 # Exploring Data Distribution | Set 2

Terms related to data dissemination research

` - & gt; Boxplot - & gt; Frequency Table - & gt; Histogram - & gt; 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: