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.
Loading Libraries
Python3
import numpy as npimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as plt |
Loading Data
Python3
data = pd.read_csv("../data/state.csv")# Adding a new column with derived data data['PopulationInMillions'] = data['Population']/1000000print (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
Python3
# Histogram Population In Millionsfig, 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 plot superposed over histogram.
Code – Density Plot for the data
Python3
# Density Plot - Populationfig, 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("Density Plot - Population", fontsize = 20) |
- Output :
