15 Python Pipelines Every Modern Data Scientist Should Have Ready (2026 Edition)
Stop rewriting exploratory data analysis, data cleaning, and XGBoost training loops from scratch. Here are the core production pipelines to accelerate your workflow.
Re-writing boilerplate data imputation, outlier detection, and cross-validation loops drains analytical momentum. Professional data scientists maintain a personal library of modular scripts.
Automated EDA & IQR Outlier Diagnostics
def run_automated_eda(df):
summary = {"rows": len(df), "missing": df.isnull().sum().to_dict()}
for col in df.select_dtypes(include=[np.number]).columns:
q1, q3 = df[col].quantile(0.25), df[col].quantile(0.75)
iqr = q3 - q1
summary[col + "_outliers"] = int(((df[col] < q1 - 1.5*iqr) | (df[col] > q3 + 1.5*iqr)).sum())
return summaryOpen Source vs. Pro
Get the free starter scripts on GitHub or grab all 15 production pipelines below.