├── .DS_Store ├── Data Visualisation with Facets ├── .DS_Store ├── Facets Notebook.ipynb └── Loan prediction dataset │ ├── test.csv │ └── train.csv ├── Data Visualisation with Power BI ├── Financial Sample.xlsx ├── Iris.csv └── tips.csv ├── Data Visualisation with PyViz ├── PyViz Notebook.ipynb ├── Readme.md └── binder │ └── environment.yml ├── Data Visualisation with Tableau ├── .DS_Store ├── Cluster Analysis with Tableau │ ├── Images │ │ ├── .DS_Store │ │ ├── Creating Clusters from World Economic Indicators Data1.gif │ │ ├── Creating Clusters from World Economic Indicators Data2.gif │ │ ├── Creating Clusters from World Economic Indicators Data3.gif │ │ ├── Creating Clusters from World Economic Indicators Data5.png │ │ ├── Creating Clusters from World Economic Indicators Data6.gif │ │ ├── Creating Clusters from World Economic Indicators Data7.gif │ │ ├── Creating Clusters from World Economic Indicators Data8.png │ │ ├── Creating Clusters from World Economic Indicators Data9.png │ │ ├── Describing Clusters1.png │ │ ├── Describing Clusters2.png │ │ ├── Describing Clusters3.gif │ │ ├── Describing Clusters4.png │ │ ├── Formatting the Data Source1.gif │ │ ├── Formatting the Data Source2.png │ │ ├── Formatting the Data Source3.png │ │ ├── Tableau Environment.png │ │ ├── The criterion for Clustering in Tableau .png │ │ └── Visualizing K-Means algorithm.gif │ └── World Indicators.xlsx ├── Data Viz with tableau │ ├── images and gifs │ │ ├── .DS_Store │ │ ├── Dashboard │ │ │ ├── adding interactiveness.gif │ │ │ └── creating dashboard.gif │ │ ├── Emphasize the Results │ │ │ ├── Adding filter.png │ │ │ ├── adding color\.png │ │ │ ├── adding filter and color.gif │ │ │ └── key findings.gif │ │ ├── Introduction to tableau │ │ │ ├── Tableau Product suite.png │ │ │ ├── Tableau Products.png │ │ │ └── installation.png │ │ ├── Map View │ │ │ ├── Hierarchy.gif │ │ │ ├── creating a map view.gif │ │ │ ├── getting into details.gif │ │ │ ├── getting into details.png │ │ │ ├── hierarchy.png │ │ │ └── key findings.gif │ │ ├── Story │ │ │ ├── Buildign a story.gif │ │ │ └── making a conclusion.gif │ │ ├── Tableau with Python │ │ │ ├── .DS_Store │ │ │ ├── Sentiment analysis.png │ │ │ ├── Tabpy connection.png │ │ │ └── script.png │ │ ├── Tableau with R │ │ │ ├── Tableau with R.gif │ │ │ ├── connection1.png │ │ │ └── connection2.png │ │ ├── Tableau with SQL │ │ │ └── Tableau with SQL.gif │ │ └── getting started │ │ │ ├── .DS_Store │ │ │ ├── Refining the view1.png │ │ │ ├── Refining the view2.png │ │ │ ├── Refining the view3.png │ │ │ ├── Refining the view4.png │ │ │ ├── Refining the viewgif.gif │ │ │ ├── Tableau Workspace.png │ │ │ ├── connectingToData2.gif │ │ │ ├── connecting_to_dataource.gif │ │ │ ├── creating a view.gif │ │ │ └── creating a view.png │ ├── .DS_Store │ ├── README.md │ ├── Sample-Superstore .xls │ └── reviews.csv ├── Quadrant Analysis in Tableau │ ├── .DS_Store │ ├── Dynamic Quadrant chart.twbx │ ├── Sample-Superstore .xls │ └── images │ │ ├── Discount Parameter.png │ │ ├── GartnerMQ.png │ │ ├── Parameters.png │ │ ├── Profit Ratio Parameter.png │ │ ├── Quadrant color formula.gif │ │ ├── Quadrant color settings.gif │ │ ├── Refrence lines1.gif │ │ ├── Tableau Environment.png │ │ ├── Update Discount Parameter.gif │ │ ├── Update Profit ratio parameter.gif │ │ ├── actions.png │ │ ├── connecting to data source.gif │ │ ├── creating Parameter actions.png │ │ ├── creating Parameters.png │ │ ├── creating scatter plot.gif │ │ ├── creating scatter plot1.png │ │ ├── df.head().png │ │ ├── df.info().png │ │ └── dynamic quadrant chart.gif ├── SQL with Tableau │ └── Images and gif │ │ ├── 1.installation.png │ │ ├── 10. join.png │ │ ├── 11. blend.png │ │ ├── 12. custom sql.gif │ │ ├── 13. data source filters.gif │ │ ├── 14. analysis in Tableau1.png │ │ ├── 15.analysis in Tableau2.png │ │ ├── 16. analysis in Tableau3.png │ │ ├── 2.relaitonal Database.png │ │ ├── 3.MSSQL main screen.png │ │ ├── 4.MSSQL credentials.gif │ │ ├── 5. MSSQL explore.gif │ │ ├── 6. Tableau SQL connection.png │ │ ├── 7. TAbleau SQL credentials.gif │ │ ├── 8. Tableau SQL accessing tables.gif │ │ └── 9. Orders.gif ├── Spreadsheets with Tableau │ ├── .DS_Store │ ├── Data │ │ ├── .DS_Store │ │ ├── Coffee Sales Data set │ │ │ ├── Coffee Consumption.xlsx │ │ │ └── Coffee Sales by Year.xlsx │ │ └── flight Incident dataset.xlsx │ └── Images and gif │ │ ├── Adding more data.gif │ │ ├── Aliases.gif │ │ ├── Custom SPlit.gif │ │ ├── Custom Split.png │ │ ├── Data Blending.png │ │ ├── Data Blending2.png │ │ ├── Data Interpreter.gif │ │ ├── Metagrid.png │ │ ├── Pivot table.gif │ │ ├── Split.gif │ │ ├── ad-hoc calculations.gif │ │ ├── anomaly in excel vs Tableau due to relationship.png │ │ ├── calculations 1.gif │ │ ├── calculations 2.gif │ │ ├── data blending.gif │ │ ├── data1.png │ │ ├── data2.png │ │ ├── data3.png │ │ ├── edit Relationships.gif │ │ ├── edit relationships.png │ │ ├── exporting crosstab data.gif │ │ ├── installation.png │ │ ├── overview.png │ │ └── table calc.gif └── Wordclouds with Tableau │ ├── Improving the word cloud.twb │ ├── Word_cloud.twb │ ├── common_words.xlsx │ ├── movies.xlsx │ ├── test.csv │ └── text.txt ├── Data-Visualisation-with-R ├── Barplot │ ├── Horizontal bar plot.png │ └── Vertical bar plot.png ├── Basic Plots │ ├── All variables.png │ ├── Basic Plot.png │ ├── Ozone vs Wind.png │ ├── high density.png │ ├── lables and titles.png │ └── line and point plot.png ├── Boxplots │ ├── box plots multiple.png │ └── boxplot_single.png ├── Histogram │ ├── histogram.png │ └── histogram_color.png ├── Multiple charts │ └── multiple charts.png ├── Packages │ ├── Lattice │ │ ├── kde.png │ │ ├── scatterplot matrix.png │ │ └── scatterplot+2 factors.png │ ├── Plotly │ │ ├── Plotly_color_size.png │ │ ├── Plotly_markers.png │ │ ├── Plotly_scatter.png │ │ ├── Plotly_style_scatterplot.png │ │ ├── plotly1.html │ │ ├── plotly2.html │ │ └── plotly3.html │ └── ggplot2 │ │ ├── ggplot_scatter.png │ │ ├── size_ggplot.png │ │ ├── styling with symbols.png │ │ └── styling_scatter_ggplo2.png ├── README.md ├── Visualisation geographical data │ ├── ABC_locations.csv │ ├── Basic geog plot.png │ ├── base map.png │ ├── styling map.png │ └── top of base map.png └── airquality.csv ├── Exploratory Data Visualisation with Altair └── Altair_Notebook.ipynb ├── README.md ├── Tableau-Projects ├── README.md ├── Recreating-Gapminder-in-Tableau │ ├── README.md │ └── data │ │ ├── countries_total.csv │ │ ├── income_per_person.csv │ │ ├── life_expectancy_years.csv │ │ └── population_total.csv ├── Screenshot 2018-10-31 at 1.20.01 PM.png └── haunted_places.csv ├── Visualising-Geospatial-data-with-Python ├── Folium_Notebook.ipynb ├── README.md ├── map.gif ├── plot_data.html └── world-countries.json └── images ├── .DS_Store ├── Altair.png ├── Facets.png ├── Folium.png └── PyViz.gif /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/.DS_Store -------------------------------------------------------------------------------- /Data Visualisation with Facets /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/Data Visualisation with Facets /.DS_Store -------------------------------------------------------------------------------- /Data Visualisation with Facets /Loan prediction dataset/test.csv: -------------------------------------------------------------------------------- 1 | Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area 2 | LP001015,Male,Yes,0,Graduate,No,5720,0,110,360,1,Urban 3 | LP001022,Male,Yes,1,Graduate,No,3076,1500,126,360,1,Urban 4 | LP001031,Male,Yes,2,Graduate,No,5000,1800,208,360,1,Urban 5 | LP001035,Male,Yes,2,Graduate,No,2340,2546,100,360,,Urban 6 | LP001051,Male,No,0,Not Graduate,No,3276,0,78,360,1,Urban 7 | LP001054,Male,Yes,0,Not Graduate,Yes,2165,3422,152,360,1,Urban 8 | LP001055,Female,No,1,Not Graduate,No,2226,0,59,360,1,Semiurban 9 | LP001056,Male,Yes,2,Not Graduate,No,3881,0,147,360,0,Rural 10 | LP001059,Male,Yes,2,Graduate,,13633,0,280,240,1,Urban 11 | LP001067,Male,No,0,Not Graduate,No,2400,2400,123,360,1,Semiurban 12 | LP001078,Male,No,0,Not Graduate,No,3091,0,90,360,1,Urban 13 | LP001082,Male,Yes,1,Graduate,,2185,1516,162,360,1,Semiurban 14 | LP001083,Male,No,3+,Graduate,No,4166,0,40,180,,Urban 15 | LP001094,Male,Yes,2,Graduate,,12173,0,166,360,0,Semiurban 16 | LP001096,Female,No,0,Graduate,No,4666,0,124,360,1,Semiurban 17 | LP001099,Male,No,1,Graduate,No,5667,0,131,360,1,Urban 18 | LP001105,Male,Yes,2,Graduate,No,4583,2916,200,360,1,Urban 19 | LP001107,Male,Yes,3+,Graduate,No,3786,333,126,360,1,Semiurban 20 | LP001108,Male,Yes,0,Graduate,No,9226,7916,300,360,1,Urban 21 | LP001115,Male,No,0,Graduate,No,1300,3470,100,180,1,Semiurban 22 | LP001121,Male,Yes,1,Not Graduate,No,1888,1620,48,360,1,Urban 23 | LP001124,Female,No,3+,Not Graduate,No,2083,0,28,180,1,Urban 24 | LP001128,,No,0,Graduate,No,3909,0,101,360,1,Urban 25 | LP001135,Female,No,0,Not Graduate,No,3765,0,125,360,1,Urban 26 | LP001149,Male,Yes,0,Graduate,No,5400,4380,290,360,1,Urban 27 | LP001153,Male,No,0,Graduate,No,0,24000,148,360,0,Rural 28 | LP001163,Male,Yes,2,Graduate,No,4363,1250,140,360,,Urban 29 | LP001169,Male,Yes,0,Graduate,No,7500,3750,275,360,1,Urban 30 | LP001174,Male,Yes,0,Graduate,No,3772,833,57,360,,Semiurban 31 | LP001176,Male,No,0,Graduate,No,2942,2382,125,180,1,Urban 32 | LP001177,Female,No,0,Not Graduate,No,2478,0,75,360,1,Semiurban 33 | LP001183,Male,Yes,2,Graduate,No,6250,820,192,360,1,Urban 34 | LP001185,Male,No,0,Graduate,No,3268,1683,152,360,1,Semiurban 35 | LP001187,Male,Yes,0,Graduate,No,2783,2708,158,360,1,Urban 36 | LP001190,Male,Yes,0,Graduate,No,2740,1541,101,360,1,Urban 37 | LP001203,Male,No,0,Graduate,No,3150,0,176,360,0,Semiurban 38 | LP001208,Male,Yes,2,Graduate,,7350,4029,185,180,1,Urban 39 | LP001210,Male,Yes,0,Graduate,Yes,2267,2792,90,360,1,Urban 40 | LP001211,Male,No,0,Graduate,Yes,5833,0,116,360,1,Urban 41 | LP001219,Male,No,0,Graduate,No,3643,1963,138,360,1,Urban 42 | LP001220,Male,Yes,0,Graduate,No,5629,818,100,360,1,Urban 43 | LP001221,Female,No,0,Graduate,No,3644,0,110,360,1,Urban 44 | LP001226,Male,Yes,0,Not Graduate,No,1750,2024,90,360,1,Semiurban 45 | LP001230,Male,No,0,Graduate,No,6500,2600,200,360,1,Semiurban 46 | LP001231,Female,No,0,Graduate,No,3666,0,84,360,1,Urban 47 | LP001232,Male,Yes,0,Graduate,No,4260,3900,185,,,Urban 48 | LP001237,Male,Yes,,Not Graduate,No,4163,1475,162,360,1,Urban 49 | LP001242,Male,No,0,Not Graduate,No,2356,1902,108,360,1,Semiurban 50 | LP001268,Male,No,0,Graduate,No,6792,3338,187,,1,Urban 51 | LP001270,Male,Yes,3+,Not Graduate,Yes,8000,250,187,360,1,Semiurban 52 | LP001284,Male,Yes,1,Graduate,No,2419,1707,124,360,1,Urban 53 | LP001287,,Yes,3+,Not Graduate,No,3500,833,120,360,1,Semiurban 54 | LP001291,Male,Yes,1,Graduate,No,3500,3077,160,360,1,Semiurban 55 | LP001298,Male,Yes,2,Graduate,No,4116,1000,30,180,1,Urban 56 | LP001312,Male,Yes,0,Not Graduate,Yes,5293,0,92,360,1,Urban 57 | LP001313,Male,No,0,Graduate,No,2750,0,130,360,0,Urban 58 | LP001317,Female,No,0,Not Graduate,No,4402,0,130,360,1,Rural 59 | LP001321,Male,Yes,2,Graduate,No,3613,3539,134,180,1,Semiurban 60 | LP001323,Female,Yes,2,Graduate,No,2779,3664,176,360,0,Semiurban 61 | LP001324,Male,Yes,3+,Graduate,No,4720,0,90,180,1,Semiurban 62 | LP001332,Male,Yes,0,Not Graduate,No,2415,1721,110,360,1,Semiurban 63 | LP001335,Male,Yes,0,Graduate,Yes,7016,292,125,360,1,Urban 64 | LP001338,Female,No,2,Graduate,No,4968,0,189,360,1,Semiurban 65 | LP001347,Female,No,0,Graduate,No,2101,1500,108,360,0,Rural 66 | LP001348,Male,Yes,3+,Not Graduate,No,4490,0,125,360,1,Urban 67 | LP001351,Male,Yes,0,Graduate,No,2917,3583,138,360,1,Semiurban 68 | LP001352,Male,Yes,0,Not Graduate,No,4700,0,135,360,0,Semiurban 69 | LP001358,Male,Yes,0,Graduate,No,3445,0,130,360,0,Semiurban 70 | LP001359,Male,Yes,0,Graduate,No,7666,0,187,360,1,Semiurban 71 | LP001361,Male,Yes,0,Graduate,No,2458,5105,188,360,0,Rural 72 | LP001366,Female,No,,Graduate,No,3250,0,95,360,1,Semiurban 73 | LP001368,Male,No,0,Graduate,No,4463,0,65,360,1,Semiurban 74 | LP001375,Male,Yes,1,Graduate,,4083,1775,139,60,1,Urban 75 | LP001380,Male,Yes,0,Graduate,Yes,3900,2094,232,360,1,Rural 76 | LP001386,Male,Yes,0,Not Graduate,No,4750,3583,144,360,1,Semiurban 77 | LP001400,Male,No,0,Graduate,No,3583,3435,155,360,1,Urban 78 | LP001407,Male,Yes,0,Graduate,No,3189,2367,186,360,1,Urban 79 | LP001413,Male,No,0,Graduate,Yes,6356,0,50,360,1,Rural 80 | LP001415,Male,Yes,1,Graduate,No,3413,4053,,360,1,Semiurban 81 | LP001419,Female,Yes,0,Graduate,No,7950,0,185,360,1,Urban 82 | LP001420,Male,Yes,3+,Graduate,No,3829,1103,163,360,0,Urban 83 | LP001428,Male,Yes,3+,Graduate,No,72529,0,360,360,1,Urban 84 | LP001445,Male,Yes,2,Not Graduate,No,4136,0,149,480,0,Rural 85 | LP001446,Male,Yes,0,Graduate,No,8449,0,257,360,1,Rural 86 | LP001450,Male,Yes,0,Graduate,No,4456,0,131,180,0,Semiurban 87 | LP001452,Male,Yes,2,Graduate,No,4635,8000,102,180,1,Rural 88 | LP001455,Male,Yes,0,Graduate,No,3571,1917,135,360,1,Urban 89 | LP001466,Male,No,0,Graduate,No,3066,0,95,360,1,Semiurban 90 | LP001471,Male,No,2,Not Graduate,No,3235,2015,77,360,1,Semiurban 91 | LP001472,Female,No,0,Graduate,,5058,0,200,360,1,Rural 92 | LP001475,Male,Yes,0,Graduate,Yes,3188,2286,130,360,,Rural 93 | LP001483,Male,Yes,3+,Graduate,No,13518,0,390,360,1,Rural 94 | LP001486,Male,Yes,1,Graduate,No,4364,2500,185,360,1,Semiurban 95 | LP001490,Male,Yes,2,Not Graduate,No,4766,1646,100,360,1,Semiurban 96 | LP001496,Male,Yes,1,Graduate,No,4609,2333,123,360,0,Semiurban 97 | LP001499,Female,Yes,3+,Graduate,No,6260,0,110,360,1,Semiurban 98 | LP001500,Male,Yes,1,Graduate,No,3333,4200,256,360,1,Urban 99 | LP001501,Male,Yes,0,Graduate,No,3500,3250,140,360,1,Semiurban 100 | LP001517,Male,Yes,3+,Graduate,No,9719,0,61,360,1,Urban 101 | LP001527,Male,Yes,3+,Graduate,No,6835,0,188,360,,Semiurban 102 | LP001534,Male,No,0,Graduate,No,4452,0,131,360,1,Rural 103 | LP001542,Female,Yes,0,Graduate,No,2262,0,,480,0,Semiurban 104 | LP001547,Male,Yes,1,Graduate,No,3901,0,116,360,1,Urban 105 | LP001548,Male,Yes,2,Not Graduate,No,2687,0,50,180,1,Rural 106 | LP001558,Male,No,0,Graduate,No,2243,2233,107,360,,Semiurban 107 | LP001561,Female,Yes,0,Graduate,No,3417,1287,200,360,1,Semiurban 108 | LP001563,,No,0,Graduate,No,1596,1760,119,360,0,Urban 109 | LP001567,Male,Yes,3+,Graduate,No,4513,0,120,360,1,Rural 110 | LP001568,Male,Yes,0,Graduate,No,4500,0,140,360,1,Semiurban 111 | LP001573,Male,Yes,0,Not Graduate,No,4523,1350,165,360,1,Urban 112 | LP001584,Female,No,0,Graduate,Yes,4742,0,108,360,1,Semiurban 113 | LP001587,Male,Yes,,Graduate,No,4082,0,93,360,1,Semiurban 114 | LP001589,Female,No,0,Graduate,No,3417,0,102,360,1,Urban 115 | LP001591,Female,Yes,2,Graduate,No,2922,3396,122,360,1,Semiurban 116 | LP001599,Male,Yes,0,Graduate,No,4167,4754,160,360,1,Rural 117 | LP001601,Male,No,3+,Graduate,No,4243,4123,157,360,,Semiurban 118 | LP001607,Female,No,0,Not Graduate,No,0,1760,180,360,1,Semiurban 119 | LP001611,Male,Yes,1,Graduate,No,1516,2900,80,,0,Rural 120 | LP001613,Female,No,0,Graduate,No,1762,2666,104,360,0,Urban 121 | LP001622,Male,Yes,2,Graduate,No,724,3510,213,360,0,Rural 122 | LP001627,Male,No,0,Graduate,No,3125,0,65,360,1,Urban 123 | LP001650,Male,Yes,0,Graduate,No,2333,3803,146,360,1,Rural 124 | LP001651,Male,Yes,3+,Graduate,No,3350,1560,135,360,1,Urban 125 | LP001652,Male,No,0,Graduate,No,2500,6414,187,360,0,Rural 126 | LP001655,Female,No,0,Graduate,No,12500,0,300,360,0,Urban 127 | LP001660,Male,No,0,Graduate,No,4667,0,120,360,1,Semiurban 128 | LP001662,Male,No,0,Graduate,No,6500,0,71,360,0,Urban 129 | LP001663,Male,Yes,2,Graduate,No,7500,0,225,360,1,Urban 130 | LP001667,Male,No,0,Graduate,No,3073,0,70,180,1,Urban 131 | LP001695,Male,Yes,1,Not Graduate,No,3321,2088,70,,1,Semiurban 132 | LP001703,Male,Yes,0,Graduate,No,3333,1270,124,360,1,Urban 133 | LP001718,Male,No,0,Graduate,No,3391,0,132,360,1,Rural 134 | LP001728,Male,Yes,1,Graduate,Yes,3343,1517,105,360,1,Rural 135 | LP001735,Female,No,1,Graduate,No,3620,0,90,360,1,Urban 136 | LP001737,Male,No,0,Graduate,No,4000,0,83,84,1,Urban 137 | LP001739,Male,Yes,0,Graduate,No,4258,0,125,360,1,Urban 138 | LP001742,Male,Yes,2,Graduate,No,4500,0,147,360,1,Rural 139 | LP001757,Male,Yes,1,Graduate,No,2014,2925,120,360,1,Rural 140 | LP001769,,No,,Graduate,No,3333,1250,110,360,1,Semiurban 141 | LP001771,Female,No,3+,Graduate,No,4083,0,103,360,,Semiurban 142 | LP001785,Male,No,0,Graduate,No,4727,0,150,360,0,Rural 143 | LP001787,Male,Yes,3+,Graduate,No,3089,2999,100,240,1,Rural 144 | LP001789,Male,Yes,3+,Not Graduate,,6794,528,139,360,0,Urban 145 | LP001791,Male,Yes,0,Graduate,Yes,32000,0,550,360,,Semiurban 146 | LP001794,Male,Yes,2,Graduate,Yes,10890,0,260,12,1,Rural 147 | LP001797,Female,No,0,Graduate,No,12941,0,150,300,1,Urban 148 | LP001815,Male,No,0,Not Graduate,No,3276,0,90,360,1,Semiurban 149 | LP001817,Male,No,0,Not Graduate,Yes,8703,0,199,360,0,Rural 150 | LP001818,Male,Yes,1,Graduate,No,4742,717,139,360,1,Semiurban 151 | LP001822,Male,No,0,Graduate,No,5900,0,150,360,1,Urban 152 | LP001827,Male,No,0,Graduate,No,3071,4309,180,360,1,Urban 153 | LP001831,Male,Yes,0,Graduate,No,2783,1456,113,360,1,Urban 154 | LP001842,Male,No,0,Graduate,No,5000,0,148,360,1,Rural 155 | LP001853,Male,Yes,1,Not Graduate,No,2463,2360,117,360,0,Urban 156 | LP001855,Male,Yes,2,Graduate,No,4855,0,72,360,1,Rural 157 | LP001857,Male,No,0,Not Graduate,Yes,1599,2474,125,300,1,Semiurban 158 | LP001862,Male,Yes,2,Graduate,Yes,4246,4246,214,360,1,Urban 159 | LP001867,Male,Yes,0,Graduate,No,4333,2291,133,350,1,Rural 160 | LP001878,Male,No,1,Graduate,No,5823,2529,187,360,1,Semiurban 161 | LP001881,Male,Yes,0,Not Graduate,No,7895,0,143,360,1,Rural 162 | LP001886,Male,No,0,Graduate,No,4150,4256,209,360,1,Rural 163 | LP001906,Male,No,0,Graduate,,2964,0,84,360,0,Semiurban 164 | LP001909,Male,No,0,Graduate,No,5583,0,116,360,1,Urban 165 | LP001911,Female,No,0,Graduate,No,2708,0,65,360,1,Rural 166 | LP001921,Male,No,1,Graduate,No,3180,2370,80,240,,Rural 167 | LP001923,Male,No,0,Not Graduate,No,2268,0,170,360,0,Semiurban 168 | LP001933,Male,No,2,Not Graduate,No,1141,2017,120,360,0,Urban 169 | LP001943,Male,Yes,0,Graduate,No,3042,3167,135,360,1,Urban 170 | LP001950,Female,Yes,3+,Graduate,,1750,2935,94,360,0,Semiurban 171 | LP001959,Female,Yes,1,Graduate,No,3564,0,79,360,1,Rural 172 | LP001961,Female,No,0,Graduate,No,3958,0,110,360,1,Rural 173 | LP001973,Male,Yes,2,Not Graduate,No,4483,0,130,360,1,Rural 174 | LP001975,Male,Yes,0,Graduate,No,5225,0,143,360,1,Rural 175 | LP001979,Male,No,0,Graduate,No,3017,2845,159,180,0,Urban 176 | LP001995,Male,Yes,0,Not Graduate,No,2431,1820,110,360,0,Rural 177 | LP001999,Male,Yes,2,Graduate,,4912,4614,160,360,1,Rural 178 | LP002007,Male,Yes,2,Not Graduate,No,2500,3333,131,360,1,Urban 179 | LP002009,Female,No,0,Graduate,No,2918,0,65,360,,Rural 180 | LP002016,Male,Yes,2,Graduate,No,5128,0,143,360,1,Rural 181 | LP002017,Male,Yes,3+,Graduate,No,15312,0,187,360,,Urban 182 | LP002018,Male,Yes,2,Graduate,No,3958,2632,160,360,1,Semiurban 183 | LP002027,Male,Yes,0,Graduate,No,4334,2945,165,360,1,Semiurban 184 | LP002028,Male,Yes,2,Graduate,No,4358,0,110,360,1,Urban 185 | LP002042,Female,Yes,1,Graduate,No,4000,3917,173,360,1,Rural 186 | LP002045,Male,Yes,3+,Graduate,No,10166,750,150,,1,Urban 187 | LP002046,Male,Yes,0,Not Graduate,No,4483,0,135,360,,Semiurban 188 | LP002047,Male,Yes,2,Not Graduate,No,4521,1184,150,360,1,Semiurban 189 | LP002056,Male,Yes,2,Graduate,No,9167,0,235,360,1,Semiurban 190 | LP002057,Male,Yes,0,Not Graduate,No,13083,0,,360,1,Rural 191 | LP002059,Male,Yes,2,Graduate,No,7874,3967,336,360,1,Rural 192 | LP002062,Female,Yes,1,Graduate,No,4333,0,132,84,1,Rural 193 | LP002064,Male,No,0,Graduate,No,4083,0,96,360,1,Urban 194 | LP002069,Male,Yes,2,Not Graduate,,3785,2912,180,360,0,Rural 195 | LP002070,Male,Yes,3+,Not Graduate,No,2654,1998,128,360,0,Rural 196 | LP002077,Male,Yes,1,Graduate,No,10000,2690,412,360,1,Semiurban 197 | LP002083,Male,No,0,Graduate,Yes,5833,0,116,360,1,Urban 198 | LP002090,Male,Yes,1,Graduate,No,4796,0,114,360,0,Semiurban 199 | LP002096,Male,Yes,0,Not Graduate,No,2000,1600,115,360,1,Rural 200 | LP002099,Male,Yes,2,Graduate,No,2540,700,104,360,0,Urban 201 | LP002102,Male,Yes,0,Graduate,Yes,1900,1442,88,360,1,Rural 202 | LP002105,Male,Yes,0,Graduate,Yes,8706,0,108,480,1,Rural 203 | LP002107,Male,Yes,3+,Not Graduate,No,2855,542,90,360,1,Urban 204 | LP002111,Male,Yes,,Graduate,No,3016,1300,100,360,,Urban 205 | LP002117,Female,Yes,0,Graduate,No,3159,2374,108,360,1,Semiurban 206 | LP002118,Female,No,0,Graduate,No,1937,1152,78,360,1,Semiurban 207 | LP002123,Male,Yes,0,Graduate,No,2613,2417,123,360,1,Semiurban 208 | LP002125,Male,Yes,1,Graduate,No,4960,2600,187,360,1,Semiurban 209 | LP002148,Male,Yes,1,Graduate,No,3074,1083,146,360,1,Semiurban 210 | LP002152,Female,No,0,Graduate,No,4213,0,80,360,1,Urban 211 | LP002165,,No,1,Not