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

=PY( df = xl("SalesData!A1:F200000", headers=True); summary = df.groupby(["Year", "Region"]).agg( Total_Sales = ("Amount", "sum"), Avg_Order = ("Amount", "mean"), Transaction_Count = ("OrderID", "nunique") ).reset_index(); summary ) You get a compact aggregated table ready for reporting. Need to run a regression or forecast next quarter? Scikit-learn and statsmodels work inside Excel:

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. Excel Python- fei su gao ding shu ju fen xi yu chu li

=PY( from sklearn.linear_model import LinearRegression import numpy as np df = xl("HistoricalData!A1:B100", headers=True); X = df[["Month"]].values; y = df["Sales"].values; model = LinearRegression().fit(X, y); prediction = model.predict([[13]]) # next month prediction[0] ) Result appears in the cell – 95, 103.2, whatever your model predicts. No need to export. Excel charts are decent but limited. Python’s seaborn creates publication-quality plots directly in the worksheet: =PY( df = xl("SalesData

=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. But as datasets grow into the hundreds of