Challenge: Average Metrics Across Taxi Types
Great! As for now, we have our dataset cleared from abnormally long rides and rides with ending time preceded starting. As we investigated, it happened because of misusage of 12 and 24-hour formats.
Let's try to find out some interesting insights from this dataset.
Swipe to start coding
- Apply
.total_seconds()function todurationcolumn usingmapandlambdafunctions. - Group observations by taxi type (
vendor_idcolumn). Then, choose columnsdist_meters,duration, and calculate mean. Then apply functionavg_mtodist_metersandavg_durtoduration. The functions are defined in the code.
Soluzione
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Completion rate improved to 3.23Challenge: Average Metrics Across Taxi Types
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Great! As for now, we have our dataset cleared from abnormally long rides and rides with ending time preceded starting. As we investigated, it happened because of misusage of 12 and 24-hour formats.
Let's try to find out some interesting insights from this dataset.
Swipe to start coding
- Apply
.total_seconds()function todurationcolumn usingmapandlambdafunctions. - Group observations by taxi type (
vendor_idcolumn). Then, choose columnsdist_meters,duration, and calculate mean. Then apply functionavg_mtodist_metersandavg_durtoduration. The functions are defined in the code.
Soluzione
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Awesome!
Completion rate improved to 3.23single
