Pandas for Finance
The Python data toolkit for finance analysts. yfinance, DataFrames, returns, joins, time-series, and a final backtest in 15 episodes.
15 episodes
1
Why Pandas When You Have Excel: 5 Years of Apple in 5 Lines | Pandas for Finance Ep1
2
The DataFrame Mental Model: Index, Columns, Series, Vectorized | Pandas for Finance Ep2
3
Loading & Caching Real Data: Parquet, load_or_download Pattern | Pandas for Finance Ep3
4
Filter Rows: Boolean Masks, .query(), .nlargest, Date Slices | Pandas for Finance Ep4
5
Pick Tickers and Columns: Brackets, .loc, .iloc, drop, rename | Pandas for Finance Ep5
6
Returns: Daily, Wealth Curve, Annualized, Sharpe, Drawdown | Pandas for Finance Ep6
7
GroupBy on Time: Buckets, Pivots, Named Aggregations | Pandas for Finance Ep7
8
Joins: Prices x Sector Lookup, merge() and the SQL JOIN | Pandas for Finance Ep8
9
Dates and the Trading Calendar: .dt, bdate_range, Calendar Gaps | Pandas for Finance Ep9
10
Resampling: Daily to Weekly OHLC, Monthly Returns, Yearly Matrix | Pandas for Finance Ep10
11
Rolling Windows: Moving Avg, Bollinger, Volatility, Drawdown | Pandas for Finance Ep11
12
Cleaning Market Data: Nulls, Forward-Fill, Adj Close Trap, Outliers | Pandas for Finance Ep12
13
Writing Out: Excel, Parquet, DuckDB — One Pipeline, Four Formats | Pandas for Finance Ep13
14
Capstone Backtest: Equal-Weight Portfolio vs SPY in Pandas | Pandas for Finance Ep14
15
Pandas + DuckDB at Scale: SQL on Parquet, 100× Faster | Pandas for Finance Ep15