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n python: Question: Binning or discretization can reduce the risk of de-anonymization in a dataset. If the 'act_moving_seconds' is replaced with the nearest integer-valued minute, what is the modified number of unique identifiers present in this column? Steps: replace 'act_moving_seconds' with the nearest integer-valued minute. find the new/modified number of unique identifiers for this column ( same steps as Q.4 )

n python: Question: Binning or discretization can reduce the risk of de-anonymization in a dataset. If the 'act_moving_seconds' is replaced with the nearest integer-valued minute, what is the modified number of unique identifiers present in this column? Steps: replace 'act_moving_seconds' with the nearest integer-valued minute. find the new/modified number of unique identifiers for this column ( same steps as Q.4 )

Computer Networking: A Top-Down Approach (7th Edition)
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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In python:

Question:

Binning or discretization can reduce the risk of de-anonymization in a dataset.

If the 'act_moving_seconds' is replaced with the nearest integer-valued minute,
what is the modified number of unique identifiers present in this column?

Steps:

  • replace 'act_moving_seconds' with the nearest integer-valued minute.
  • find the new/modified number of unique identifiers for this column ( same steps as Q.4 )


To replace the values in a column, take a look at this following example (code example is in screen shot)

 

CSV file code: 

url = "https://raw.githubusercontent.com/nat-data/data/main/jeddah_strava_segments.csv"
df = pd.read_csv(url)
O ## Example for replacing values in a column ## In this example, we are replacing the user_ID with 42 x user_ID ## We don't want to disturb the original dataframe. df_example = df.copy() print(df_example.filter(['user_id']).head(5)) print("\n \n Example: replacing the user_ID with 10 x user_ID \n \n") ## Pay attention to this : df_example['user_id'] = df_example.apply (lambda row: row['user_id']*42 , axis=1) print(df_example.filter(['user_id']).head (5))
Transcribed Image Text:O ## Example for replacing values in a column ## In this example, we are replacing the user_ID with 42 x user_ID ## We don't want to disturb the original dataframe. df_example = df.copy() print(df_example.filter(['user_id']).head(5)) print("\n \n Example: replacing the user_ID with 10 x user_ID \n \n") ## Pay attention to this : df_example['user_id'] = df_example.apply (lambda row: row['user_id']*42 , axis=1) print(df_example.filter(['user_id']).head (5))
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