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)
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))](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fe837985f-2989-4a25-a05d-02efdd5ff4d2%2F4ef43d87-81ce-4726-9cc2-fb236f452a6b%2F1vci9s9_processed.png&w=3840&q=75)
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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