cudf-analytics

# cuDF Analytics Skill GPU-accelerated data analysis using NVIDIA RAPIDS cuDF. cuDF provides a pandas-like API that runs on NVIDIA GPUs, enabling massive speedups on large datasets. ## When to Use This Skill Use this skill when: - Analyzing CSV files, datasets, or tabular data - Computing statistical summaries (mean, median, std, quartiles) - Performing groupby aggregations - Detecting anomalies or outliers in data - Profiling datasets with millions of rows - Computing correlation matrices ## Initialization (REQUIRED) Always start every script with this boilerplate. It tests actual GPU operations, not just import. ```python import pandas as pd try: import cudf # Smoke-test: verify GPU compute AND host transfer both work _test = cudf.Series([1, 2, 3]) assert _test.sum() == 6 assert _test.to_pandas().tolist() == [1, 2, 3] GPU = True except Exception as e: print(f"[GPU] cudf unavailable, falling back to pandas: {e}") GPU = False def read_csv(path): return cudf.read_csv(path) if GPU else pd.read_csv(path) def to_pd(df): """Convert cuDF DataFrame/Series to pandas. Use this instead of .to_pandas() directly.""" if not GPU: return df try: return df.to_pandas() except Exception as e: print(f"[GPU] .to_pandas() failed, using Arrow fallback: {e}") return df.to_arrow().to_pandas() ``` ## Quick Reference cuDF mirrors the pandas API. Common operations: ### Read Data ```python df = read_csv("data.csv") ``` ### Statistical Summary ```python # Use to_pd() when you need pandas output summary = to_pd(df[["value", "score"]].describe()) # Scalar values work directly with float() mean_val = float(df["value"].mean()) q1 = float(df["value"].quantile(0.25)) # Correlation corr = float(df["value"].corr(df["score"])) ``` ### Groupby Aggregation ```python result = df.groupby("category").agg({ "revenue": ["sum", "mean", "count"], "quantity": ["sum", "mean"], }) result_pd = to_pd(result) ``` ### Anomaly Detection (IQR Method) ```python col = "value" Q1 = float(df[col].quantile(0.25)) Q3 = float(df[col].quantile(0.75)) IQR = Q3 - Q1 lower = Q1 - 1.5 * IQR upper = Q3 + 1.5 * IQR outliers = to_pd(df[(df[col] < lower) | (df[col] > upper)]) ``` ### Anomaly Detection (Z-Score Method) ```python mean = float(df[col].mean()) std = float(df[col].std()) df["z_score"] = (df[col] - mean) / std anomalies = to_pd(df[df["z_score"].abs() > 3]) ``` ### Filtering and Selection ```python # Filter rows filtered = df[df["status"] == "active"] # Select columns subset = df[["name", "revenue", "date"]] # Sort sorted_df = df.sort_values("revenue", ascending=False) # Convert to pandas for final output / iteration result_pd = to_pd(sorted_df) ``` ## Data Type Requirements cuDF requires explicit type specification for optimal performance: - Use `float32` or `float64` for numeric data - Use `int32` or `int64` for integer data - String columns use cuDF's string dtype automatically ## Output Guidelines When reporting analysis results: - Include dataset dimensions (rows x columns) - Show key statistics in formatted tables - Highlight notable patterns, trends, or anomalies - Provide both summary statistics and specific examples - Note any data quality issues (missing values, outliers)