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In [1]:

    
import pandas as pd
from numpy import random as rnd
import matplotlib.pyplot as plt
from IPython.display import display



In [2]:

    
# Имена 
names = ['Bob','Jessica','Mary','John','Mel']

rnd.seed(100)
random_names = [
    names[
        rnd.randint(low=0,high=len(names))
    ] for i in range(1000)
]
random_names[:10]









    Out[2]:





['Bob', 'Bob', 'John', 'Bob', 'Mary', 'Mel', 'Mary', 'Mary', 'Mary', 'Mary']



In [3]:

    
# Количество рожденных
rnd.seed(300)
births = [rnd.randint(low=0,high=1000) for i in range(1000)]

births[:10]









    Out[3]:





[209, 481, 917, 714, 9, 725, 91, 103, 406, 505]



In [4]:

    
# Упаковка
BabyDataSet = list(zip(random_names,births))
BabyDataSet[:10]









    Out[4]:





[('Bob', 209),
 ('Bob', 481),
 ('John', 917),
 ('Bob', 714),
 ('Mary', 9),
 ('Mel', 725),
 ('Mary', 91),
 ('Mary', 103),
 ('Mary', 406),
 ('Mary', 505)]



In [5]:

    
# Датафрейм
df = pd.DataFrame(data = BabyDataSet, columns=['Names', 'Births'])
df[:10]



In [6]:

    
df.head()



In [7]:

    
# уникальные значения в столбце
df['Names'].unique()









    Out[7]:





array(['Bob', 'John', 'Mary', 'Mel', 'Jessica'], dtype=object)



In [8]:

    
# описание столбца
df['Names'].describe()









    Out[8]:





count     1000
unique       5
top       John
freq       212
Name: Names, dtype: object



In [9]:

    
# группировка
name = df.groupby('Names')
df = name.sum()
df



In [10]:

    
Sorted = df.sort_values(['Births'], ascending=False)
Sorted.head(1)



In [11]:

    
df['Births'].plot.bar()
plt.show()

print("The most popular name")
df.sort_values(by='Births', ascending=False)









    












    



The most popular name






    Out[11]:







  
    
      
      Births
    
    
      Names
      
    
  
  
    
      Mel
      106947
    
    
      John
      105402
    
    
      Bob
      94634
    
    
      Jessica
      92289
    
    
      Mary
      92007



In [ ]:

	Names	Births
0	Bob	209
1	Bob	481
2	John	917
3	Bob	714
4	Mary	9
5	Mel	725
6	Mary	91
7	Mary	103
8	Mary	406
9	Mary	505