Time Series Basics

Time Series

Let me give you the basics of working with dates and times in Pandas. Time series data is everywhere — stock prices, weather data, website traffic. Pandas makes handling it surprisingly easy.

Converting to Datetime

First things first — convert strings to proper datetime objects:

import pandas as pd

data = {
    'Date': ['2024-01-15', '2024-02-20', '2024-03-25'],
    'Sales': [100, 150, 200]
}
df = pd.DataFrame(data)

df['Date'] = pd.to_datetime(df['Date'])
print(df.dtypes)
    

Once you have proper datetime objects, you unlock the `.dt` accessor. It's like having a Swiss Army knife for dates.

The dt Accessor

Extract date components easily:

df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month
df['Day'] = df['Date'].dt.day_name()
print(df)
    

This creates new columns with year, month number, and day name. One thing that confused me at first was `day_name()` — it returns strings like "Monday" instead of numbers.

Resampling

Need to aggregate time series data by time periods?

df.set_index('Date', inplace=True)
monthly = df['Sales'].resample('M').sum()
print(monthly)
    

`resample('M')` groups data by month. You can use 'D' for daily, 'W' for weekly, 'Y' for yearly. Think of it like groupby but for time periods.

Key Takeaways

• `pd.to_datetime()` converts strings to datetime objects
• The `.dt` accessor provides year, month, day components
• `resample()` aggregates data by time periods
• Set the Date column as index before resampling