Graduate,No,2038,4027,100,360,1,Rural 212 | LP002167,Female,No,0,Graduate,No,2362,0,55,360,1,Urban 213 | LP002168,Male,No,0,Graduate,No,5333,2400,200,360,0,Rural 214 | LP002172,Male,Yes,3+,Graduate,Yes,5384,0,150,360,1,Semiurban 215 | LP002176,Male,No,0,Graduate,No,5708,0,150,360,1,Rural 216 | LP002183,Male,Yes,0,Not Graduate,No,3754,3719,118,,1,Rural 217 | LP002184,Male,Yes,0,Not Graduate,No,2914,2130,150,300,1,Urban 218 | LP002186,Male,Yes,0,Not Graduate,No,2747,2458,118,36,1,Semiurban 219 | LP002192,Male,Yes,0,Graduate,No,7830,2183,212,360,1,Rural 220 | LP002195,Male,Yes,1,Graduate,Yes,3507,3148,212,360,1,Rural 221 | LP002208,Male,Yes,1,Graduate,No,3747,2139,125,360,1,Urban 222 | LP002212,Male,Yes,0,Graduate,No,2166,2166,108,360,,Urban 223 | LP002240,Male,Yes,0,Not Graduate,No,3500,2168,149,360,1,Rural 224 | LP002245,Male,Yes,2,Not Graduate,No,2896,0,80,480,1,Urban 225 | LP002253,Female,No,1,Graduate,No,5062,0,152,300,1,Rural 226 | LP002256,Female,No,2,Graduate,Yes,5184,0,187,360,0,Semiurban 227 | LP002257,Female,No,0,Graduate,No,2545,0,74,360,1,Urban 228 | LP002264,Male,Yes,0,Graduate,No,2553,1768,102,360,1,Urban 229 | LP002270,Male,Yes,1,Graduate,No,3436,3809,100,360,1,Rural 230 | LP002279,Male,No,0,Graduate,No,2412,2755,130,360,1,Rural 231 | LP002286,Male,Yes,3+,Not Graduate,No,5180,0,125,360,0,Urban 232 | LP002294,Male,No,0,Graduate,No,14911,14507,130,360,1,Semiurban 233 | LP002298,,No,0,Graduate,Yes,2860,2988,138,360,1,Urban 234 | LP002306,Male,Yes,0,Graduate,No,1173,1594,28,180,1,Rural 235 | LP002310,Female,No,1,Graduate,No,7600,0,92,360,1,Semiurban 236 | LP002311,Female,Yes,0,Graduate,No,2157,1788,104,360,1,Urban 237 | LP002316,Male,No,0,Graduate,No,2231,2774,176,360,0,Urban 238 | LP002321,Female,No,0,Graduate,No,2274,5211,117,360,0,Semiurban 239 | LP002325,Male,Yes,2,Not Graduate,No,6166,13983,102,360,1,Rural 240 | LP002326,Male,Yes,2,Not Graduate,No,2513,1110,107,360,1,Semiurban 241 | LP002329,Male,No,0,Graduate,No,4333,0,66,480,1,Urban 242 | LP002333,Male,No,0,Not Graduate,No,3844,0,105,360,1,Urban 243 | LP002339,Male,Yes,0,Graduate,No,3887,1517,105,360,0,Semiurban 244 | LP002344,Male,Yes,0,Graduate,No,3510,828,105,360,1,Semiurban 245 | LP002346,Male,Yes,0,Graduate,,2539,1704,125,360,0,Rural 246 | LP002354,Female,No,0,Not Graduate,No,2107,0,64,360,1,Semiurban 247 | LP002355,,Yes,0,Graduate,No,3186,3145,150,180,0,Semiurban 248 | LP002358,Male,Yes,2,Graduate,Yes,5000,2166,150,360,1,Urban 249 | LP002360,Male,Yes,,Graduate,No,10000,0,,360,1,Urban 250 | LP002375,Male,Yes,0,Not Graduate,Yes,3943,0,64,360,1,Semiurban 251 | LP002376,Male,No,0,Graduate,No,2925,0,40,180,1,Rural 252 | LP002383,Male,Yes,3+,Graduate,No,3242,437,142,480,0,Urban 253 | LP002385,Male,Yes,,Graduate,No,3863,0,70,300,1,Semiurban 254 | LP002389,Female,No,1,Graduate,No,4028,0,131,360,1,Semiurban 255 | LP002394,Male,Yes,2,Graduate,No,4010,1025,120,360,1,Urban 256 | LP002397,Female,Yes,1,Graduate,No,3719,1585,114,360,1,Urban 257 | LP002399,Male,No,0,Graduate,,2858,0,123,360,0,Rural 258 | LP002400,Female,Yes,0,Graduate,No,3833,0,92,360,1,Rural 259 | LP002402,Male,Yes,0,Graduate,No,3333,4288,160,360,1,Urban 260 | LP002412,Male,Yes,0,Graduate,No,3007,3725,151,360,1,Rural 261 | LP002415,Female,No,1,Graduate,,1850,4583,81,360,,Rural 262 | LP002417,Male,Yes,3+,Not Graduate,No,2792,2619,171,360,1,Semiurban 263 | LP002420,Male,Yes,0,Graduate,No,2982,1550,110,360,1,Semiurban 264 | LP002425,Male,No,0,Graduate,No,3417,738,100,360,,Rural 265 | LP002433,Male,Yes,1,Graduate,No,18840,0,234,360,1,Rural 266 | LP002440,Male,Yes,2,Graduate,No,2995,1120,184,360,1,Rural 267 | LP002441,Male,No,,Graduate,No,3579,3308,138,360,,Semiurban 268 | LP002442,Female,Yes,1,Not Graduate,No,3835,1400,112,480,0,Urban 269 | LP002445,Female,No,1,Not Graduate,No,3854,3575,117,360,1,Rural 270 | LP002450,Male,Yes,2,Graduate,No,5833,750,49,360,0,Rural 271 | LP002471,Male,No,0,Graduate,No,3508,0,99,360,1,Rural 272 | LP002476,Female,Yes,3+,Not Graduate,No,1635,2444,99,360,1,Urban 273 | LP002482,Female,No,0,Graduate,Yes,3333,3916,212,360,1,Rural 274 | LP002485,Male,No,1,Graduate,No,24797,0,240,360,1,Semiurban 275 | LP002495,Male,Yes,2,Graduate,No,5667,440,130,360,0,Semiurban 276 | LP002496,Female,No,0,Graduate,No,3500,0,94,360,0,Semiurban 277 | LP002523,Male,Yes,3+,Graduate,No,2773,1497,108,360,1,Semiurban 278 | LP002542,Male,Yes,0,Graduate,,6500,0,144,360,1,Urban 279 | LP002550,Female,No,0,Graduate,No,5769,0,110,180,1,Semiurban 280 | LP002551,Male,Yes,3+,Not Graduate,,3634,910,176,360,0,Semiurban 281 | LP002553,,No,0,Graduate,No,29167,0,185,360,1,Semiurban 282 | LP002554,Male,No,0,Graduate,No,2166,2057,122,360,1,Semiurban 283 | LP002561,Male,Yes,0,Graduate,No,5000,0,126,360,1,Rural 284 | LP002566,Female,No,0,Graduate,No,5530,0,135,360,,Urban 285 | LP002568,Male,No,0,Not Graduate,No,9000,0,122,360,1,Rural 286 | LP002570,Female,Yes,2,Graduate,No,10000,11666,460,360,1,Urban 287 | LP002572,Male,Yes,1,Graduate,,8750,0,297,360,1,Urban 288 | LP002581,Male,Yes,0,Not Graduate,No,2157,2730,140,360,,Rural 289 | LP002584,Male,No,0,Graduate,,1972,4347,106,360,1,Rural 290 | LP002592,Male,No,0,Graduate,No,4983,0,141,360,1,Urban 291 | LP002593,Male,Yes,1,Graduate,No,8333,4000,,360,1,Urban 292 | LP002599,Male,Yes,0,Graduate,No,3667,2000,170,360,1,Semiurban 293 | LP002604,Male,Yes,2,Graduate,No,3166,2833,145,360,1,Urban 294 | LP002605,Male,No,0,Not Graduate,No,3271,0,90,360,1,Rural 295 | LP002609,Female,Yes,0,Graduate,No,2241,2000,88,360,0,Urban 296 | LP002610,Male,Yes,1,Not Graduate,,1792,2565,128,360,1,Urban 297 | LP002612,Female,Yes,0,Graduate,No,2666,0,84,480,1,Semiurban 298 | LP002614,,No,0,Graduate,No,6478,0,108,360,1,Semiurban 299 | LP002630,Male,No,0,Not Graduate,,3808,0,83,360,1,Rural 300 | LP002635,Female,Yes,2,Not Graduate,No,3729,0,117,360,1,Semiurban 301 | LP002639,Male,Yes,2,Graduate,No,4120,0,128,360,1,Rural 302 | LP002644,Male,Yes,1,Graduate,Yes,7500,0,75,360,1,Urban 303 | LP002651,Male,Yes,1,Graduate,,6300,0,125,360,0,Urban 304 | LP002654,Female,No,,Graduate,Yes,14987,0,177,360,1,Rural 305 | LP002657,,Yes,1,Not Graduate,Yes,570,2125,68,360,1,Rural 306 | LP002711,Male,Yes,0,Graduate,No,2600,700,96,360,1,Semiurban 307 | LP002712,Male,No,2,Not Graduate,No,2733,1083,180,360,,Semiurban 308 | LP002721,Male,Yes,2,Graduate,Yes,7500,0,183,360,1,Rural 309 | LP002735,Male,Yes,2,Not Graduate,No,3859,0,121,360,1,Rural 310 | LP002744,Male,Yes,1,Graduate,No,6825,0,162,360,1,Rural 311 | LP002745,Male,Yes,0,Graduate,No,3708,4700,132,360,1,Semiurban 312 | LP002746,Male,No,0,Graduate,No,5314,0,147,360,1,Urban 313 | LP002747,Female,No,3+,Graduate,No,2366,5272,153,360,0,Rural 314 | LP002754,Male,No,,Graduate,No,2066,2108,104,84,1,Urban 315 | LP002759,Male,Yes,2,Graduate,No,5000,0,149,360,1,Rural 316 | LP002760,Female,No,0,Graduate,No,3767,0,134,300,1,Urban 317 | LP002766,Female,Yes,0,Graduate,No,7859,879,165,180,1,Semiurban 318 | LP002769,Female,Yes,0,Graduate,No,4283,0,120,360,1,Rural 319 | LP002774,Male,Yes,0,Not Graduate,No,1700,2900,67,360,0,Urban 320 | LP002775,,No,0,Not Graduate,No,4768,0,125,360,1,Rural 321 | LP002781,Male,No,0,Graduate,No,3083,2738,120,360,1,Urban 322 | LP002782,Male,Yes,1,Graduate,No,2667,1542,148,360,1,Rural 323 | LP002786,Female,Yes,0,Not Graduate,No,1647,1762,181,360,1,Urban 324 | LP002790,Male,Yes,3+,Graduate,No,3400,0,80,120,1,Urban 325 | LP002791,Male,No,1,Graduate,,16000,5000,40,360,1,Semiurban 326 | LP002793,Male,Yes,0,Graduate,No,5333,0,90,360,1,Rural 327 | LP002802,Male,No,0,Graduate,No,2875,2416,95,6,0,Semiurban 328 | LP002803,Male,Yes,1,Not Graduate,,2600,618,122,360,1,Semiurban 329 | LP002805,Male,Yes,2,Graduate,No,5041,700,150,360,1,Urban 330 | LP002806,Male,Yes,3+,Graduate,Yes,6958,1411,150,360,1,Rural 331 | LP002816,Male,Yes,1,Graduate,No,3500,1658,104,360,,Semiurban 332 | LP002823,Male,Yes,0,Graduate,No,5509,0,143,360,1,Rural 333 | LP002825,Male,Yes,3+,Graduate,No,9699,0,300,360,1,Urban 334 | LP002826,Female,Yes,1,Not Graduate,No,3621,2717,171,360,1,Urban 335 | LP002843,Female,Yes,0,Graduate,No,4709,0,113,360,1,Semiurban 336 | LP002849,Male,Yes,0,Graduate,No,1516,1951,35,360,1,Semiurban 337 | LP002850,Male,No,2,Graduate,No,2400,0,46,360,1,Urban 338 | LP002853,Female,No,0,Not Graduate,No,3015,2000,145,360,,Urban 339 | LP002856,Male,Yes,0,Graduate,No,2292,1558,119,360,1,Urban 340 | LP002857,Male,Yes,1,Graduate,Yes,2360,3355,87,240,1,Rural 341 | LP002858,Female,No,0,Graduate,No,4333,2333,162,360,0,Rural 342 | LP002860,Male,Yes,0,Graduate,Yes,2623,4831,122,180,1,Semiurban 343 | LP002867,Male,No,0,Graduate,Yes,3972,4275,187,360,1,Rural 344 | LP002869,Male,Yes,3+,Not Graduate,No,3522,0,81,180,1,Rural 345 | LP002870,Male,Yes,1,Graduate,No,4700,0,80,360,1,Urban 346 | LP002876,Male,No,0,Graduate,No,6858,0,176,360,1,Rural 347 | LP002878,Male,Yes,3+,Graduate,No,8334,0,260,360,1,Urban 348 | LP002879,Male,Yes,0,Graduate,No,3391,1966,133,360,0,Rural 349 | LP002885,Male,No,0,Not Graduate,No,2868,0,70,360,1,Urban 350 | LP002890,Male,Yes,2,Not Graduate,No,3418,1380,135,360,1,Urban 351 | LP002891,Male,Yes,0,Graduate,Yes,2500,296,137,300,1,Rural 352 | LP002899,Male,Yes,2,Graduate,No,8667,0,254,360,1,Rural 353 | LP002901,Male,No,0,Graduate,No,2283,15000,106,360,,Rural 354 | LP002907,Male,Yes,0,Graduate,No,5817,910,109,360,1,Urban 355 | LP002920,Male,Yes,0,Graduate,No,5119,3769,120,360,1,Rural 356 | LP002921,Male,Yes,3+,Not Graduate,No,5316,187,158,180,0,Semiurban 357 | LP002932,Male,Yes,3+,Graduate,No,7603,1213,197,360,1,Urban 358 | LP002935,Male,Yes,1,Graduate,No,3791,1936,85,360,1,Urban 359 | LP002952,Male,No,0,Graduate,No,2500,0,60,360,1,Urban 360 | LP002954,Male,Yes,2,Not Graduate,No,3132,0,76,360,,Rural 361 | LP002962,Male,No,0,Graduate,No,4000,2667,152,360,1,Semiurban 362 | LP002965,Female,Yes,0,Graduate,No,8550,4255,96,360,,Urban 363 | LP002969,Male,Yes,1,Graduate,No,2269,2167,99,360,1,Semiurban 364 | LP002971,Male,Yes,3+,Not Graduate,Yes,4009,1777,113,360,1,Urban 365 | LP002975,Male,Yes,0,Graduate,No,4158,709,115,360,1,Urban 366 | LP002980,Male,No,0,Graduate,No,3250,1993,126,360,,Semiurban 367 | LP002986,Male,Yes,0,Graduate,No,5000,2393,158,360,1,Rural 368 | LP002989,Male,No,0,Graduate,Yes,9200,0,98,180,1,Rural 369 | -------------------------------------------------------------------------------- /Data Visualisation with Facets /Loan prediction dataset/train.csv: -------------------------------------------------------------------------------- 1 | Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status 2 | LP001002,Male,No,0,Graduate,No,5849,0,,360,1,Urban,Y 3 | LP001003,Male,Yes,1,Graduate,No,4583,1508,128,360,1,Rural,N 4 | LP001005,Male,Yes,0,Graduate,Yes,3000,0,66,360,1,Urban,Y 5 | LP001006,Male,Yes,0,Not Graduate,No,2583,2358,120,360,1,Urban,Y 6 | LP001008,Male,No,0,Graduate,No,6000,0,141,360,1,Urban,Y 7 | LP001011,Male,Yes,2,Graduate,Yes,5417,4196,267,360,1,Urban,Y 8 | LP001013,Male,Yes,0,Not Graduate,No,2333,1516,95,360,1,Urban,Y 9 | LP001014,Male,Yes,3+,Graduate,No,3036,2504,158,360,0,Semiurban,N 10 | LP001018,Male,Yes,2,Graduate,No,4006,1526,168,360,1,Urban,Y 11 | LP001020,Male,Yes,1,Graduate,No,12841,10968,349,360,1,Semiurban,N 12 | LP001024,Male,Yes,2,Graduate,No,3200,700,70,360,1,Urban,Y 13 | LP001027,Male,Yes,2,Graduate,,2500,1840,109,360,1,Urban,Y 14 | LP001028,Male,Yes,2,Graduate,No,3073,8106,200,360,1,Urban,Y 15 | LP001029,Male,No,0,Graduate,No,1853,2840,114,360,1,Rural,N 16 | LP001030,Male,Yes,2,Graduate,No,1299,1086,17,120,1,Urban,Y 17 | LP001032,Male,No,0,Graduate,No,4950,0,125,360,1,Urban,Y 18 | LP001034,Male,No,1,Not Graduate,No,3596,0,100,240,,Urban,Y 19 | LP001036,Female,No,0,Graduate,No,3510,0,76,360,0,Urban,N 20 | LP001038,Male,Yes,0,Not Graduate,No,4887,0,133,360,1,Rural,N 21 | LP001041,Male,Yes,0,Graduate,,2600,3500,115,,1,Urban,Y 22 | LP001043,Male,Yes,0,Not Graduate,No,7660,0,104,360,0,Urban,N 23 | LP001046,Male,Yes,1,Graduate,No,5955,5625,315,360,1,Urban,Y 24 | LP001047,Male,Yes,0,Not Graduate,No,2600,1911,116,360,0,Semiurban,N 25 | LP001050,,Yes,2,Not Graduate,No,3365,1917,112,360,0,Rural,N 26 | LP001052,Male,Yes,1,Graduate,,3717,2925,151,360,,Semiurban,N 27 | LP001066,Male,Yes,0,Graduate,Yes,9560,0,191,360,1,Semiurban,Y 28 | LP001068,Male,Yes,0,Graduate,No,2799,2253,122,360,1,Semiurban,Y 29 | LP001073,Male,Yes,2,Not Graduate,No,4226,1040,110,360,1,Urban,Y 30 | LP001086,Male,No,0,Not Graduate,No,1442,0,35,360,1,Urban,N 31 | LP001087,Female,No,2,Graduate,,3750,2083,120,360,1,Semiurban,Y 32 | LP001091,Male,Yes,1,Graduate,,4166,3369,201,360,,Urban,N 33 | LP001095,Male,No,0,Graduate,No,3167,0,74,360,1,Urban,N 34 | LP001097,Male,No,1,Graduate,Yes,4692,0,106,360,1,Rural,N 35 | LP001098,Male,Yes,0,Graduate,No,3500,1667,114,360,1,Semiurban,Y 36 | LP001100,Male,No,3+,Graduate,No,12500,3000,320,360,1,Rural,N 37 | LP001106,Male,Yes,0,Graduate,No,2275,2067,,360,1,Urban,Y 38 | LP001109,Male,Yes,0,Graduate,No,1828,1330,100,,0,Urban,N 39 | LP001112,Female,Yes,0,Graduate,No,3667,1459,144,360,1,Semiurban,Y 40 | LP001114,Male,No,0,Graduate,No,4166,7210,184,360,1,Urban,Y 41 | LP001116,Male,No,0,Not Graduate,No,3748,1668,110,360,1,Semiurban,Y 42 | LP001119,Male,No,0,Graduate,No,3600,0,80,360,1,Urban,N 43 | LP001120,Male,No,0,Graduate,No,1800,1213,47,360,1,Urban,Y 44 | LP001123,Male,Yes,0,Graduate,No,2400,0,75,360,,Urban,Y 45 | LP001131,Male,Yes,0,Graduate,No,3941,2336,134,360,1,Semiurban,Y 46 | LP001136,Male,Yes,0,Not Graduate,Yes,4695,0,96,,1,Urban,Y 47 | LP001137,Female,No,0,Graduate,No,3410,0,88,,1,Urban,Y 48 | LP001138,Male,Yes,1,Graduate,No,5649,0,44,360,1,Urban,Y 49 | LP001144,Male,Yes,0,Graduate,No,5821,0,144,360,1,Urban,Y 50 | LP001146,Female,Yes,0,Graduate,No,2645,3440,120,360,0,Urban,N 51 | LP001151,Female,No,0,Graduate,No,4000,2275,144,360,1,Semiurban,Y 52 | LP001155,Female,Yes,0,Not Graduate,No,1928,1644,100,360,1,Semiurban,Y 53 | LP001157,Female,No,0,Graduate,No,3086,0,120,360,1,Semiurban,Y 54 | LP001164,Female,No,0,Graduate,No,4230,0,112,360,1,Semiurban,N 55 | LP001179,Male,Yes,2,Graduate,No,4616,0,134,360,1,Urban,N 56 | LP001186,Female,Yes,1,Graduate,Yes,11500,0,286,360,0,Urban,N 57 | LP001194,Male,Yes,2,Graduate,No,2708,1167,97,360,1,Semiurban,Y 58 | LP001195,Male,Yes,0,Graduate,No,2132,1591,96,360,1,Semiurban,Y 59 | LP001197,Male,Yes,0,Graduate,No,3366,2200,135,360,1,Rural,N 60 | LP001198,Male,Yes,1,Graduate,No,8080,2250,180,360,1,Urban,Y 61 | LP001199,Male,Yes,2,Not Graduate,No,3357,2859,144,360,1,Urban,Y 62 | LP001205,Male,Yes,0,Graduate,No,2500,3796,120,360,1,Urban,Y 63 | LP001206,Male,Yes,3+,Graduate,No,3029,0,99,360,1,Urban,Y 64 | LP001207,Male,Yes,0,Not Graduate,Yes,2609,3449,165,180,0,Rural,N 65 | LP001213,Male,Yes,1,Graduate,No,4945,0,,360,0,Rural,N 66 | LP001222,Female,No,0,Graduate,No,4166,0,116,360,0,Semiurban,N 67 | LP001225,Male,Yes,0,Graduate,No,5726,4595,258,360,1,Semiurban,N 68 | LP001228,Male,No,0,Not Graduate,No,3200,2254,126,180,0,Urban,N 69 | LP001233,Male,Yes,1,Graduate,No,10750,0,312,360,1,Urban,Y 70 | LP001238,Male,Yes,3+,Not Graduate,Yes,7100,0,125,60,1,Urban,Y 71 | LP001241,Female,No,0,Graduate,No,4300,0,136,360,0,Semiurban,N 72 | LP001243,Male,Yes,0,Graduate,No,3208,3066,172,360,1,Urban,Y 73 | LP001245,Male,Yes,2,Not Graduate,Yes,1875,1875,97,360,1,Semiurban,Y 74 | LP001248,Male,No,0,Graduate,No,3500,0,81,300,1,Semiurban,Y 75 | LP001250,Male,Yes,3+,Not Graduate,No,4755,0,95,,0,Semiurban,N 76 | LP001253,Male,Yes,3+,Graduate,Yes,5266,1774,187,360,1,Semiurban,Y 77 | LP001255,Male,No,0,Graduate,No,3750,0,113,480,1,Urban,N 78 | LP001256,Male,No,0,Graduate,No,3750,4750,176,360,1,Urban,N 79 | LP001259,Male,Yes,1,Graduate,Yes,1000,3022,110,360,1,Urban,N 80 | LP001263,Male,Yes,3+,Graduate,No,3167,4000,180,300,0,Semiurban,N 81 | LP001264,Male,Yes,3+,Not Graduate,Yes,3333,2166,130,360,,Semiurban,Y 82 | LP001265,Female,No,0,Graduate,No,3846,0,111,360,1,Semiurban,Y 83 | LP001266,Male,Yes,1,Graduate,Yes,2395,0,,360,1,Semiurban,Y 84 | LP001267,Female,Yes,2,Graduate,No,1378,1881,167,360,1,Urban,N 85 | LP001273,Male,Yes,0,Graduate,No,6000,2250,265,360,,Semiurban,N 86 | LP001275,Male,Yes,1,Graduate,No,3988,0,50,240,1,Urban,Y 87 | LP001279,Male,No,0,Graduate,No,2366,2531,136,360,1,Semiurban,Y 88 | LP001280,Male,Yes,2,Not Graduate,No,3333,2000,99,360,,Semiurban,Y 89 | LP001282,Male,Yes,0,Graduate,No,2500,2118,104,360,1,Semiurban,Y 90 | LP001289,Male,No,0,Graduate,No,8566,0,210,360,1,Urban,Y 91 | LP001310,Male,Yes,0,Graduate,No,5695,4167,175,360,1,Semiurban,Y 92 | LP001316,Male,Yes,0,Graduate,No,2958,2900,131,360,1,Semiurban,Y 93 | LP001318,Male,Yes,2,Graduate,No,6250,5654,188,180,1,Semiurban,Y 94 | LP001319,Male,Yes,2,Not Graduate,No,3273,1820,81,360,1,Urban,Y 95 | LP001322,Male,No,0,Graduate,No,4133,0,122,360,1,Semiurban,Y 96 | LP001325,Male,No,0,Not Graduate,No,3620,0,25,120,1,Semiurban,Y 97 | LP001326,Male,No,0,Graduate,,6782,0,,360,,Urban,N 98 | LP001327,Female,Yes,0,Graduate,No,2484,2302,137,360,1,Semiurban,Y 99 | LP001333,Male,Yes,0,Graduate,No,1977,997,50,360,1,Semiurban,Y 100 | LP001334,Male,Yes,0,Not Graduate,No,4188,0,115,180,1,Semiurban,Y 101 | LP001343,Male,Yes,0,Graduate,No,1759,3541,131,360,1,Semiurban,Y 102 | LP001345,Male,Yes,2,Not Graduate,No,4288,3263,133,180,1,Urban,Y 103 | LP001349,Male,No,0,Graduate,No,4843,3806,151,360,1,Semiurban,Y 104 | LP001350,Male,Yes,,Graduate,No,13650,0,,360,1,Urban,Y 105 | LP001356,Male,Yes,0,Graduate,No,4652,3583,,360,1,Semiurban,Y 106 | LP001357,Male,,,Graduate,No,3816,754,160,360,1,Urban,Y 107 | LP001367,Male,Yes,1,Graduate,No,3052,1030,100,360,1,Urban,Y 108 | LP001369,Male,Yes,2,Graduate,No,11417,1126,225,360,1,Urban,Y 109 | LP001370,Male,No,0,Not Graduate,,7333,0,120,360,1,Rural,N 110 | LP001379,Male,Yes,2,Graduate,No,3800,3600,216,360,0,Urban,N 111 | LP001384,Male,Yes,3+,Not Graduate,No,2071,754,94,480,1,Semiurban,Y 112 | LP001385,Male,No,0,Graduate,No,5316,0,136,360,1,Urban,Y 113 | LP001387,Female,Yes,0,Graduate,,2929,2333,139,360,1,Semiurban,Y 114 | LP001391,Male,Yes,0,Not Graduate,No,3572,4114,152,,0,Rural,N 115 | LP001392,Female,No,1,Graduate,Yes,7451,0,,360,1,Semiurban,Y 116 | LP001398,Male,No,0,Graduate,,5050,0,118,360,1,Semiurban,Y 117 | LP001401,Male,Yes,1,Graduate,No,14583,0,185,180,1,Rural,Y 118 | LP001404,Female,Yes,0,Graduate,No,3167,2283,154,360,1,Semiurban,Y 119 | LP001405,Male,Yes,1,Graduate,No,2214,1398,85,360,,Urban,Y 120 | LP001421,Male,Yes,0,Graduate,No,5568,2142,175,360,1,Rural,N 121 | LP001422,Female,No,0,Graduate,No,10408,0,259,360,1,Urban,Y 122 | LP001426,Male,Yes,,Graduate,No,5667,2667,180,360,1,Rural,Y 123 | LP001430,Female,No,0,Graduate,No,4166,0,44,360,1,Semiurban,Y 124 | LP001431,Female,No,0,Graduate,No,2137,8980,137,360,0,Semiurban,Y 125 | LP001432,Male,Yes,2,Graduate,No,2957,0,81,360,1,Semiurban,Y 126 | LP001439,Male,Yes,0,Not Graduate,No,4300,2014,194,360,1,Rural,Y 127 | LP001443,Female,No,0,Graduate,No,3692,0,93,360,,Rural,Y 128 | LP001448,,Yes,3+,Graduate,No,23803,0,370,360,1,Rural,Y 129 | LP001449,Male,No,0,Graduate,No,3865,1640,,360,1,Rural,Y 130 | LP001451,Male,Yes,1,Graduate,Yes,10513,3850,160,180,0,Urban,N 131 | LP001465,Male,Yes,0,Graduate,No,6080,2569,182,360,,Rural,N 132 | LP001469,Male,No,0,Graduate,Yes,20166,0,650,480,,Urban,Y 133 | LP001473,Male,No,0,Graduate,No,2014,1929,74,360,1,Urban,Y 134 | LP001478,Male,No,0,Graduate,No,2718,0,70,360,1,Semiurban,Y 135 | LP001482,Male,Yes,0,Graduate,Yes,3459,0,25,120,1,Semiurban,Y 136 | LP001487,Male,No,0,Graduate,No,4895,0,102,360,1,Semiurban,Y 137 | LP001488,Male,Yes,3+,Graduate,No,4000,7750,290,360,1,Semiurban,N 138 | LP001489,Female,Yes,0,Graduate,No,4583,0,84,360,1,Rural,N 139 | LP001491,Male,Yes,2,Graduate,Yes,3316,3500,88,360,1,Urban,Y 140 | LP001492,Male,No,0,Graduate,No,14999,0,242,360,0,Semiurban,N 141 | LP001493,Male,Yes,2,Not Graduate,No,4200,1430,129,360,1,Rural,N 142 | LP001497,Male,Yes,2,Graduate,No,5042,2083,185,360,1,Rural,N 143 | LP001498,Male,No,0,Graduate,No,5417,0,168,360,1,Urban,Y 144 | LP001504,Male,No,0,Graduate,Yes,6950,0,175,180,1,Semiurban,Y 145 | LP001507,Male,Yes,0,Graduate,No,2698,2034,122,360,1,Semiurban,Y 146 | LP001508,Male,Yes,2,Graduate,No,11757,0,187,180,1,Urban,Y 147 | LP001514,Female,Yes,0,Graduate,No,2330,4486,100,360,1,Semiurban,Y 148 | LP001516,Female,Yes,2,Graduate,No,14866,0,70,360,1,Urban,Y 149 | LP001518,Male,Yes,1,Graduate,No,1538,1425,30,360,1,Urban,Y 150 | LP001519,Female,No,0,Graduate,No,10000,1666,225,360,1,Rural,N 151 | LP001520,Male,Yes,0,Graduate,No,4860,830,125,360,1,Semiurban,Y 152 | LP001528,Male,No,0,Graduate,No,6277,0,118,360,0,Rural,N 153 | LP001529,Male,Yes,0,Graduate,Yes,2577,3750,152,360,1,Rural,Y 154 | LP001531,Male,No,0,Graduate,No,9166,0,244,360,1,Urban,N 155 | LP001532,Male,Yes,2,Not Graduate,No,2281,0,113,360,1,Rural,N 156 | LP001535,Male,No,0,Graduate,No,3254,0,50,360,1,Urban,Y 157 | LP001536,Male,Yes,3+,Graduate,No,39999,0,600,180,0,Semiurban,Y 158 | LP001541,Male,Yes,1,Graduate,No,6000,0,160,360,,Rural,Y 