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.
Opgave
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.
Løsning
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# Load libraries
import pandas as pd
from datetime import timedelta
# Loading dataset, creating duration column
url = 'https://drive.google.com/uc?id=1pQCA5C4Yvm86rjUIneefI31LNfoywtrU'
df = pd.read_csv(url, parse_dates = ['pickup_datetime', 'dropoff_datetime', 'dropoff_calculated'])
df["duration"] = pd.to_timedelta(df["duration"])
# Defining functions
avg_m = lambda x: str(round(x/1000, 2)) + ' km'
avg_dur = lambda x: pd.to_timedelta(round(x, 0), unit = "S")
# Task 1 - use total_seconds method to duration column
df['duration'] = df['duration'].map(lambda x: x.total_seconds())
# Task 2 - calculate average distance and duration across taxi types
print(df.groupby('vendor_id')[['dist_meters', 'duration']].mean().agg({'dist_meters': avg_m, 'duration': avg_dur}))
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Sektion 4. Kapitel 8
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# Load libraries
import pandas as pd
from datetime import timedelta
# Loading dataset, creating duration column
url = 'https://drive.google.com/uc?id=1pQCA5C4Yvm86rjUIneefI31LNfoywtrU'
df = pd.read_csv(url, parse_dates = ['pickup_datetime', 'dropoff_datetime', 'dropoff_calculated'])
df["duration"] = pd.to_timedelta(df["duration"])
# Defining functions
avg_m = lambda x: str(round(x/1000, 2)) + ' km'
avg_dur = lambda x: pd.to_timedelta(round(x, 0), unit = "S")
# Task 1 - use total_seconds method to duration column
df['duration'] = df['___'].map(lambda x: x.___())
# Task 2 - calculate average distance and duration across taxi types
print(df.___('vendor_id')[['___', '___']].___().agg({'___': ___, 'duration': ___}))
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