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.



In [2]:

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



In [3]:

    
df = pd.read_csv("data/heights_weights_genders.csv")



In [4]:

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



In [5]:

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









    Out[5]:





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



In [6]:

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









    Out[6]:





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



In [7]:

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









    Out[7]:





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



In [8]:

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



In [9]:

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



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









    Out[10]:





Height     61.894441
Weight    122.934096
dtype: float64



In [11]:

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









    Out[11]:





Height     63.730924
Weight    136.117583
dtype: float64



In [12]:

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









    Out[12]:





Height     65.563565
Weight    148.810926
dtype: float64



In [14]:

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









    Out[14]:





Height     63.708774
Weight    135.860093
dtype: float64



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



In [15]:

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









    Out[15]:





Height     2.696284
Weight    19.022468
dtype: float64



In [16]:

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









    Out[16]:





Height     2.863362
Weight    19.781155
dtype: float64



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