159 | LP001543,Male,Yes,1,Graduate,No,9538,0,187,360,1,Urban,Y 160 | LP001546,Male,No,0,Graduate,,2980,2083,120,360,1,Rural,Y 161 | LP001552,Male,Yes,0,Graduate,No,4583,5625,255,360,1,Semiurban,Y 162 | LP001560,Male,Yes,0,Not Graduate,No,1863,1041,98,360,1,Semiurban,Y 163 | LP001562,Male,Yes,0,Graduate,No,7933,0,275,360,1,Urban,N 164 | LP001565,Male,Yes,1,Graduate,No,3089,1280,121,360,0,Semiurban,N 165 | LP001570,Male,Yes,2,Graduate,No,4167,1447,158,360,1,Rural,Y 166 | LP001572,Male,Yes,0,Graduate,No,9323,0,75,180,1,Urban,Y 167 | LP001574,Male,Yes,0,Graduate,No,3707,3166,182,,1,Rural,Y 168 | LP001577,Female,Yes,0,Graduate,No,4583,0,112,360,1,Rural,N 169 | LP001578,Male,Yes,0,Graduate,No,2439,3333,129,360,1,Rural,Y 170 | LP001579,Male,No,0,Graduate,No,2237,0,63,480,0,Semiurban,N 171 | LP001580,Male,Yes,2,Graduate,No,8000,0,200,360,1,Semiurban,Y 172 | LP001581,Male,Yes,0,Not Graduate,,1820,1769,95,360,1,Rural,Y 173 | LP001585,,Yes,3+,Graduate,No,51763,0,700,300,1,Urban,Y 174 | LP001586,Male,Yes,3+,Not Graduate,No,3522,0,81,180,1,Rural,N 175 | LP001594,Male,Yes,0,Graduate,No,5708,5625,187,360,1,Semiurban,Y 176 | LP001603,Male,Yes,0,Not Graduate,Yes,4344,736,87,360,1,Semiurban,N 177 | LP001606,Male,Yes,0,Graduate,No,3497,1964,116,360,1,Rural,Y 178 | LP001608,Male,Yes,2,Graduate,No,2045,1619,101,360,1,Rural,Y 179 | LP001610,Male,Yes,3+,Graduate,No,5516,11300,495,360,0,Semiurban,N 180 | LP001616,Male,Yes,1,Graduate,No,3750,0,116,360,1,Semiurban,Y 181 | LP001630,Male,No,0,Not Graduate,No,2333,1451,102,480,0,Urban,N 182 | LP001633,Male,Yes,1,Graduate,No,6400,7250,180,360,0,Urban,N 183 | LP001634,Male,No,0,Graduate,No,1916,5063,67,360,,Rural,N 184 | LP001636,Male,Yes,0,Graduate,No,4600,0,73,180,1,Semiurban,Y 185 | LP001637,Male,Yes,1,Graduate,No,33846,0,260,360,1,Semiurban,N 186 | LP001639,Female,Yes,0,Graduate,No,3625,0,108,360,1,Semiurban,Y 187 | LP001640,Male,Yes,0,Graduate,Yes,39147,4750,120,360,1,Semiurban,Y 188 | LP001641,Male,Yes,1,Graduate,Yes,2178,0,66,300,0,Rural,N 189 | LP001643,Male,Yes,0,Graduate,No,2383,2138,58,360,,Rural,Y 190 | LP001644,,Yes,0,Graduate,Yes,674,5296,168,360,1,Rural,Y 191 | LP001647,Male,Yes,0,Graduate,No,9328,0,188,180,1,Rural,Y 192 | LP001653,Male,No,0,Not Graduate,No,4885,0,48,360,1,Rural,Y 193 | LP001656,Male,No,0,Graduate,No,12000,0,164,360,1,Semiurban,N 194 | LP001657,Male,Yes,0,Not Graduate,No,6033,0,160,360,1,Urban,N 195 | LP001658,Male,No,0,Graduate,No,3858,0,76,360,1,Semiurban,Y 196 | LP001664,Male,No,0,Graduate,No,4191,0,120,360,1,Rural,Y 197 | LP001665,Male,Yes,1,Graduate,No,3125,2583,170,360,1,Semiurban,N 198 | LP001666,Male,No,0,Graduate,No,8333,3750,187,360,1,Rural,Y 199 | LP001669,Female,No,0,Not Graduate,No,1907,2365,120,,1,Urban,Y 200 | LP001671,Female,Yes,0,Graduate,No,3416,2816,113,360,,Semiurban,Y 201 | LP001673,Male,No,0,Graduate,Yes,11000,0,83,360,1,Urban,N 202 | LP001674,Male,Yes,1,Not Graduate,No,2600,2500,90,360,1,Semiurban,Y 203 | LP001677,Male,No,2,Graduate,No,4923,0,166,360,0,Semiurban,Y 204 | LP001682,Male,Yes,3+,Not Graduate,No,3992,0,,180,1,Urban,N 205 | LP001688,Male,Yes,1,Not Graduate,No,3500,1083,135,360,1,Urban,Y 206 | LP001691,Male,Yes,2,Not Graduate,No,3917,0,124,360,1,Semiurban,Y 207 | LP001692,Female,No,0,Not Graduate,No,4408,0,120,360,1,Semiurban,Y 208 | LP001693,Female,No,0,Graduate,No,3244,0,80,360,1,Urban,Y 209 | LP001698,Male,No,0,Not Graduate,No,3975,2531,55,360,1,Rural,Y 210 | LP001699,Male,No,0,Graduate,No,2479,0,59,360,1,Urban,Y 211 | LP001702,Male,No,0,Graduate,No,3418,0,127,360,1,Semiurban,N 212 | LP001708,Female,No,0,Graduate,No,10000,0,214,360,1,Semiurban,N 213 | LP001711,Male,Yes,3+,Graduate,No,3430,1250,128,360,0,Semiurban,N 214 | LP001713,Male,Yes,1,Graduate,Yes,7787,0,240,360,1,Urban,Y 215 | LP001715,Male,Yes,3+,Not Graduate,Yes,5703,0,130,360,1,Rural,Y 216 | LP001716,Male,Yes,0,Graduate,No,3173,3021,137,360,1,Urban,Y 217 | LP001720,Male,Yes,3+,Not Graduate,No,3850,983,100,360,1,Semiurban,Y 218 | LP001722,Male,Yes,0,Graduate,No,150,1800,135,360,1,Rural,N 219 | LP001726,Male,Yes,0,Graduate,No,3727,1775,131,360,1,Semiurban,Y 220 | LP001732,Male,Yes,2,Graduate,,5000,0,72,360,0,Semiurban,N 221 | LP001734,Female,Yes,2,Graduate,No,4283,2383,127,360,,Semiurban,Y 222 | LP001736,Male,Yes,0,Graduate,No,2221,0,60,360,0,Urban,N 223 | LP001743,Male,Yes,2,Graduate,No,4009,1717,116,360,1,Semiurban,Y 224 | LP001744,Male,No,0,Graduate,No,2971,2791,144,360,1,Semiurban,Y 225 | LP001749,Male,Yes,0,Graduate,No,7578,1010,175,,1,Semiurban,Y 226 | LP001750,Male,Yes,0,Graduate,No,6250,0,128,360,1,Semiurban,Y 227 | LP001751,Male,Yes,0,Graduate,No,3250,0,170,360,1,Rural,N 228 | LP001754,Male,Yes,,Not Graduate,Yes,4735,0,138,360,1,Urban,N 229 | LP001758,Male,Yes,2,Graduate,No,6250,1695,210,360,1,Semiurban,Y 230 | LP001760,Male,,,Graduate,No,4758,0,158,480,1,Semiurban,Y 231 | LP001761,Male,No,0,Graduate,Yes,6400,0,200,360,1,Rural,Y 232 | LP001765,Male,Yes,1,Graduate,No,2491,2054,104,360,1,Semiurban,Y 233 | LP001768,Male,Yes,0,Graduate,,3716,0,42,180,1,Rural,Y 234 | LP001770,Male,No,0,Not Graduate,No,3189,2598,120,,1,Rural,Y 235 | LP001776,Female,No,0,Graduate,No,8333,0,280,360,1,Semiurban,Y 236 | LP001778,Male,Yes,1,Graduate,No,3155,1779,140,360,1,Semiurban,Y 237 | LP001784,Male,Yes,1,Graduate,No,5500,1260,170,360,1,Rural,Y 238 | LP001786,Male,Yes,0,Graduate,,5746,0,255,360,,Urban,N 239 | LP001788,Female,No,0,Graduate,Yes,3463,0,122,360,,Urban,Y 240 | LP001790,Female,No,1,Graduate,No,3812,0,112,360,1,Rural,Y 241 | LP001792,Male,Yes,1,Graduate,No,3315,0,96,360,1,Semiurban,Y 242 | LP001798,Male,Yes,2,Graduate,No,5819,5000,120,360,1,Rural,Y 243 | LP001800,Male,Yes,1,Not Graduate,No,2510,1983,140,180,1,Urban,N 244 | LP001806,Male,No,0,Graduate,No,2965,5701,155,60,1,Urban,Y 245 | LP001807,Male,Yes,2,Graduate,Yes,6250,1300,108,360,1,Rural,Y 246 | LP001811,Male,Yes,0,Not Graduate,No,3406,4417,123,360,1,Semiurban,Y 247 | LP001813,Male,No,0,Graduate,Yes,6050,4333,120,180,1,Urban,N 248 | LP001814,Male,Yes,2,Graduate,No,9703,0,112,360,1,Urban,Y 249 | LP001819,Male,Yes,1,Not Graduate,No,6608,0,137,180,1,Urban,Y 250 | LP001824,Male,Yes,1,Graduate,No,2882,1843,123,480,1,Semiurban,Y 251 | LP001825,Male,Yes,0,Graduate,No,1809,1868,90,360,1,Urban,Y 252 | LP001835,Male,Yes,0,Not Graduate,No,1668,3890,201,360,0,Semiurban,N 253 | LP001836,Female,No,2,Graduate,No,3427,0,138,360,1,Urban,N 254 | LP001841,Male,No,0,Not Graduate,Yes,2583,2167,104,360,1,Rural,Y 255 | LP001843,Male,Yes,1,Not Graduate,No,2661,7101,279,180,1,Semiurban,Y 256 | LP001844,Male,No,0,Graduate,Yes,16250,0,192,360,0,Urban,N 257 | LP001846,Female,No,3+,Graduate,No,3083,0,255,360,1,Rural,Y 258 | LP001849,Male,No,0,Not Graduate,No,6045,0,115,360,0,Rural,N 259 | LP001854,Male,Yes,3+,Graduate,No,5250,0,94,360,1,Urban,N 260 | LP001859,Male,Yes,0,Graduate,No,14683,2100,304,360,1,Rural,N 261 | LP001864,Male,Yes,3+,Not Graduate,No,4931,0,128,360,,Semiurban,N 262 | LP001865,Male,Yes,1,Graduate,No,6083,4250,330,360,,Urban,Y 263 | LP001868,Male,No,0,Graduate,No,2060,2209,134,360,1,Semiurban,Y 264 | LP001870,Female,No,1,Graduate,No,3481,0,155,36,1,Semiurban,N 265 | LP001871,Female,No,0,Graduate,No,7200,0,120,360,1,Rural,Y 266 | LP001872,Male,No,0,Graduate,Yes,5166,0,128,360,1,Semiurban,Y 267 | LP001875,Male,No,0,Graduate,No,4095,3447,151,360,1,Rural,Y 268 | LP001877,Male,Yes,2,Graduate,No,4708,1387,150,360,1,Semiurban,Y 269 | LP001882,Male,Yes,3+,Graduate,No,4333,1811,160,360,0,Urban,Y 270 | LP001883,Female,No,0,Graduate,,3418,0,135,360,1,Rural,N 271 | LP001884,Female,No,1,Graduate,No,2876,1560,90,360,1,Urban,Y 272 | LP001888,Female,No,0,Graduate,No,3237,0,30,360,1,Urban,Y 273 | LP001891,Male,Yes,0,Graduate,No,11146,0,136,360,1,Urban,Y 274 | LP001892,Male,No,0,Graduate,No,2833,1857,126,360,1,Rural,Y 275 | LP001894,Male,Yes,0,Graduate,No,2620,2223,150,360,1,Semiurban,Y 276 | LP001896,Male,Yes,2,Graduate,No,3900,0,90,360,1,Semiurban,Y 277 | LP001900,Male,Yes,1,Graduate,No,2750,1842,115,360,1,Semiurban,Y 278 | LP001903,Male,Yes,0,Graduate,No,3993,3274,207,360,1,Semiurban,Y 279 | LP001904,Male,Yes,0,Graduate,No,3103,1300,80,360,1,Urban,Y 280 | LP001907,Male,Yes,0,Graduate,No,14583,0,436,360,1,Semiurban,Y 281 | LP001908,Female,Yes,0,Not Graduate,No,4100,0,124,360,,Rural,Y 282 | LP001910,Male,No,1,Not Graduate,Yes,4053,2426,158,360,0,Urban,N 283 | LP001914,Male,Yes,0,Graduate,No,3927,800,112,360,1,Semiurban,Y 284 | LP001915,Male,Yes,2,Graduate,No,2301,985.7999878,78,180,1,Urban,Y 285 | LP001917,Female,No,0,Graduate,No,1811,1666,54,360,1,Urban,Y 286 | LP001922,Male,Yes,0,Graduate,No,20667,0,,360,1,Rural,N 287 | LP001924,Male,No,0,Graduate,No,3158,3053,89,360,1,Rural,Y 288 | LP001925,Female,No,0,Graduate,Yes,2600,1717,99,300,1,Semiurban,N 289 | LP001926,Male,Yes,0,Graduate,No,3704,2000,120,360,1,Rural,Y 290 | LP001931,Female,No,0,Graduate,No,4124,0,115,360,1,Semiurban,Y 291 | LP001935,Male,No,0,Graduate,No,9508,0,187,360,1,Rural,Y 292 | LP001936,Male,Yes,0,Graduate,No,3075,2416,139,360,1,Rural,Y 293 | LP001938,Male,Yes,2,Graduate,No,4400,0,127,360,0,Semiurban,N 294 | LP001940,Male,Yes,2,Graduate,No,3153,1560,134,360,1,Urban,Y 295 | LP001945,Female,No,,Graduate,No,5417,0,143,480,0,Urban,N 296 | LP001947,Male,Yes,0,Graduate,No,2383,3334,172,360,1,Semiurban,Y 297 | LP001949,Male,Yes,3+,Graduate,,4416,1250,110,360,1,Urban,Y 298 | LP001953,Male,Yes,1,Graduate,No,6875,0,200,360,1,Semiurban,Y 299 | LP001954,Female,Yes,1,Graduate,No,4666,0,135,360,1,Urban,Y 300 | LP001955,Female,No,0,Graduate,No,5000,2541,151,480,1,Rural,N 301 | LP001963,Male,Yes,1,Graduate,No,2014,2925,113,360,1,Urban,N 302 | LP001964,Male,Yes,0,Not Graduate,No,1800,2934,93,360,0,Urban,N 303 | LP001972,Male,Yes,,Not Graduate,No,2875,1750,105,360,1,Semiurban,Y 304 | LP001974,Female,No,0,Graduate,No,5000,0,132,360,1,Rural,Y 305 | LP001977,Male,Yes,1,Graduate,No,1625,1803,96,360,1,Urban,Y 306 | LP001978,Male,No,0,Graduate,No,4000,2500,140,360,1,Rural,Y 307 | LP001990,Male,No,0,Not Graduate,No,2000,0,,360,1,Urban,N 308 | LP001993,Female,No,0,Graduate,No,3762,1666,135,360,1,Rural,Y 309 | LP001994,Female,No,0,Graduate,No,2400,1863,104,360,0,Urban,N 310 | LP001996,Male,No,0,Graduate,No,20233,0,480,360,1,Rural,N 311 | LP001998,Male,Yes,2,Not Graduate,No,7667,0,185,360,,Rural,Y 312 | LP002002,Female,No,0,Graduate,No,2917,0,84,360,1,Semiurban,Y 313 | LP002004,Male,No,0,Not Graduate,No,2927,2405,111,360,1,Semiurban,Y 314 | LP002006,Female,No,0,Graduate,No,2507,0,56,360,1,Rural,Y 315 | LP002008,Male,Yes,2,Graduate,Yes,5746,0,144,84,,Rural,Y 316 | LP002024,,Yes,0,Graduate,No,2473,1843,159,360,1,Rural,N 317 | LP002031,Male,Yes,1,Not Graduate,No,3399,1640,111,180,1,Urban,Y 318 | LP002035,Male,Yes,2,Graduate,No,3717,0,120,360,1,Semiurban,Y 319 | LP002036,Male,Yes,0,Graduate,No,2058,2134,88,360,,Urban,Y 320 | LP002043,Female,No,1,Graduate,No,3541,0,112,360,,Semiurban,Y 321 | LP002050,Male,Yes,1,Graduate,Yes,10000,0,155,360,1,Rural,N 322 | LP002051,Male,Yes,0,Graduate,No,2400,2167,115,360,1,Semiurban,Y 323 | LP002053,Male,Yes,3+,Graduate,No,4342,189,124,360,1,Semiurban,Y 324 | LP002054,Male,Yes,2,Not Graduate,No,3601,1590,,360,1,Rural,Y 325 | LP002055,Female,No,0,Graduate,No,3166,2985,132,360,,Rural,Y 326 | LP002065,Male,Yes,3+,Graduate,No,15000,0,300,360,1,Rural,Y 327 | LP002067,Male,Yes,1,Graduate,Yes,8666,4983,376,360,0,Rural,N 328 | LP002068,Male,No,0,Graduate,No,4917,0,130,360,0,Rural,Y 329 | LP002082,Male,Yes,0,Graduate,Yes,5818,2160,184,360,1,Semiurban,Y 330 | LP002086,Female,Yes,0,Graduate,No,4333,2451,110,360,1,Urban,N 331 | LP002087,Female,No,0,Graduate,No,2500,0,67,360,1,Urban,Y 332 | LP002097,Male,No,1,Graduate,No,4384,1793,117,360,1,Urban,Y 333 | LP002098,Male,No,0,Graduate,No,2935,0,98,360,1,Semiurban,Y 334 | LP002100,Male,No,,Graduate,No,2833,0,71,360,1,Urban,Y 335 | LP002101,Male,Yes,0,Graduate,,63337,0,490,180,1,Urban,Y 336 | LP002103,,Yes,1,Graduate,Yes,9833,1833,182,180,1,Urban,Y 337 | LP002106,Male,Yes,,Graduate,Yes,5503,4490,70,,1,Semiurban,Y 338 | LP002110,Male,Yes,1,Graduate,,5250,688,160,360,1,Rural,Y 339 | LP002112,Male,Yes,2,Graduate,Yes,2500,4600,176,360,1,Rural,Y 340 | LP002113,Female,No,3+,Not Graduate,No,1830,0,,360,0,Urban,N 341 | LP002114,Female,No,0,Graduate,No,4160,0,71,360,1,Semiurban,Y 342 | LP002115,Male,Yes,3+,Not Graduate,No,2647,1587,173,360,1,Rural,N 343 | LP002116,Female,No,0,Graduate,No,2378,0,46,360,1,Rural,N 344 | LP002119,Male,Yes,1,Not Graduate,No,4554,1229,158,360,1,Urban,Y 345 | LP002126,Male,Yes,3+,Not Graduate,No,3173,0,74,360,1,Semiurban,Y 346 | LP002128,Male,Yes,2,Graduate,,2583,2330,125,360,1,Rural,Y 347 | LP002129,Male,Yes,0,Graduate,No,2499,2458,160,360,1,Semiurban,Y 348 | LP002130,Male,Yes,,Not Graduate,No,3523,3230,152,360,0,Rural,N 349 | LP002131,Male,Yes,2,Not Graduate,No,3083,2168,126,360,1,Urban,Y 350 | LP002137,Male,Yes,0,Graduate,No,6333,4583,259,360,,Semiurban,Y 351 | LP002138,Male,Yes,0,Graduate,No,2625,6250,187,360,1,Rural,Y 352 | LP002139,Male,Yes,0,Graduate,No,9083,0,228,360,1,Semiurban,Y 353 | LP002140,Male,No,0,Graduate,No,8750,4167,308,360,1,Rural,N 354 | LP002141,Male,Yes,3+,Graduate,No,2666,2083,95,360,1,Rural,Y 355 | LP002142,Female,Yes,0,Graduate,Yes,5500,0,105,360,0,Rural,N 356 | LP002143,Female,Yes,0,Graduate,No,2423,505,130,360,1,Semiurban,Y 357 | LP002144,Female,No,,Graduate,No,3813,0,116,180,1,Urban,Y 358 | LP002149,Male,Yes,2,Graduate,No,8333,3167,165,360,1,Rural,Y 359 | LP002151,Male,Yes,1,Graduate,No,3875,0,67,360,1,Urban,N 360 | LP002158,Male,Yes,0,Not Graduate,No,3000,1666,100,480,0,Urban,N 361 | LP002160,Male,Yes,3+,Graduate,No,5167,3167,200,360,1,Semiurban,Y 362 | LP002161,Female,No,1,Graduate,No,4723,0,81,360,1,Semiurban,N 363 | LP002170,Male,Yes,2,Graduate,No,5000,3667,236,360,1,Semiurban,Y 364 | LP002175,Male,Yes,0,Graduate,No,4750,2333,130,360,1,Urban,Y 365 | LP002178,Male,Yes,0,Graduate,No,3013,3033,95,300,,Urban,Y 366 | LP002180,Male,No,0,Graduate,Yes,6822,0,141,360,1,Rural,Y 367 | LP002181,Male,No,0,Not Graduate,No,6216,0,133,360,1,Rural,N 368 | LP002187,Male,No,0,Graduate,No,2500,0,96,480,1,Semiurban,N 369 | LP002188,Male,No,0,Graduate,No,5124,0,124,,0,Rural,N 370 | LP002190,Male,Yes,1,Graduate,No,6325,0,175,360,1,Semiurban,Y 371 | LP002191,Male,Yes,0,Graduate,No,19730,5266,570,360,1,Rural,N 372 | LP002194,Female,No,0,Graduate,Yes,15759,0,55,360,1,Semiurban,Y 373 | LP002197,Male,Yes,2,Graduate,No,5185,0,155,360,1,Semiurban,Y 374 | LP002201,Male,Yes,2,Graduate,Yes,9323,7873,380,300,1,Rural,Y 375 | LP002205,Male,No,1,Graduate,No,3062,1987,111,180,0,Urban,N 376 | LP002209,Female,No,0,Graduate,,2764,1459,110,360,1,Urban,Y 377 | LP002211,Male,Yes,0,Graduate,No,4817,923,120,180,1,Urban,Y 378 | LP002219,Male,Yes,3+,Graduate,No,8750,4996,130,360,1,Rural,Y 379 | LP002223,Male,Yes,0,Graduate,No,4310,0,130,360,,Semiurban,Y 380 | LP002224,Male,No,0,Graduate,No,3069,0,71,480,1,Urban,N 381 | LP002225,Male,Yes,2,Graduate,No,5391,0,130,360,1,Urban,Y 382 | LP002226,Male,Yes,0,Graduate,,3333,2500,128,360,1,Semiurban,Y 383 | LP002229,Male,No,0,Graduate,No,5941,4232,296,360,1,Semiurban,Y 384 | LP002231,Female,No,0,Graduate,No,6000,0,156,360,1,Urban,Y 385 | LP002234,Male,No,0,Graduate,Yes,7167,0,128,360,1,Urban,Y 386 | LP002236,Male,Yes,2,Graduate,No,4566,0,100,360,1,Urban,N 387 | LP002237,Male,No,1,Graduate,,3667,0,113,180,1,Urban,Y 388 | LP002239,Male,No,0,Not Graduate,No,2346,1600,132,360,1,Semiurban,Y 389 | LP002243,Male,Yes,0,Not Graduate,No,3010,3136,,360,0,Urban,N 390 | LP002244,Male,Yes,0,Graduate,No,2333,2417,136,360,1,Urban,Y 391 | LP002250,Male,Yes,0,Graduate,No,5488,0,125,360,1,Rural,Y 392 | LP002255,Male,No,3+,Graduate,No,9167,0,185,360,1,Rural,Y 393 | LP002262,Male,Yes,3+,Graduate,No,9504,0,275,360,1,Rural,Y 394 | LP002263,Male,Yes,0,Graduate,No,2583,2115,120,360,,Urban,Y 395 | LP002265,Male,Yes,2,Not Graduate,No,1993,1625,113,180,1,Semiurban,Y 396 | LP002266,Male,Yes,2,Graduate,No,3100,1400,113,360,1,Urban,Y 397 | LP002272,Male,Yes,2,Graduate,No,3276,484,135,360,,Semiurban,Y 398 | LP002277,Female,No,0,Graduate,No,3180,0,71,360,0,Urban,N 399 | LP002281,Male,Yes,0,Graduate,No,3033,1459,95,360,1,Urban,Y 400 | LP002284,Male,No,0,Not Graduate,No,3902,1666,109,360,1,Rural,Y 401 | LP002287,Female,No,0,Graduate,No,1500,1800,103,360,0,Semiurban,N 402 | LP002288,Male,Yes,2,Not Graduate,No,2889,0,45,180,0,Urban,N 403 | LP002296,Male,No,0,Not Graduate,No,2755,0,65,300,1,Rural,N 404 | LP002297,Male,No,0,Graduate,No,2500,20000,103,360,1,Semiurban,Y 405 | LP002300,Female,No,0,Not Graduate,No,1963,0,53,360,1,Semiurban,Y 406 | LP002301,Female,No,0,Graduate,Yes,7441,0,194,360,1,Rural,N 407 | LP002305,Female,No,0,Graduate,No,4547,0,115,360,1,Semiurban,Y 408 | LP002308,Male,Yes,0,Not Graduate,No,2167,2400,115,360,1,Urban,Y 409 | LP002314,Female,No,0,Not Graduate,No,2213,0,66,360,1,Rural,Y 410 | LP002315,Male,Yes,1,Graduate,No,8300,0,152,300,0,Semiurban,N 411 | LP002317,Male,Yes,3+,Graduate,No,81000,0,360,360,0,Rural,N 412 | LP002318,Female,No,1,Not Graduate,Yes,3867,0,62,360,1,Semiurban,N 413 | LP002319,Male,Yes,0,Graduate,,6256,0,160,360,,Urban,Y 414 | LP002328,Male,Yes,0,Not Graduate,No,6096,0,218,360,0,Rural,N 415 | LP002332,Male,Yes,0,Not Graduate,No,2253,2033,110,360,1,Rural,Y 416 | LP002335,Female,Yes,0,Not Graduate,No,2149,3237,178,360,0,Semiurban,N 417 | LP002337,Female,No,0,Graduate,No,2995,0,60,360,1,Urban,Y 418 | LP002341,Female,No,1,Graduate,No,2600,0,160,360,1,Urban,N 419 | LP002342,Male,Yes,2,Graduate,Yes,1600,20000,239,360,1,Urban,N 420 | LP002345,Male,Yes,0,Graduate,No,1025,2773,112,360,1,Rural,Y 421 | LP002347,Male,Yes,0,Graduate,No,3246,1417,138,360,1,Semiurban,Y 422 | LP002348,Male,Yes,0,Graduate,No,5829,0,138,360,1,Rural,Y 423 | LP002357,Female,No,0,Not Graduate,No,2720,0,80,,0,Urban,N 424 | LP002361,Male,Yes,0,Graduate,No,1820,1719,100,360,1,Urban,Y 425 | LP002362,Male,Yes,1,Graduate,No,7250,1667,110,,0,Urban,N 426 | LP002364,Male,Yes,0,Graduate,No,14880,0,96,360,1,Semiurban,Y 427 | LP002366,Male,Yes,0,Graduate,No,2666,4300,121,360,1,Rural,Y 428 | LP002367,Female,No,1,Not Graduate,No,4606,0,81,360,1,Rural,N 429 | LP002368,Male,Yes,2,Graduate,No,5935,0,133,360,1,Semiurban,Y 430 | LP002369,Male,Yes,0,Graduate,No,2920,16.12000084,87,360,1,Rural,Y 431 | LP002370,Male,No,0,Not Graduate,No,2717,0,60,180,1,Urban,Y 432 | LP002377,Female,No,1,Graduate,Yes,8624,0,150,360,1,Semiurban,Y 433 | LP002379,Male,No,0,Graduate,No,6500,0,105,360,0,Rural,N 434 | LP002386,Male,No,0,Graduate,,12876,0,405,360,1,Semiurban,Y 435 | LP002387,Male,Yes,0,Graduate,No,2425,2340,143,360,1,Semiurban,Y 436 | LP002390,Male,No,0,Graduate,No,3750,0,100,360,1,Urban,Y 437 | LP002393,Female,,,Graduate,No,10047,0,,240,1,Semiurban,Y 438 | LP002398,Male,No,0,Graduate,No,1926,1851,50,360,1,Semiurban,Y 439 | LP002401,Male,Yes,0,Graduate,No,2213,1125,,360,1,Urban,Y 440 | LP002403,Male,No,0,Graduate,Yes,10416,0,187,360,0,Urban,N 441 | LP002407,Female,Yes,0,Not Graduate,Yes,7142,0,138,360,1,Rural,Y 442 | LP002408,Male,No,0,Graduate,No,3660,5064,187,360,1,Semiurban,Y 443 | LP002409,Male,Yes,0,Graduate,No,7901,1833,180,360,1,Rural,Y 444 | LP002418,Male,No,3+,Not Graduate,No,4707,1993,148,360,1,Semiurban,Y 445 | LP002422,Male,No,1,Graduate,No,37719,0,152,360,1,Semiurban,Y 446 | LP002424,Male,Yes,0,Graduate,No,7333,8333,175,300,,Rural,Y 447 | LP002429,Male,Yes,1,Graduate,Yes,3466,1210,130,360,1,Rural,Y 448 | LP002434,Male,Yes,2,Not Graduate,No,4652,0,110,360,1,Rural,Y 449 | LP002435,Male,Yes,0,Graduate,,3539,1376,55,360,1,Rural,N 450 | LP002443,Male,Yes,2,Graduate,No,3340,1710,150,360,0,Rural,N 451 | LP002444,Male,No,1,Not Graduate,Yes,2769,1542,190,360,,Semiurban,N 452 | LP002446,Male,Yes,2,Not Graduate,No,2309,1255,125,360,0,Rural,N 453 | LP002447,Male,Yes,2,Not Graduate,No,1958,1456,60,300,,Urban,Y 454 | LP002448,Male,Yes,0,Graduate,No,3948,1733,149,360,0,Rural,N 455 | LP002449,Male,Yes,0,Graduate,No,2483,2466,90,180,0,Rural,Y 456 | LP002453,Male,No,0,Graduate,Yes,7085,0,84,360,1,Semiurban,Y 457 | LP002455,Male,Yes,2,Graduate,No,3859,0,96,360,1,Semiurban,Y 458 | LP002459,Male,Yes,0,Graduate,No,4301,0,118,360,1,Urban,Y 459 | LP002467,Male,Yes,0,Graduate,No,3708,2569,173,360,1,Urban,N 460 | LP002472,Male,No,2,Graduate,No,4354,0,136,360,1,Rural,Y 461 | LP002473,Male,Yes,0,Graduate,No,8334,0,160,360,1,Semiurban,N 462 | LP002478,,Yes,0,Graduate,Yes,2083,4083,160,360,,Semiurban,Y 463 | LP002484,Male,Yes,3+,Graduate,No,7740,0,128,180,1,Urban,Y 464 | LP002487,Male,Yes,0,Graduate,No,3015,2188,153,360,1,Rural,Y 465 | LP002489,Female,No,1,Not Graduate,,5191,0,132,360,1,Semiurban,Y 466 | LP002493,Male,No,0,Graduate,No,4166,0,98,360,0,Semiurban,N 467 | LP002494,Male,No,0,Graduate,No,6000,0,140,360,1,Rural,Y 468 | LP002500,Male,Yes,3+,Not Graduate,No,2947,1664,70,180,0,Urban,N 469 | LP002501,,Yes,0,Graduate,No,16692,0,110,360,1,Semiurban,Y 470 | LP002502,Female,Yes,2,Not Graduate,,210,2917,98,360,1,Semiurban,Y 471 | LP002505,Male,Yes,0,Graduate,No,4333,2451,110,360,1,Urban,N 472 | LP002515,Male,Yes,1,Graduate,Yes,3450,2079,162,360,1,Semiurban,Y 473 | LP002517,Male,Yes,1,Not Graduate,No,2653,1500,113,180,0,Rural,N 474 | LP002519,Male,Yes,3+,Graduate,No,4691,0,100,360,1,Semiurban,Y 475 | LP002522,Female,No,0,Graduate,Yes,2500,0,93,360,,Urban,Y 476 | LP002524,Male,No,2,Graduate,No,5532,4648,162,360,1,Rural,Y 477 | LP002527,Male,Yes,2,Graduate,Yes,16525,1014,150,360,1,Rural,Y 478 | LP002529,Male,Yes,2,Graduate,No,6700,1750,230,300,1,Semiurban,Y 479 | LP002530,,Yes,2,Graduate,No,2873,1872,132,360,0,Semiurban,N 480 | LP002531,Male,Yes,1,Graduate,Yes,16667,2250,86,360,1,Semiurban,Y 481 | LP002533,Male,Yes,2,Graduate,No,2947,1603,,360,1,Urban,N 482 | LP002534,Female,No,0,Not Graduate,No,4350,0,154,360,1,Rural,Y 483 | LP002536,Male,Yes,3+,Not Graduate,No,3095,0,113,360,1,Rural,Y 484 | LP002537,Male,Yes,0,Graduate,No,2083,3150,128,360,1,Semiurban,Y 485 | LP002541,Male,Yes,0,Graduate,No,10833,0,234,360,1,Semiurban,Y 486 | LP002543,Male,Yes,2,Graduate,No,8333,0,246,360,1,Semiurban,Y 487 | LP002544,Male,Yes,1,Not Graduate,No,1958,2436,131,360,1,Rural,Y 488 | LP002545,Male,No,2,Graduate,No,3547,0,80,360,0,Rural,N 489 | LP002547,Male,Yes,1,Graduate,No,18333,0,500,360,1,Urban,N 490 | LP002555,Male,Yes,2,Graduate,Yes,4583,2083,160,360,1,Semiurban,Y 491 | LP002556,Male,No,0,Graduate,No,2435,0,75,360,1,Urban,N 492 | LP002560,Male,No,0,Not Graduate,No,2699,2785,96,360,,Semiurban,Y 493 | LP002562,Male,Yes,1,Not Graduate,No,5333,1131,186,360,,Urban,Y 494 | LP002571,Male,No,0,Not Graduate,No,3691,0,110,360,1,Rural,Y 495 | LP002582,Female,No,0,Not Graduate,Yes,17263,0,225,360,1,Semiurban,Y 496 | LP002585,Male,Yes,0,Graduate,No,3597,2157,119,360,0,Rural,N 497 | LP002586,Female,Yes,1,Graduate,No,3326,913,105,84,1,Semiurban,Y 498 | LP002587,Male,Yes,0,Not Graduate,No,2600,1700,107,360,1,Rural,Y 499 | LP002588,Male,Yes,0,Graduate,No,4625,2857,111,12,,Urban,Y 500 | LP002600,Male,Yes,1,Graduate,Yes,2895,0,95,360,1,Semiurban,Y 501 | LP002602,Male,No,0,Graduate,No,6283,4416,209,360,0,Rural,N 502 | LP002603,Female,No,0,Graduate,No,645,3683,113,480,1,Rural,Y 503 | LP002606,Female,No,0,Graduate,No,3159,0,100,360,1,Semiurban,Y 504 | LP002615,Male,Yes,2,Graduate,No,4865,5624,208,360,1,Semiurban,Y 505 | LP002618,Male,Yes,1,Not Graduate,No,4050,5302,138,360,,Rural,N 506 | LP002619,Male,Yes,0,Not Graduate,No,3814,1483,124,300,1,Semiurban,Y 507 | LP002622,Male,Yes,2,Graduate,No,3510,4416,243,360,1,Rural,Y 508 | LP002624,Male,Yes,0,Graduate,No,20833,6667,480,360,,Urban,Y 509 | LP002625,,No,0,Graduate,No,3583,0,96,360,1,Urban,N 510 | LP002626,Male,Yes,0,Graduate,Yes,2479,3013,188,360,1,Urban,Y 511 | LP002634,Female,No,1,Graduate,No,13262,0,40,360,1,Urban,Y 512 | LP002637,Male,No,0,Not Graduate,No,3598,1287,100,360,1,Rural,N 513 | LP002640,Male,Yes,1,Graduate,No,6065,2004,250,360,1,Semiurban,Y 514 | LP002643,Male,Yes,2,Graduate,No,3283,2035,148,360,1,Urban,Y 515 | LP002648,Male,Yes,0,Graduate,No,2130,6666,70,180,1,Semiurban,N 516 | LP002652,Male,No,0,Graduate,No,5815,3666,311,360,1,Rural,N 517 | LP002659,Male,Yes,3+,Graduate,No,3466,3428,150,360,1,Rural,Y 518 | LP002670,Female,Yes,2,Graduate,No,2031,1632,113,480,1,Semiurban,Y 519 | LP002682,Male,Yes,,Not Graduate,No,3074,1800,123,360,0,Semiurban,N 520 | LP002683,Male,No,0,Graduate,No,4683,1915,185,360,1,Semiurban,N 521 | LP002684,Female,No,0,Not Graduate,No,3400,0,95,360,1,Rural,N 522 | LP002689,Male,Yes,2,Not Graduate,No,2192,1742,45,360,1,Semiurban,Y 523 | LP002690,Male,No,0,Graduate,No,2500,0,55,360,1,Semiurban,Y 524 | LP002692,Male,Yes,3+,Graduate,Yes,5677,1424,100,360,1,Rural,Y 525 | LP002693,Male,Yes,2,Graduate,Yes,7948,7166,480,360,1,Rural,Y 526 | LP002697,Male,No,0,Graduate,No,4680,2087,,360,1,Semiurban,N 527 | LP002699,Male,Yes,2,Graduate,Yes,17500,0,400,360,1,Rural,Y 528 | LP002705,Male,Yes,0,Graduate,No,3775,0,110,360,1,Semiurban,Y 529 | LP002706,Male,Yes,1,Not Graduate,No,5285,1430,161,360,0,Semiurban,Y 530 | LP002714,Male,No,1,Not Graduate,No,2679,1302,94,360,1,Semiurban,Y 531 | LP002716,Male,No,0,Not Graduate,No,6783,0,130,360,1,Semiurban,Y 532 | LP002717,Male,Yes,0,Graduate,No,1025,5500,216,360,,Rural,Y 533 | LP002720,Male,Yes,3+,Graduate,No,4281,0,100,360,1,Urban,Y 534 | LP002723,Male,No,2,Graduate,No,3588,0,110,360,0,Rural,N 535 | LP002729,Male,No,1,Graduate,No,11250,0,196,360,,Semiurban,N 536 | LP002731,Female,No,0,Not Graduate,Yes,18165,0,125,360,1,Urban,Y 537 | LP002732,Male,No,0,Not Graduate,,2550,2042,126,360,1,Rural,Y 538 | LP002734,Male,Yes,0,Graduate,No,6133,3906,324,360,1,Urban,Y 539 | LP002738,Male,No,2,Graduate,No,3617,0,107,360,1,Semiurban,Y 540 | LP002739,Male,Yes,0,Not Graduate,No,2917,536,66,360,1,Rural,N 541 | LP002740,Male,Yes,3+,Graduate,No,6417,0,157,180,1,Rural,Y 542 | LP002741,Female,Yes,1,Graduate,No,4608,2845,140,180,1,Semiurban,Y 543 | LP002743,Female,No,0,Graduate,No,2138,0,99,360,0,Semiurban,N 544 | LP002753,Female,No,1,Graduate,,3652,0,95,360,1,Semiurban,Y 545 | LP002755,Male,Yes,1,Not Graduate,No,2239,2524,128,360,1,Urban,Y 546 | LP002757,Female,Yes,0,Not Graduate,No,3017,663,102,360,,Semiurban,Y 547 | LP002767,Male,Yes,0,Graduate,No,2768,1950,155,360,1,Rural,Y 548 | LP002768,Male,No,0,Not Graduate,No,3358,0,80,36,1,Semiurban,N 549 | LP002772,Male,No,0,Graduate,No,2526,1783,145,360,1,Rural,Y 550 | LP002776,Female,No,0,Graduate,No,5000,0,103,360,0,Semiurban,N 551 | LP002777,Male,Yes,0,Graduate,No,2785,2016,110,360,1,Rural,Y 552 | LP002778,Male,Yes,2,Graduate,Yes,6633,0,,360,0,Rural,N 553 | LP002784,Male,Yes,1,Not Graduate,No,2492,2375,,360,1,Rural,Y 554 | LP002785,Male,Yes,1,Graduate,No,3333,3250,158,360,1,Urban,Y 555 | LP002788,Male,Yes,0,Not Graduate,No,2454,2333,181,360,0,Urban,N 556 | LP002789,Male,Yes,0,Graduate,No,3593,4266,132,180,0,Rural,N 557 | LP002792,Male,Yes,1,Graduate,No,5468,1032,26,360,1,Semiurban,Y 558 | LP002794,Female,No,0,Graduate,No,2667,1625,84,360,,Urban,Y 559 | LP002795,Male,Yes,3+,Graduate,Yes,10139,0,260,360,1,Semiurban,Y 560 | LP002798,Male,Yes,0,Graduate,No,3887,2669,162,360,1,Semiurban,Y 561 | LP002804,Female,Yes,0,Graduate,No,4180,2306,182,360,1,Semiurban,Y 562 | LP002807,Male,Yes,2,Not Graduate,No,3675,242,108,360,1,Semiurban,Y 563 | LP002813,Female,Yes,1,Graduate,Yes,19484,0,600,360,1,Semiurban,Y 564 | LP002820,Male,Yes,0,Graduate,No,5923,2054,211,360,1,Rural,Y 565 | LP002821,Male,No,0,Not Graduate,Yes,5800,0,132,360,1,Semiurban,Y 566 | LP002832,Male,Yes,2,Graduate,No,8799,0,258,360,0,Urban,N 567 | LP002833,Male,Yes,0,Not Graduate,No,4467,0,120,360,,Rural,Y 568 | LP002836,Male,No,0,Graduate,No,3333,0,70,360,1,Urban,Y 569 | LP002837,Male,Yes,3+,Graduate,No,3400,2500,123,360,0,Rural,N 570 | LP002840,Female,No,0,Graduate,No,2378,0,9,360,1,Urban,N 571 | LP002841,Male,Yes,0,Graduate,No,3166,2064,104,360,0,Urban,N 572 | LP002842,Male,Yes,1,Graduate,No,3417,1750,186,360,1,Urban,Y 573 | LP002847,Male,Yes,,Graduate,No,5116,1451,165,360,0,Urban,N 574 | LP002855,Male,Yes,2,Graduate,No,16666,0,275,360,1,Urban,Y 575 | LP002862,Male,Yes,2,Not Graduate,No,6125,1625,187,480,1,Semiurban,N 576 | LP002863,Male,Yes,3+,Graduate,No,6406,0,150,360,1,Semiurban,N 577 | LP002868,Male,Yes,2,Graduate,No,3159,461,108,84,1,Urban,Y 578 | LP002872,,Yes,0,Graduate,No,3087,2210,136,360,0,Semiurban,N 579 | LP002874,Male,No,0,Graduate,No,3229,2739,110,360,1,Urban,Y 580 | LP002877,Male,Yes,1,Graduate,No,1782,2232,107,360,1,Rural,Y 581 | LP002888,Male,No,0,Graduate,,3182,2917,161,360,1,Urban,Y 582 | LP002892,Male,Yes,2,Graduate,No,6540,0,205,360,1,Semiurban,Y 583 | LP002893,Male,No,0,Graduate,No,1836,33837,90,360,1,Urban,N 584 | LP002894,Female,Yes,0,Graduate,No,3166,0,36,360,1,Semiurban,Y 585 | LP002898,Male,Yes,1,Graduate,No,1880,0,61,360,,Rural,N 586 | LP002911,Male,Yes,1,Graduate,No,2787,1917,146,360,0,Rural,N 587 | LP002912,Male,Yes,1,Graduate,No,4283,3000,172,84,1,Rural,N 588 | LP002916,Male,Yes,0,Graduate,No,2297,1522,104,360,1,Urban,Y 589 | LP002917,Female,No,0,Not Graduate,No,2165,0,70,360,1,Semiurban,Y 590 | LP002925,,No,0,Graduate,No,4750,0,94,360,1,Semiurban,Y 591 | LP002926,Male,Yes,2,Graduate,Yes,2726,0,106,360,0,Semiurban,N 592 | LP002928,Male,Yes,0,Graduate,No,3000,3416,56,180,1,Semiurban,Y 593 | LP002931,Male,Yes,2,Graduate,Yes,6000,0,205,240,1,Semiurban,N 594 | LP002933,,No,3+,Graduate,Yes,9357,0,292,360,1,Semiurban,Y 595 | LP002936,Male,Yes,0,Graduate,No,3859,3300,142,180,1,Rural,Y 596 | LP002938,Male,Yes,0,Graduate,Yes,16120,0,260,360,1,Urban,Y 597 | LP002940,Male,No,0,Not Graduate,No,3833,0,110,360,1,Rural,Y 598 | LP002941,Male,Yes,2,Not Graduate,Yes,6383,1000,187,360,1,Rural,N 599 | LP002943,Male,No,,Graduate,No,2987,0,88,360,0,Semiurban,N 600 | LP002945,Male,Yes,0,Graduate,Yes,9963,0,180,360,1,Rural,Y 601 | LP002948,Male,Yes,2,Graduate,No,5780,0,192,360,1,Urban,Y 602 | LP002949,Female,No,3+,Graduate,,416,41667,350,180,,Urban,N 603 | LP002950,Male,Yes,0,Not Graduate,,2894,2792,155,360,1,Rural,Y 604 | LP002953,Male,Yes,3+,Graduate,No,5703,0,128,360,1,Urban,Y 605 | LP002958,Male,No,0,Graduate,No,3676,4301,172,360,1,Rural,Y 606 | LP002959,Female,Yes,1,Graduate,No,12000,0,496,360,1,Semiurban,Y 607 | LP002960,Male,Yes,0,Not Graduate,No,2400,3800,,180,1,Urban,N 608 | LP002961,Male,Yes,1,Graduate,No,3400,2500,173,360,1,Semiurban,Y 609 | LP002964,Male,Yes,2,Not Graduate,No,3987,1411,157,360,1,Rural,Y 610 | LP002974,Male,Yes,0,Graduate,No,3232,1950,108,360,1,Rural,Y 611 | LP002978,Female,No,0,Graduate,No,2900,0,71,360,1,Rural,Y 612 | LP002979,Male,Yes,3+,Graduate,No,4106,0,40,180,1,Rural,Y 613 | LP002983,Male,Yes,1,Graduate,No,8072,240,253,360,1,Urban,Y 614 | LP002984,Male,Yes,2,Graduate,No,7583,0,187,360,1,Urban,Y 615 | LP002990,Female,No,0,Graduate,Yes,4583,0,133,360,0,Semiurban,N 616 | -------------------------------------------------------------------------------- /Data Visualisation with Power BI/Financial Sample.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/Data Visualisation with Power BI/Financial Sample.xlsx -------------------------------------------------------------------------------- /Data Visualisation with Power BI/Iris.csv: -------------------------------------------------------------------------------- 1 | Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species 2 | 1,5.1,3.5,1.4,0.2,Iris-setosa 3 | 2,4.9,3.0,1.4,0.2,Iris-setosa 4 | 3,4.7,3.2,1.3,0.2,Iris-setosa 5 | 4,4.6,3.1,1.5,0.2,Iris-setosa 6 | 5,5.0,3.6,1.4,0.2,Iris-setosa 7 | 6,5.4,3.9,1.7,0.4,Iris-setosa 8 | 7,4.6,3.4,1.4,0.3,Iris-setosa 9 | 8,5.0,3.4,1.5,0.2,Iris-setosa 10 | 9,4.4,2.9,1.4,0.2,Iris-setosa 11 | 10,4.9,3.1,1.5,0.1,Iris-setosa 12 | 11,5.4,3.7,1.5,0.2,Iris-setosa 13 | 12,4.8,3.4,1.6,0.2,Iris-setosa 14 | 13,4.8,3.0,1.4,0.1,Iris-setosa 15 | 14,4.3,3.0,1.1,0.1,Iris-setosa 16 | 15,5.8,4.0,1.2,0.2,Iris-setosa 17 | 16,5.7,4.4,1.5,0.4,Iris-setosa 18 | 17,5.4,3.9,1.3,0.4,Iris-setosa 19 | 18,5.1,3.5,1.4,0.3,Iris-setosa 20 | 19,5.7,3.8,1.7,0.3,Iris-setosa 21 | 20,5.1,3.8,1.5,0.3,Iris-setosa 22 | 21,5.4,3.4,1.7,0.2,Iris-setosa 23 | 22,5.1,3.7,1.5,0.4,Iris-setosa 24 | 23,4.6,3.6,1.0,0.2,Iris-setosa 25 | 24,5.1,3.3,1.7,0.5,Iris-setosa 26 | 25,4.8,3.4,1.9,0.2,Iris-setosa 27 | 26,5.0,3.0,1.6,0.2,Iris-setosa 28 | 27,5.0,3.4,1.6,0.4,Iris-setosa 29 | 28,5.2,3.5,1.5,0.2,Iris-setosa 30 | 29,5.2,3.4,1.4,0.2,Iris-setosa 31 | 30,4.7,3.2,1.6,0.2,Iris-setosa 32 | 31,4.8,3.1,1.6,0.2,Iris-setosa 33 | 32,5.4,3.4,1.5,0.4,Iris-setosa 34 | 33,5.2,4.1,1.5,0.1,Iris-setosa 35 | 34,5.5,4.2,1.4,0.2,Iris-setosa 36 | 35,4.9,3.1,1.5,0.1,Iris-setosa 37 | 36,5.0,3.2,1.2,0.2,Iris-setosa 38 | 37,5.5,3.5,1.3,0.2,Iris-setosa 39 | 38,4.9,3.1,1.5,0.1,Iris-setosa 40 | 39,4.4,3.0,1.3,0.2,Iris-setosa 41 | 40,5.1,3.4,1.5,0.2,Iris-setosa 42 | 41,5.0,3.5,1.3,0.3,Iris-setosa 43 | 42,4.5,2.3,1.3,0.3,Iris-setosa 44 | 43,4.4,3.2,1.3,0.2,Iris-setosa 45 | 44,5.0,3.5,1.6,0.6,Iris-setosa 46 | 45,5.1,3.8,1.9,0.4,Iris-setosa 47 | 46,4.8,3.0,1.4,0.3,Iris-setosa 48 | 47,5.1,3.8,1.6,0.2,Iris-setosa 49 | 48,4.6,3.2,1.4,0.2,Iris-setosa 50 | 49,5.3,3.7,1.5,0.2,Iris-setosa 51 | 50,5.0,3.3,1.4,0.2,Iris-setosa 52 | 51,7.0,3.2,4.7,1.4,Iris-versicolor 53 | 52,6.4,3.2,4.5,1.5,Iris-versicolor 54 | 53,6.9,3.1,4.9,1.5,Iris-versicolor 55 | 54,5.5,2.3,4.0,1.3,Iris-versicolor 56 | 55,6.5,2.8,4.6,1.5,Iris-versicolor 57 | 56,5.7,2.8,4.5,1.3,Iris-versicolor 58 | 57,6.3,3.3,4.7,1.6,Iris-versicolor 59 | 58,4.9,2.4,3.3,1.0,Iris-versicolor 60 | 59,6.6,2.9,4.6,1.3,Iris-versicolor 61 | 60,5.2,2.7,3.9,1.4,Iris-versicolor 62 | 61,5.0,2.0,3.5,1.0,Iris-versicolor 63 | 62,5.9,3.0,4.2,1.5,Iris-versicolor 64 | 63,6.0,2.2,4.0,1.0,Iris-versicolor 65 | 64,6.1,2.9,4.7,1.4,Iris-versicolor 66 | 65,5.6,2.9,3.6,1.3,Iris-versicolor 67 | 66,6.7,3.1,4.4,1.4,Iris-versicolor 68 | 67,5.6,3.0,4.5,1.5,Iris-versicolor 69 | 68,5.8,2.7,4.1,1.0,Iris-versicolor 70 | 69,6.2,2.2,4.5,1.5,Iris-versicolor 71 | 70,5.6,2.5,3.9,1.1,Iris-versicolor 72 | 71,5.9,3.2,4.8,1.8,Iris-versicolor 73 | 72,6.1,2.8,4.0,1.3,Iris-versicolor 74 | 73,6.3,2.5,4.9,1.5,Iris-versicolor 75 | 74,6.1,2.8,4.7,1.2,Iris-versicolor 76 | 75,6.4,2.9,4.3,1.3,Iris-versicolor 77 | 76,6.6,3.0,4.4,1.4,Iris-versicolor 78 | 77,6.8,2.8,4.8,1.4,Iris-versicolor 79 | 78,6.7,3.0,5.0,1.7,Iris-versicolor 80 | 79,6.0,2.9,4.5,1.5,Iris-versicolor 81 | 80,5.7,2.6,3.5,1.0,Iris-versicolor 82 | 81,5.5,2.4,3.8,1.1,Iris-versicolor 83 | 82,5.5,2.4,3.7,1.0,Iris-versicolor 84 | 83,5.8,2.7,3.9,1.2,Iris-versicolor 85 | 84,6.0,2.7,5.1,1.6,Iris-versicolor 86 | 85,5.4,3.0,4.5,1.5,Iris-versicolor 87 | 86,6.0,3.4,4.5,1.6,Iris-versicolor 88 | 87,6.7,3.1,4.7,1.5,Iris-versicolor 89 | 88,6.3,2.3,4.4,1.3,Iris-versicolor 90 | 89,5.6,3.0,4.1,1.3,Iris-versicolor 91 | 90,5.5,2.5,4.0,1.3,Iris-versicolor 92 | 91,5.5,2.6,4.4,1.2,Iris-versicolor 93 | 92,6.1,3.0,4.6,1.4,Iris-versicolor 94 | 93,5.8,2.6,4.0,1.2,Iris-versicolor 95 | 94,5.0,2.3,3.3,1.0,Iris-versicolor 96 | 95,5.6,2.7,4.2,1.3,Iris-versicolor 97 | 96,5.7,3.0,4.2,1.2,Iris-versicolor 98 | 97,5.7,2.9,4.2,1.3,Iris-versicolor 99 | 98,6.2,2.9,4.3,1.3,Iris-versicolor 100 | 99,5.1,2.5,3.0,1.1,Iris-versicolor 101 | 100,5.7,2.8,4.1,1.3,Iris-versicolor 102 | 101,6.3,3.3,6.0,2.5,Iris-virginica 103 | 102,5.8,2.7,5.1,1.9,Iris-virginica 104 | 103,7.1,3.0,5.9,2.1,Iris-virginica 105 | 104,6.3,2.9,5.6,1.8,Iris-virginica 106 | 105,6.5,3.0,5.8,2.2,Iris-virginica 107 | 106,7.6,3.0,6.6,2.1,Iris-virginica 108 | 107,4.9,2.5,4.5,1.7,Iris-virginica 109 | 108,7.3,2.9,6.3,1.8,Iris-virginica 110 | 109,6.7,2.5,5.8,1.8,Iris-virginica 111 | 110,7.2,3.6,6.1,2.5,Iris-virginica 112 | 111,6.5,3.2,5.1,2.0,Iris-virginica 113 | 112,6.4,2.7,5.3,1.9,Iris-virginica 114 | 113,6.8,3.0,5.5,2.1,Iris-virginica 115 | 114,5.7,2.5,5.0,2.0,Iris-virginica 116 | 115,5.8,2.8,5.1,2.4,Iris-virginica 117 | 116,6.4,3.2,5.3,2.3,Iris-virginica 118 | 117,6.5,3.0,5.5,1.8,Iris-virginica 119 | 118,7.7,3.8,6.7,2.2,Iris-virginica 120 | 119,7.7,2.6,6.9,2.3,Iris-virginica 121 | 120,6.0,2.2,5.0,1.5,Iris-virginica 122 | 121,6.9,3.2,5.7,2.3,Iris-virginica 123 | 122,5.6,2.8,4.9,2.0,Iris-virginica 124 | 123,7.7,2.8,6.7,2.0,Iris-virginica 125 | 124,6.3,2.7,4.9,1.8,Iris-virginica 126 | 125,6.7,3.3,5.7,2.1,Iris-virginica 127 | 126,7.2,3.2,6.0,1.8,Iris-virginica 128 | 127,6.2,2.8,4.8,1.8,Iris-virginica 129 | 128,6.1,3.0,4.9,1.8,Iris-virginica 130 | 129,6.4,2.8,5.6,2.1,Iris-virginica 131 | 130,7.2,3.0,5.8,1.6,Iris-virginica 132 | 131,7.4,2.8,6.1,1.9,Iris-virginica 133 | 132,7.9,3.8,6.4,2.0,Iris-virginica 134 | 133,6.4,2.8,5.6,2.2,Iris-virginica 135 | 134,6.3,2.8,5.1,1.5,Iris-virginica 136 | 135,6.1,2.6,5.6,1.4,Iris-virginica 137 | 136,7.7,3.0,6.1,2.3,Iris-virginica 138 | 137,6.3,3.4,5.6,2.4,Iris-virginica 139 | 138,6.4,3.1,5.5,1.8,Iris-virginica 140 | 139,6.0,3.0,4.8,1.8,Iris-virginica 141 | 140,6.9,3.1,5.4,2.1,Iris-virginica 142 | 141,6.7,3.1,5.6,2.4,Iris-virginica 143 | 142,6.9,3.1,5.1,2.3,Iris-virginica 144 | 143,5.8,2.7,5.1,1.9,Iris-virginica 145 | 144,6.8,3.2,5.9,2.3,Iris-virginica 146 | 145,6.7,3.3,5.7,2.5,Iris-virginica 147 | 146,6.7,3.0,5.2,2.3,Iris-virginica 148 | 147,6.3,2.5,5.0,1.9,Iris-virginica 149 | 148,6.5,3.0,5.2,2.0,Iris-virginica 150 | 149,6.2,3.4,5.4,2.3,Iris-virginica 151 | 150,5.9,3.0,5.1,1.8,Iris-virginica 152 | -------------------------------------------------------------------------------- /Data Visualisation with Power BI/tips.csv: -------------------------------------------------------------------------------- 1 | "total_bill","tip","sex","smoker","day","time","size" 2 | 16.99,1.01,"Female","No","Sun","Dinner",2 3 | 10.34,1.66,"Male","No","Sun","Dinner",3 4 | 21.01,3.5,"Male","No","Sun","Dinner",3 5 | 23.68,3.31,"Male","No","Sun","Dinner",2 6 | 24.59,3.61,"Female","No","Sun","Dinner",4 7 | 25.29,4.71,"Male","No","Sun","Dinner",4 8 | 8.77,2,"Male","No","Sun","Dinner",2 9 | 26.88,3.12,"Male","No","Sun","Dinner",4 10 | 15.04,1.96,"Male","No","Sun","Dinner",2 11 | 14.78,3.23,"Male","No","Sun","Dinner",2 12 | 10.27,1.71,"Male","No","Sun","Dinner",2 13 | 35.26,5,"Female","No","Sun","Dinner",4 14 | 15.42,1.57,"Male","No","Sun","Dinner",2 15 | 18.43,3,"Male","No","Sun","Dinner",4 16 | 14.83,3.02,"Female","No","Sun","Dinner",2 17 | 21.58,3.92,"Male","No","Sun","Dinner",2 18 | 10.33,1.67,"Female","No","Sun","Dinner",3 19 | 16.29,3.71,"Male","No","Sun","Dinner",3 20 | 16.97,3.5,"Female","No","Sun","Dinner",3 21 | 20.65,3.35,"Male","No","Sat","Dinner",3 22 | 17.92,4.08,"Male","No","Sat","Dinner",2 23 | 20.29,2.75,"Female","No","Sat","Dinner",2 24 | 15.77,2.23,"Female","No","Sat","Dinner",2 25 | 39.42,7.58,"Male","No","Sat","Dinner",4 26 | 19.82,3.18,"Male","No","Sat","Dinner",2 27 | 17.81,2.34,"Male","No","Sat","Dinner",4 28 | 13.37,2,"Male","No","Sat","Dinner",2 29 | 12.69,2,"Male","No","Sat","Dinner",2 30 | 21.7,4.3,"Male","No","Sat","Dinner",2 31 | 19.65,3,"Female","No","Sat","Dinner",2 32 | 9.55,1.45,"Male","No","Sat","Dinner",2 33 | 18.35,2.5,"Male","No","Sat","Dinner",4 34 | 15.06,3,"Female","No","Sat","Dinner",2 35 | 20.69,2.45,"Female","No","Sat","Dinner",4 36 | 17.78,3.27,"Male","No","Sat","Dinner",2 37 | 24.06,3.6,"Male","No","Sat","Dinner",3 38 | 16.31,2,"Male","No","Sat","Dinner",3 39 | 16.93,3.07,"Female","No","Sat","Dinner",3 40 | 18.69,2.31,"Male","No","Sat","Dinner",3 41 | 31.27,5,"Male","No","Sat","Dinner",3 42 | 16.04,2.24,"Male","No","Sat","Dinner",3 43 | 17.46,2.54,"Male","No","Sun","Dinner",2 44 | 13.94,3.06,"Male","No","Sun","Dinner",2 45 | 9.68,1.32,"Male","No","Sun","Dinner",2 46 | 30.4,5.6,"Male","No","Sun","Dinner",4 47 | 18.29,3,"Male","No","Sun","Dinner",2 48 | 22.23,5,"Male","No","Sun","Dinner",2 49 | 32.4,6,"Male","No","Sun","Dinner",4 50 | 28.55,2.05,"Male","No","Sun","Dinner",3 51 | 18.04,3,"Male","No","Sun","Dinner",2 52 | 12.54,2.5,"Male","No","Sun","Dinner",2 53 | 10.29,2.6,"Female","No","Sun","Dinner",2 54 | 34.81,5.2,"Female","No","Sun","Dinner",4 55 | 9.94,1.56,"Male","No","Sun","Dinner",2 56 | 25.56,4.34,"Male","No","Sun","Dinner",4 57 | 19.49,3.51,"Male","No","Sun","Dinner",2 58 | 38.01,3,"Male","Yes","Sat","Dinner",4 59 | 26.41,1.5,"Female","No","Sat","Dinner",2 60 | 11.24,1.76,"Male","Yes","Sat","Dinner",2 61 | 48.27,6.73,"Male","No","Sat","Dinner",4 62 | 20.29,3.21,"Male","Yes","Sat","Dinner",2 63 | 13.81,2,"Male","Yes","Sat","Dinner",2 64 | 11.02,1.98,"Male","Yes","Sat","Dinner",2 65 | 18.29,3.76,"Male","Yes","Sat","Dinner",4 66 | 17.59,2.64,"Male","No","Sat","Dinner",3 67 | 20.08,3.15,"Male","No","Sat","Dinner",3 68 | 16.45,2.47,"Female","No","Sat","Dinner",2 69 | 3.07,1,"Female","Yes","Sat","Dinner",1 70 | 20.23,2.01,"Male","No","Sat","Dinner",2 71 | 15.01,2.09,"Male","Yes","Sat","Dinner",2 72 | 12.02,1.97,"Male","No","Sat","Dinner",2 73 | 17.07,3,"Female","No","Sat","Dinner",3 74 | 26.86,3.14,"Female","Yes","Sat","Dinner",2 75 | 25.28,5,"Female","Yes","Sat","Dinner",2 76 | 14.73,2.2,"Female","No","Sat","Dinner",2 77 | 10.51,1.25,"Male","No","Sat","Dinner",2 78 | 17.92,3.08,"Male","Yes","Sat","Dinner",2 79 | 27.2,4,"Male","No","Thur","Lunch",4 80 | 22.76,3,"Male","No","Thur","Lunch",2 81 | 17.29,2.71,"Male","No","Thur","Lunch",2 82 | 19.44,3,"Male","Yes","Thur","Lunch",2 83 | 16.66,3.4,"Male","No","Thur","Lunch",2 84 | 10.07,1.83,"Female","No","Thur","Lunch",1 85 | 32.68,5,"Male","Yes","Thur","Lunch",2 86 | 15.98,2.03,"Male","No","Thur","Lunch",2 87 | 34.83,5.17,"Female","No","Thur","Lunch",4 88 | 13.03,2,"Male","No","Thur","Lunch",2 89 | 18.28,4,"Male","No","Thur","Lunch",2 90 | 24.71,5.85,"Male","No","Thur","Lunch",2 91 | 21.16,3,"Male","No","Thur","Lunch",2 92 | 28.97,3,"Male","Yes","Fri","Dinner",2 93 | 22.49,3.5,"Male","No","Fri","Dinner",2 94 | 5.75,1,"Female","Yes","Fri","Dinner",2 95 | 16.32,4.3,"Female","Yes","Fri","Dinner",2 96 | 22.75,3.25,"Female","No","Fri","Dinner",2 97 | 40.17,4.73,"Male","Yes","Fri","Dinner",4 98 | 27.28,4,"Male","Yes","Fri","Dinner",2 99 | 12.03,1.5,"Male","Yes","Fri","Dinner",2 100 | 21.01,3,"Male","Yes","Fri","Dinner",2 101 | 