Excel Python- Fei Su Gao Ding Shu Ju Fen Xi Yu Chu Li Review

For decades, Microsoft Excel has been the world’s most accessible data tool. But as datasets grow into the hundreds of thousands of rows, Excel’s traditional formula engine and manual operations become sluggish, error-prone, and limiting.

=PY( df = xl("A1:G10000", headers=True); # Remove duplicates df = df.drop_duplicates(); # Fill missing values with median df["Price"] = df["Price"].fillna(df["Price"].median()); # Standardize text df["Product"] = df["Product"].str.strip().str.lower(); df ) The cleaned DataFrame spills back into your grid instantly – 10,000 rows processed in under 1 second. VLOOKUP/XLOOKUP are great for one match. But merging three tables with different keys? Python’s merge() is your friend. Excel Python- fei su gao ding shu ju fen xi yu chu li

=PY( orders = xl("Orders!A1:D5000", headers=True); customers = xl("Customers!A1:C2000", headers=True); products = xl("Products!A1:B1000", headers=True); merged = orders.merge(customers, on="CustomerID").merge(products, on="ProductID"); merged["TotalValue"] = merged["Quantity"] * merged["UnitPrice"]; merged ) One line of code replaces dozens of helper columns and volatile array formulas. Excel pivot tables are interactive but slow on large data. Python’s groupby + agg gives you the same results instantly: For decades, Microsoft Excel has been the world’s