Mathew K AnalyticsLesson 12 · Data analytics zero to hero
Pandas Time Series Analysis Tutorial | Data Analytics #12
Video twelve of the 30-part series, and the last stop in the pandas block: dates as an index, resampling, rolling windows, and percent change. We're using a…
- CourseData analytics zero to hero
- Lesson12 of 30
- Video12 min
- FormatJupyter notebook · 10 code cells
- Data1 dataset
What you'll learn
Datasets used in this lesson
Save these next to the notebook. In Google Colab, upload them with the 📁 icon on the left first.
- aapl_ohlcv.csv24.3 KB
📓 Full notebook
Download .ipynbData Analytics Zero to Hero, Video 12: Time Series with Pandas#
- Video twelve of the 30-part series, and the last stop in the pandas block: dates as an index, resampling, rolling windows, and percent change.
- We're using a real dataset: about a year of actual AAPL daily stock price and volume history.
- Let's jump straight in.
Before You Start#
- Open a new Jupyter Notebook in VS Code and select your Python interpreter as the kernel.
- Place aapl_ohlcv.csv in the same folder as this notebook.
Part 1: Dates as the Index#
import pandas as pd
df = pd.read_csv('aapl_ohlcv.csv', parse_dates=['Date'])
df = df.set_index('Date').sort_index()
print(df.head(3))
print(df.index)
print(df.loc['2025-08'].shape)
print(df.loc['2025-08':'2025-09'].head(3))
Part 2: Resampling#
weekly_close = df['Close'].resample('W').mean()
print(weekly_close.head(5))
monthly_summary = df['Close'].resample('ME').agg(['first', 'last', 'min', 'max'])
print(monthly_summary.head(5))
monthly_volume = df['Volume'].resample('ME').sum()
print(monthly_volume.head(3))
Part 3: Rolling Windows#
df['MA20'] = df['Close'].rolling(window=20).mean()
print(df[['Close', 'MA20']].tail(5))
df['MA5'] = df['Close'].rolling(window=5).mean()
df['Above20MA'] = df['MA5'] > df['MA20']
print(df[['MA5', 'MA20', 'Above20MA']].tail(5))
Part 4: pct_change, shift, and diff#
df['DailyReturn'] = df['Close'].pct_change(fill_method=None)
print(df['DailyReturn'].describe())
df['PriceYesterday'] = df['Close'].shift(1)
df['PriceChange'] = df['Close'].diff()
print(df[['Close', 'PriceYesterday', 'PriceChange']].tail(3))
cumulative_return = (1 + df['DailyReturn']).cumprod() - 1
print(cumulative_return.tail(3))
last_valid = cumulative_return.dropna().iloc[-1]
print(f"Total return over the period: {last_valid:.2%}")
Wrap-Up: What You Learned#
- Turning a date column into a real DatetimeIndex with parse_dates and set_index, and slicing it with partial date strings.
- resample, for bucketing a time series into weekly or monthly summaries.
- rolling, for moving averages and other smoothed statistics.
- pct_change, diff, and shift, for comparing each row to an earlier point in time.
- Computing a compounded cumulative return from daily percent changes.
- All of it on a real year of AAPL trading data. This wraps up the pandas block. Video thirteen starts a visualization block with Matplotlib, turning tables like these into real charts. Subscribe so it lands automatically see you there.
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