Advanced Python Interview Questions for Data Analysts & Scientists! #Python #DataScience #Interview

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

1️⃣ How do you detect and handle outliers in Python using libraries like pandas and NumPy?

Detection: Use statistical methods such as the Interquartile Range (IQR) or Z-score.
Handling: Options include removing outliers or capping/flooring extreme values.

Example using IQR:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'value': [10, 12, 14, 15, 100, 13, 11]})
Q1 = df['value'].quantile(0.25)
Q3 = df['value'].quantile(0.75)
IQR = Q3 - Q1

# Define bounds
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR

# Filter out outliers
df_filtered = df[(df['value'] v= lower_bound) & (df['value'] v= upper_bound)]
print(df_filtered)

2️⃣ How do you visualize data distributions and relationships using Matplotlib or Seaborn?

Matplotlib: Offers basic plotting (line, bar, scatter plots).
Seaborn: Provides enhanced statistical plots (histograms, boxplots, pairplots).

Example using Seaborn:

import seaborn as sns
import pandas as pd

# Sample data
df = pd.DataFrame({
'category': ['A', 'B', 'A', 'B', 'C'],
'value': [10, 20, 15, 25, 30]
})

# Create a boxplot to visualize distribution per category

3️⃣ What is feature scaling, and why is it important in machine learning? Explain normalization vs. standardization.

Feature Scaling: Ensures all features contribute equally to model performance by adjusting their ranges.
Normalization: Rescales features to a range of [0, 1].
Standardization: Centers data around zero with a standard deviation of one.

Example using scikit-learn:

import numpy as np

# Normalization
minmax_scaler = MinMaxScaler()

# Standardization
standard_scaler = StandardScaler()

print("Normalized:\n", normalized_data)
print("Standardized:\n", standardized_data)

4️⃣ How do you implement a machine learning pipeline using scikit-learn?

Pipeline: Combines preprocessing steps and model training into a single workflow.
Benefits: Ensures reproducibility and cleaner code.

Example:

# Load data
iris = load_iris()

# Define pipeline
pipeline = Pipeline([
('scaler', StandardScaler()),
('classifier', LogisticRegression())
])

# Train model

5️⃣ How do you work with time series data in Python, including resampling and date manipulation?

Pandas Time Series: Use the DatetimeIndex for time-based indexing.

Resampling: Change the frequency of your time series data (e.g., daily to monthly).

Example:

import pandas as pd

# Create a date range
data = pd.DataFrame({'value': range(10)}, index=dates)

# Resample data to a 2-day frequency
print(resampled)

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