Challenge: Fixing the Issues
Well, in the last chapter you saw, that there were only two rides with negative durations where minutes in both columns were different. But if paid your attention to seconds, you might notice, that that were the minute ending and starting (59 seconds, and 00 respectively). It means that all the inconsistencies can be interpreted as misuages of 12 and 24-hour formats.
Since we have investigated the real reason for the issue, we can now fix it! Let me remind you of one of the ways to replace values in dataframe based on some condition - .where function.
1df['col_name'].where(~(condition), inplace = True, other = values_to_replace)
Using the following approach all the values in col_name will be replaced with values_to_replace if (condition) is True.
Swipe to start coding
- For all the trips with negative
durationadd 12 hours todropoff_datetimecolumn. - Calculate column
durationagain. - Print first 5 rows of updated
df.
Oplossing
Bedankt voor je feedback!
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Completion rate improved to 3.23Challenge: Fixing the Issues
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Well, in the last chapter you saw, that there were only two rides with negative durations where minutes in both columns were different. But if paid your attention to seconds, you might notice, that that were the minute ending and starting (59 seconds, and 00 respectively). It means that all the inconsistencies can be interpreted as misuages of 12 and 24-hour formats.
Since we have investigated the real reason for the issue, we can now fix it! Let me remind you of one of the ways to replace values in dataframe based on some condition - .where function.
1df['col_name'].where(~(condition), inplace = True, other = values_to_replace)
Using the following approach all the values in col_name will be replaced with values_to_replace if (condition) is True.
Swipe to start coding
- For all the trips with negative
durationadd 12 hours todropoff_datetimecolumn. - Calculate column
durationagain. - Print first 5 rows of updated
df.
Oplossing
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Awesome!
Completion rate improved to 3.23single
