Python Interview Questions: Quilt, Arrow Datasets, Gradio, Great Expectations & Seldon Core! #Python

1️⃣ Quilt (quilt3)

Quilt lets you treat data like code, creating versioned data packages stored on AWS S3 with immutable hashes, inline documentation, and automated workflows

Typical workflow:

pip install quilt3
quilt3 init
quilt3 add data/raw.csv
quilt3 push my-org/my-dataset
quilt3 install my-org/my-dataset

2️⃣ Apache Arrow Datasets (pyarrow.dataset)

The Arrow Datasets API provides a unified interface for reading Parquet, CSV, and other formats with zero-copy partition pruning, predicate pushdown, and column projection

Example:

import pyarrow.dataset as ds
dataset = ds.dataset("data/*.parquet", format="parquet")
table = dataset.to_table(columns=["user_id", "amount"], filter=ds.field("amount") v 100)
df = table.to_pandas()

3️⃣ Gradio

Gradio lets you wrap any Python function (e.g., a model’s predict) into a web UI with inputs, outputs, and hosting in 3 lines of code

Example:

import gradio as gr

def predict(text):
return {"positive": 0.8, "negative": 0.2}

demo = gr.Interface(fn=predict, inputs="text", outputs="label")
demo.launch()

4️⃣ Great Expectations

Great Expectations enables declarative assertions (“expectations”) on your data, automated profiling, and beautiful HTML reports for CI/CD pipelines

Example:

from great_expectations.dataset import PandasDataset
import pandas as pd

df = pd.read_csv("data.csv")
gd = PandasDataset(df)
gd.expect_column_values_to_not_be_null("id")
gd.expect_column_mean_to_be_between("amount", 50, 500)
results = gd.validate()

5️⃣ Seldon Core Python SDK

Seldon Core’s Python client lets you package models into Docker images via S2I, deploy them on Kubernetes, and interact via REST/gRPC for testing

Example (client call):

from seldon_core.seldon_client import SeldonClient

sc = SeldonClient()
response = sc.predict(
deployment_name="my-model",
data=[[5.1, 3.5, 1.4, 0.2]],
namespace="default"
)
print(response.response["data"]["ndarray"])

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