Exploring Data Distribution | Set 1

Terms of Data Dissemination Research

 -" Boxplot -" Frequency Table -" Histogram -" Density Plot 

• Boxplot: it is based on data percentiles as shown in the image below. The top and bottom of the boxplot represent the 75th ^th and 25th ^th percentiles of the data. The extended lines are known as whiskers, which include the range of the rest of the data. 
  

  

  To get link to csv file being 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 " )   # Add a new derived data column 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 ( 9 ,  15 )   ax1 = sns.boxplot (x = data.PopulationInMillions, orient = "v" ) ax1.set_ylabel ( " Population by Sta te in Millions " , fontsize = 15 ) ax1.set_title ( "Population - BoxPlot" , fontsize = 20 )

  Output:

  

• Frequency Table: is a tool for spreading data across evenly spaced ranges, segments and tells us how many values ​​are in each segment.

  Code # 1: Adding a column to execute crosstab and group functionality.

  # Perform binning action, binning has been made # selected to highlight the output for 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: crosstab — frequency table type

  # Cross Tab - frequency table type   pd.crosstab (data.PopulationInMillionsBins, data.Abbreviation, margins = True )

  Output:

  

  Code # 3: GroupBy — frequency table type

  # Groupby - frequency table type    data.groupby (data.PopulationInMillionsBins) [ ’Abbreviation’ ]. apply ( ’, ’ . join)

  Output: