1. What is the output of this code?
A. Series with values [3, 1, NaN]
B. Series with values [3, 1]
C. KeyError
D. Series with values [1, 3, NaN]
Correct answer: A.
2. What does this code produce?
A. 3
B. 4
C. 5
D. 6
Correct answer: C.
3. What is the result?
A. [1, 2, 3]
B. [1, 0, 0]
C. [0, 0, 0]
D. [1, 2, 0]
Correct answer: B.
4. What does this output?
A. 10
B. 20
C. 30
D. IndexError
Correct answer: B.
5. What is returned?
A. 1
B. 2
C. 3
D. KeyError
Correct answer: B.
6. What does this code output?
A. 0
B. 1
C. 2
D. 3
Correct answer: B.
7. What is the result?
A. 0
B. 1
C. 2
D. Raises error
Correct answer: B.
8. What does this produce?
A. (1, 2)
B. (2, 1)
C. (2, 2)
D. (1, 1)
Correct answer: B.
9. What is printed?
A. 1-2-3
B. ['1-2-3']
C. Series
D. Error
Correct answer: A.
10. What does this code return?
A. (3, 1)
B. (1, 3)
C. (1, 1)
D. Depends on random seed
Correct answer: C.
11. What is the result?
A. 4
B. 5
C. 6
D. NaN
Correct answer: B.
12. What does this output?
A. (3, 1)
B. (3, 2)
C. (1, 3)
D. Error
Correct answer: B.
13. What is returned?
A. 1
B. 2
C. 3
D. KeyError
Correct answer: B.
14. What does this code output?
A. (1, 3)
B. (3, 1)
C. (3, 3)
D. (1, 1)
Correct answer: B.
15. What is the result?
A. (2, 1)
B. (1, 2)
C. (2, 2)
D. (1, 1)
Correct answer: B.
16. What does this print?
A. 0
B. 1
C. 2
D. 3
Correct answer: B.
17. What is the output?
A. True
B. False
C. None
D. Error
Correct answer: B.
18. What does this code return?
A. [3, 1, 2]
B. [1, 2, 3]
C. [3.0, 1.0, 2.0]
D. [3.0, 1.0, 2.0] sorted
Correct answer: C.
19. What is printed?
A. True
B. False
C. None
D. Error
Correct answer: A.
20. What does this produce?
A. (3, 0)
B. (0, 1)
C. (3, 1)
D. (1, 3)
Correct answer: C.
import pandas as pd
s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
print(s.reindex(['c', 'a', 'd']))
A. Series with values [3, 1, NaN]
B. Series with values [3, 1]
C. KeyError
D. Series with values [1, 3, NaN]
Correct answer: A.
2. What does this code produce?
import pandas as pd
df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
print(df.assign(c=lambda x: x['a'] + x['b'])['c'].iloc[1])
A. 3
B. 4
C. 5
D. 6
Correct answer: C.
3. What is the result?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
df.loc[df['a'] > 1, 'a'] = 0
print(df['a'].tolist())
A. [1, 2, 3]
B. [1, 0, 0]
C. [0, 0, 0]
D. [1, 2, 0]
Correct answer: B.
4. What does this output?
import pandas as pd
s = pd.Series([10, 20, 30], index=[2, 0, 1])
print(s.sort_index().iloc[0])
A. 10
B. 20
C. 30
D. IndexError
Correct answer: B.
5. What is returned?
import pandas as pd
df = pd.DataFrame({'a': [1, 1, 2]})
print(df['a'].value_counts().loc[1])
A. 1
B. 2
C. 3
D. KeyError
Correct answer: B.
6. What does this code output?
import pandas as pd
s = pd.Series([1, 2, 3])
print(s.map({1: 'a', 2: 'b'}).isna().sum())
A. 0
B. 1
C. 2
D. 3
Correct answer: B.
7. What is the result?
import pandas as pd
df = pd.DataFrame({'a': [1, None, 3]})
print(df['a'].astype('Int64').isna().sum())
A. 0
B. 1
C. 2
D. Raises error
Correct answer: B.
8. What does this produce?
import pandas as pd
df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
print(df.filter(regex='a').shape)
A. (1, 2)
B. (2, 1)
C. (2, 2)
D. (1, 1)
Correct answer: B.
9. What is printed?
import pandas as pd
s = pd.Series(['1', '2', '3'])
print(s.str.cat(sep='-'))
A. 1-2-3
B. ['1-2-3']
C. Series
D. Error
Correct answer: A.
10. What does this code return?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.sample(n=1).shape)
A. (3, 1)
B. (1, 3)
C. (1, 1)
D. Depends on random seed
Correct answer: C.
11. What is the result?
import pandas as pd
s = pd.Series([1, 2, 3, 4])
print(s.rolling(2).sum().iloc[-1])
A. 4
B. 5
C. 6
D. NaN
Correct answer: B.
12. What does this output?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.eval('b = a * 2').shape)
A. (3, 1)
B. (3, 2)
C. (1, 3)
D. Error
Correct answer: B.
