Skip to content
Related Articles

Related Articles

Improve Article

Exploring Data Distribution | Set 1

  • Last Updated : 21 Jan, 2019

Whenever we work in data science and machine learning, our approach of handling the data and finding something useful out of it is based on the distribution of the data.
Distribution means that how data can be present in different possible ways, the percentage of specific data, identifying the outliers. So, data distribution is the way of using graphical methods to organize and display useful information.

Terms related to Exploration of Data Distribution

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning - Basic Level Course

-> Boxplot
-> Frequency Table
-> Histogram 
-> Density Plot
  • Boxplot : It is based on the percentiles of the data as shown in the figure below. The top and bottom of the boxplot are 75th and 25th percentile of the data. The extended lines are known as whiskers that includes the range of rest of the data.



    To get the link to csv file used, click here.

    Code #1 : Loading Libraries




    import numpy as np
    import pandas as pd
    import seaborn as sns
    import matplotlib.pyplot as plt

    Code #2: Loading Data




    data = pd.read_csv("../data/state.csv")
      
    # Adding a new column with derived data 
    data['PopulationInMillions'] = data['Population']/1000000
      
    print (data.head(10))

    Output :

    Code #3 : BoxPlot




    # BoxPlot Population In Millions
    fig, ax1 = plt.subplots()
    fig.set_size_inches(915)
      
    ax1 = sns.boxplot(x = data.PopulationInMillions, orient ="v")
    ax1.set_ylabel("Population by State in Millions", fontsize = 15)
    ax1.set_title("Population - BoxPlot", fontsize = 20)

    Output :

  • Frequency Table : It is a tool to distribute the data into equally spaced ranges, segments and tells us how many values fall in each segment.

    Code #1: Adding a column to perform crosstab and groupby functionality.




    # Perform the binning action, the bins have been
    # chosen to accentuate the output for the Frequency Table
      
    data['PopulationInMillionsBins'] = pd.cut(
        data.PopulationInMillions, bins =[0, 1, 2, 5, 8, 12, 15, 20, 50])
      
    print (data.head(10))

    Output :

    Code #2: Cross Tab – a type of Frequency Table




    # Cross Tab - a type of Frequency Table
      
    pd.crosstab(data.PopulationInMillionsBins, data.Abbreviation, margins = True)

    Output :

    Code #3: GroupBy – a type of Frequency Table




    # Groupby - a type of Frequency Table
      
    data.groupby(data.PopulationInMillionsBins)['Abbreviation'].apply(', '.join)

    Output :




My Personal Notes arrow_drop_up
Recommended Articles
Page :