Leer Date and Time-Based Features

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Extracting calendar-based features from date and time information is a powerful technique in temporal feature engineering. By breaking down a timestamp into components such as the day of the week, month, or whether it falls on a weekend, you can capture recurring patterns or seasonality that often drive changes in real-world data. These features are especially valuable in time series forecasting, where many phenomena – like sales, traffic, or energy usage – follow predictable cycles tied to the calendar. For example, demand might spike on weekends or drop during certain months, and models that incorporate this information can make more accurate predictions.


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import pandas as pd

# Create a sample datetime index
dates = pd.date_range(start="2024-06-01", end="2024-06-14", freq="D")
df = pd.DataFrame(index=dates)

# Extract day of week (Monday=0, Sunday=6)
df["day_of_week"] = df.index.dayofweek

# Extract month
df["month"] = df.index.month

# Determine if the date is a weekend (Saturday=5, Sunday=6)
df["is_weekend"] = df.index.dayofweek >= 5

print(df)

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