n\n
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\t\tStatistics per gender:
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\tMedian:
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{median}
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\tFirst Quartile:
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{first_quartile}
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\tThird Quartile:
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{third_quartile}
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\tMean:
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{mean}
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\tStd. deviation:
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{std_dev}
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"""
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output_file.write(result)
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@ -0,0 +1,40 @@
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"""
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author: Wojciech Janota
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laboratory: Lab 2, ex 2
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"""
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import pandas as pd
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from scipy.stats import zscore
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input_data = pd.read_csv("zb_6.txt", sep=',')
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# replace all non-numeric values to NaN
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input_data = input_data.map(lambda x: pd.to_numeric(x, errors='coerce'))
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# replace all NaN values with median
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input_data.fillna(input_data.median(), inplace=True)
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# # Check for NaN values in prepared DataFrame
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# nan_mask = input_data.isna().any(axis=1)
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# rows_with_nan = input_data[nan_mask]
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# print(rows_with_nan)
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# Find outliers using z-score (interquartile range)
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numeric_columns = input_data[['a_1', 'a_2', 'a_3']]
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z_scores = zscore(numeric_columns)
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# Threshold for finding outliers (over and under 3 std dev)
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threshold = 3
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# Matrix of detected outliers
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outliers = (abs(z_scores > threshold).any(axis=1))
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# Filter out only the rows with outliers
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outliers_rows = input_data[outliers]
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outliers_rows.to_csv("detected_outliers.csv", sep=',', index_label="index")
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# Remove detected outliers form the original data and write to new file
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cleaned_data = input_data.drop(outliers_rows.index)
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cleaned_data.to_csv("zb_6_cleaned_outliers.csv", sep=',', index_label="index")
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@ -0,0 +1,73 @@
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General statistics:
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Median:
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Person 39.50
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Age 39.00
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Height 169.50
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pre.weight 72.00
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Diet 2.00
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weight6weeks 68.95
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Name: 50%, dtype: float64
|
||||||
|
First Quartile:
|
||||||
|
Person 20.25
|
||||||
|
Age 32.25
|
||||||
|
Height 164.25
|
||||||
|
pre.weight 66.00
|
||||||
|
Diet 1.00
|
||||||
|
weight6weeks 61.85
|
||||||
|
Name: 25%, dtype: float64
|
||||||
|
Third Quartile:
|
||||||
|
Person 58.750
|
||||||
|
Age 46.750
|
||||||
|
Height 174.750
|
||||||
|
pre.weight 78.000
|
||||||
|
Diet 3.000
|
||||||
|
weight6weeks 73.825
|
||||||
|
Name: 75%, dtype: float64
|
||||||
|
Mean:
|
||||||
|
Person 39.500000
|
||||||
|
Age 39.153846
|
||||||
|
Height 170.820513
|
||||||
|
pre.weight 72.525641
|
||||||
|
Diet 2.038462
|
||||||
|
weight6weeks 68.680769
|
||||||
|
Name: mean, dtype: float64
|
||||||
|
Std. deviation:
|
||||||
|
Person 22.660538
|
||||||
|
Age 9.815277
|
||||||
|
Height 11.276621
|
||||||
|
pre.weight 8.723344
|
||||||
|
Diet 0.812920
|
||||||
|
weight6weeks 8.924504
|
||||||
|
Name: std, dtype: float64
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
Statistics per gender:
|
||||||
|
Median:
|
||||||
|
Person Age Height pre.weight Diet weight6weeks
|
||||||
|
gender
|
||||||
|
0 33.0 37.0 169.0 67.0 2.0 62.4
|
||||||
|
1 47.0 39.0 175.0 79.0 2.0 73.9
|
||||||
|
First Quartile:
|
||||||
|
Person Age Height pre.weight Diet weight6weeks
|
||||||
|
gender
|
||||||
|
0 12.0 31.0 163.0 63.0 1.0 60.0
|
||||||
|
1 23.0 35.0 166.0 76.0 1.0 71.6
|
||||||
|
Third Quartile:
|
||||||
|
Person Age Height pre.weight Diet weight6weeks
|
||||||
|
gender
|
||||||
|
0 55.0 48.0 171.0 72.0 3.0 68.1
|
||||||
|
1 70.0 44.0 182.0 83.0 3.0 79.1
|
||||||
|
Mean:
|
||||||
|
Person Age Height pre.weight Diet weight6weeks
|
||||||
|
gender
|
||||||
|
0 33.555556 39.000000 167.577778 67.755556 2.022222 64.035556
|
||||||
|
1 47.606061 39.363636 175.242424 79.030303 2.060606 75.015152
|
||||||
|
Std. deviation:
|
||||||
|
Person Age Height pre.weight Diet weight6weeks
|
||||||
|
gender
|
||||||
|
0 21.363260 11.190499 9.306618 7.772783 0.811533 8.463670
|
||||||
|
1 22.149971 7.716938 12.326370 4.940172 0.826869 4.629398
|
||||||
|
|
||||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue