Correlation of height and weight in Men & Women

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 numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')

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In [3]:

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In [4]:

df.groupby('Gender').count()

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Out[4]:

Height
Weight

Gender

Female
5000
5000

Male
5000
5000

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In [5]:

df.plot(kind='scatter', y='Height', x='Weight')

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Out[5]:

<matplotlib.axes._subplots.AxesSubplot at 0x106964f60>

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In [6]:

df[df['Gender'] == 'Male'].plot(kind='scatter', y='Height', x='Weight')

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Out[6]:

<matplotlib.axes._subplots.AxesSubplot at 0x1069e0ac8>

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In [7]:

df[df['Gender'] == 'Female'].plot(kind='scatter', y='Height', x='Weight')

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Out[7]:

<matplotlib.axes._subplots.AxesSubplot at 0x107b11cf8>

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In [8]:

df[df['Gender'] == 'Female'].corr()

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Out[8]:

Height
Weight

Height
1.000000
0.849609

Weight
0.849609
1.000000

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In [9]:

df[df['Gender'] == 'Male'].corr()

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Out[9]:

Height
Weight

Height
1.000000
0.862979

Weight
0.862979
1.000000

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In [10]:

df[df['Gender'] == 'Female'].quantile(.25)

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Out[10]:

Height     61.894441
Weight    122.934096
dtype: float64

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In [11]:

df[df['Gender'] == 'Female'].quantile(.5)

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Out[11]:

Height     63.730924
Weight    136.117583
dtype: float64

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In [12]:

df[df['Gender'] == 'Female'].quantile(.75)

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Out[12]:

Height     65.563565
Weight    148.810926
dtype: float64

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In [14]:

df[df['Gender'] == 'Female'].mean()

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Out[14]:

Height     63.708774
Weight    135.860093
dtype: float64

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In [ ]:

#Working out the standard deviation in weight and height for women

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In [15]:

df[df['Gender'] == 'Female'].std()

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Out[15]:

Height     2.696284
Weight    19.022468
dtype: float64

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In [16]:

df[df['Gender'] == 'Male'].std()

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Out[16]:

Height     2.863362
Weight    19.781155
dtype: float64

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In [ ]:

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