Explain the features of Python / Say something about the benefits of using Python?
Python is a MUST for students and working professionals to become a great Software Engineer specially when they are working in Web Development Domain. I will list down some of the key advantages of learning Python:
○ Simple and easy to learn:
* Learning python programming language is easy and fun.
* Compared to other language, like, Java or C++, its syntax is a way lot easier.
* You also don’t have to worry about the missing semicolons (;) in the end!
* It is more expressive means that it is more understandable and readable.
* Python is a great language for the beginner-level programmers.
* It supports the development of a wide range of applications from simple text processing to WWW browsers to games.
* Easy-to-learn − Python has few keywords, simple structure, and a clearly defined syntax. This makes it easy for Beginners to pick up the language quickly.
* Easy-to-read − Python code is more clearly defined and readable. It's almost like plain and simple English.
* Easy-to-maintain − Python's source code is fairly easy-to-maintain.
Features of Python
○ Python is Interpreted −
* Python is processed at runtime by the interpreter.
* You do not need to compile your program before executing it. This is similar to PERL and PHP.
○ Python is Interactive −
* Python has support for an interactive mode which allows interactive testing and debugging of snippets of code.
* You can open the interactive terminal also referred to as Python prompt and interact with the interpreter directly to write your programs.
○ Python is Object-Oriented −
* Python not only supports functional and structured programming methods, but Object Oriented Principles.
○ Scripting Language —
* Python can be used as a scripting language or it can be compliled to byte-code for building large applications.
○ Dynammic language —
* It provides very high-level dynamic data types and supports dynamic type checking.
○ Garbage collection —
* Garbage collection is a process where the objects that are no longer reachable are freed from memory.
* Memory management is very important while writing programs and python supports automatic garbage collection, which is one of the main problems in writing programs using C & C++.
○ Large Open Source Community —
* Python has a large open source community and which is one of its main strength.
* And its libraries, from open source 118 thousand plus and counting.
* If you are stuck with an issue, you don’t have to worry at all because python has a huge community for help. So, if you have any queries, you can directly seek help from millions of python community members.
* A broad standard library − Python's bulk of the library is very portable and cross-platform compatible on UNIX, Windows, and Macintosh.
* Extendable − You can add low-level modules to the Python interpreter. These modules enable programmers to add to or customize their tools to be more efficient.
○ Cross-platform Language —
* Python is a Cross-platform language or Portable language.
* Python can run on a wide variety of hardware platforms and has the same interface on all platforms.
* Python can run on different platforms such as Windows, Linux, