If people chose a number between 0 and Infinity truly at random, we would not expect a sample of a couple hundred people to frequently choose the same number. Let's write a function to start to assess randomness in human choice. Define a function called calculate_unique . Inputs: data - DataFrame • variable - name of column in data to summarize (string) Output: • num_unique, num_total, num_unique/num_total Procedure: 1. Calculate the number of unique responses in the specified variable of the input DataFrame. Store this in num_unique. (Hint: there is a unique() method in pandas) 2. Calculate the number of total responses. Store this in num_total 3. Return, num_unique, num_total, and the proportion of unique responses ( num_unique/num_total)- Return all three, separated by commas, in the return statement. : def calculate_unique(data, variable): : assert callable(calculate_unique) : test_df = pd.DataFrame ( {'ID' : [1, 2, 3], 'response' : ['a', 'a', 'b']}) num_unique, num_total, prop_unique = calculate_unique (test_df, 'response') assert (num_unique == 2) assert(num_total == 3)

If people chose a number between 0 and Infinity truly at random, we would not expect a sample of a couple hundred people to frequently choose the same number. Let's write a function to start to assess randomness in human choice. Define a function called calculate_unique . Inputs: data - DataFrame • variable - name of column in data to summarize (string) Output: • num_unique, num_total, num_unique/num_total Procedure: 1. Calculate the number of unique responses in the specified variable of the input DataFrame. Store this in num_unique. (Hint: there is a unique() method in pandas) 2. Calculate the number of total responses. Store this in num_total 3. Return, num_unique, num_total, and the proportion of unique responses ( num_unique/num_total)- Return all three, separated by commas, in the return statement. : def calculate_unique(data, variable): : assert callable(calculate_unique) : test_df = pd.DataFrame ( {'ID' : [1, 2, 3], 'response' : ['a', 'a', 'b']}) num_unique, num_total, prop_unique = calculate_unique (test_df, 'response') assert (num_unique == 2) assert(num_total == 3)

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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Question

If people chose a number between 0 and Infinity truly at random, we would not expect a sample of a couple hundred people to frequently choose the same
number.
Let's write a function to start to assess randomness in human choice.
Define a function called calculate_unique .
Inputs:
• data - DataFrame
variable
- name of column in data to summarize (string)
Output:
num_unique, num_total, num_unique/num_total
Procedure:
1. Calculate the number of unique responses in the specified variable of the input DataFrame. Store this in num_unique . (Hint: there is a unique( )
method in pandas)
2. Calculate the number of total responses. Store this in num_total
3. Return, num_unique, num_total, and the proportion of unique responses ( num_unique/num_total)- Return all three, separated by commas, in
the return statement.
In [ ]: def calculate_unique (data, variable):
In [ ]: assert callable(calculate_unique)
In [ ]: test_df
pd. DataFrame ({'ID' :
[1, 2, 3],
'response' : ['a', 'a', 'b']})
num_unique, num_total, prop_unique
assert(num_unique
calculate_unique(test_df, 'response')
%3D
2)
3)
assert(num total
==

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