In this lab, we will look at building visualizations known as density plots to estimate the probability density for a given set of data.

You will be able to:

• Plot and interpret density plots and comment on the shape of the plot
• Estimate probabilities for continuous variables by using interpolation

Let's import the necessary libraries for this lab.

# Import required libraries
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import pandas as pd 

• Import the dataset 'weight-height.csv' as a pandas dataframe.

• Next, calculate the mean and standard deviation for weights and heights for men and women individually. You can simply use the pandas .mean() and .std() to do so.

Hint: Use your pandas dataframe subsetting skills like loc(), iloc(), and groupby()

data = None
male_df =  None
female_df =  None

# Male Height mean: 69.02634590621737
# Male Height sd: 2.8633622286606517
# Male Weight mean: 187.0206206581929
# Male Weight sd: 19.781154516763813
# Female Height mean: 63.708773603424916
# Female Height sd: 2.696284015765056
# Female Weight mean: 135.8600930074687
# Female Weight sd: 19.022467805319007

Male Height mean: 69.02634590621737
Male Height sd: 2.8633622286606517
Male Weight mean: 187.0206206581929
Male Weight sd: 19.781154516763813
Female Height mean: 63.708773603424916
Female Height sd: 2.696284015765056
Female Weight mean: 135.8600930074687
Female Weight sd: 19.022467805319007

• Make sure to create overlapping plots
• Use binsize = 10, set alpha level so that overlap can be visualized

# Your code here

# Record your observations - are these inline with your personal observations?

• Write a density function density() that uses interpolation and takes in a random variable
• Use np.histogram()
• The function should return two lists carrying x and y coordinates for plotting the density function

def density(x):
    
    pass


# Generate test data and test the function - uncomment to run the test
# np.random.seed(5)
# mu, sigma = 0, 0.1 # mean and standard deviation
# s = np.random.normal(mu, sigma, 100)
# x,y = density(s)
# plt.plot(x,y, label = 'test')
# plt.legend()

# Your code here 

# Your code here 

# Record your observations - are these inline with your personal observations?


# What is the takeaway when comparing male and female heights and weights?

# Code for heights here

# Code for weights here

# Your comments on the two approaches here. 
# are they similar? what makes them different if they are?

In this lesson, you learned how to build the probability density curves visually for a given dataset and compare the distributions visually by looking at the spread, center, and overlap. This is a useful EDA technique and can be used to answer some initial questions before embarking on a complex analytics journey.