# 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 .

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

 ` 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:

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