12.46,1.5,"Male","No","Fri","Dinner",2 102 | 11.35,2.5,"Female","Yes","Fri","Dinner",2 103 | 15.38,3,"Female","Yes","Fri","Dinner",2 104 | 44.3,2.5,"Female","Yes","Sat","Dinner",3 105 | 22.42,3.48,"Female","Yes","Sat","Dinner",2 106 | 20.92,4.08,"Female","No","Sat","Dinner",2 107 | 15.36,1.64,"Male","Yes","Sat","Dinner",2 108 | 20.49,4.06,"Male","Yes","Sat","Dinner",2 109 | 25.21,4.29,"Male","Yes","Sat","Dinner",2 110 | 18.24,3.76,"Male","No","Sat","Dinner",2 111 | 14.31,4,"Female","Yes","Sat","Dinner",2 112 | 14,3,"Male","No","Sat","Dinner",2 113 | 7.25,1,"Female","No","Sat","Dinner",1 114 | 38.07,4,"Male","No","Sun","Dinner",3 115 | 23.95,2.55,"Male","No","Sun","Dinner",2 116 | 25.71,4,"Female","No","Sun","Dinner",3 117 | 17.31,3.5,"Female","No","Sun","Dinner",2 118 | 29.93,5.07,"Male","No","Sun","Dinner",4 119 | 10.65,1.5,"Female","No","Thur","Lunch",2 120 | 12.43,1.8,"Female","No","Thur","Lunch",2 121 | 24.08,2.92,"Female","No","Thur","Lunch",4 122 | 11.69,2.31,"Male","No","Thur","Lunch",2 123 | 13.42,1.68,"Female","No","Thur","Lunch",2 124 | 14.26,2.5,"Male","No","Thur","Lunch",2 125 | 15.95,2,"Male","No","Thur","Lunch",2 126 | 12.48,2.52,"Female","No","Thur","Lunch",2 127 | 29.8,4.2,"Female","No","Thur","Lunch",6 128 | 8.52,1.48,"Male","No","Thur","Lunch",2 129 | 14.52,2,"Female","No","Thur","Lunch",2 130 | 11.38,2,"Female","No","Thur","Lunch",2 131 | 22.82,2.18,"Male","No","Thur","Lunch",3 132 | 19.08,1.5,"Male","No","Thur","Lunch",2 133 | 20.27,2.83,"Female","No","Thur","Lunch",2 134 | 11.17,1.5,"Female","No","Thur","Lunch",2 135 | 12.26,2,"Female","No","Thur","Lunch",2 136 | 18.26,3.25,"Female","No","Thur","Lunch",2 137 | 8.51,1.25,"Female","No","Thur","Lunch",2 138 | 10.33,2,"Female","No","Thur","Lunch",2 139 | 14.15,2,"Female","No","Thur","Lunch",2 140 | 16,2,"Male","Yes","Thur","Lunch",2 141 | 13.16,2.75,"Female","No","Thur","Lunch",2 142 | 17.47,3.5,"Female","No","Thur","Lunch",2 143 | 34.3,6.7,"Male","No","Thur","Lunch",6 144 | 41.19,5,"Male","No","Thur","Lunch",5 145 | 27.05,5,"Female","No","Thur","Lunch",6 146 | 16.43,2.3,"Female","No","Thur","Lunch",2 147 | 8.35,1.5,"Female","No","Thur","Lunch",2 148 | 18.64,1.36,"Female","No","Thur","Lunch",3 149 | 11.87,1.63,"Female","No","Thur","Lunch",2 150 | 9.78,1.73,"Male","No","Thur","Lunch",2 151 | 7.51,2,"Male","No","Thur","Lunch",2 152 | 14.07,2.5,"Male","No","Sun","Dinner",2 153 | 13.13,2,"Male","No","Sun","Dinner",2 154 | 17.26,2.74,"Male","No","Sun","Dinner",3 155 | 24.55,2,"Male","No","Sun","Dinner",4 156 | 19.77,2,"Male","No","Sun","Dinner",4 157 | 29.85,5.14,"Female","No","Sun","Dinner",5 158 | 48.17,5,"Male","No","Sun","Dinner",6 159 | 25,3.75,"Female","No","Sun","Dinner",4 160 | 13.39,2.61,"Female","No","Sun","Dinner",2 161 | 16.49,2,"Male","No","Sun","Dinner",4 162 | 21.5,3.5,"Male","No","Sun","Dinner",4 163 | 12.66,2.5,"Male","No","Sun","Dinner",2 164 | 16.21,2,"Female","No","Sun","Dinner",3 165 | 13.81,2,"Male","No","Sun","Dinner",2 166 | 17.51,3,"Female","Yes","Sun","Dinner",2 167 | 24.52,3.48,"Male","No","Sun","Dinner",3 168 | 20.76,2.24,"Male","No","Sun","Dinner",2 169 | 31.71,4.5,"Male","No","Sun","Dinner",4 170 | 10.59,1.61,"Female","Yes","Sat","Dinner",2 171 | 10.63,2,"Female","Yes","Sat","Dinner",2 172 | 50.81,10,"Male","Yes","Sat","Dinner",3 173 | 15.81,3.16,"Male","Yes","Sat","Dinner",2 174 | 7.25,5.15,"Male","Yes","Sun","Dinner",2 175 | 31.85,3.18,"Male","Yes","Sun","Dinner",2 176 | 16.82,4,"Male","Yes","Sun","Dinner",2 177 | 32.9,3.11,"Male","Yes","Sun","Dinner",2 178 | 17.89,2,"Male","Yes","Sun","Dinner",2 179 | 14.48,2,"Male","Yes","Sun","Dinner",2 180 | 9.6,4,"Female","Yes","Sun","Dinner",2 181 | 34.63,3.55,"Male","Yes","Sun","Dinner",2 182 | 34.65,3.68,"Male","Yes","Sun","Dinner",4 183 | 23.33,5.65,"Male","Yes","Sun","Dinner",2 184 | 45.35,3.5,"Male","Yes","Sun","Dinner",3 185 | 23.17,6.5,"Male","Yes","Sun","Dinner",4 186 | 40.55,3,"Male","Yes","Sun","Dinner",2 187 | 20.69,5,"Male","No","Sun","Dinner",5 188 | 20.9,3.5,"Female","Yes","Sun","Dinner",3 189 | 30.46,2,"Male","Yes","Sun","Dinner",5 190 | 18.15,3.5,"Female","Yes","Sun","Dinner",3 191 | 23.1,4,"Male","Yes","Sun","Dinner",3 192 | 15.69,1.5,"Male","Yes","Sun","Dinner",2 193 | 19.81,4.19,"Female","Yes","Thur","Lunch",2 194 | 28.44,2.56,"Male","Yes","Thur","Lunch",2 195 | 15.48,2.02,"Male","Yes","Thur","Lunch",2 196 | 16.58,4,"Male","Yes","Thur","Lunch",2 197 | 7.56,1.44,"Male","No","Thur","Lunch",2 198 | 10.34,2,"Male","Yes","Thur","Lunch",2 199 | 43.11,5,"Female","Yes","Thur","Lunch",4 200 | 13,2,"Female","Yes","Thur","Lunch",2 201 | 13.51,2,"Male","Yes","Thur","Lunch",2 202 | 18.71,4,"Male","Yes","Thur","Lunch",3 203 | 12.74,2.01,"Female","Yes","Thur","Lunch",2 204 | 13,2,"Female","Yes","Thur","Lunch",2 205 | 16.4,2.5,"Female","Yes","Thur","Lunch",2 206 | 20.53,4,"Male","Yes","Thur","Lunch",4 207 | 16.47,3.23,"Female","Yes","Thur","Lunch",3 208 | 26.59,3.41,"Male","Yes","Sat","Dinner",3 209 | 38.73,3,"Male","Yes","Sat","Dinner",4 210 | 24.27,2.03,"Male","Yes","Sat","Dinner",2 211 | 12.76,2.23,"Female","Yes","Sat","Dinner",2 212 | 30.06,2,"Male","Yes","Sat","Dinner",3 213 | 25.89,5.16,"Male","Yes","Sat","Dinner",4 214 | 48.33,9,"Male","No","Sat","Dinner",4 215 | 13.27,2.5,"Female","Yes","Sat","Dinner",2 216 | 28.17,6.5,"Female","Yes","Sat","Dinner",3 217 | 12.9,1.1,"Female","Yes","Sat","Dinner",2 218 | 28.15,3,"Male","Yes","Sat","Dinner",5 219 | 11.59,1.5,"Male","Yes","Sat","Dinner",2 220 | 7.74,1.44,"Male","Yes","Sat","Dinner",2 221 | 30.14,3.09,"Female","Yes","Sat","Dinner",4 222 | 12.16,2.2,"Male","Yes","Fri","Lunch",2 223 | 13.42,3.48,"Female","Yes","Fri","Lunch",2 224 | 8.58,1.92,"Male","Yes","Fri","Lunch",1 225 | 15.98,3,"Female","No","Fri","Lunch",3 226 | 13.42,1.58,"Male","Yes","Fri","Lunch",2 227 | 16.27,2.5,"Female","Yes","Fri","Lunch",2 228 | 10.09,2,"Female","Yes","Fri","Lunch",2 229 | 20.45,3,"Male","No","Sat","Dinner",4 230 | 13.28,2.72,"Male","No","Sat","Dinner",2 231 | 22.12,2.88,"Female","Yes","Sat","Dinner",2 232 | 24.01,2,"Male","Yes","Sat","Dinner",4 233 | 15.69,3,"Male","Yes","Sat","Dinner",3 234 | 11.61,3.39,"Male","No","Sat","Dinner",2 235 | 10.77,1.47,"Male","No","Sat","Dinner",2 236 | 15.53,3,"Male","Yes","Sat","Dinner",2 237 | 10.07,1.25,"Male","No","Sat","Dinner",2 238 | 12.6,1,"Male","Yes","Sat","Dinner",2 239 | 32.83,1.17,"Male","Yes","Sat","Dinner",2 240 | 35.83,4.67,"Female","No","Sat","Dinner",3 241 | 29.03,5.92,"Male","No","Sat","Dinner",3 242 | 27.18,2,"Female","Yes","Sat","Dinner",2 243 | 22.67,2,"Male","Yes","Sat","Dinner",2 244 | 17.82,1.75,"Male","No","Sat","Dinner",2 245 | 18.78,3,"Female","No","Thur","Dinner",2 246 | -------------------------------------------------------------------------------- /Data Visualisation with PyViz/Readme.md: -------------------------------------------------------------------------------- 1 |  2 | 3 | The Jupyter notebook doesn't show the intearctive plots. Eitheruse the notebook locally or use Binder for viewing the interactive plots. 4 | 5 | 6 | -------------------------------------------------------------------------------- /Data Visualisation with PyViz/binder/environment.yml: -------------------------------------------------------------------------------- 1 | # file created by pyctdev: 2 | # /cio/mc3/bin/doit env_export2 --env-name-again=pyviz --env-file=examples/environment.yml --package-name=pyviz -c pyviz/label/dev -c defaults --pin-deps 3 | 4 | name: pyviz 5 | channels: 6 | - pyviz/label/dev 7 | - defaults 8 | dependencies: 9 | - bokeh ==1.0.0dev6 10 | - cartopy 11 | - cffi 12 | - dask ==0.18.2 13 | - datashader ==0.6.8 14 | - fastparquet 15 | - geopandas 16 | - geoviews ==1.5.4a6 17 | - holoviews ==1.11.0a4 18 | - hvplot ==0.2.1 19 | - ipython ==5.* 20 | - ipywidgets ==7.* 21 | - jupyter 22 | - netcdf4 23 | - networkx 24 | - notebook >=5.5 25 | - numpy ==1.14.5 26 | - pandas ==0.23.4 27 | - panel ==0.1.0a3 28 | - param ==1.8.0a2 29 | - parambokeh ==0.2.3 30 | - paramnb ==2.0.4 31 | - phantomjs 32 | - pyct 33 | - python ==3.6.* 34 | - python-snappy 35 | - rise 36 | - scikit-image 37 | - selenium 38 | - streamz ==0.3.0 39 | - tornado ==4.5.3 40 | - xarray ==0.10.3 41 | -------------------------------------------------------------------------------- /Data Visualisation with Tableau/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/Data Visualisation with Tableau/.DS_Store -------------------------------------------------------------------------------- /Data Visualisation with Tableau/Cluster Analysis with Tableau/Images/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/Data Visualisation with 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Analysts is to arrange the insights of our data in such a way that everybody who sees them is able to understand their implications and how to act on them clearly. 4 | 5 | Tableau is a data analytics and visualization tool used widely in the industry today. Many businesses even consider it indispensable for data-science-related work. Tableau's ease of use comes from the fact that it has a drag and drop interface. This feature helps to perform tasks like sorting, comparing and analyzing, very easily and fast. Tableau is also compatible with multiple sources, including Excel, SQL Server, and cloud-based data repositories which makes it an excellent choice for Data Scientists. 6 | 7 | **This is an excerpt taken from a tutorial I wrote on datacamp with the same name.Please find the link to the tutorial [here](https://medium.com/@parulnith/data-visualisation-with-tableau-150f99a39bba)** 8 | -------------------------------------------------------------------------------- /Data Visualisation with Tableau/Data Viz with tableau/Sample-Superstore .xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/Data Visualisation with Tableau/Data Viz with tableau/Sample-Superstore .xls -------------------------------------------------------------------------------- /Data Visualisation with Tableau/Data Viz with tableau/reviews.csv: -------------------------------------------------------------------------------- 1 | S.No,Reviews 1,I feel so LUCKY to have found this used (phone to us & not used hard at all) 2,nice phone 3,Very pleased 4,It works good but it goes slow sometimes but its a very good phone I love it 5,Great phone to replace my lost phone. The only thing is the volume up button does not work 6,I already had a phone with problems... I know it stated it was used 7,The charging port was loose. I got that soldered in. Then needed a new battery as well. $100 later (not including cost of purchase) I have a usable phone. The phone should not have been sold in the state it was in. 8,Phone looks good but wouldn't stay charged 9,I originally was using the Samsung S2 Galaxy for Sprint and wanted to return back to the Samsung EPIC 4G for Sprint because I really missed the keyboard 10,This is a great product it came after two days of ordering it. There was only one little blemish on the side 11,These guys are the best! I had a little situation with my item but they quickly fixed the issue. I was pleased and will definitely be buying another phon from them if I need one. 12,I'm really disappointed about my phone and service. The phone went out on me over a week ago. Instead of handling it when issue first surfaced. I've been told to do this and do that. Now I'm stick with no phone and I'm out 55 dollars for service that I'm not using. I still haven't received a mailing label to return item. This was my first purchase on amazon. I'm not rushing to use theirs or smartphones 288 business Anytime soon. I thought I was getting a deal but it seems like I'm the one who lucked out. 13,Ordered this phone as a replacement for the same model until my contract expires and I can get a new one. Seller confirmation said delivery could take up to 7 days. Seller sent out the phone within hours of receiving the order and I had the phone the next day. Phone looks better than described was able to transfer data from the old one to the new one with no problems. Highly recommend this seller 14,Had this phone before and loved it but was not working so I got this phone. One thing is the SD card slot does not open up when I try to access it in file managment 15,I was able to get the phone I previously owned...with a keyboard and touch screen. It's the best phone and I love it. I still had to clean the device with my service provider 16,I brought this phone as a replacement for my daughter 17,I love the phone. It does everything I need and works great. I purchased four of these phones through a seller that shipped from Amazon's warehouse. My only problem is that the phone didn't come with a micro sd card. I couldn't use the camera until I got the card. No problems getting these activated at Sprint. 18,The battery was old & had been over used because it barely holds a charge. Otherwise 19,pros-beautiful screen 20,I purchased this phone in December as a christmas present to my son. I called sprint to activate the phone only to find out the ESN wasn't clear. I was told to come into the store and upon research the phone wasn't clear. I reached out to this company to inform them that this ESN wasn't clean. I was told to send the phone back and they would send out another phone. I asked the representative to provide me with the ESN # so that I could call and make sure it was clear. I was told that they didn't have to phone available and was guaranteed it would be clear. I should've known something was wrong then. I received a second phone that turned out not have a clear ESN. The representative at Sprint informed me that the seller of these phones are aware the the ESN isn't clear. So once again I had to send another phone back. I asked for a refund for the phone and shipping since the sent me two phones that didn't have a clear ESN. I received my refund for the phone but have yet to see a refund for the shipping. I wouldn't suggest you buy anything from them. 21,Phone good just a little slow phone old but it's a great phone temporary right now. thank you for the great deal 22,Phone's speaker little low. Overall very happy with the phone. I would purchase another cell phone from Chubbietech. I am satisfied. 23,the phone was great and in good condition. My Daughter is so happy the be a andriod user now !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 24,Phone works great. No problems at all 25,was not in good condition but does work good 26,Just... not good. The phone has great screen resolution 27,as described 28,Perfect in every way. 29,One of the phones have a bad charger port. I want to send it back 30,Just got this phone and it is a great phone. It's easy to use. 31,The phone was great but it had gotten old so it was time for a replacement.it was great while it lasted. 32,This phone came in great condition! Great price and it brings back great memories of owning this phone! 33,Met all of my expectations. I can't complain at all. 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In the movie, James Cameron introduced an interesting visual effect concept that made it possible for the viewers to get behind the eyes of the cyborg called Terminator. This effect came to be known as the Terminator Vision and in a way, it segmented humans from the background. It might have sounded totally out of place then, but Image segmentation forms a vital part of many Image processing techniques today. 2 | 3 | Image Segmentation 4 | We all are pretty aware of the endless possibilities offered by Photoshop or similar graphics editors that take a person from one image and place them into another. However, the first step of doing this is identifying where that person is in the source image and this is where Image Segmentation comes into play. There are many libraries written for Image Analysis purpose. In this article, we will be discussing in detail about scikit-image, a Python-based image processing library. 5 | 6 | The entire code can also be accessed from the Github Repository associated with this article. 7 | Scikit-image 8 | 9 | scikit-image.org 10 | Scikit-image is a Python package dedicated to image processing. 11 | 12 | Installation 13 | scikit-image can be installed as follows: 14 | 15 | pip install -U scikit-image(Linux and OSX) 16 | pip install scikit-image(Windows) 17 | # For Conda-based distributions 18 | conda install scikit-image 19 | Overview of Images in Python 20 | Before proceeding with the technicalities of Image Segmentation, it is essential to get a little familiar with the scikit image ecosystem and how it handles images. 21 | 22 | Importing a GrayScale Image from the skimage library 23 | The skimage data module contains some inbuilt example data sets which are generally stored in jpeg or png format. 24 | 25 | from skimage import data 26 | import numpy as np 27 | import matplotlib.pyplot as plt 28 | image = data.binary_blobs() 29 | plt.imshow(image, cmap='gray') 30 | 31 | Importing a Colored Image from the skimage library 32 | from skimage import data 33 | import numpy as np 34 | import matplotlib.pyplot as plt 35 | image = data.astronaut() 36 | plt.imshow(image) 37 | 38 | Importing an image from an external source 39 | # The I/O module is used for importing the image 40 | from skimage import data 41 | import numpy as np 42 | import matplotlib.pyplot as plt 43 | from skimage import io 44 | image = io.imread('skimage_logo.png') 45 | plt.imshow(image); 46 | 47 | Loading multiple images 48 | images = io.ImageCollection('../images/*.png:../images/*.jpg') 49 | print('Type:', type(images)) 50 | images.files 51 | Out[]: Type: 52 | Saving images 53 | #Saving file as ‘logo.png’ 54 | io.imsave('logo.png', logo) 55 | Image segmentation 56 | Now that we have an idea about scikit-image, let us get into details of Image Segmentation. Image Segmentation is essentially the process of partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. 57 | 58 | In this article, we will approach the Segmentation process as a combination of Supervised and Unsupervised algorithms. 59 | 60 | 61 | Some of the Segmentation Algorithms available in the scikit-image library 62 | Supervised segmentation: Some prior knowledge, possibly from human input, is used to guide the algorithm. 63 | 64 | Unsupervised segmentation: No prior knowledge is required. These algorithms attempt to subdivide images into meaningful regions automatically. The user may still be able to tweak certain settings to obtain desired outputs. 65 | 66 | Let’s begin with the simplest algorithm called Thresholding. 67 | 68 | Thresholding 69 | It is the simplest way to segment objects from a background by choosing pixels above or below a certain threshold. This is generally helpful when we intend to segment objects from their background. You can read more about thresholding here. 70 | 71 | Let’s try this on an image of a textbook which comes preloaded with the scikit-image dataset. 72 | 73 | Basic Imports 74 | import numpy as np 75 | import matplotlib.pyplot as plt 76 | import skimage.data as data 77 | import skimage.segmentation as seg 78 | import skimage.filters as filters 79 | import skimage.draw as draw 80 | import skimage.color as color 81 | A simple function to plot the images 82 | 83 | def image_show(image, nrows=1, ncols=1, cmap='gray'): 84 | fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(14, 14)) 85 | ax.imshow(image, cmap='gray') 86 | ax.axis('off') 87 | return fig, ax 88 | Image 89 | text = data.page() 90 | image_show(text) 91 | 92 | This image is a little darker but maybe we can still pick a value that will give us a reasonable segmentation without any advanced algorithms. Now to help us in picking that value, we will use a Histogram. 93 | 94 | A histogram is a graph showing the number of pixels in an image at different intensity values found in that image. Simply put, a histogram is a graph wherein the x-axis shows all the values that are in the image while the y-axis shows the frequency of those values. 95 | 96 | fig, ax = plt.subplots(1, 1) 97 | ax.hist(text.ravel(), bins=32, range=[0, 256]) 98 | ax.set_xlim(0, 256); 99 | 100 | Our example happens to be an 8-bit image so we have a total of 256 possible values on the x-axis. We observe that there is a concentration of pixels that are fairly light(0: black, 255: white). That’s most likely our fairly light text background but then the rest of it is kind of smeared out. An ideal segmentation histogram would be bimodal and fairly separated so that we could pick a number right in the middle. Now, let’s just try and make a few segmented images based on simple thresholding. 101 | 102 | Supervised thresholding 103 | Since we will be choosing the thresholding value ourselves, we call it supervised thresholding. 