Visualizing the data

• First, we will import Pandas as pd, seaborn as sns, Pyplot as plt, and ggplot

  import pandas as <<your code goes here>>
  import seaborn as <<your code goes here>>
  import matplotlib.pyplot as <<your code goes here>>
  from statsmodels.graphics.gofplots import qqplot
  

• Next, we will load the Titanic dataset we used earlier

  train = pd.read_csv('/cxldata/datasets/project/titanic/train.csv')
  

• Now, we will create a function named box_plots to plot the data for a particular column from our dataset, and then we will call this function using the Age column

  def <<your code goes here>>(df):
      plt.figure(figsize=(10, 4))
      plt.title("Box Plot")
      sns.boxplot(df)
      plt.show()
  
  box_plots(train['Age'])
  

  Notice the datapoints lying towards the extreme right, those are the outliers.

• Now, we will create a function named hist_plots to plot the data for a particular column from our dataset, and then we will call this function using the Age column

  def <<your code goes here>>(df):
      plt.figure(figsize=(10, 4))
      plt.hist(df)
      plt.title("Histogram Plot")
      plt.show()
  
  hist_plots(train['Age'])
  

  Notice the tail at the end of the histogram towards the extreme right, those are the outliers.

• Now, we will create a function named scatter_plots to plot the data for a particular column from our dataset, and then we will call this function using the Age and Fare columns

  def <<your code goes here>>(df1,df2):
      fig, ax = plt.subplots(figsize=(10,4))
      ax.scatter(df1,df2)
      ax.set_xlabel('Age')
      ax.set_ylabel('Fare')
      plt.title("Scatter Plot")
      plt.show()
  
  scatter_plots(train['Age'],<<your code goes here>>)
  

  Notice the datapoints above the densely populated scatter plot, those are the outliers.

• Now, we will create a function named dist_plots to plot the data for a particular column from our dataset, and then we will call this function using the Fare column

  def <<your code goes here>>(df):
      plt.figure(figsize=(10, 4))
      sns.distplot(df)
      plt.title("Distribution plot")
      sns.despine()
      plt.show()
  
  dist_plots(train['Fare'])
  

  Notice the datapoints lying towards the tail on extreme right, those are the outliers.

• Finally, we will create a function named qq_plots to plot the data for a particular column from our dataset, and then we will call this function using the Fare column

  def <<your code goes here>>(df):
      plt.figure(figsize=(10, 4))
      qqplot(df,line='s')
      plt.title("Normal QQPlot")
      plt.show()
  
  qq_plots(train['Fare'])
  

  Notice the datapoints lying towards the extreme top right corner, those are the outliers.

We can observe the points beyond the regular boundaries, which are the outliers in these cases.