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



In [2]:

    
!pip install xlrd
!pip install matplotlib









    



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In [3]:

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









    



/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py:1035: UserWarning: Duplicate key in file "/Users/mercybenzaquen/.matplotlib/matplotlibrc", line #2
  (fname, cnt))



In [4]:

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



In [5]:

    
df.tail()



In [6]:

    
df_males = df[df['Gender'] == 'Male']



In [7]:

    
df_females = df[df['Gender'] == 'Female']

MEDIAN



In [8]:

    
plt.style.use('fivethirtyeight')



In [9]:

    
df_females.median()









    Out[9]:





Height     63.730924
Weight    136.117583
dtype: float64



In [10]:

    
df_males.median()









    Out[10]:





Height     69.027709
Weight    187.033546
dtype: float64

MEAN



In [11]:

    
df_females.mean()









    Out[11]:





Height     63.708774
Weight    135.860093
dtype: float64



In [13]:

    
df_males.mean()









    Out[13]:





Height     69.026346
Weight    187.020621
dtype: float64

RANGE



In [16]:

    
df_females['Height'].max() - df_females['Height'].min()









    Out[16]:





19.126452540972608



In [17]:

    
df_females['Weight'].max() - df_females['Weight'].min()









    Out[17]:





137.53708702680598



In [18]:

    
df_males['Height'].max() - df_males['Height'].min()









    Out[18]:





20.591837414639791



In [19]:

    
df_males['Weight'].max() - df_males['Weight'].min()









    Out[19]:





157.08675905728799

IQR



In [55]:

    
iqr_f = df_females.quantile(q=0.75)- df_females.quantile(q=0.25)
iqr_f









    Out[55]:





Height     3.669124
Weight    25.876830
dtype: float64



In [56]:

    
iqr_m = df_males.quantile(q=0.75)- df_males.quantile(q=0.25)
iqr_m









    Out[56]:





Height     3.814065
Weight    26.470034
dtype: float64

UAL and LAL



In [64]:

    
UAL_f= (iqr_f*1.5) + df_females.quantile(q=0.75)
UAL_f









    Out[64]:





Height     71.067251
Weight    187.626171
dtype: float64



In [65]:

    
LAL_f= df_females.quantile(q=0.25) - (iqr_f*1.5)  
LAL_f









    Out[65]:





Height    56.390756
Weight    84.118851
dtype: float64



In [66]:

    
UAL_m= (iqr_m*1.5) + df_males.quantile(q=0.75)
UAL_m









    Out[66]:





Height     76.709840
Weight    240.062854
dtype: float64



In [67]:

    
LAL_m= df_males.quantile(q=0.25) - (iqr_m*1.5)  
LAL_m









    Out[67]:





Height     61.453582
Weight    134.182716
dtype: float64

CORRELATION COEFICIENT



In [16]:

    
df_females.corr()



In [10]:

    
df_males.corr()

OUTLIERS



In [50]:

    
len(df_males)









    Out[50]:





5000



In [34]:

    
len(df_females[df_females['Height']> 71.284662])









    Out[34]:





7



In [35]:

    
len(df_females[df_females['Height']< 56.173345])  #In total 25 outliers









    Out[35]:





17



In [69]:

    
len(df_females[df_females['Weight']> 188.515978])









    Out[69]:





14



In [70]:

    
len(df_females[df_females['Weight']< 83.229044]) #In total 25 outliers









    Out[70]:





11



In [40]:

    
len(df_males[df_males['Height']> 76.709840])









    Out[40]:





18



In [44]:

    
len(df_males[df_males['Height']< 61.453582])  #In total 46 outliers









    Out[44]:





28



In [45]:

    
len(df_males[df_males['Weight']>240.062854])









    Out[45]:





26



In [71]:

    
len(df_males[df_males['Weight']< 134.182716]) #In total 46 outliers









    Out[71]:





20

GRAPHS



In [17]:

    
#fig, ax = plt.subplots()
ax= df_males.plot(kind='scatter', y='Height', x='Weight', color='darkblue', figsize= (7,5))
ax_f= df_females.plot(kind='scatter', y='Height', x='Weight', color='orange', figsize= (7,5))
ax_f.set_ylim([50, 75])
ax_f.set_xlim([60, 220])









    Out[17]:





(60, 220)

The correlation coeficient of weight and height for men and women is very similar, with 84% for women and 86% in men.



In [ ]:



In [ ]:

	Gender	Height	Weight
9995	Female	66.172652	136.777454
9996	Female	67.067155	170.867906
9997	Female	63.867992	128.475319
9998	Female	69.034243	163.852461
9999	Female	61.944246	113.649103