Topic: 25 Important Questions on Handling Datasets of All Types in Python

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1. What are the common types of datasets?
Structured, unstructured, and semi-structured.

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2. How do you load a CSV file in Python?
Using pandas.read_csv() function.

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3. How to check for missing values in a dataset?
Using df.isnull().sum() in pandas.

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4. What methods can you use to handle missing data?
Remove rows/columns, mean/median/mode imputation, interpolation.

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5. How to detect outliers in data?
Using boxplots, z-score, or interquartile range (IQR) methods.

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6. What is data normalization?
Scaling data to a specific range, often \[0,1].

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7. What is data standardization?
Rescaling data to have zero mean and unit variance.

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8. How to encode categorical variables?
Label encoding or one-hot encoding.

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9. What libraries help with image data processing in Python?
OpenCV, Pillow, scikit-image.

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10. How do you load and preprocess images for ML models?
Resize, normalize pixel values, data augmentation.

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11. How can audio data be loaded in Python?
Using libraries like librosa or scipy.io.wavfile.

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12. What are MFCCs in audio processing?
Mel-frequency cepstral coefficients – features extracted from audio signals.

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13. How do you preprocess text data?
Tokenization, removing stopwords, stemming, lemmatization.

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14. What is TF-IDF?
A technique to weigh words based on frequency and importance.

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15. How do you handle variable-length sequences in text or time series?
Padding sequences or using packed sequences.

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16. How to handle time series missing data?
Forward fill, backward fill, interpolation.

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17. What is data augmentation?
Creating new data samples by transforming existing data.

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18. How to split datasets into training and testing sets?
Using train_test_split from scikit-learn.

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19. What is batch processing in ML?
Processing data in small batches during training for efficiency.

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20. How to save and load datasets efficiently?
Using formats like HDF5, pickle, or TFRecord.

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21. What is feature scaling and why is it important?
Adjusting features to a common scale to improve model training.

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22. How to detect and remove duplicate data?
Using df.duplicated() and df.drop_duplicates().

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23. What is one-hot encoding and when to use it?
Converting categorical variables to binary vectors, used for nominal categories.

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24. How to handle imbalanced datasets?
Techniques like oversampling, undersampling, or synthetic data generation (SMOTE).

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25. How to visualize datasets in Python?
Using matplotlib, seaborn, or plotly for charts and graphs.

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In Python, handling CSV files is straightforward using the built-in csv module for reading and writing tabular data, or pandas for advanced analysis—essential for data processing tasks like importing/exporting datasets in interviews.

# Reading CSV with csv module (basic)
import csv
with open('data.csv', 'r') as file:
    reader = csv.reader(file)
    data = list(reader)  # data = [['Name', 'Age'], ['Alice', '30'], ['Bob', '25']]

# Writing CSV with csv module
import csv
with open('output.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(['Name', 'Age'])  # Header
    writer.writerows([['Alice', 30], ['Bob', 25]])  # Data rows

# Advanced: Reading with pandas (handles headers, missing values)
import pandas as pd
df = pd.read_csv('data.csv')  # df = DataFrame with columns 'Name', 'Age'
print(df.head())  # Output: First 5 rows preview

# Writing with pandas
df.to_csv('output.csv', index=False)  # Saves without row indices

#python #csv #pandas #datahandling #fileio #interviewtips

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