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Time Series Analysis & DatetimeIndex
pd.to_datetime, date parsing, dt accessor, resampling, and rolling windows
Pandas was originally created at AQR Capital Management for quantitative financial time series. It provides exceptional datetime manipulation capabilities.
Working with Datetime in Pandas
pd.to_datetime(col): Converts strings and timestamps into robust pd.Timestamp objects.
The .dt accessor: Extracts date components (.dt.year, .dt.day_name(), .dt.hour).
Setting a DatetimeIndex: When dates are the DataFrame index, powerful date slicing (df["2024-01":"2024-03"]) is enabled.
df.resample("ME").mean(): Frequency conversion (e.g. daily to monthly end), like a groupby for time intervals.
df["price"].rolling(window=7).mean(): Computes moving averages over time windows.
import pandas as pd
import numpy as np
# Create daily date range for January 2024
dates = pd.date_range(start="2024-01-01", periods=10, freq="D")
df = pd.DataFrame({
"price": [100, 102, 105, 103, 108, 112, 110, 115, 118, 120]
}, index=dates)
# Compute 3-day rolling moving average
df["3_day_ma"] = df["price"].rolling(window=3).mean()
print("Stock prices with Moving Average:
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Test Yourself With Exercise
Which method frequency-converts a time-series DataFrame (e.g. from daily transactions to monthly sums)?