In this tutorial, we design an end-to-end, production-style analytics and modeling pipeline using Vaex to operate efficiently on millions of rows without materializing data in memory. We generate a realistic, large-scale dataset, engineer rich behavioral and city-level features using lazy expressions and approximate statistics, and aggregate insights at scale. We then integrate Vaex with scikit-learn to train and evaluate a predictive model, demonstrating how Vaex can act as the backbone for high-performance exploratory analysis and machine-learning workflows.
import os, time, json, numpy as np, pandas as pd
import vaex
import vaex.ml
from vaex.ml.sklearn import Predictor
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, average_precision_score
print(“Python:”, __import__(“sys”).version.split()[0])
print(“vaex:”, vaex.__version__)
print(“numpy:”, np.__version__)
print(“pandas:”, pd.__version__)
rng = np.random.default_rng(7)
n = 2_000_000
cities = np.array([“Montreal”,”Toronto”,”Vancouver”,”Calgary”,”Ottawa”,”Edmonton”,”Quebec City”,”Winnipeg”], dtype=object)
city = rng.choice(cities, size=n, replace=True, p=np.array([0.16,0.18,0.12,0.10,0.10,0.10,0.10,0.14]))
age = rng.integers(18, 75, size=n, endpoint=False).astype(“int32”)
tenure_m = rng.integers(0, 180, size=n, endpoint=False).astype(“int32”)
tx = rng.poisson(lam=22, size=n).astype(“int32”)
base_income = rng.lognormal(mean=10.6, sigma=0.45, size=n).astype(“float64”)
city_mult = pd.Series({“Montreal”:0.92,”Toronto”:1.05,”Vancouver”:1.10,”Calgary”:1.02,”Ottawa”:1.00,”Edmonton”:0.98,”Quebec City”:0.88,”Winnipeg”:0.90}).reindex(city).to_numpy()
income = (base_income * city_mult * (1.0 + 0.004*(age-35)) * (1.0 + 0.0025*np.minimum(tenure_m,120))).astype(“float64”)
income = np.clip(income, 18_000, 420_000)
noise = rng.normal(0, 1, size=n).astype(“float64”)
score_latent = (
0.55*np.log1p(income/1000.0)
+ 0.28*np.log1p(tx)
+ 0.18*np.sqrt(np.maximum(tenure_m,0)/12.0 + 1e-9)
– 0.012*(age-40)
+ 0.22*(city == “Vancouver”).astype(“float64”)
+ 0.15*(city == “Toronto”).astype(“float64”)
+ 0.10*(city == “Ottawa”).astype(“float64”)
+ 0.65*noise
)
p = 1.0/(1.0 + np.exp(-(score_latent – np.quantile(score_latent, 0.70))))
target = (rng.random(n) < p).astype(“int8”)
df = vaex.from_arrays(city=city, age=age, tenure_m=tenure_m, tx=tx, income=income, target=target)
df[“income_k”] = df.income / 1000.0
df[“tenure_y”] = df.tenure_m / 12.0
df[“log_income”] = df.income.log1p()
df[“tx_per_year”] = df.tx / (df.tenure_y + 0.25)
df[“value_score”] = (0.35*df.log_income + 0.20*df.tx_per_year + 0.10*df.tenure_y – 0.015*df.age)
df[“is_new”] = df.tenure_m < 6
df[“is_senior”] = df.age >= 60
print(“\nRows:”, len(df), “Columns:”, len(df.get_column_names()))
print(df[[“city”,”age”,”tenure_m”,”tx”,”income”,”income_k”,”value_score”,”target”]].head(5))
We generate a large, realistic synthetic dataset and initialize a Vaex DataFrame to work lazily on millions of rows. We engineer core numerical features directly as expressions so no intermediate data is materialized. We validate the setup by inspecting schema, row counts, and a small sample of computed values.
df = encoder.fit_transform(df)
city_map = encoder.labels_[“city”]
inv_city_map = {v:k for k,v in city_map.items()}
n_cities = len(city_map)
p95_income_k_by_city = df.percentile_approx(“income_k”, 95, binby=”label_encoded_city”, shape=n_cities, limits=[-0.5, n_cities-0.5])
p50_value_by_city = df.percentile_approx(“value_score”, 50, binby=”label_encoded_city”, shape=n_cities, limits=[-0.5, n_cities-0.5])
avg_income_k_by_city = df.mean(“income_k”, binby=”label_encoded_city”, shape=n_cities, limits=[-0.5, n_cities-0.5])
target_rate_by_city = df.mean(“target”, binby=”label_encoded_city”, shape=n_cities, limits=[-0.5, n_cities-0.5])
n_by_city = df.count(binby=”label_encoded_city”, shape=n_cities, limits=[-0.5, n_cities-0.5])
p95_income_k_by_city = np.asarray(p95_income_k_by_city).reshape(-1)
p50_value_by_city = np.asarray(p50_value_by_city).reshape(-1)
avg_income_k_by_city = np.asarray(avg_income_k_by_city).reshape(-1)
target_rate_by_city = np.asarray(target_rate_by_city).reshape(-1)
n_by_city = np.asarray(n_by_city).reshape(-1)
city_table = pd.DataFrame({
“city_id”: np.arange(n_cities),
“city”: [inv_city_map[i] for i in range(n_cities)],
“n”: n_by_city.astype(“int64”),
“avg_income_k”: avg_income_k_by_city,
“p95_income_k”: p95_income_k_by_city,
“median_value_score”: p50_value_by_city,
“target_rate”: target_rate_by_city
}).sort_values([“target_rate”,”p95_income_k”], ascending=False)
print(“\nCity summary:”)
print(city_table.to_string(index=False))
df_city_features = vaex.from_pandas(city_table[[“city”,”p95_income_k”,”avg_income_k”,”median_value_score”,”target_rate”]], copy_index=False)
df = df.join(df_city_features, on=”city”, rsuffix=”_city”)
df[“income_vs_city_p95”] = df.income_k / (df.p95_income_k + 1e-9)
df[“value_vs_city_median”] = df.value_score – df.median_value_score
We encode categorical city data and compute scalable, approximate per-city statistics using binning-based operations. We assemble these aggregates into a city-level table and join them back to the main dataset. We then derive relative features that compare each record against its city context.
“age”,”tenure_y”,”tx”,”income_k”,”log_income”,”tx_per_year”,”value_score”,
“p95_income_k”,”avg_income_k”,”median_value_score”,”target_rate”,
“income_vs_city_p95″,”value_vs_city_median”
]
scaler = vaex.ml.StandardScaler(features=features_num, with_mean=True, with_std=True, prefix=”z_”)
df = scaler.fit_transform(df)
features = [“z_”+f for f in features_num] + [“label_encoded_city”]
df_train, df_test = df.split_random([0.80, 0.20], random_state=42)
model = LogisticRegression(max_iter=250, solver=”lbfgs”, n_jobs=None)
vaex_model = Predictor(model=model, features=features, target=”target”, prediction_name=”pred”)
t0 = time.time()
vaex_model.fit(df=df_train)
fit_s = time.time() – t0
df_test = vaex_model.transform(df_test)
y_true = df_test[“target”].to_numpy()
y_pred = df_test[“pred”].to_numpy()
auc = roc_auc_score(y_true, y_pred)
ap = average_precision_score(y_true, y_pred)
print(“\nModel:”)
print(“fit_seconds:”, round(fit_s, 3))
print(“test_auc:”, round(float(auc), 4))
print(“test_avg_precision:”, round(float(ap), 4))
We standardize all numeric features using Vaex’s ML utilities and prepare a consistent feature vector for modeling. We split the dataset without loading the entire dataset into memory. We train a logistic regression model through Vaex’s sklearn wrapper and evaluate its predictive performance.
cuts = np.quantile(y_pred, deciles)
cuts[0] = -np.inf
cuts[-1] = np.inf
bucket = np.digitize(y_pred, cuts[1:-1], right=True).astype(“int32”)
df_test_local = vaex.from_arrays(y_true=y_true.astype(“int8”), y_pred=y_pred.astype(“float64″), bucket=bucket)
lift = df_test_local.groupby(by=”bucket”, agg={“n”: vaex.agg.count(), “rate”: vaex.agg.mean(“y_true”), “avg_pred”: vaex.agg.mean(“y_pred”)}).sort(“bucket”)
lift_pd = lift.to_pandas_df()
baseline = float(df_test_local[“y_true”].mean())
lift_pd[“lift”] = lift_pd[“rate”] / (baseline + 1e-12)
print(“\nDecile lift table:”)
print(lift_pd.to_string(index=False))
We analyze model behavior by segmenting predictions into deciles and computing lift metrics. We calculate baseline rates and compare them across score buckets to assess ranking quality. We summarize the results in a compact lift table that reflects real-world model diagnostics.
