⏳ Time Series Resampling
Resampling allows you to change the frequency of your time series data, e.g., from daily to monthly.
💡 Quick Tip:
Mastering this concept will significantly boost your Python data science skills!
💻 Code Example:
import pandas as pd import numpy as np np.random.seed(42) # 1. Create daily pynfinity user activity series (1 year) dates = pd.date_range(start="2024-01-01", periods=365, freq="D") df = pd.DataFrame({ "sessions": np.random.poisson(lam=120, size=365), "signups" : np.random.poisson(lam=5, size=365), "revenue" : np.round(np.random.exponential(scale=500, size=365), 2), }, index=dates) print("Daily data (first 5 rows):") print(df.head()) # 2. Weekly resampling — total sessions per week weekly = df.resample("W").agg({ "sessions": "sum", "signups" : "sum", "revenue" : "sum", }) print("\nWeekly totals (last 4 weeks):") print(weekly.tail(4)) # 3. Monthly average revenue monthly_avg = df["revenue"].resample("ME").mean().round(2) print("\nMonthly avg revenue:") print(monthly_avg) # 4. Rolling 7-day average (smoothing) df["sessions_7d_avg"] = df["sessions"].rolling(window=7).mean().round(1) # 5. Lag features (yesterday vs today growth) df["sessions_prev_day"] = df["sessions"].shift(1) df["day_over_day_pct"] = ((df["sessions"] - df["sessions_prev_day"]) / df["sessions_prev_day"] * 100).round(2) print("\nWith rolling avg & lag:") print(df[["sessions", "sessions_7d_avg", "day_over_day_pct"]].tail(5)) # 6. Expanding cumulative stats df["cumulative_revenue"] = df["revenue"].expanding().sum().round(2) print(f"\nTotal revenue for the year: ₹{df['cumulative_revenue'].iloc[-1]:,.2f}")
Keep exploring and happy coding! 💻
