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# Exploring Data Distribution | Set 2

• Last Updated : 21 Jan, 2019

Prerequisite: Exploring Data Distribution | Set 1

Terms related to Exploration of Data Distribution

```-> Boxplot
-> Frequency Table
-> Histogram
-> Density Plot
```

To get the link to `csv `file used, 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"``)`` ` `# Adding a new column with derived data ``data[``'PopulationInMillions'``] ``=` `data[``'Population'``]``/``1000000`` ` `print` `(data.head(``10``))`

Output : • Histogram: It is a way of visualizing data distribution through frequency table with bins on the x-axis and data count on 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 related to histogram as it shows data-values being distributed as continuous line. It is a smoothed histogram version. The output below is the density plor superposed over histogram.

Code – Density Plot for the data

 `# 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``)`

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