Assignment #3: Using the heights_weights_genders.csv, analyze the difference between the height weight correlation in women and men.

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In [2]:

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

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In [3]:

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Out[3]:

Gender
Height
Weight

0
Male
73.847017
241.893563

1
Male
68.781904
162.310473

2
Male
74.110105
212.740856

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In [45]:

male = df[df['Gender']=='Male']
print("Male correlation: " + str(male.corr()['Height']['Weight']))

female = df[df['Gender']=='Female']
print("Female correlation: " + str(female.corr()['Height']['Weight']))

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Male correlation: 0.862978848616
Female correlation: 0.849608591419

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The positive correlation between height and weight is slightly higher for males.

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In [71]:

ax.set_prop_cycle('color', ['grey'])

fig, ax = plt.subplots(figsize=(5,5))
for category, group in df.groupby('Gender'):
ax.plot(group['Height'], group['Weight'], marker='o', linestyle='', label=category, markeredgewidth=0)

ax.set_ylabel('Weight')
ax.set_xlabel('Height')

ax.grid()
ax.set_axisbelow(True)
ax.grid(linestyle=':', linewidth='0.5', color='darkgrey')
ax.minorticks_on()
ax.grid(which='minor', linestyle=':', linewidth='0.5', color='darkgrey')

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)

ax.tick_params(which='both',
top='off',
left='off',
right='off',
bottom='off')

ax.set_xlim(45,90)
ax.set_ylim(45,300)
ax.legend(loc='lower right')

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Out[71]:

<matplotlib.legend.Legend at 0x110828240>

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