os.makedirs(out_dir, exist_ok=True)
parquet_path = os.path.join(out_dir, “customers_vaex.parquet”)
state_path = os.path.join(out_dir, “vaex_pipeline.json”)
base_cols = [“city”,”label_encoded_city”,”age”,”tenure_m”,”tenure_y”,”tx”,”income”,”income_k”,”value_score”,”target”]
export_cols = base_cols + [“z_”+f for f in features_num]
df_export = df[export_cols].sample(n=500_000, random_state=1)
if os.path.exists(parquet_path):
os.remove(parquet_path)
df_export.export_parquet(parquet_path)
pipeline_state = {
“vaex_version”: vaex.__version__,
“encoder_labels”: {k: {str(kk): int(vv) for kk,vv in v.items()} for k,v in encoder.labels_.items()},
“scaler_mean”: [float(x) for x in scaler.mean_],
“scaler_std”: [float(x) for x in scaler.std_],
“features_num”: features_num,
“export_cols”: export_cols,
}
with open(state_path, “w”) as f:
json.dump(pipeline_state, f)
df_reopen = vaex.open(parquet_path)
df_reopen[“income_k”] = df_reopen.income / 1000.0
df_reopen[“tenure_y”] = df_reopen.tenure_m / 12.0
df_reopen[“log_income”] = df_reopen.income.log1p()
df_reopen[“tx_per_year”] = df_reopen.tx / (df_reopen.tenure_y + 0.25)
df_reopen[“value_score”] = (0.35*df_reopen.log_income + 0.20*df_reopen.tx_per_year + 0.10*df_reopen.tenure_y – 0.015*df_reopen.age)
df_city_features = vaex.from_pandas(city_table[[“city”,”p95_income_k”,”avg_income_k”,”median_value_score”,”target_rate”]], copy_index=False)
df_reopen = df_reopen.join(df_city_features, on=”city”, rsuffix=”_city”)
df_reopen[“income_vs_city_p95”] = df_reopen.income_k / (df_reopen.p95_income_k + 1e-9)
df_reopen[“value_vs_city_median”] = df_reopen.value_score – df_reopen.median_value_score
with open(state_path, “r”) as f:
st = json.load(f)
labels_city = {k: int(v) for k,v in st[“encoder_labels”][“city”].items()}
df_reopen[“label_encoded_city”] = df_reopen.city.map(labels_city, default_value=-1)
for i, feat in enumerate(st[“features_num”]):
mean_i = st[“scaler_mean”][i]
std_i = st[“scaler_std”][i] if st[“scaler_std”][i] != 0 else 1.0
df_reopen[“z_”+feat] = (df_reopen[feat] – mean_i) / std_i
df_reopen = vaex_model.transform(df_reopen)
print(“\nArtifacts written:”)
print(parquet_path)
print(state_path)
print(“\nReopened parquet predictions (head):”)
print(df_reopen[[“city”,”income_k”,”value_score”,”pred”,”target”]].head(8))
print(“\nDone.”)
We export a large, feature-complete sample to Parquet and persist the full preprocessing state for reproducibility. We reload the data and deterministically rebuild all engineered features from saved metadata. We run inference on the reloaded dataset to confirm that the pipeline remains stable and deployable end-to-end.
In conclusion, we demonstrated how Vaex enables fast, memory-efficient data processing while still supporting advanced feature engineering, aggregation, and model integration. We showed that approximate statistics, lazy computation, and out-of-core execution allow us to scale cleanly from analysis to deployment-ready artifacts. By exporting reproducible features and persisting the full pipeline state, we closed the loop from raw data to inference, illustrating how Vaex fits naturally into modern large-data Python workflows.
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