104 | 105 | text_segmented = text > (value concluded from histogram i.e 50,70,120 ) 106 | image_show(text_segmented); 107 | 108 | 109 | 110 | Left: text>50 | Middle : text > 70 | Right : text >120 111 | We didn’t get any ideal results since the shadow on the left creates problems. Let’s try with unsupervised thresholding now. 112 | 113 | Unsupervised thresholding 114 | Scikit-image has a number of automatic thresholding methods, which require no input in choosing an optimal threshold. Some of the methods are : otsu, li, local. 115 | 116 | text_threshold = filters.threshold_ # Hit tab with the cursor after the underscore to get all the methods. 117 | image_show(text < text_threshold); 118 | 119 | 120 | Left: otsu || Right: li 121 | In the case of local, we also need to specify the block_size . Offset helps to tune the image for better results. 122 | 123 | text_threshold = filters.threshold_local(text,block_size=51, offset=10) 124 | image_show(text > text_threshold); 125 | 126 | local thresholding 127 | This is pretty good and has got rid of the noisy regions to a large extent. 128 | 129 | Supervised segmentation 130 | Thresholding is a very basic segmentation process and will not work properly in a high-contrast image for which we will be needing more advanced tools. 131 | 132 | For this section, we will use an example image which is freely available and attempt to segment the head portion using supervised segmentation techniques. 133 | 134 | # import the image 135 | from skimage import io 136 | image = io.imread('girl.jpg') 137 | plt.imshow(image); 138 | 139 | source 140 | Before doing any segmentation on an image, it is a good idea to de-noise it using some filters. 141 | However, in our case, the image is not very noisy, so we will take it as it is. Next step would be to convert the image to grayscale with rgb2gray. 142 | 143 | image_gray = color.rgb2gray(image) 144 | image_show(image_gray); 145 | 146 | We will use two segmentation methods which work on entirely different principles. 147 | 148 | Active contour segmentation 149 | Active Contour segmentation also called as snakes and is initialized using a user-defined contour or line, around the area of interest and this contour then slowly contracts and is attracted or repelled from light and edges. 150 | 151 | For our example image, let’s draw a circle around the person’s head to initialize the snake. 152 | 153 | def circle_points(resolution, center, radius): 154 | """ 155 | Generate points which define a circle on an image.Centre refers to the centre of the circle 156 | """ 157 | radians = np.linspace(0, 2*np.pi, resolution) 158 | c = center[1] + radius*np.cos(radians)#polar co-ordinates 159 | r = center[0] + radius*np.sin(radians) 160 | 161 | return np.array([c, r]).T 162 | # Exclude last point because a closed path should not have duplicate points 163 | points = circle_points(200, [80, 250], 80)[:-1] 164 | The above calculations calculate x and y co-ordinates of the points on the periphery of the circle. Since we have given the resolution to be 200, it will calculate 200 such points. 165 | fig, ax = image_show(image) 166 | ax.plot(points[:, 0], points[:, 1], '--r', lw=3) 167 | 168 | The algorithm then segments the face of a person from the rest of an image by fitting a closed curve to the edges of the face. 169 | 170 | snake = seg.active_contour(image_gray, points) 171 | fig, ax = image_show(image) 172 | ax.plot(points[:, 0], points[:, 1], '--r', lw=3) 173 | ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3); 174 | 175 | We can tweak the parameters called alpha and beta. Higher values of alpha will make this snake contract faster while beta makes the snake smoother. 176 | 177 | snake = seg.active_contour(image_gray, points,alpha=0.06,beta=0.3) 178 | fig, ax = image_show(image) 179 | ax.plot(points[:, 0], points[:, 1], '--r', lw=3) 180 | ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3); 181 | 182 | Random walker segmentation 183 | In this method, a user interactively labels a small number of pixels which are known as labels. Each unlabeled pixel is then imagined to release a random walker and one can then determine the probability of a random walker starting at each unlabeled pixel and reaching one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, high-quality image segmentation may be obtained. Read the Reference paper here. 184 | 185 | We will re-use the seed values from our previous example here. We could have 186 | done different initializations but for simplicity let’s stick to circles. 187 | 188 | image_labels = np.zeros(image_gray.shape, dtype=np.uint8) 189 | The random walker algorithm expects a label image as input. So we will have the bigger circle that encompasses the person’s entire face and another smaller circle near the middle of the face. 190 | 191 | indices = draw.circle_perimeter(80, 250,20)#from here 192 | image_labels[indices] = 1 193 | image_labels[points[:, 1].astype(np.int), points[:, 0].astype(np.int)] = 2 194 | image_show(image_labels); 195 | 196 | Now, let’s use Random Walker and see what happens. 197 | 198 | image_segmented = seg.random_walker(image_gray, image_labels) 199 | # Check our results 200 | fig, ax = image_show(image_gray) 201 | ax.imshow(image_segmented == 1, alpha=0.3); 202 | 203 | It doesn’t look like it’s grabbing edges as we wanted. To resolve this situation we can tune in the beta parameter until we get the desired results. After several attempts, a value of 3000 works reasonably well. 204 | 205 | image_segmented = seg.random_walker(image_gray, image_labels, beta = 3000) 206 | # Check our results 207 | fig, ax = image_show(image_gray) 208 | ax.imshow(image_segmented == 1, alpha=0.3); 209 | 210 | That’s all for Supervised Segmentation where we had to provide certain inputs and also had to tweak certain parameters. However, it is not always possible to have a human looking at an image and then deciding what inputs to give or where to start from. Fortunately, for those situations, we have Unsupervised segmentation techniques. 211 | 212 | Unsupervised segmentation 213 | Unsupervised segmentation requires no prior knowledge. Consider an image that is so large that it is not feasible to consider all pixels simultaneously. So in such cases, Unsupervised segmentation can breakdown the image into several sub-regions, so instead of millions of pixels, you have tens to hundreds of regions. Let’s look at two such algorithms: 214 | 215 | SLIC( Simple Linear Iterative Clustering) 216 | SLIC algorithm actually uses a machine learning algorithm called K-Means under the hood. It takes in all the pixel values of the image and tries to separate them out into the given number of sub-regions. Read the Reference Paper here. 217 | 218 | SLIC works in color so we will use the original image. 219 | 220 | image_slic = seg.slic(image,n_segments=155) 221 | All we’re doing is just setting each sub-image or sub-region that we have found, to the average of that region which makes it look less like a patchwork of randomly assigned colors and more like an image that has been decomposed into areas that are kind of similar. 222 | 223 | # label2rgb replaces each discrete label with the average interior color 224 | image_show(color.label2rgb(image_slic, image, kind='avg')); 225 | 226 | We’ve reduced this image from 512*512 = 262,000 pixels down to 155 regions. 227 | 228 | Felzenszwalb 229 | This algorithm also uses a machine learning algorithm called minimum-spanning tree clustering under the hood. Felzenszwaib doesn’t tell us the exact number of clusters that the image will be partitioned into. It’s going to run and generate as many clusters as it thinks is appropriate for that 230 | given scale or zoom factor on the image. The Reference Paper can be accessed here. 231 | 232 | image_felzenszwalb = seg.felzenszwalb(image) 233 | image_show(image_felzenszwalb); 234 | 235 | These are a lot of regions. Let’s calculate the number of unique regions. 236 | 237 | np.unique(image_felzenszwalb).size 238 | 3368 239 | Now let’s recolour them using the region average just as we did in the SLIC algorithm. 240 | 241 | image_felzenszwalb_colored = color.label2rgb(image_felzenszwalb, image, kind='avg') 242 | image_show(image_felzenszwalb_colored); 243 | Now we get reasonable smaller regions. If we wanted still fewer regions, we could change the scale parameter or start here and combine them. This approach is sometimes called over-segmentation. 244 | 245 | 246 | This almost looks more like a posterized image which is essentially just a reduction in the number of colours. 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Memorial Parkway,Huntsville,Alabama,35801-5930,34.7430949,-86.6009553 3 | 3650 Galleria Circle,Hoover,Alabama,35244-2346,33.377649,-86.81242 4 | 8251 Eastchase Parkway,Montgomery,Alabama,36117,32.363889,-86.150884 5 | 5225 Commercial Boulevard,Juneau,Alaska,99801-7210,58.3592,-134.483 6 | 330 West Dimond Blvd,Anchorage,Alaska,99515-1950,61.143266,-149.884217 7 | 4125 DeBarr Road,Anchorage,Alaska,99508-3115,61.210815,-149.804337 8 | 3911 Highway 69,Prescott,Arizona,86301-6717,34.548993,-112.3954274 9 | 3901 West Costco Drive,Tucson,Arizona,85741-2864,32.3262177,-111.0491606 10 | 6255 East Grant Road,Tucson,Arizona,85712-5834,32.2522189,-110.859554 11 | 17550 N. 79th Ave.,Glendale,Arizona,85308-8711,33.643277,-112.233474 12 | 2887 S Market St,Gilbert,Arizona,85296-6303,33.296095,-111.745647 13 | 1445 West Elliot Road,Tempe,Arizona,85284-1103,33.347289,-111.961862 14 | 15255 North Hayden Road,Scottsdale,Arizona,85260-2507,33.6241344,-111.9019118 15 | 1415 North Arizona Avenue,Gilbert,Arizona,85233-1616,33.376244,-111.840765 16 | 595 S Galleria Way,Chandler,Arizona,85226-4932,33.293696,-111.899509 17 | 1444 S Sossaman Rd,Mesa,Arizona,85209-3400,33.3886808,-111.6675437 18 | 2450 E Beardsley Rd,Phoenix,Arizona,85050-1300,33.670943,-112.028124 19 | 19001 N. 27th Ave,Phoenix,Arizona,85027-5036,33.659688,-112.115841 20 | 3801 N. 33rd Avenue,Phoenix,Arizona,85017-4508,33.49179,-112.128379 21 | 1646 W. Montebello,Phoenix,Arizona,85015-2557,33.5206919,-112.095189 22 | 4502 East Oak Street,Phoenix,Arizona,85008-2411,33.473335,-111.985531 23 | 10000 W. McDowell Road,Avondale,Arizona,85323,33.467156,-112.274404 24 | 4570 E Cactus Rd,Phoenix,Arizona,85032,33.600961,-111.983793 25 | 2345 Fenton Pkwy,San Diego,California,92108-4743,32.7789832,-117.1269067 26 | 1300 Dana Drive,Redding,California,96003-4071,40.586449,-122.345675 27 | 2100 Dr Martin Luther King Jr Way,Chico,California,95928-4422,39.7214025,-121.8096578 28 | 7981 East Stockton Boulevard,Sacramento,California,95823-9606,38.4677597,-121.4154069 29 | 1600 Expo Parkway,Sacramento,California,95815-4228,38.597139,-121.452409 30 | 2299 Bronze Star Drive,Woodland,California,95776-5409,38.6699409,-121.7259571 31 | 11260 White Rock Road,Rancho Cordova,California,95742-6571,38.588195,-121.263769 32 | 6750 Stanford Ranch Road,Roseville,California,95678-1907,38.78061,-121.267423 33 | 1800 Cavitt Court,Folsom,California,95630-6235,38.645206,-121.109598 34 | 7000 Auburn Blvd,Citrus Heights,California,95621-4342,38.6903,-121.29859 35 | 1006 West Wabash Avenue,Eureka,California,95501-2121,40.7919385,-124.1825438 36 | 1900 Santa Rosa Avenue,Santa Rosa,California,95407-7636,38.4204562,-122.7134804 37 | 3801 Pelandale Ave.,Modesto,California,95356-8300,37.7003612,-121.0671386 38 | 1445 R Street,Merced,California,95340-5850,37.3019943,-120.4940012 39 | 2440 Daniels St,Manteca,California,95336-6745,37.784874,-121.260014 40 | 1616 East Hammer Lane,Stockton,California,95210-4119,38.018791,-121.292757 41 | 1709 Automation Parkway,San Jose,California,95131-1866,37.3886293,-121.8829132 42 | 5301 Almaden Expressway,San Jose,California,95118-3603,37.253214,-121.879999 43 | 2201 Senter Road,San Jose,California,95112-2627,37.309657,-121.851571 44 | 220 Sylvania Avenue,Santa Cruz,California,95060-2161,36.985104,-122.033615 45 | 1601 Coleman Avenue,Santa Clara,California,95050-3100,37.357021,-121.938843 46 | 300 Vintage Way,Novato,California,94945-5007,38.087238,-122.551076 47 | 4801 Central Avenue,Richmond,California,94804-5878,37.898774,-122.320125 48 | 198 Plaza Drive,Vallejo,California,94591-3702,38.133192,-122.213745 49 | 5101 Business Center Drive,Fairfield,California,94585-1624,38.215406,-122.144911 50 | 1900 Davis Street,San Leandro,California,94577-1209,37.7181741,-122.1809332 51 | 2800 Independence Drive,Livermore,California,94550-7628,37.702588,-121.812265 52 | 22330 Hathaway Avenue,Hayward,California,94541-4861,37.667516,-122.10278 53 | 43621 Pacific Commons Blvd,Fremont,California,94538-3809,37.5031232,-121.9748814 54 | 3150 Fostoria Way,Danville,California,94526-5553,37.78259,-121.972853 55 | 2201 Verne Roberts Circle,Antioch,California,94509-7911,38.009119,-121.837904 56 | 1001 Metro Center Boulevard,Foster City,California,94404-2177,37.561366,-122.275144 57 | 450 10th Street,San Francisco,California,94103-4304,37.770488,-122.411106 58 | 150 Lawrence Station Road,Sunnyvale,California,94086-5328,37.372027,-121.994779 59 | 451 South Airport Boulevard,South San Francisco,California,94080-6909,37.6422308,-122.4010744 60 | 1600 El Camino Real,South San Francisco,California,94080-1206,37.6654356,-122.4490857 61 | 1000 North Rengstorff Avenue,Mountain View,California,94043-1716,37.420676,-122.095061 62 | 801 Tioga Avenue,Sand City,California,93955-3051,36.6170705,-121.8474767 63 | 1339 North Davis Road,Salinas,California,93907-1988,36.703939,-121.668608 64 | 4500 W Shaw,Fresno,California,93722-6200,36.80989,-119.874081 65 | 380 West Ashlan Avenue,Clovis,California,93612-5611,36.792187,-119.714342 66 | 1141 West Avenue L,Lancaster,California,93534-7077,34.66129,-118.151429 67 | 1335 South Bradley,Santa Maria,California,93454-8006,34.936541,-120.42084 68 | 1540 Froom Ranch Way,San Luis Obispo,California,93405-7211,35.251917,-120.691013 69 | 1405 W Cameron Ave,Visalia,California,93277-9527,36.292806,-119.306638 70 | 7095 Market Place Drive,Goleta,California,93117-5905,34.42789,-119.874572 71 | 2001 East Ventura Boulevard,Oxnard,California,93030-1813,34.2251071,-119.148567 72 | 22633 Savi Ranch Parkway,Yorba Linda,California,92887-4664,33.87567,-117.74385 73 | 480 McKinley Street,Corona,California,92879-1291,33.8899732,-117.5208947 74 | 11000 Garden Grove Boulevard,Garden Grove,California,92843-1206,33.773511,-117.940271 75 | 900 South Harbor Boulevard,Fullerton,California,92832-3098,33.862449,-117.92211 76 | 2655 El Camino Real,Tustin,California,92782-8918,33.7282,-117.796294 77 | 2700 Park Ave,Tustin,California,92782-2708,33.699689,-117.822618 78 | 17900 Newhope Street,Fountain Valley,California,92708-5404,33.703448,-117.931978 79 | 27972 Cabot Road,Laguna Niguel,California,92677-1211,33.5569565,-117.6788758 80 | 33961 Doheny Park Road,San Juan Capistrano,California,92675-4836,33.469498,-117.677697 81 | 115 Technology West Drive,Irvine,California,92618-2408,33.6570006,-117.7397337 82 | 12700 Day Street,Moreno Valley,California,92553-7531,33.935555,-117.276579 83 | 29315 Central Ave,Lake Elsinore,California,92532-2212,33.696337,-117.339182 84 | 1099 East Hospitality Lane,San Bernardino,California,92408-2836,34.068044,-117.263077 85 | 14555 Valley Center Drive,Victorville,California,92392-4216,34.5155574,-117.3196428 86 | 2030 North Imperial Avenue,El Centro,California,92243-1323,32.8152064,-115.5700028 87 | 72-800 Dinah Shore Drive,Palm Desert,California,92211-0817,33.7996392,-116.3819564 88 | 12350 Carmel Mountain Road,San Diego,California,92128-4697,32.987241,-117.077675 89 | 650 Gateway Center Drive,San Diego,California,92102-4594,32.712739,-117.11613 90 | 1755 Hacienda Drive,Vista,California,92081-4546,33.1879309,-117.2767568 91 | 101 Town Center Parkway,Santee,California,92071-5899,32.840642,-116.987356 92 | 12155 Tech Center Drive,Poway,California,92064-7156,32.93649,-117.033125 93 | 951 Palomar Airport Road,Carlsbad,California,92011-1110,33.1201381,-117.3165503 94 | 8125 Fletcher Parkway,La Mesa,California,91942-2934,32.773971,-117.024305 95 | 1130 Broadway,Chula Vista,California,91911-2707,32.6089545,-117.0809526 96 | 895 East H Street,Chula Vista,California,91910-7807,32.637276,-117.022199 97 | 2207 West Commonwealth Avenue,Alhambra,California,91803-1302,34.089515,-118.148531 98 | 520 N Lone Hill Ave,San Dimas,California,91773-1725,34.112139,-117.82633 99 | 9404 Central Avenue,Montclair,California,91763-2421,34.083039,-117.690661 100 | 17550 Castleton St.,City of Industry,California,91748-1701,33.994075,-117.922 101 | 11800 4th Street,Rancho Cucamonga,California,91739-9318,34.078576,-117.5482714 102 | 13111 Peyton Drive,Chino Hills,California,91709-6002,34.014463,-117.741815 103 | 1220 West Foothill Boulevard,Azusa,California,91702-2819,34.131982,-117.925681 104 | 1051 Burbank Blvd.,Burbank,California,91506-1421,34.185739,-118.324045 105 | 6100 Sepulveda Boulevard,Van Nuys,California,91411-2503,34.181613,-118.46401 106 | 18649 Via Princessa,Santa Clarita,California,91387-4935,34.4027129,-118.4550432 107 | 5700 Lindero Canyon Road,Westlake Village,California,91362-4063,34.152307,-118.797131 108 | 8810 Tampa Avenue,Northridge,California,91324-3519,34.230765,-118.551069 109 | 21300 Roscoe Boulevard,Canoga Park,California,91304-4200,34.218644,-118.596119 110 | 2200 East Willow Street,Signal Hill,California,90806-2132,33.8043173,-118.166725 111 | 340 Lakewood Center Mall,Lakewood,California,90712-2409,33.850463,-118.136453 112 | 12324 Hoxie Avenue,Norwalk,California,90650-2266,33.919958,-118.102519 113 | 1345 North Montebello Boulevard,Montebello,California,90640-2585,34.032917,-118.097463 114 | 101 N Beach Blvd,La Habra,California,90631-4468,33.932029,-117.969786 115 | 2751 Sky Park Drive,Torrance,California,90505-5351,33.800813,-118.34993 116 | 3560 West Century Blvd,Inglewood,California,90303-1201,33.943257,-118.333856 117 | 13463 Washington Boulevard,Marina del Rey,California,90292-5658,33.992576,-118.446821 118 | 14501 Hindry Avenue,Hawthorne,California,90250-6748,33.898805,-118.372745 119 | 2901 Los Feliz Boulevard,Los Angeles,California,90039-1502,34.1285583,-118.2636796 120 | 1051 Hume Way,Vacaville,California,95687,38.3506756,-121.9817708 121 | 2955 North Tegner Road,Turlock,California,95380,37.519131,-120.888552 122 | 3250 W. Grant Line Road,Tracy,California,95377,37.7510679,-121.475091 123 | 7251 Camino Arroyo,Gilroy,California,95020,37.008207,-121.556447 124 | 5901 Redwood Drive,Rohnert Park,California,94928,38.3527259,-122.717092 125 | 28505 Hesperian Blvd,Hayward,California,94545,37.6163221,-122.0889509 126 | 2300 Middlefield Rd,Redwood City,California,94063,37.478656,-122.216527 127 | 7100 N. Abby Street,Fresno,California,93720,36.839426,-119.78774 128 | 4900 Panama Lane,Bakersfield,California,93308,35.297271,-119.058275 129 | 3800 Rosedale Highway,Bakersfield,California,93308,35.384837,-119.048452 130 | 27220 Heather Ridge Road,Laguna Niguel,California,92677,33.567618,-117.711571 131 | 26610 Ynez Road,Temecula,California,92591,33.521227,-117.154362 132 | 16505 Sierra Lakes Parkway,Fontana,California,92336,34.137557,-117.442672 133 | 79795 Hwy 111,La Quinta,California,92253,33.704896,-116.272801 134 | 4605 Morena Boulevard,San Diego,California,92117,32.8234958,-117.2282851 135 | 7803 Othello Ave,San Diego,California,92111,32.816251,-117.153631 136 | 13550 W Paxton St,Pacoima,California,91331,34.2725894,-118.4276271 137 | 5401 Katella Ave,Cypress,California,90720,33.8031834,-118.038976 138 | 12530 Prairie Ave,Hawthorne,California,90250,33.918618,-118.342576 139 | 6333 Telegraph Rd,Commerce,California,90040,33.9947586,-118.1430614 140 | 170 Cooley Mesa Rd,Gypsum,Colorado,81637-9707,39.6433872,-106.8885487 141 | 5885 Barnes Road,Colorado Springs,Colorado,80922-3512,38.892736,-104.71771 142 | 18414 Cottonwood Dr,Parker,Colorado,80138-8876,39.5580896,-104.7758852 143 | 8686 Park Meadows Center Drive,Littleton,Colorado,80124-5129,39.561203,-104.874753 144 | 7900 West Quincy Avenue,Littleton,Colorado,80123-2472,39.638285,-105.082856 145 | 4000 River Point Parkway,Sheridan,Colorado,80110-3316,39.644445,-105.006451 146 | 6400 West 92nd Avenue,Westminster,Colorado,80031-2952,39.862237,-105.0661949 147 | 600 Marshall Road,Superior,Colorado,80027-9730,39.9576167,-105.1699298 148 | 16375 N Washington St,Thornton,Colorado,80023-8907,39.991358,-104.983163 149 | 1471 South Havana,Aurora,Colorado,80012-4013,39.690471,-104.86808 150 | 5195 Wadsworth Blvd.,Arvada,Colorado,80002-4617,39.790074,-105.082637 151 | 5050 N Nevada Ave,Colorado Springs,Colorado,80918,38.9037505,-104.8172136 152 | 779 Connecticut Ave,Norwalk,Connecticut,06854-1615,41.0919457,-73.4515808 153 | 200 Federal Road,Brookfield,Connecticut,06804-2527,41.4422731,-73.4051279 154 | 3600 East Main Street,Waterbury,Connecticut,06705-3851,41.542672,-72.9686675 155 | 1718 