13. What is returned?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.query('a % 2 == 0')['a'].iloc[0])
A. 1
B. 2
C. 3
D. KeyError
Correct answer: B.
14. What does this code output?
import pandas as pd
s = pd.Series([1, 2, 3])
print(s.to_frame().shape)
A. (1, 3)
B. (3, 1)
C. (3, 3)
D. (1, 1)
Correct answer: B.
15. What is the result?
import pandas as pd
df = pd.DataFrame({'a': [1, 2]})
print(df.T.shape)
A. (2, 1)
B. (1, 2)
C. (2, 2)
D. (1, 1)
Correct answer: B.
16. What does this print?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.shift(1)['a'].isna().sum())
A. 0
B. 1
C. 2
D. 3
Correct answer: B.
17. What is the output?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.duplicated().any())
A. True
B. False
C. None
D. Error
Correct answer: B.
18. What does this code return?
import pandas as pd
s = pd.Series([3, 1, 2])
print(s.rank().tolist())
A. [3, 1, 2]
B. [1, 2, 3]
C. [3.0, 1.0, 2.0]
D. [3.0, 1.0, 2.0] sorted
Correct answer: C.
19. What is printed?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.memory_usage(deep=True).iloc[1] > 0)
A. True
B. False
C. None
D. Error
Correct answer: A.
20. What does this produce?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.select_dtypes(include='int').shape)
A. (3, 0)
B. (0, 1)
C. (3, 1)
D. (1, 3)
Correct answer: C.
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1. What is the output of this code? import pandas as pd s = pd.Series([1, 2, 3], index=['a', 'b', 'c']) print(s.reindex(['c', 'a', 'd'])) A. Series with values [3, 1, NaN] B. Series with values [3, 1] C. KeyError D. Series with values [1, 3, NaN] Correct…
I have sent you some real and important questions based on my reading of the book "Pandas Cookbook 2025".
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1. What is the output of this code?
A. False
B. True
C. Raises AttributeError
D. None
Correct answer: B.
2. What does this code return?
A. None
B. 'index'
C. 'a'
D. Raises KeyError
Correct answer: C.
3. What is the result?
A. [1, 2, 3]
B. [2, 3, 4]
C. [1, 3, 5]
D. Error
Correct answer: B.
4. What does this code output?
A. [1, 2]
B. [2, 3]
C. [3, 2]
D. [3, 1]
Correct answer: C.
5. What is printed?
A. 1
B. 2
C. 3
D. Error
Correct answer: A.
6. What does this code return?
A. 0
B. 1
C. 2
D. 3
Correct answer: B.
7. What is the output?
A. 3
B. 5
C. 6
D. Error
Correct answer: C.
8. What does this code produce?
A. (1, 4)
B. (2, 2)
C. (4, 1)
D. Error
Correct answer: B.
9. What is returned?
A. [10, 20]
B. [30, 10]
C. [20, 30]
D. Error
Correct answer: B.
10. What does this output?
A. False
B. True
C. None
D. Error
Correct answer: B.
11. What is the result?
A. True
B. False
C. None
D. Error
Correct answer: B.
12. What does this code return?
A. (3,)
B. (6,)
C. (2, 3)
D. Error
Correct answer: B.
13. What is printed?
A. (1, 3)
B. (3, 2)
C. (3, 1)
D. (1, 2)
Correct answer: B.
14. What does this code output?
A. (3,)
B. (3, 1)
C. (1, 3)
D. Error
Correct answer: A.
15. What is the result?
A. 0
B. 1
C. 2
D. Error
Correct answer: A.
16. What does this code return?
A. 0
B. 1
C. 2
D. Error
Correct answer: B.
17. What is printed?
A. True
B. False
C. None
D. Error
Correct answer: A.
18. What does this code output?
A. True
B. False
C. None
D. Error
Correct answer: B.
19. What is the result?
A. True
B. False
C. None
D. Error
Correct answer: A.
20. What does this code output?
A. True
B. False
C. None
D. Error
Correct answer: A.
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import pandas as pd
idx = pd.Index(['a', 'b', 'c'])
print(idx.is_unique)
A. False
B. True
C. Raises AttributeError
D. None
Correct answer: B.
2. What does this code return?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.set_index('a').index.name)
A. None
B. 'index'
C. 'a'
D. Raises KeyError
Correct answer: C.
3. What is the result?
import pandas as pd
s = pd.Series([1, 2, 3])
print(s.add(1).tolist())
A. [1, 2, 3]
B. [2, 3, 4]
C. [1, 3, 5]
D. Error
Correct answer: B.
4. What does this code output?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.nlargest(2, 'a')['a'].tolist())
A. [1, 2]
B. [2, 3]
C. [3, 2]
D. [3, 1]
Correct answer: C.
5. What is printed?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.nsmallest(1, 'a').iloc[0, 0])
A. 1
B. 2
C. 3
D. Error
Correct answer: A.
