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.

df['col_name'].where(~(condition), inplace = True, other = values_to_replace)


              1
            
df['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.

Compito

Swipe to start coding

For all the trips with negative duration add 12 hours to dropoff_datetime column.
Calculate column duration again.
Print first 5 rows of updated df.

Soluzione

# Load libraries

import pandas as pd

from datetime import timedelta

# Loading dataset, creating duration column, and filtering to negative durations

url = 'https://drive.google.com/uc?id=1YV5bKobzYxVAWyB7VlxNH6dmfP4tHBui'

df = pd.read_csv(url, parse_dates = ['pickup_datetime', 'dropoff_datetime', 'dropoff_calculated'])

df["duration"] = pd.to_timedelta(df["duration"])

# Task 1: add 12 hours to dropoff duration for negative durations

df['dropoff_datetime'].where(~(df.duration < timedelta(0)), other = df.dropoff_datetime + timedelta(hours = 12), inplace = True)

# Task 2: recalculate duration column

df['duration'] = df['dropoff_datetime'] - df['pickup_datetime']

# Task 3: inspect first 10 rows with negative duration

print(df[df['duration'] < timedelta(0)][["pickup_datetime", "dropoff_datetime", "trip_duration", "dropoff_calculated"]].head())

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 4. Capitolo 7

# Load libraries

import pandas as pd

from datetime import timedelta

# Loading dataset, creating duration column, and filtering to negative durations

url = 'https://drive.google.com/uc?id=1YV5bKobzYxVAWyB7VlxNH6dmfP4tHBui'

df = pd.read_csv(url, parse_dates = ['pickup_datetime', 'dropoff_datetime', 'dropoff_calculated'])

df["duration"] = pd.to_timedelta(df["duration"])

# Task 1: add 12 hours to dropoff duration for negative durations

df['___'].___(~(___ < timedelta(0)), other = ___ + timedelta(___ = ___), inplace = True)

# Task 2: recalculate duration column

df['duration'] = df['___'] - df['___']

# Task 3: inspect first 10 rows with negative duration

print(df[df['duration'] < timedelta(0)][["pickup_datetime", "dropoff_datetime", "trip_duration", "dropoff_calculated"]].___())

Chieda ad AI

Chieda pure quello che desidera o provi una delle domande suggerite per iniziare la nostra conversazione