Boston Post Road,Milford,Connecticut,06460-2718,41.249661,-73.023977 156 | 75 Freshwater Blvd,Enfield,Connecticut,06082-3854,41.9936047,-72.5754399 157 | 900 Center Boulevard,Newark,Delaware,19702-3221,39.677736,-75.64737 158 | 17800 Congress Avenue,Boca Raton,Florida,33487,26.411955,-80.1008839 159 | 6275 Naples Boulevard,Naples,Florida,34109-2030,26.22026,-81.77492 160 | 10088 Gulf Center Drive,Fort Myers,Florida,33913-8961,26.489631,-81.785933 161 | 7171 Cypress Lake Drive,Fort Myers,Florida,33907-6521,26.544447,-81.8742089 162 | 10921 Causeway Blvd.,Brandon,Florida,33511-2903,27.921491,-82.330936 163 | 1873 West Lantana Road,Lantana,Florida,33462-2698,26.5872905,-80.0726927 164 | 11001 Southern Blvd - Ste 160,Royal Palm Beach,Florida,33411-4240,26.6810314,-80.2195563 165 | 3250 Northlake Boulevard,Lake Park,Florida,33403-1702,26.8083519,-80.0879667 166 | 1890 South University Drive,Davie,Florida,33324-5847,26.0969039,-80.2509582 167 | 13450 SW 120th St,Kendall,Florida,33186-7393,25.655668,-80.412069 168 | 14585 Biscayne Boulevard,North Miami Beach,Florida,33181-1209,25.909564,-80.156203 169 | 8300 Park Boulevard,Miami,Florida,33126-3832,25.774109,-80.330536 170 | 1800 West Sample Road,Pompano Beach,Florida,33064-1324,26.273566,-80.148255 171 | 15915 Pines Blvd,Pembroke Pines,Florida,33027-1201,26.009617,-80.360331 172 | 16580 NW 59th Avenue,Miami,Florida,33014-5611,25.923451,-80.298706 173 | 2101 Water Bridge Blvd,Orlando,Florida,32837-9283,28.404252,-81.40671 174 | 3333 University Boulevard,Winter Park,Florida,32792-7428,28.59907,-81.30024 175 | 741 Orange Avenue,Altamonte Springs,Florida,32714-3031,28.6618672,-81.4176945 176 | 4067 Lagniappe Way,Tallahassee,Florida,32317-1201,30.4606461,-84.2124709 177 | 4901 Gate Parkway,Jacksonville,Florida,32246-4405,30.252071,-81.533776 178 | 2655 Gulf to Bay Blvd.,Clearwater,Florida,33759,27.9604528,-82.7275752 179 | 100 Cobb Parkway,Ringgold,Georgia,30742,34.9326477,-85.2547988 180 | 1550 Mall of Georgia Blvd,Buford,Georgia,30519-6551,34.0620378,-83.9943156 181 | 2900 Cumberland Mall,Atlanta,Georgia,30339-8107,33.8816153,-84.4694359 182 | 6350 Peachtree Dunwoody Rd N.E.,Atlanta,Georgia,30328-4527,33.927816,-84.352462 183 | 1700 Mount Zion Road,Morrow,Georgia,30260-3014,33.572052,-84.333989 184 | 645 Barrett Parkway,Kennesaw,Georgia,30144-4922,34.0092178,-84.5686483 185 | 3980 Venture Drive,Duluth,Georgia,30096-5077,33.947764,-84.143039 186 | 2855 Jordan Ct,Alpharetta,Georgia,30004-3869,34.090513,-84.277902 187 | 500 Brookhaven Ave,Atlanta,Georgia,30319,33.8709805,-84.3350283 188 | 73-5600 Maiau Street,Kailua-Kona (Hawaii),Hawaii,96740-2630,19.687344,-156.017457 189 | 333A Keahole Street,Honolulu (Oahu),Hawaii,96825-3428,21.282117,-157.7119219 190 | 94-1231 Ka Uka Blvd,Waipahu (Oahu),Hawaii,96797-4495,21.4188931,-158.0063848 191 | 4300 Nuhou St,Lihue,Hawaii,96766-8002,21.9665355,-159.3799149 192 | 540 Haleakala Highway,Kahului (Maui),Hawaii,96732-2302,20.887082,-156.451364 193 | 4589 Kapolei Parkway,Kapolei (Oahu),Hawaii,96707-1879,21.3272958,-158.0881083 194 | 525 Alakawa Street,Iwilei (Oahu),Hawaii,96817,21.3184739,-157.870711 195 | 355 East Neider Avenue,Coeur D\'Alene,Idaho,83815-3723,47.7080223,-116.7819023 196 | 2051 S. Cole Road,Boise,Idaho,83709-2815,43.585487,-116.276686 197 | 16700 N Marketplace Blvd,Nampa,Idaho,83687-7909,43.615175,-116.592784 198 | 731 Pole Line Road,Twin Falls,Idaho,83301-3036,42.591998,-114.4656395 199 | 305 West Quinn Rd,Pocatello,Idaho,83201-4988,42.905739,-112.468931 200 | 7311 North Melvina Ave,Niles,Illinois,60714-3905,42.014665,-87.7815541 201 | 7300 S. Cicero Ave,Bedford Park,Illinois,60629-5817,41.7600708,-87.7418497 202 | 2746 N. Clybourn Avenue,Chicago,Illinois,60614-1006,41.9297729,-87.6770447 203 | 1901 West 22nd Street,Oak Brook,Illinois,60523-1785,41.845569,-87.964888 204 | 9915 W 159th St,Orland Park,Illinois,60467-4572,41.6011911,-87.8618256 205 | 1375 North Meacham Rd,Schaumburg,Illinois,60173-4805,42.0565401,-88.0449252 206 | 250 N. Randall Rd.,Lake in the Hills,Illinois,60156-5943,42.1788808,-88.3387302 207 | 505 W. Army Trail Road,Bloomingdale,Illinois,60108-1391,41.9396832,-88.1323182 208 | 680 S. Rand Road,Lake Zurich,Illinois,60047-3409,42.187135,-88.093412 209 | 1320 S. Route 59,Naperville,Illinois,60564,41.7443388,-88.2058422 210 | 830 E Boughton Rd,Bolingbrook,Illinois,60440,41.7265993,-88.0325566 211 | 221 S. Randall Rd,St. Charles,Illinois,60174,41.9073026,-88.3400191 212 | 8400 W North Ave,Melrose Park,Illinois,60160,41.9091302,-87.8367543 213 | 999 N. Elmhurst Road,Mount Prospect,Illinois,60056,42.0831646,-87.9332458 214 | 25901 Riverwoods Rd,Mettawa,Illinois,60045,42.240048,-87.9057161 215 | 2900 Patriot Blvd,Glenview,Illinois,60026,42.1090138,-87.8214507 216 | 1310 E. 79th Ave,Merrillville,Indiana,46410-5768,41.4738435,-87.3218679 217 | 9010 Michigan Road,Indianapolis,Indiana,46268,39.916735,-86.226227 218 | 6110 East 86th Street,Castleton,Indiana,46250,39.913779,-86.061793 219 | 7205 Mills Civic Parkway,West Des Moines,Iowa,50266-8140,41.562811,-93.804976 220 | 9350 Marshall Drive,Lenexa,Kansas,66215-3845,38.95885,-94.733002 221 | 12221 Blue Valley Parkway,Overland Park,Kansas,66213,38.906906,-94.670999 222 | 5020 Norton Healthcare Blvd,Louisville,Kentucky,40241-2835,38.3163673,-85.5728641 223 | 2400 Five Lees Lane,Lanham,Maryland,20706,38.9731445,-76.8160858 224 | 10 Monocacy Blvd.,Frederick,Maryland,21701-6554,39.4018344,-77.4075364 225 | 9919 Pulaski Highway,Baltimore,Maryland,21220-1423,39.358195,-76.444565 226 | 7077 Arundel Mills Circle,Hanover,Maryland,21076-1387,39.15582,-76.730744 227 | 6675 Marie Curie Drive,Elkridge,Maryland,21075-6457,39.186369,-76.793645 228 | 575 East Ordnance Road,Glen Burnie,Maryland,21060-6575,39.2035401,-76.5939428 229 | 880 Russell Avenue,Gaithersburg,Maryland,20879-3506,39.155646,-77.214124 230 | 10925 Baltimore Avenue (Route 1),Beltsville,Maryland,20705-2117,37.228312,-77.3903001 231 | 16006 Crain Highway SE,Brandywine,Maryland,20613-8081,38.6645584,-76.8747015 232 | 71 Second Avenue,Waltham,Massachusetts,02451-1107,42.395066,-71.265264 233 | 120 Stockwell Drive,Avon,Massachusetts,02322-1149,42.136598,-71.066458 234 | 400 Commercial Circle,Dedham,Massachusetts,02026-2635,42.230996,-71.17497 235 | 11 Newbury Street (Route 1),Danvers,Massachusetts,01923-1014,37.226693,-77.401371 236 | 119 Daggett Drive,West Springfield,Massachusetts,01089-4672,42.13216,-72.620485 237 | 2 Mystic View Road,Everett,Massachusetts,2149,42.4017251,-71.0695264 238 | 5100 28th Street SE,Grand Rapids,Michigan,49512-2049,42.910564,-85.542713 239 | 4901 Wilson Avenue,Wyoming,Michigan,49418-8788,42.875818,-85.76385 240 | 400 Brown Road,Auburn Hills,Michigan,48326-1305,42.7070854,-83.2963467 241 | 45460 Market Street,Shelby Township,Michigan,48315-6204,42.6295851,-82.98077 242 | 2343 South Telegraph Rd,Bloomfield,Michigan,48302-0254,42.606916,-83.293322 243 | 20000 Haggerty Rd,Livonia,Michigan,48152-1011,42.432885,-83.430471 244 | 13700 Middlebelt Road,Livonia,Michigan,48150-2215,42.3813419,-83.332026 245 | 6700 Whitmore Lake Rd,Brighton,Michigan,48116-2160,42.5044786,-83.7594455 246 | 30550 Stephenson Highway,Madison Heights,Michigan,48071-1611,42.5161919,-83.117129 247 | 27118 Gratiot Avenue,Roseville,Michigan,48066-2915,42.4959314,-82.9371897 248 | 3000 Commerce Crossing,Commerce Tnshp,Michigan,48390,42.534006,-83.444799 249 | 12547 Riverdale Blvd.,Coon Rapids,Minnesota,55448-6708,45.1985438,-93.3502188 250 | 5801 16th Street West,St. Louis Park,Minnesota,55416-1446,44.9676254,-93.3495534 251 | 11330 Fountains Dr N,Maple Grove,Minnesota,55369-7200,45.09298,-93.424382 252 | 12011 Technology Drive,Eden Prairie,Minnesota,55344-3620,44.8603208,-93.4358767 253 | 1431 Beam Ave,Maplewood,Minnesota,55109-1064,45.029375,-93.039964 254 | 14050 Burnhaven Dr,Burnsville,Minnesota,55337,44.7481179,-93.2934839 255 | 241 East Linwood Blvd,Kansas City,Missouri,64111-1119,39.0682148,-94.5815625 256 | 19040 E. Valley View Pkwy,Independence,Missouri,64055-7004,39.0372034,-94.3551426 257 | 200 Costco Way,St. Peters,Missouri,63376-4385,38.797262,-90.607339 258 | 4200 Rusty Road,St. Louis,Missouri,63128-1973,38.506798,-90.3408851 259 | 301 Highlands Blvd Drive,Manchester,Missouri,63011,38.597951,-90.506914 260 | 2330 US Highway 93 N,Kalispell,Montana,59901-2547,48.174177,-114.29897 261 | 3220 Northern Pacific Avenue,Missoula,Montana,59808-1338,46.89293,-114.037358 262 | 2505 Catron Street,Bozeman,Montana,59718-7993,45.71334,-111.070653 263 | 2195 E Custer Ave,Helena,Montana,59602-1217,46.616615,-112.0049737 264 | 2290 King Avenue West,Billings,Montana,59102-7415,45.7550163,-108.5727142 265 | 12300 West Dodge Rd,Omaha,Nebraska,68154-2382,41.265224,-96.10534 266 | 700 Old Clear Creek Road,Carson City,Nevada,89705-6853,39.1123484,-119.8451288 267 | 2200 Harvard Way,Reno,Nevada,89502-4000,39.503102,-119.785095 268 | 4810 Galleria Parkway,Sparks,Nevada,89436-9605,39.579204,-119.738792 269 | 801 S. Pavilion Center Drive,Las Vegas,Nevada,89144-4566,36.1633476,-115.3318905 270 | 6555 N Decatur Blvd,Las Vegas,Nevada,89131-2796,36.278946,-115.209445 271 | 222 S Martin Luther King Blvd,Las Vegas,Nevada,89106-4305,36.1721288,-115.1608236 272 | 791 Marks Street,Henderson,Nevada,89014-8601,36.06985,-115.037166 273 | 311 Daniel Webster Highway,Nashua,New Hampshire,03060-5702,42.7023,-71.443126 274 | 2210 Route 27 North,Edison,New Jersey,08817-3314,40.5317305,-74.3766237 275 | 325 Promenade Blvd.,Bridgewater,New Jersey,08807-3457,40.5644881,-74.5553081 276 | 465 Route 70,Brick,New Jersey,08723-4049,40.0547573,-74.1563178 277 | 100 Centerton Road,Mount Laurel,New Jersey,08054-6103,39.9505868,-74.8761132 278 | 245 Stafford Park Blvd,Stafford Township,New Jersey,08050-2734,39.70721,-74.287661 279 | 156 SR-10 West,East Hanover,New Jersey,07936-2107,40.8096853,-74.381266 280 | 315 Route 15,Wharton,New Jersey,07885-1224,40.906896,-74.569445 281 | 2835 Route 35 South,Hazlet,New Jersey,07730-1516,40.4194098,-74.1705136 282 | 80 South River Street,Hackensack,New Jersey,07601-7110,40.876239,-74.038818 283 | 77 Willowbrook Boulevard,Wayne,New Jersey,07470-7055,40.8872413,-74.2541309 284 | 1055 Hudson St,Union,New Jersey,07083-6809,40.6931,-74.2873 285 | 20 Bridewell Place,Clifton,New Jersey,07014-1724,40.830625,-74.1366964 286 | 500 Eubank Blvd SE,Albuquerque,New Mexico,87123-3338,35.068225,-106.530385 287 | 9955 Coors Bypass NW,Albuquerque,New Mexico,87114-5963,35.19547,-106.66139 288 | 1420 Renaissance Boulevard NE,Albuquerque,New Mexico,87107-6811,35.136386,-106.614209 289 | 3000 Middle Country Road,Nesconset,New York,11767-1072,40.862057,-73.130236 290 | 625 Broadhollow Road,Melville,New York,11747-5007,40.757604,-73.420634 291 | 125 Beacon Drive,Holbrook,New York,11741-4309,40.773266,-73.050016 292 | 10 Garret Place,Commack,New York,11725-5421,40.8067522,-73.2865925 293 | 1250 Old Country Road,Westbury,New York,11590-5641,40.743347,-73.601117 294 | 605 Rockaway Turnpike,Lawrence,New York,11559-1029,40.6307824,-73.7398576 295 | 976 3rd Avenue,Brooklyn,New York,11232-2400,40.6554813,-74.0090742 296 | 32-50 Vernon Boulevard,Long Island City,New York,11106-4927,40.7673216,-73.9375771 297 | 50 Overlook Boulevard,Nanuet,New York,10954-5292,41.107253,-74.025274 298 | 1 Industrial Lane,New Rochelle,New York,10805-1203,40.896403,-73.7938571 299 | 20 Stew Leonard Drive,Yonkers,New York,10710-7202,40.9749617,-73.8612905 300 | 2975 Richmond Avenue,Staten Island,New York,10314-5851,40.57268,-74.168663 301 | 61-35 Junction Boulevard,Rego Park,New York,11374,40.7333304,-73.8635717 302 | 1 Westchester Ave.,Port Chester,New York,10573,40.997205,-73.661561 303 | 517 E 117th St,New York,New York,10035,40.7953918,-73.9318516 304 | 5351 Gingerwood Dr,Wilmington,North Carolina,28405-3091,34.2471728,-77.8738996 305 | 500 Tyvola Road,Charlotte,North Carolina,28217-3504,35.162075,-80.883958 306 | 2125 Matthews Township Pkwy,Matthews,North Carolina,28105-5766,35.130096,-80.704742 307 | 1510 North Pointe Drive,Durham,North Carolina,27705-3405,36.028911,-78.914527 308 | 2838 Wake Forest Rd,Raleigh,North Carolina,27609-7840,35.818255,-78.622315 309 | 4201 West Wendover Ave,Greensboro,North Carolina,27407-1908,36.058064,-79.883838 310 | 1085 Hanes Mall Blvd.,Winston Salem,North Carolina,27103-1310,36.064731,-80.317952 311 | 9691 Waterstone Blvd,Cincinnati,Ohio,45249-8220,39.2956999,-84.3032 312 | 1100 East Kemper Road,Springdale,Ohio,45246-3321,39.288172,-84.4510214 313 | 1409 Golden Gate Blvd.,Cleveland,Ohio,44124-1808,41.5241698,-81.4467262 314 | 3405 West Central Ave,Toledo,Ohio,43606-1402,41.6773358,-83.6241301 315 | 1500 Gemini Place,Columbus,Ohio,43240-7002,40.150574,-82.976965 316 | 16690 Royalton Rd,Strongsville,Ohio,44136,41.3130793,-81.8162777 317 | 35804 Detroit Road,Avon,Ohio,44011,41.461881,-82.012682 318 | 2500 Northeast Highway 20,Bend,Oregon,97701-6277,44.057267,-121.267616 319 | 3639 Crater Lake Highway,Medford,Oregon,97504-9259,42.3687759,-122.858378 320 | 2828 Chad Drive,Eugene,Oregon,97408-7337,44.089117,-123.06635 321 | 3130 Killdeer Ave,Albany,Oregon,97321-5325,44.6391268,-123.065814 322 | 1010 Hawthorne Avenue SE,Salem,Oregon,97301-5090,44.918976,-122.995309 323 | 4849 NE 138th Ave,Portland,Oregon,97230-3401,45.559288,-122.523747 324 | 7855 SW Dartmouth Ave,Tigard,Oregon,97223-8401,45.4367116,-122.7564796 325 | 1255 NE 48th Ave,Hillsboro,Oregon,97124-5008,45.535709,-122.9355 326 | 25900 SW Heather Place,Wilsonville,Oregon,97070-5785,45.332949,-122.764093 327 | 13130 SE 84th Avenue,Clackamas,Oregon,97015-9733,45.429738,-122.571376 328 | 15901 SW Jenkins,Aloha,Oregon,97006-5098,45.509085,-122.841919 329 | 4141 NE Stephens,Roseburg,Oregon,97470,43.2590258,-123.3513746 330 | 1804 SE Ensign Lane,Warrenton,Oregon,97146,46.1472442,-123.9174036 331 | 740 Upper State Road,North Wales,Pennsylvania,19454-1403,40.2391985,-75.2351214 332 | 201 Allendale Road,King of Prussia,Pennsylvania,19406-1634,40.091369,-75.385931 333 | 1875 Hempstead Road,Lancaster,Pennsylvania,17601-5671,40.052357,-76.256924 334 | 5125 Jonestown Road Suite 221,Harrisburg,Pennsylvania,17112-2983,40.3098913,-76.7996506 335 | 1050 Cranberry Square Drive,Cranberry Township,Pennsylvania,16066-6142,40.691215,-80.107991 336 | 501 W Waterfront Dr,West Homestead,Pennsylvania,15120-5009,40.4067922,-79.9195722 337 | 202 Costco Drive,Pittsburgh,Pennsylvania,15205,40.4581284,-80.1671087 338 | 1021 Woodruff Rd,Greenville,South Carolina,29607-4108,34.8360858,-82.311008 339 | 1021 Oak Forest Lane,Myrtle Beach,South Carolina,29577-9795,33.705698,-78.915906 340 | 3050 Ashley Town Center,Charleston,South Carolina,29414-5664,32.7953856,-80.0369654 341 | 211 W Blackstock Rd,Spartanburg,South Carolina,29301-1382,34.9370256,-81.9906978 342 | 3775 Hacks Cross Road,Memphis,Tennessee,38125-2302,35.0465639,-89.7972209 343 | 2431 North Germantown Parkway,Cordova,Tennessee,38016-4494,35.19204,-89.794989 344 | 6670 Charlotte Pike,Nashville,Tennessee,37209-4202,36.1392669,-86.885891 345 | 98 Seaboard Lane,Brentwood,Tennessee,37027-2930,35.963436,-86.818149 346 | 10401 Research Blvd,Austin,Texas,78759-5712,30.3968644,-97.7456423 347 | 5611 Utsa Blvd,San Antonio,Texas,78249-1619,29.576287,-98.597156 348 | 15330 IH-35 North,Selma,Texas,78154-3814,29.598391,-98.277915 349 | 12405 N. Gessner Rd.,Houston,Texas,77064-1170,29.9536092,-95.5468109 350 | 1150 Bunker Hill Road,Houston,Texas,77055-6208,29.787755,-95.529263 351 | 3836 Richmond Ave,Houston,Texas,77027-5802,29.7327829,-95.4389565 352 | 5300 Overton Ridge Blvd,Fort Worth,Texas,76132-3301,32.6775654,-97.4087829 353 | 2601 E. State Hwy 114,Southlake,Texas,76092-6668,32.9419217,-97.1121332 354 | 600 West Arbrook Blvd,Arlington,Texas,76014-3702,32.6846619,-97.1127486 355 | 1701 Dallas Pkwy,Plano,Texas,75093-4520,33.023989,-96.831701 356 | 3800 N. Central Expressway,Plano,Texas,75074-2221,33.0441379,-96.699179 357 | 851 S. State Hwy 121,Lewisville,Texas,75067-4158,32.9952011,-96.9648545 358 | 6101 Gateway West Blvd,El Paso,Texas,79925,31.783497,-106.410297 359 | 1201 N. Loop 1604 East,San Antonio,Texas,78232,29.611904,-98.474805 360 | 250 West Highway 67,Duncanville,Texas,75137,32.620946,-96.910279 361 | 1225 State Hwy 276,Rockwall,Texas,75032,32.9087304,-96.4454985 362 | 835 North 3050 East,St. George,Utah,84790-9041,37.122515,-113.523381 363 | 3656 Wall Avenue,Ogden,Utah,84405-7101,41.197413,-111.9793725 364 | 3747 South 2700 West,West Valley,Utah,84119-4903,40.6903769,-111.9541263 365 | 1818 South 300 West,Salt Lake City,Utah,84115-1805,40.730657,-111.90294 366 | 648 East 800 South,Orem,Utah,84097-6528,40.2822443,-111.6798494 367 | 11100 S. Auto Mall Drive,Sandy,Utah,84070-4171,40.5506675,-111.8934445 368 | 198 North 1200 East,Lehi,Utah,84043-2294,40.389719,-111.8229955 369 | 573 West 100 North,West Bountiful,Utah,84010-7018,40.889379,-111.895108 370 | 5201 South Intermountain Drive,Murray,Utah,84107,40.6577119,-111.889873 371 | 218 Lower Mountain View Drive,Colchester,Vermont,05446-5830,44.5034,-73.173988 372 | 12121 Jefferson Avenue,Newport News,Virginia,23602-6916,37.106044,-76.496559 373 | 850 Glenrock Road,Norfolk,Virginia,23502-3702,36.851561,-76.199681 374 | 3700 Price Club Boulevard,Midlothian,Virginia,23112-3371,37.438778,-77.587539 375 | 9650 West Broad Street,Glen Allen,Virginia,23060-4115,37.642294,-77.561159 376 | 1830 Reservoir Street,Harrisonburg,Virginia,22801-8742,38.428597,-78.85613 377 | 251 Front Royal Pike,Winchester,Virginia,22602-7319,39.157827,-78.157929 378 | 1200 South Fern Street,Arlington,Virginia,22202-2862,38.8624735,-77.0567423 379 | 2700 Potomac Mills Circle - Ste 200,Woodbridge,Virginia,22192-4653,38.642245,-77.294447 380 | 7373 Boston Boulevard,Springfield,Virginia,22153-2805,38.739883,-77.197301 381 | 4725 West Ox Road,Fairfax,Virginia,22030-6101,38.849315,-77.371784 382 | 1300 Edwards Ferry Road,Leesburg,Virginia,20176-3355,39.115223,-77.533198 383 | 21398 Price/Cascades Plaza,Sterling,Virginia,20164-6607,39.023821,-77.401854 384 | 14390 Chantilly Crossing Lane,Chantilly,Virginia,20151-2117,38.8951069,-77.4430209 385 | 10701 Sudley Manor Drive,Manassas,Virginia,20109-2845,38.783572,-77.5164269 386 | 3102 Plank Road #600,Fredericksburg,Virginia,22407,38.293479,-77.515701 387 | 8629 120th Avenue NE,Kirkland,Washington,98033-5865,47.681717,-122.182086 388 | 301 Fifth Street,Clarkston,Washington,99403-1860,46.4229672,-117.0442722 389 | 8505A W. Gage Blvd,Kennewick,Washington,99336-8120,46.225654,-119.235246 390 | 5601 East Sprague Avenue,Spokane,Washington,99212-0826,47.658358,-117.332852 391 | 7619 North Division Street,Spokane,Washington,99208-5613,47.724927,-117.411489 392 | 2310 Longfibre Rd,Union Gap,Washington,98903-1513,46.562354,-120.499306 393 | 375 Highline Drive South,East Wenatchee,Washington,98802-5344,47.401812,-120.280459 394 | 6720 NE 84th Street,Vancouver,Washington,98665-2016,45.684942,-122.604718 395 | 1470 Marvin Rd NE,Lacey,Washington,98516-3870,47.05934,-122.760723 396 | 5500 Littlerock Road,Tumwater,Washington,98512-7363,46.996336,-122.916699 397 | 3900 20th St. East,Fife,Washington,98424-1818,47.238297,-122.373955 398 | 2219 South 37th Street,Tacoma,Washington,98409-7473,47.224683,-122.469182 399 | 10000 Mickelberry Rd. NW,Silverdale,Washington,98383-8302,47.656571,-122.680673 400 | 955 West Washington St,Sequim,Washington,98382-3266,48.0770692,-123.1254206 401 | 955 West Washington St,Sequim,Washington,98382-3266,48.0770692,-123.1254206 402 | 1201 39th SW,Puyallup,Washington,98373-3803,47.156472,-122.308125 403 | 16616 Twin Lakes Ave,Marysville,Washington,98271-4701,48.147561,-122.191237 404 | 1725 S. Burlington Blvd,Burlington,Washington,98233-3223,48.453882,-122.338038 405 | 4299 Guide Meridian Street,Bellingham,Washington,98226-6475,48.7974507,-122.4882889 406 | 10200 19th Avenue SE,Everett,Washington,98208-4256,47.903556,-122.209275 407 | 400 Costco Drive Suite 150,Tukwila,Washington,98188-4808,47.445595,-122.24834 408 | 4401 4th Avenue South,Seattle,Washington,98134-2389,47.5649224,-122.3304533 409 | 1175 North 205th Street,Seattle,Washington,98133-3206,47.77567,-122.344819 410 | 24008 Snohomish-Woodinville Rd SE,Woodinville,Washington,98072-9743,47.779015,-122.1484548 411 | 19105 Highway 99,Lynnwood,Washington,98036-5228,47.82549,-122.309915 412 | 1801 10th Avenue NW,Issaquah,Washington,98027-5384,47.5512165,-122.0528913 413 | 35100 Enchanted Parkway South,Federal Way,Washington,98003-8314,47.2869797,-122.3135201 414 | 19610 SE 1st St,Vancouver,Washington,98607,45.6212994,-122.4591346 415 | 10990 Harbor Hill Dr,Gig Harbor,Washington,98335,47.3577482,-122.6038876 416 | 27520 Covington Way SE,Covington,Washington,98042,47.354838,-122.121185 417 | 2150 Deming Way,Middleton,Wisconsin,53562-5507,43.100195,-89.522751 418 | 950 Port Washington Rd,Grafton,Wisconsin,53024-9201,43.3246907,-87.9216153 419 | -------------------------------------------------------------------------------- /Data-Visualisation-with-R/Visualisation geographical data/Basic geog plot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/Data-Visualisation-with-R/Visualisation geographical data/Basic geog plot.png -------------------------------------------------------------------------------- /Data-Visualisation-with-R/Visualisation geographical data/base map.png: -------------------------------------------------------------------------------- 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20,37,9.2,65,6,18 12,120,11.5,73,6,19 13,137,10.3,76,6,20 135,269,4.1,84,7,1 49,248,9.2,85,7,2 32,236,9.2,81,7,3 64,175,4.6,83,7,5 40,314,10.9,83,7,6 77,276,5.1,88,7,7 97,267,6.3,92,7,8 97,272,5.7,92,7,9 85,175,7.4,89,7,10 10,264,14.3,73,7,12 27,175,14.9,81,7,13 7,48,14.3,80,7,15 48,260,6.9,81,7,16 35,274,10.3,82,7,17 61,285,6.3,84,7,18 79,187,5.1,87,7,19 63,220,11.5,85,7,20 16,7,6.9,74,7,21 80,294,8.6,86,7,24 108,223,8,85,7,25 20,81,8.6,82,7,26 52,82,12,86,7,27 82,213,7.4,88,7,28 50,275,7.4,86,7,29 64,253,7.4,83,7,30 59,254,9.2,81,7,31 39,83,6.9,81,8,1 9,24,13.8,81,8,2 16,77,7.4,82,8,3 122,255,4,89,8,7 89,229,10.3,90,8,8 110,207,8,90,8,9 44,192,11.5,86,8,12 28,273,11.5,82,8,13 65,157,9.7,80,8,14 22,71,10.3,77,8,16 59,51,6.3,79,8,17 23,115,7.4,76,8,18 31,244,10.9,78,8,19 44,190,10.3,78,8,20 21,259,15.5,77,8,21 9,36,14.3,72,8,22 45,212,9.7,79,8,24 168,238,3.4,81,8,25 73,215,8,86,8,26 76,203,9.7,97,8,28 118,225,2.3,94,8,29 84,237,6.3,96,8,30 85,188,6.3,94,8,31 96,167,6.9,91,9,1 78,197,5.1,92,9,2 73,183,2.8,93,9,3 91,189,4.6,93,9,4 47,95,7.4,87,9,5 32,92,15.5,84,9,6 20,252,10.9,80,9,7 23,220,10.3,78,9,8 21,230,10.9,75,9,9 24,259,9.7,73,9,10 44,236,14.9,81,9,11 21,259,15.5,76,9,12 28,238,6.3,77,9,13 9,24,10.9,71,9,14 13,112,11.5,71,9,15 46,237,6.9,78,9,16 18,224,13.8,67,9,17 13,27,10.3,76,9,18 24,238,10.3,68,9,19 16,201,8,82,9,20 13,238,12.6,64,9,21 23,14,9.2,71,9,22 36,139,10.3,81,9,23 7,49,10.3,69,9,24 14,20,16.6,63,9,25 30,193,6.9,70,9,26 14,191,14.3,75,9,28 18,131,8,76,9,29 20,223,11.5,68,9,30 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data Visualisation Libraries 2 | 3 | *Don’t simply show data, tell a story with it!* 4 | 5 | Yes, we have data and we have insights, now what? Obviously, the next step would be to communicate these findings with people so that they can take the necessary actions. One of the most effective ways to communicate data is through storytelling. But to be effective storytellers, we need to simplify things and not complicate them so that the real essence of the analysis is not lost. 6 | 7 | ## About this repository 8 | This repository contains the files linked to the articles that I wrote on **Medium** regarding different Data Visualisation libraries. 9 | 10 | ### Python 11 | * [PyViz: Simplifying the Data Visualisation process in Python.](https://towardsdatascience.com/pyviz-simplifying-the-data-visualisation-process-in-python-1b6d2cb728f1) 12 | 13 | * [Visualising Machine Learning Datasets with Google’s FACETS.](https://towardsdatascience.com/visualising-machine-learning-datasets-with-googles-facets-462d923251b3) 14 | 15 | * [Exploratory Data Visualisation with Altair](https://medium.com/analytics-vidhya/exploratory-data-visualisation-with-altair-b8d85494795c) 16 | 17 | * [Visualising Geospatial data with Python using Folium](https://medium.com/datadriveninvestor/visualising-geospatial-data-with-python-d3b1c519f31) 18 | 19 | * [Animations with Matplotlib](https://towardsdatascience.com/animations-with-matplotlib-d96375c5442c) 20 | 21 | --- 22 | 23 | ### R 24 | * [A Comprehensive Guide to Data Visualisation in R for Beginners](https://towardsdatascience.com/a-guide-to-data-visualisation-in-r-for-beginners-ef6d41a34174) 25 | 26 | --- 27 | 28 | ### Tableau 29 | 30 | * [Data Visualisation with Tableau](https://medium.com/@parulnith/data-visualisation-with-tableau-150f99a39bba) 31 | * [Python with Tableau](https://lnkd.in/fKjCwqT) 32 | * [R with Tableau](https://lnkd.in/fduEeat) 33 | * [SQL with Tableau](https://medium.com/@parulnith/using-tableau-to-leverage-sql-287365f90d3?source=friends_link&sk=c5a1cfbf08d4a6e926b884ffedbf87b1) 34 | * [Spreadsheets with Tableau](https://www.datacamp.com/community/tutorials/spreadsheets-tableau) 35 | * [Word Clouds in Tableau: Quick & Easy](https://towardsdatascience.com/word-clouds-in-tableau-quick-easy-e71519cf507a) 36 | * [Quadrant Analysis with Tableau](https://www.datacamp.com/community/tutorials/quadrant-analysis-in-tableau) 37 | * [Cluster Analysis with Tableau](https://www.datacamp.com/community/tutorials/quadrant-analysis-in-tableau) 38 | --- 39 | 40 | ### PowerBI 41 | * [Data Visualization with Power BI](https://www.datacamp.com/community/tutorials/data-visualisation-powerbi) 42 | * [SQL with PowerBI](https://www.datacamp.com/community/tutorials/sql-with-powerbi) 43 | --- 44 | 45 | ### Drag and Drop Visualisation tools 46 | [10 Free tools to get started with Data Visualisation-Easily & Instantly](https://towardsdatascience.com/10-free-tools-to-instantly-get-started-with-data-visualisation-d7fadb5f6dce) 47 | * Data Wrapper 48 | * RAWGraphs 49 | * Charted 50 | * Chart Studio 51 | * Fastcharts 52 | * Palladio 53 | * Openheatmap 54 | * MyHeatMap 55 | * Chartbuilder 56 | * Timeline.js 57 | 58 | 59 | -------------------------------------------------------------------------------- /Tableau-Projects/README.md: -------------------------------------------------------------------------------- 1 | # Tableau-Projects 2 | 3 | Was playing around with some spooky data in hashtag#Tableau and just realised it was Halloween's 🎃 today. So completed just in time to share it with all of you. 4 | Original Data source: https://lnkd.in/fB2W_JK 5 | Scraped, cleaned and geocoded data source: https://lnkd.in/fdMZ45H 6 | -------------------------------------------------------------------------------- /Tableau-Projects/Recreating-Gapminder-in-Tableau/README.md: -------------------------------------------------------------------------------- 1 | # Recreating Gapminder in Tableau: A Humble tribute to Hans Rosling 2 | 3 | Hans Rosling in his famous [video](https://www.gapminder.org/videos/the-joy-of-stats/) showed told us the story of the world’s 200 countries over 200 years using 120,000 numbers — in just four minutes. Plotting life expectancy against income for every country since 1810, Hans showed how the world we live in is radically different from the world most of us imagine to be. 4 | 5 | _I will try to recreate the same visualization (as shown in the video/talk) to analyse how Life Expectancy in years (health) and GDP per capita (wealth) have changed over time in the world for various countries.This will a small tribute to the master storyteller who passed away on 7 February 2017._ 6 | 7 | This is an excerpt from my Medium article with the same name. For the complete article, refer [here](https://medium.com/analytics-vidhya/recreating-gapminder-in-tableau-a-humble-tribute-to-hans-rosling-53de74b18ec) 8 | -------------------------------------------------------------------------------- /Tableau-Projects/Recreating-Gapminder-in-Tableau/data/countries_total.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/Tableau-Projects/Recreating-Gapminder-in-Tableau/data/countries_total.csv -------------------------------------------------------------------------------- /Tableau-Projects/Screenshot 2018-10-31 at 1.20.01 PM.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/Tableau-Projects/Screenshot 2018-10-31 at 1.20.01 PM.png -------------------------------------------------------------------------------- /Tableau-Projects/haunted_places.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/Tableau-Projects/haunted_places.csv -------------------------------------------------------------------------------- /Visualising-Geospatial-data-with-Python/Folium_Notebook.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Visualising Geospatial data with Python\n" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 22, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import folium\n", 17 | "import pandas as pd" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 23, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "## Country coordinates for plotting\n", 27 | "country_geo = 'world-countries.json'" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 24, 33 | "metadata": {}, 34 | "outputs": [ 35 | { 36 | "data": { 37 | "text/plain": [ 38 | "(5656458, 6)" 39 | ] 40 | }, 41 | "execution_count": 24, 42 | "metadata": {}, 43 | "output_type": "execute_result" 44 | } 45 | ], 46 | "source": [ 47 | "# Read in the World Development Indicators Database\n", 48 | "data = pd.read_csv('Indicators.csv')\n", 49 | "data.shape" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 25, 55 | "metadata": { 56 | "scrolled": true 57 | }, 58 | "outputs": [ 59 | { 60 | "data": { 61 | "text/html": [ 62 | "
\n", 63 | "\n", 76 | "\n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | "
CountryNameCountryCodeIndicatorNameIndicatorCodeYearValue
0Arab WorldARBAdolescent fertility rate (births per 1,000 wo...SP.ADO.TFRT19601.335609e+02
1Arab WorldARBAge dependency ratio (% of working-age populat...SP.POP.DPND19608.779760e+01
2Arab WorldARBAge dependency ratio, old (% of working-age po...SP.POP.DPND.OL19606.634579e+00
3Arab WorldARBAge dependency ratio, young (% of working-age ...SP.POP.DPND.YG19608.102333e+01
4Arab WorldARBArms exports (SIPRI trend indicator values)MS.MIL.XPRT.KD19603.000000e+06
\n", 136 | "
" 137 | ], 138 | "text/plain": [ 139 | " CountryName CountryCode IndicatorName \\\n", 140 | "0 Arab World ARB Adolescent fertility rate (births per 1,000 wo... \n", 141 | "1 Arab World ARB Age dependency ratio (% of working-age populat... \n", 142 | "2 Arab World ARB Age dependency ratio, old (% of working-age po... \n", 143 | "3 Arab World ARB Age dependency ratio, young (% of working-age ... \n", 144 | "4 Arab World ARB Arms exports (SIPRI trend indicator values) \n", 145 | "\n", 146 | " IndicatorCode Year Value \n", 147 | "0 SP.ADO.TFRT 1960 1.335609e+02 \n", 148 | "1 SP.POP.DPND 1960 8.779760e+01 \n", 149 | "2 SP.POP.DPND.OL 1960 6.634579e+00 \n", 150 | "3 SP.POP.DPND.YG 1960 8.102333e+01 \n", 151 | "4 MS.MIL.XPRT.KD 1960 3.000000e+06 " 152 | ] 153 | }, 154 | "execution_count": 25, 155 | "metadata": {}, 156 | "output_type": "execute_result" 157 | } 158 | ], 159 | "source": [ 160 | "data.head()" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": 26, 166 | "metadata": { 167 | "scrolled": true 168 | }, 169 | "outputs": [ 170 | { 171 | "data": { 172 | "text/plain": [ 173 | "0 Adolescent fertility rate (births per 1,000 wo...\n", 174 | "1 Age dependency ratio (% of working-age populat...\n", 175 | "2 Age dependency ratio, old (% of working-age po...\n", 176 | "3 Age dependency ratio, young (% of working-age ...\n", 177 | "4 Arms exports (SIPRI trend indicator values)\n", 178 | "5 Arms imports (SIPRI trend indicator values)\n", 179 | "6 Birth rate, crude (per 1,000 people)\n", 180 | "7 CO2 emissions (kt)\n", 181 | "8 CO2 emissions (metric tons per capita)\n", 182 | "9 CO2 emissions from gaseous fuel consumption (%...\n", 183 | "10 CO2 emissions from liquid fuel consumption (% ...\n", 184 | "11 CO2 emissions from liquid fuel consumption (kt)\n", 185 | "12 CO2 emissions from solid fuel consumption (% o...\n", 186 | "13 Death rate, crude (per 1,000 people)\n", 187 | "14 Fertility rate, total (births per woman)\n", 188 | "15 Fixed telephone subscriptions\n", 189 | "16 Fixed telephone subscriptions (per 100 people)\n", 190 | "17 Hospital beds (per 1,000 people)\n", 191 | "18 International migrant stock (% of population)\n", 192 | "19 International migrant stock, total\n", 193 | "20 Life expectancy at birth, female (years)\n", 194 | "21 Life expectancy at birth, male (years)\n", 195 | "22 Life expectancy at birth, total (years)\n", 196 | "23 Merchandise exports (current US$)\n", 197 | "24 Merchandise exports by the reporting economy (...\n", 198 | "Name: IndicatorName, dtype: object" 199 | ] 200 | }, 201 | "execution_count": 26, 202 | "metadata": {}, 203 | "output_type": "execute_result" 204 | } 205 | ], 206 | "source": [ 207 | "data.IndicatorName[:25]" 208 | ] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "metadata": {}, 213 | "source": [ 214 | "Getting the Life expectancy at birth, female (years)data from the entire dataset" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": 27, 220 | "metadata": {}, 221 | "outputs": [ 222 | { 223 | "data": { 224 | "text/html": [ 225 | "
\n", 226 | "\n", 239 | "\n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | "
CountryNameCountryCodeIndicatorNameIndicatorCodeYearValue
5377669Arab WorldARBLife expectancy at birth, female (years)SP.DYN.LE00.FE.IN201372.536117
5377670Arab WorldARBLife expectancy at birth, male (years)SP.DYN.LE00.MA.IN201368.848383
5377671Arab WorldARBLife expectancy at birth, total (years)SP.DYN.LE00.IN201370.631305
5378129Caribbean small statesCSSLife expectancy at birth, female (years)SP.DYN.LE00.FE.IN201374.757382
5378130Caribbean small statesCSSLife expectancy at birth, male (years)SP.DYN.LE00.MA.IN201369.183365
\n", 299 | "
" 300 | ], 301 | "text/plain": [ 302 | " CountryName CountryCode \\\n", 303 | "5377669 Arab World ARB \n", 304 | "5377670 Arab World ARB \n", 305 | "5377671 Arab World ARB \n", 306 | "5378129 Caribbean small states CSS \n", 307 | "5378130 Caribbean small states CSS \n", 308 | "\n", 309 | " IndicatorName IndicatorCode Year \\\n", 310 | "5377669 Life expectancy at birth, female (years) SP.DYN.LE00.FE.IN 2013 \n", 311 | "5377670 Life expectancy at birth, male (years) SP.DYN.LE00.MA.IN 2013 \n", 312 | "5377671 Life expectancy at birth, total (years) SP.DYN.LE00.IN 2013 \n", 313 | "5378129 Life expectancy at birth, female (years) SP.DYN.LE00.FE.IN 2013 \n", 314 | "5378130 Life expectancy at birth, male (years) SP.DYN.LE00.MA.IN 2013 \n", 315 | "\n", 316 | " Value \n", 317 | "5377669 72.536117 \n", 318 | "5377670 68.848383 \n", 319 | "5377671 70.631305 \n", 320 | "5378129 74.757382 \n", 321 | "5378130 69.183365 " 322 | ] 323 | }, 324 | "execution_count": 27, 325 | "metadata": {}, 326 | "output_type": "execute_result" 327 | } 328 | ], 329 | "source": [ 330 | "\n", 331 | "\n", 332 | "hist_indicator = 'Life expectancy at birth'\n", 333 | "hist_year = 2013\n", 334 | "mask1 = data['IndicatorName'].str.contains(hist_indicator) \n", 335 | "mask2 = data['Year'].isin([hist_year])\n", 336 | "\n", 337 | "# apply our mask\n", 338 | "stage = data[mask1 & mask2]\n", 339 | "stage.head()" 340 | ] 341 | }, 342 | { 343 | "cell_type": "markdown", 344 | "metadata": {}, 345 | "source": [ 346 | "## Setting data for plotting.\n", 347 | "Create a data frame with just the country codes and the values we want plotted." 348 | ] 349 | }, 350 | { 351 | "cell_type": "code", 352 | "execution_count": 28, 353 | "metadata": {}, 354 | "outputs": [ 355 | { 356 | "data": { 357 | "text/html": [ 358 | "
\n", 359 | "\n", 372 | "\n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | "
CountryCodeValue
5377669ARB72.536117
5377670ARB68.848383
5377671ARB70.631305
5378129CSS74.757382
5378130CSS69.183365
\n", 408 | "
" 409 | ], 410 | "text/plain": [ 411 | " CountryCode Value\n", 412 | "5377669 ARB 72.536117\n", 413 | "5377670 ARB 68.848383\n", 414 | "5377671 ARB 70.631305\n", 415 | "5378129 CSS 74.757382\n", 416 | "5378130 CSS 69.183365" 417 | ] 418 | }, 419 | "execution_count": 28, 420 | "metadata": {}, 421 | "output_type": "execute_result" 422 | } 423 | ], 424 | "source": [ 425 | "data_to_plot = stage[['CountryCode','Value']]\n", 426 | "data_to_plot.head()" 427 | ] 428 | }, 429 | { 430 | "cell_type": "markdown", 431 | "metadata": {}, 432 | "source": [ 433 | "## Label for the map" 434 | ] 435 | }, 436 | { 437 | "cell_type": "code", 438 | "execution_count": 29, 439 | "metadata": {}, 440 | "outputs": [], 441 | "source": [ 442 | "hist_indicator = stage.iloc[0]['IndicatorName']" 443 | ] 444 | }, 445 | { 446 | "cell_type": "markdown", 447 | "metadata": {}, 448 | "source": [ 449 | "# Visualising the Map" 450 | ] 451 | }, 452 | { 453 | "cell_type": "code", 454 | "execution_count": 30, 455 | "metadata": {}, 456 | "outputs": [], 457 | "source": [ 458 | "map = folium.Map(location=[100, 0], zoom_start=1.5)\n" 459 | ] 460 | }, 461 | { 462 | "cell_type": "code", 463 | "execution_count": 31, 464 | "metadata": {}, 465 | "outputs": [], 466 | "source": [ 467 | "# choropleth maps bind Pandas Data Frames and json geometries.\n", 468 | "map.choropleth(geo_data=country_geo, data=data_to_plot,\n", 469 | " columns=['CountryCode', 'Value'],\n", 470 | " key_on='feature.id',\n", 471 | " fill_color='YlGnBu', fill_opacity=0.7, line_opacity=0.2,\n", 472 | " legend_name=hist_indicator)" 473 | ] 474 | }, 475 | { 476 | "cell_type": "code", 477 | "execution_count": 32, 478 | "metadata": {}, 479 | "outputs": [], 480 | "source": [ 481 | "country_geo = 'world-countries.json'" 482 | ] 483 | }, 484 | { 485 | "cell_type": "code", 486 | "execution_count": 33, 487 | "metadata": {}, 488 | "outputs": [], 489 | "source": [ 490 | "map.save('plot_data.html')" 491 | ] 492 | }, 493 | { 494 | "cell_type": "code", 495 | "execution_count": 19, 496 | "metadata": { 497 | "scrolled": false 498 | }, 499 | "outputs": [ 500 | { 501 | "data": { 502 | "text/html": [ 503 | "" 504 | ], 505 | "text/plain": [ 506 | "" 507 | ] 508 | }, 509 | "execution_count": 19, 510 | "metadata": {}, 511 | "output_type": "execute_result" 512 | } 513 | ], 514 | "source": [ 515 | "\n", 516 | "# Import the Folium interactive html file\n", 517 | "from IPython.display import HTML\n", 518 | "HTML('')" 519 | ] 520 | }, 521 | { 522 | "cell_type": "code", 523 | "execution_count": null, 524 | "metadata": {}, 525 | "outputs": [], 526 | "source": [] 527 | } 528 | ], 529 | "metadata": { 530 | "kernelspec": { 531 | "display_name": "Python 3", 532 | "language": "python", 533 | "name": "python3" 534 | }, 535 | "language_info": { 536 | "codemirror_mode": { 537 | "name": "ipython", 538 | "version": 3 539 | }, 540 | "file_extension": ".py", 541 | "mimetype": "text/x-python", 542 | "name": "python", 543 | "nbconvert_exporter": "python", 544 | "pygments_lexer": "ipython3", 545 | "version": "3.6.5" 546 | } 547 | }, 548 | "nbformat": 4, 549 | "nbformat_minor": 2 550 | } 551 | -------------------------------------------------------------------------------- /Visualising-Geospatial-data-with-Python/README.md: -------------------------------------------------------------------------------- 1 | # Visualising Geospatial data with Python 2 | 3 | An overview of the Folium library to visualize Geospatial data 4 | 5 | 6 | Data visualization is a broader term that describes an effort to help people understand the importance of data by placing it in a visual context. Patterns, trends, and correlations can be easily shown visually which otherwise might go unnoticed in textual data. It is a fundamental part of the data scientist’s toolkit. Creating visualisations is pretty easy but creating good ones is much harder. It requires an eye for detail and a good amount of expertise to create visualisations which are simple yet effective. Powerful visualisation tools and libraries are available today which have redefined the meaning of visualisation. 7 | 8 | __This is an excerpt from my article published in KDnuggets. For the complete article, refer__ [here](https://www.kdnuggets.com/2018/09/visualising-geospatial-data-python-folium.html). 9 | -------------------------------------------------------------------------------- /Visualising-Geospatial-data-with-Python/map.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/Visualising-Geospatial-data-with-Python/map.gif -------------------------------------------------------------------------------- /images/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/images/.DS_Store -------------------------------------------------------------------------------- /images/Altair.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/images/Altair.png -------------------------------------------------------------------------------- /images/Facets.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/images/Facets.png -------------------------------------------------------------------------------- /images/Folium.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/images/Folium.png -------------------------------------------------------------------------------- /images/PyViz.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/parulnith/Data-Visualisation-libraries/97b09185d011f9bd9b0ea216c60bc75f9c4bfdb0/images/PyViz.gif --------------------------------------------------------------------------------