Python Panda pipe and groupby functions

Python Panda pipe,apply, apply_map and groupby() functions

'''

def adder(a):

    #return a+2

    return np.sqrt(a)

def adder2(a):

    return a/2

import pandas as pd

d1 = {'quantity':[28,36,25,14,25,48],

      'rate':[11,22,33,44,55,66],

      }

df = pd.DataFrame(d1)

print(df)

print('*'*30)

#df1 = df.pipe(adder)

#df1=df.pipe(adder).pipe(adder2)

#df1 = df.pipe(np.mean,axis=1)

#df1 = df.apply(np.mean,axis=0)

#df1 = df.apply(np.sqrt,axis=0)

#df2 = df.applymap(np.sqrt)

#df1 = df.applymap(np.mean)

df1 = df.pipe(np.cumsum)

df2 = df.apply(np.cumsum)

df3 = df.applymap(np.cumsum)

print(df1)

print(df2)

print(df3)

'''

'''

d = {'science_marks':pd.Series([2,5]),

'english_marks':pd.Series([8,7])}

df= pd.DataFrame(d)

print(df)

print('*'*30)

df1 = df.pipe(np.mean)

df2=df.apply(np.mean,axis=0)

df3=df.applymap(np.mean)

print (df1)

print('*'*30)

print (df2)

print('*'*30)

print (df3)

'''

'''

d = {'2014':pd.Series([2,5,3,2]),

'2015':pd.Series([8,7,3,6]),

'2016':pd.Series([8,2,2,7]),

'2017':pd.Series([1,2,8,7])}

df= pd.DataFrame(d,index=['Jan','Feb','March','April'])

'''

'''

a = {'Jan':10,'Feb':15,'March':20,'April':2}

b = {'Jan':5,'Feb':20,'March':10,'April':3}

c = {'Jan':15,'Feb':25,'March':15,'April':10}

d = {'Jan':10,'Feb':10,'March':10,'April':1}

e = {'Jan':5,'Feb':5,'March':20,'April':2}

dd = {'2014':a,'2015':b,'2016':c,'2017':d,'2018':e}

df= pd.DataFrame(dd)

print(df)

print('*'*30)

df1=df.pipe(np.sum) #YEAR  WISE TOTAL SALES

df2=df.pipe(np.sum).pipe(np.max) #YEAR  WISE MAX SALES

print (df1)

print('*'*30)

print (df2)

'''

import pandas as pd

d1 = {'item':['AC','TV','CAR','TV','BIKE','TV'],

      'quantity':[28,36,25,14,25,48],

      'rate':[11,22,33,44,55,66],

      'country':['USA','PAK','JAPAN','USA','INDIA','USA']}

df = pd.DataFrame(d1)

print(df)

print('*'*30)

gp = df.groupby(['item'])

#print(gp.groups)

#print(gp.get_group(('TV')))

#print(gp.size())

#print(gp.count())

#print(gp.max())

#print(gp.min())

gp = df.groupby(['item','country'])

print(gp.groups)

#print(gp.get_group(('TV','USA')))