Advanced Python Interview Questions for Data Analysts & Scientists: LIME to Real-Time Streaming! 🚀

Here are 5 advanced Python interview questions for data analysts and scientists with detailed answers and code examples:

1️⃣ How do you use LIME (Local Interpretable Model-agnostic Explanations) for model interpretability in Python?

LIME explains individual predictions by approximating the model locally with an interpretable model.

It helps validate model behavior, especially for complex models like ensembles or deep learning networks.

Example:

import numpy as np
import lime

# Load data and train model
iris = load_iris()
model = RandomForestClassifier(n_estimators=100, random_state=42)

# Create LIME explainer for tabular data
)

# Explain prediction for a single instance

This tool provides a human-readable explanation for complex model predictions.

2️⃣ How do you handle missing data using advanced imputation techniques such as Iterative Imputer or MissForest?

Advanced imputation methods iteratively estimate missing values by modeling each feature as a function of others.

Iterative Imputer (from scikit-learn) or MissForest (from the missingpy library) can be used for this purpose.

Example using Iterative Imputer:

import numpy as np
import pandas as pd

# Create sample DataFrame with missing values
data = {
}
df = pd.DataFrame(data)

imputer = IterativeImputer(random_state=42)
print(df_imputed)

These methods preserve the underlying patterns in the data better than simple imputation methods.

3️⃣ How do you analyze graph data using NetworkX in Python?

NetworkX is used to create, manipulate, and study complex networks of nodes and edges.

It supports a wide array of graph algorithms (e.g., shortest path, centrality measures).

Example:

import networkx as nx

# Create a simple graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 4), (4, 5)])

# Compute degree centrality
print("Degree Centrality:", centrality)

# Draw the graph

This enables analysis of relationships and influence within datasets such as social networks.

4️⃣ How do you process real-time data streams using Apache Kafka and Spark Streaming in Python?

Kafka is used as a distributed streaming platform, and Spark Streaming can process these streams in near real time.

With the PySpark API, you can create a streaming context and consume messages from Kafka.

Example snippet (conceptual):

# Define schema for incoming data
schema = StructType([
StructField("id", StringType(), True),
StructField("value", StringType(), True)
])

# Read stream from Kafka topic
.option("subscribe", "my_topic") \
.load()

# Parse the JSON messages
.select(from_json(col("json_value"), schema).alias("data")).select("data.*")

# Write the streaming data to console

This setup allows scalable and fault-tolerant processing of streaming data.

5️⃣ How do you optimize memory usage for large-scale data processing using iterators and generators in Python?

Iterators and generators yield one item at a time, reducing memory overhead by not storing entire datasets in memory.

They are especially useful when working with large files or streams.

Example:

def read_large_file(file_path):
with open(file_path, 'r') as file:
for line in file:

# Process a large file line by line
# Process each line without loading the entire file
process(line)

This approach is vital for optimizing performance when handling big data.

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