{"id":240,"date":"2021-09-15T07:12:32","date_gmt":"2021-09-15T07:12:32","guid":{"rendered":"https:\/\/www.kindsonthegenius.com\/data-science\/?p=240"},"modified":"2021-09-25T13:48:37","modified_gmt":"2021-09-25T13:48:37","slug":"a-class-on-data-visualization-with-python-a-data-science-primer","status":"publish","type":"post","link":"https:\/\/www.kindsonthegenius.com\/data-science\/a-class-on-data-visualization-with-python-a-data-science-primer\/","title":{"rendered":"Class 2 – A Class on Data Visualization with Python – A Data Science Primer"},"content":{"rendered":"

In this class we would cover Data Visualization with Python. This class follows from Part 1: Your First Data Science Class<\/a>. I recommend you check out that first if you are a beginner.<\/p>\n

The following is covered in this class:<\/p>\n

    \n
  1. Our Dataset<\/a><\/li>\n
  2. Univariate Plots: Understanding Attributes Independently<\/a><\/li>\n
  3. Histogram<\/a><\/li>\n
  4. Density Plots<\/a><\/li>\n
  5. Box Plots (Whisker Plot)<\/a><\/li>\n
  6. Multivariate Plots: Relationship Variables<\/a><\/li>\n
  7. Correlation Matrix Plot<\/a><\/li>\n
  8. Scatter Matrix Plot<\/a><\/li>\n
  9. Using Heatmap – Seaborn<\/a><\/li>\n
  10. Using Matshow<\/a><\/li>\n
  11. Pairplot<\/a><\/li>\n<\/ol>\n

    Class 2 Video of Data Visualization<\/a><\/p>\n

    1. Our Datatset – The Wine Dataset<\/strong><\/h4>\n

    The dataset of wine was obtained from the UCI Machine Learning Repository. <\/span>We would use the wine.csv file which is available for free from here<\/a>. According to the documentation of this dataset, the data consists of 13 physiochemical parameters measured in 178 different wine samples from three distinct cultivars(variety produced by selective breeding) grown in Italy.<\/p>\n

    Use the code below to import your dataset:<\/p>\n

    <\/p>\n

    from<\/span> pandas<\/span> import<\/span> ExcelWriter\r\nfrom<\/span> pandas<\/span> import<\/span> ExcelFile\r\npath =<\/span> r\"\/Users\/kindsonmunonye\/Datasets\/wine.xlsx\"<\/span>\r\nwine_data =<\/span> pd.<\/span>read_excel(path, header=<\/span>0<\/span>)\r\n<\/pre>\n
    The parameters are given below:<\/p>\n
      \n
    1. Alcohol<\/li>\n
    2. Metallic Acid<\/li>\n
    3. Ash<\/li>\n
    4. Alkalinity of Ash<\/li>\n
    5. Magnesium<\/li>\n
    6. Total Phenols<\/li>\n
    7. Flavanoids<\/li>\n
    8. Flavanoid Phnols<\/li>\n
    9. Proanthocyanins<\/li>\n
    10. Color Intensity<\/li>\n
    11. Hue<\/li>\n
    12. OD280\/OD315 of Diluted wines<\/li>\n
    13. Prolines<\/li>\n<\/ol>\n
       <\/p>\n
      2. Univariate Plots: Visualising\u00a0Individual Features<\/strong><\/h4>\n
      This type of plots help use understand individual variables of our dataset independent of other variables. Some of the univariate plots we would use in this class includes, histograms, univariate scatter plots, line plots.<\/p>\n
      We begin with histogram<\/p>\n
       <\/p>\n
      3. Histograms<\/strong><\/h4>\n
      A histogram is a plot that groups the data into bins or vertical bars. Each attributes is represented with bin whose height represents the values of the attribute. An example of use of histogram is to get the count of observations in given category of the totals of certain columns.<\/p>\n
      Use the code below to get a histogram plot of the wine dataset:<\/p>\n
      # HISTOGRAM<\/span>\r\nfig =<\/span> plt.<\/span>figure(figsize =<\/span> (15<\/span>,20<\/span>))\r\nax =<\/span> fig.<\/span>gca()\r\nwine_data.<\/span>hist(ax =<\/span> ax)\r\n# wine_data.hist(ax = ax, column='Wine') for a single data column<\/span>\r\nplt.<\/span>show()\r\n<\/pre>\n
       <\/p>\n
      4. Density Plot<\/strong><\/h4>\n
      A density plot is similar to a histogram bu it uses a smooth curve to represent the data attributes. It uses the kernel density estimate to show the probability density function (PDF) of the variables.<\/p>\n
      The code below provides the density plot of the wine dataset<\/p>\n
      # DENSITY PLOT<\/span>\r\nfig =<\/span> plt.<\/span>figure(figsize =<\/span> (15<\/span>,20<\/span>))\r\nax =<\/span> fig.<\/span>gca()\r\nwine_data.<\/span>plot(ax =<\/span> ax, kind=<\/span>'density'<\/span>, subplots=<\/span>True<\/span>, layout=<\/span>(4<\/span>,4<\/span>), sharex=<\/span>False<\/span>)\r\nplt.<\/span>show()\r\n<\/pre>\n
      The kinds of plot can be changes into any of the following.<\/p>\n