6. What does this code return?
import pandas as pd
s = pd.Series([1, 2, 3])
print(s.diff().isna().sum())
A. 0
B. 1
C. 2
D. 3
Correct answer: B.
7. What is the output?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.cumsum()['a'].iloc[-1])
A. 3
B. 5
C. 6
D. Error
Correct answer: C.
8. What does this code produce?
import pandas as pd
df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
print(df.pipe(lambda x: x.shape))
A. (1, 4)
B. (2, 2)
C. (4, 1)
D. Error
Correct answer: B.
9. What is returned?
import pandas as pd
s = pd.Series([10, 20, 30])
print(s.take([2, 0]).tolist())
A. [10, 20]
B. [30, 10]
C. [20, 30]
D. Error
Correct answer: B.
10. What does this output?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.any().iloc[0])
A. False
B. True
C. None
D. Error
Correct answer: B.
11. What is the result?
import pandas as pd
df = pd.DataFrame({'a': [0, 0, 1]})
print(df.all().iloc[0])
A. True
B. False
C. None
D. Error
Correct answer: B.
12. What does this code return?
import pandas as pd
s = pd.Series(['a', 'b', 'c'])
print(s.repeat(2).shape)
A. (3,)
B. (6,)
C. (2, 3)
D. Error
Correct answer: B.
13. What is printed?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.melt().shape)
A. (1, 3)
B. (3, 2)
C. (3, 1)
D. (1, 2)
Correct answer: B.
14. What does this code output?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.stack().shape)
A. (3,)
B. (3, 1)
C. (1, 3)
D. Error
Correct answer: A.
15. What is the result?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.unstack().isna().sum().sum())
A. 0
B. 1
C. 2
D. Error
Correct answer: A.
16. What does this code return?
import pandas as pd
s = pd.Series([1, 2, 3])
print(s.to_numpy().ndim)
A. 0
B. 1
C. 2
D. Error
Correct answer: B.
17. What is printed?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.axes[0].equals(df.index))
A. True
B. False
C. None
D. Error
Correct answer: A.
18. What does this code output?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.copy(deep=False) is df)
A. True
B. False
C. None
D. Error
Correct answer: B.
19. What is the result?
import pandas as pd
s = pd.Series([1, 2, 3])
print(s.equals(pd.Series([1, 2, 3])))
A. True
B. False
C. None
D. Error
Correct answer: A.
20. What does this code output?
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.info() is None)
A. True
B. False
C. None
D. Error
Correct answer: A.
https://t.iss.one/DataAnalyticsX
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
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This GitHub repository is not a dump of tutorials.
Inside, there are 28 production-ready AI projects that can be used.
What's there:
Machine learning projects
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AI for medicine
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Generative AI applications
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→ Medical assistant tool
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Computer vision projects
→ Hand tracking system
→ Drug recognition app
→ OpenCV implementations
Data analysis dashboards
→ E-commerce analytics
→ Restaurant analytics
→ Cricket statistics tracker
And 10 more advanced projects coming soon:
→ Deepfake detection
→ Brain tumor classification
→ Driver drowsiness alert system
This is not just a collection of code files.
These are end-to-end working applications.
View the repository😲
https://github.com/KalyanM45/AI-Project-Gallery
👉 @codeprogrammer
Like and Share
Inside, there are 28 production-ready AI projects that can be used.
What's there:
Machine learning projects
→ Airbnb price forecasting
→ Air ticket cost calculator
→ Student performance tracker
AI for medicine
→ Chest disease detection
→ Heart disease prediction
→ Diabetes risk analysis
Generative AI applications
→ Live chatbot on Gemini
→ Medical assistant tool
→ Document analysis tool
Computer vision projects
→ Hand tracking system
→ Drug recognition app
→ OpenCV implementations
Data analysis dashboards
→ E-commerce analytics
→ Restaurant analytics
→ Cricket statistics tracker
And 10 more advanced projects coming soon:
→ Deepfake detection
→ Brain tumor classification
→ Driver drowsiness alert system
This is not just a collection of code files.
These are end-to-end working applications.
View the repository
https://github.com/KalyanM45/AI-Project-Gallery
Like and Share
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And what if simply changing the library would unlock all the processor cores without rewriting the code?
pandas runs joins on a single core, leaving the others idle when working with large tables.
Polars distributes join operations across all available cores and, as a result, is significantly faster than pandas on large data sets.
Why is Polars so fast:
• Processes rows in batches in parallel
• Uses all CPU cores
• Does not require any configuration
Article - pandas vs polars vs DuckDB
Run this code
👉 https://t.iss.one/DataAnalyticsX
pandas runs joins on a single core, leaving the others idle when working with large tables.
Polars distributes join operations across all available cores and, as a result, is significantly faster than pandas on large data sets.
Why is Polars so fast:
• Processes rows in batches in parallel
• Uses all CPU cores
• Does not require any configuration
Article - pandas vs polars vs DuckDB
Run this code
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