(3) (a) Consider the following interaction with Python: x= [1,2,34 ,5,6, np.nan] y= (10,1,2,5,'Missing',6.3) z= [0.1, 1.2 , np.nan ,4,5.1,0.5] df1=DataFrame({'col1':Series (z),'col2':Series (y), 'col3': Series (x)}). df1.index=['a','b','c','d','e','f'] Replace the NaN value in coll with -9, the Missing value in col2 with -99, and the NaN value in col3 with -999 with relevant functions. Name as dfl_replaced
(3) (a) Consider the following interaction with Python: x= [1,2,34 ,5,6, np.nan] y= (10,1,2,5,'Missing',6.3) z= [0.1, 1.2 , np.nan ,4,5.1,0.5] df1=DataFrame({'col1':Series (z),'col2':Series (y), 'col3': Series (x)}). df1.index=['a','b','c','d','e','f'] Replace the NaN value in coll with -9, the Missing value in col2 with -99, and the NaN value in col3 with -999 with relevant functions. Name as dfl_replaced
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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![(3) (a) Consider the following interaction with Python:
x= [1,2,34 ,5 ,6 , np.nan]
y= (10,i,2,5, 'Missing',6.3)
z= [0.1, 1.2 , np.nan , 4,5.1,0.5]
df1=DataFrame ({'col1':Series (z),'co12':Series (y),
'col3': Series (x)}).
df1.index= ['a','b','c', 'd','e','f']
Replace the NaN value in coll with -9, the Missing value in col2 with -99, and the NaN
value in col3 with -999 with relevant functions. Name as dfl_replaced
(b) Consider the following interaction with Python:
df2=DataFrame (np. array ( [[1, np.nan ,3, 8], [np.nan , 2,3,5] ,
[10,2,3, np.nan], [10,2,3 , np.nan],
[10,2,3,11]]))
df2.columns = ['one', 'two', three', four ']
df2. index= ['a','b','c
'd','e']
Remove the rows that have nan values from df2 and name as df2_row. Remove the
columns that have nan values from df2 and name as df2_column. Use relevant
functions.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F44641a1c-29ea-4b2b-8881-ff521a62b515%2F2caebc28-2d00-4d04-b3b5-e16742b2eda7%2Fbwojb9_processed.png&w=3840&q=75)
Transcribed Image Text:(3) (a) Consider the following interaction with Python:
x= [1,2,34 ,5 ,6 , np.nan]
y= (10,i,2,5, 'Missing',6.3)
z= [0.1, 1.2 , np.nan , 4,5.1,0.5]
df1=DataFrame ({'col1':Series (z),'co12':Series (y),
'col3': Series (x)}).
df1.index= ['a','b','c', 'd','e','f']
Replace the NaN value in coll with -9, the Missing value in col2 with -99, and the NaN
value in col3 with -999 with relevant functions. Name as dfl_replaced
(b) Consider the following interaction with Python:
df2=DataFrame (np. array ( [[1, np.nan ,3, 8], [np.nan , 2,3,5] ,
[10,2,3, np.nan], [10,2,3 , np.nan],
[10,2,3,11]]))
df2.columns = ['one', 'two', three', four ']
df2. index= ['a','b','c
'd','e']
Remove the rows that have nan values from df2 and name as df2_row. Remove the
columns that have nan values from df2 and name as df2_column. Use relevant
functions.
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