Top Questions People Ask About Pandas, NumPy, Matplotlib & Scikit-learn — Answered!

 Whether you're a beginner or brushing up on your skills, these are the real-world questions Python learners ask most about key libraries in data science. Let’s dive in! 🐍

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🐼 Pandas: Data Manipulation Made Easy

1. How do I handle missing data in a DataFrame?

df.fillna(0)       # Replace NaNs with 0
df.dropna()        # Remove rows with NaNs
df.isna().sum()    # Count missing values per column

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2. How can I merge or join two DataFrames?

pd.merge(df1, df2, on='id', how='inner')   # inner, left, right, outer

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3. What is the difference between loc[] and iloc[]?

• loc[] uses labels (e.g., column names)

• iloc[] uses integer positions

df.loc[0, 'name']     # label-based
df.iloc[0, 1]         # index-based

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4. How do I group data and perform aggregation?

df.groupby('category')['sales'].sum()

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5. How can I convert a column to datetime format?

df['date'] = pd.to_datetime(df['date'])

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🔢 NumPy: Fast Numerical Computation

6. How is NumPy different from a Python list?

• NumPy arrays are faster and support vectorized operations.

• Use less memory and are more efficient for math-heavy tasks.

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7. What is broadcasting in NumPy?

Broadcasting allows operations between arrays of different shapes.

arr = np.array([1, 2, 3])
arr + 5   # [6, 7, 8] — scalar is broadcasted

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8. How do I create arrays of zeros, ones, or random numbers?

np.zeros((3,3))           # 3x3 of zeros
np.ones((2,2))            # 2x2 of ones
np.random.rand(4)         # 1D array of 4 random floats

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9. How can I apply mathematical operations on arrays?

arr = np.array([1, 2, 3])
np.sqrt(arr)
np.log(arr)
arr * 2

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10. How do I reshape or flatten an array?

arr.reshape(3, 2)        # reshape to 3x2
arr.flatten()            # convert to 1D

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📊 Matplotlib: Beautiful Data Visualization

11. How do I create a basic line chart?

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [4, 5, 6])
plt.title("Line Chart")
plt.show()

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12. How can I customize the plot style, color, and size?

plt.plot(x, y, color='green', linestyle='--', linewidth=2)
plt.figure(figsize=(10,5))

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13. What’s the difference between plt.plot() and plt.scatter()?

• plot() is for line charts

• scatter() is for point plots

plt.scatter(x, y)

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14. How do I save a plot as an image?

plt.savefig("my_plot.png")

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15. How do I plot multiple charts in one figure?

plt.subplot(1, 2, 1)   # 1 row, 2 cols, first plot
plt.plot(x1, y1)

plt.subplot(1, 2, 2)   # second plot
plt.plot(x2, y2)

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🧠 Scikit-learn: ML Simplified

16. How do I split data into training and test sets?

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

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17. What are the most common models in Scikit-learn?

• LinearRegression()

• LogisticRegression()

• RandomForestClassifier()

• KNeighborsClassifier()

• SVC() (Support Vector Classifier)

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18. How do I evaluate model performance?

from sklearn.metrics import accuracy_score, confusion_matrix

accuracy_score(y_test, y_pred)
confusion_matrix(y_test, y_pred)

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19. What is the difference between fit(), transform(), and fit_transform()?

• fit(): learns the parameters (e.g., mean, std)

• transform(): applies the transformation

• fit_transform(): does both in one step

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20. How do I do hyperparameter tuning with GridSearchCV?

from sklearn.model_selection import GridSearchCV

params = {'n_neighbors': [3, 5, 7]}
grid = GridSearchCV(KNeighborsClassifier(), params, cv=5)
grid.fit(X_train, y_train)

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✨ Conclusion

These are the most common real-world questions Python learners ask when working with the most-used libraries in data science. Bookmark this post and share it with your learning buddies!