Exploring Data Distribution | Set 2

Terms related to data dissemination research

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

To get a link to the csv file in use, click here .

Loading Libraries

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

Loading data

data = pd .read_csv ( "../ data / state.csv" < / code> )   # Adding new derived data column data [ ’PopulationInMillions’ ] = data [ ’ Population’ ] / 1000000   print (data.head ( 10 ))

Output:

• Histogram: is a way to visualize the distribution of data through a table frequencies with cells along the X-axis and counting data along the Y-axis.

  Code — histogram

  # Histogram population in millions   fig, ax2 = plt.subplots () fig.set_size_inches ( 9 ,  15 )   ax2 = sns.distplot (data.PopulationInMillions, kde = False ) ax2.set_ylabel ( "Frequency" , fontsize = 15 ) ax2.set_xlabel ( "Population by State in Millions" , fontsize = 15 ) ax2.set_title ( "Population - Histogram" , fontsize = 20 )

  Output:

  

• Density plot : it is associated with a histogram as it shows the data values ​​distributed as a continuous line. This is a smoothed version of the histogram. The output below is — it is the density of the density superimposed on the histogram.

  Code — Data density plot

  # Density Plot - Population   fig, ax3 = plt.subplots () fig.set_size_inches ( 7 ,  9 )    ax3 = sns.distplot (data.Population, kde = True ) ax3.set_ylabel ( "Density" , fontsize = 15 ) ax3.set_xlabel ( " Murder Rate per Million " , fontsize = 15 ) ax3.set_title ( "Desnsity Plot - Population" , fontsize = 20 )

  Output: