Data Analytics
@DataAnalyticsX
27.9K subscribers
1.19K photos
27 videos
33 files
1.01K links
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.

Admin: @HusseinSheikho || @Hussein_Sheikho
Download Telegram
About
Blog
Apps
Platform
Join
Data Analytics
27.9K subscribers
Data Analytics
1. What is the result of the following code?

import pandas as pd
s = pd.Series([10, 20, 30], index=[1, 2, 3])
print(s[1])


A. 10
B. 20
C. 30
D. KeyError

Correct answer: A.

2. What will this code output?

import pandas as pd
s = pd.Series([10, 20, 30])
print(s.iloc[1])


A. 10
B. 20
C. 30
D. IndexError

Correct answer: B.

3. What does this print?

import pandas as pd
df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
print(df.shape)


A. (4,)
B. (2, 2)
C. (1, 4)
D. (2,)

Correct answer: B.

4. What is returned by this expression?

df["a"]


A. DataFrame
B. Series
C. list
D. ndarray

Correct answer: B.

5. What does this code output?

import pandas as pd
df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
print(df[["a"]].shape)


A. (2,)
B. (1, 2)
C. (2, 1)
D. (4, 1)

Correct answer: C.

6. What is the result?

import pandas as pd
s = pd.Series([1, 2, 3])
print(s > 1)


A. [False, True, True]
B. Series of booleans
C. ndarray of booleans
D. True

Correct answer: B.

7. What does this code produce?

import pandas as pd
s = pd.Series([1, 2, 3])
print(s[s > 1])


A. Series [2, 3]
B. Series [False, True, True]
C. [2, 3]
D. IndexError

Correct answer: A.

8. What is the output?

import pandas as pd
df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
print(df.iloc[0, 1])


A. 1
B. 2
C. 3
D. 4

Correct answer: C.

9. What does this select?

df.loc[:, "a"]


A. First row
B. First column as Series
C. First column as DataFrame
D. Entire DataFrame

Correct answer: B.

10. What will this code output?

import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3]})
print(len(df))


A. 1
B. 2
C. 3
D. Error

Correct answer: C.

11. What is returned?

df.values


A. Series
B. DataFrame
C. NumPy ndarray
D. list

Correct answer: C.

12. What does this code output?

import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3]})
print(df.index)


A. [0, 1, 2]
B. list
C. RangeIndex
D. ndarray

Correct answer: C.

13. What is the result?

df.columns


A. list
B. Series
C. Index
D. dict

Correct answer: C.

14. What does this return?

df.dtypes


A. dict
B. Series
C. DataFrame
D. ndarray

Correct answer: B.

15. What is printed?

import pandas as pd
s = pd.Series([1, None, 3])
print(s.isna().sum())


A. 0
B. 1
C. 2
D. 3

Correct answer: B.

16. What does this code output?

import pandas as pd
s = pd.Series([1, None, 3])
print(s.dropna().values)


A. [1, None, 3]
B. [None]
C. [1, 3]
D. Error

Correct answer: C.

17. What does this expression return?

df.head(1)


A. First column
B. First row as Series
C. First row as DataFrame
D. Entire DataFrame

Correct answer: C.

18. What is the output?

import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3]})
print(df.tail(1)["a"].iloc[0])


A. 1
B. 2
C. 3
D. Error

Correct answer: C.

19. What happens here?

df["c"] = df["a"] * 2


A. Raises KeyError
B. Modifies column a
C. Adds new column c
D. No effect

Correct answer: C.

20. What does this code output?

import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3]})
print(df.sum().iloc[0])


A. 1
B. 3
C. 6
D. Error

Correct answer: C.

21. What does df.mean() return?
A. scalar
B. Series
C. DataFrame
D. ndarray

Correct answer: B.

22. What is the result?

df["a"].dtype


A. int
B. numpy.int64
C. object
D. float

Correct answer: B.

23. What does this code do?

df = df.rename(columns={"a": "x"})


A. Renames index
B. Renames column a to x
C. Deletes column a
D. Copies DataFrame only

Correct answer: B.

24. What does this expression return?

df.loc[df["a"] > 1, :]


A. Boolean Series
B. Filtered DataFrame
C. Filtered Series
D. Error

Correct answer: B.

25. What is printed?

import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3]})
print(df.empty)


A. True
B. False
C. None
D. Error

Correct answer: B.

https://t.me/DataAnalyticsX 😱
Please open Telegram to view this post
VIEW IN TELEGRAM
❀1
1.09K views
Data Analytics
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 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.
❀6
2.29K views
Data Analytics
Data Analytics
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".
❀2πŸ‘1
2.18K views
Data Analytics
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

βœ… https://t.me/addlist/8_rRW2scgfRhOTc0

βœ… https://t.me/Codeprogrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
❀1
1.72K views
Data Analytics
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.

https://t.me/DataAnalyticsX βœ…
Please open Telegram to view this post
VIEW IN TELEGRAM
Telegram
Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.

Admin: @HusseinSheikho || @Hussein_Sheikho
❀8
2.5K viewsedited  
Data Analytics
Forwarded from Machine Learning with Python
AI-ML Roadmap from Scratch

πŸ‘‰ https://github.com/aadi1011/AI-ML-Roadmap-from-scratch?tab=readme-ov-file

https://t.me/CodeProgrammer 🌟

Like and Share
Please open Telegram to view this post
VIEW IN TELEGRAM
❀5
1.79K views
Data Analytics
πŸš€ Master Data Science & Programming!

Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!


πŸ”° Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer

πŸ”– Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM

🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4

🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ

πŸ’Ύ Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1

πŸ§‘β€πŸŽ“ Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC

πŸ˜€ ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT

πŸ’¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9

🐍 Python Arab| Ψ¨Ψ§ΩŠΨ«ΩˆΩ† عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab

πŸ–Š Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksβ€”insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN

πŸ“Ί Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV

πŸ“ˆ Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX

🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.me/Python53

⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY

━━━━━━━━━━━━━━━━━━
Admin: @HusseinSheikho
Please open Telegram to view this post
VIEW IN TELEGRAM
❀3
2.09K views
Data Analytics
Forwarded from Machine Learning with Python
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
β†’ Airbnb price forecasting
β†’ Air ticket cost calculator
β†’ Student performance tracker

AI for medicine
β†’ Chest disease detection
β†’ Heart disease prediction
β†’ Diabetes risk analysis

Generative AI applications
β†’ Live chatbot on Gemini
β†’ Medical assistant tool
β†’ Document analysis tool

Computer vision projects
β†’ Hand tracking system
β†’ Drug recognition app
β†’ OpenCV implementations

Data analysis dashboards
β†’ E-commerce analytics
β†’ Restaurant analytics
β†’ Cricket statistics tracker

And 10 more advanced projects coming soon:
β†’ Deepfake detection
β†’ Brain tumor classification
β†’ 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

πŸ‘‰ @codeprogrammer

Like and Share
Please open Telegram to view this post
VIEW IN TELEGRAM
❀7
2.62K views
Data Analytics
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

βœ… https://t.me/addlist/8_rRW2scgfRhOTc0

βœ… https://t.me/Codeprogrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
❀4
1.97K views
Data Analytics
Media is too big
VIEW IN TELEGRAM
OnSpace Mobile App builder: Build AI Apps in minutes

Visit website: https://www.onspace.ai/?via=tg_datas
Or Download app:https://onspace.onelink.me/za8S/h1jb6sb9?c=datas

With OnSpace, you can build website or AI Mobile Apps by chatting with AI, and publish to PlayStore or AppStore.

What will you get:
βœ”οΈ Create app or website by chatting with AI;
βœ”οΈ Integrate with Any top AI power just by giving order (like Sora2, Nanobanan Pro & Gemini 3 Pro);
βœ”οΈ Download APK,AAB file, publish to AppStore.
βœ”οΈ Add payments and monetize like in-app-purchase and Stripe.
βœ”οΈ Functional login & signup.
βœ”οΈ Database + dashboard in minutes.
βœ”οΈ Full tutorial on YouTube and within 1 day customer service
Please open Telegram to view this post
VIEW IN TELEGRAM
❀3
1.45K views
Data Analytics
Data Analytics pinned a video
Data Analytics
Data Analytics
OnSpace Mobile App builder: Build AI Apps in minutes Visit website: https://www.onspace.ai/?via=tg_datas Or Download app:https://onspace.onelink.me/za8S/h1jb6sb9?c=datas With OnSpace, you can build website or AI Mobile Apps by chatting with AI, and publish…
A great app for building and programming desktop, Android, and Telegram bots using only prompts

Just send what you want and it will design everything for you and the possibility to make money from your app πŸ‘
❀1
1.1K views
Data Analytics
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:
β€’ Processes rows in batches in parallel
β€’ Uses all CPU cores
β€’ Does not require any configuration

Article - pandas vs polars vs DuckDB
Run this code

πŸ‘‰ https://t.me/DataAnalyticsX
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘2❀1
1.19K viewsedited  
Data Analytics
Media is too big
VIEW IN TELEGRAM
πŸ›‘ Popular #SQL interview question.

How would you answer it?

https://t.me/DataAnalyticsX
❀2
961 views
Data Analytics
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

βœ… https://t.me/addlist/8_rRW2scgfRhOTc0

βœ… https://t.me/Codeprogrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
❀2
861 views
Data Analytics
πŸ‘©β€πŸ’» FREE 2026 IT Learning Kits Giveaway

πŸ”₯Whether you're preparing for #Cisco #AWS #PMP #Python #Excel #Google #Microsoft #AI or any other in-demand certification – SPOTO has got you covered!

🎁 Explore Our FREE Study Resources
Β·IT Certs E-book : https://bit.ly/3YvSMHL
Β·IT exams skill Test : https://bit.ly/4r4VHnd
Β·Python, ITIL, PMP, Excel, Cyber Security, cloud, SQL Courses : https://bit.ly/4qNWl8r
Β·Free AI online preparation material and support tools : https://bit.ly/4qKiKTN

πŸ”— Need IT Certs Exam Help？ contact: wa.link/dm4kyz
πŸ“² Join IT Study Group for insider tips & expert support:
https://chat.whatsapp.com/BEQ9WrfLnpg1SgzGQw69oM
❀3
858 views
Data Analytics
Data Analytics pinned a photo