This channels is for Programmers, Coders, Software Engineers.

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1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
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1. What is the output of this code?

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.

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Admin: @HusseinSheikho

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
→ 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

👉 @codeprogrammer

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This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer

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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.me/DataAnalyticsX

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer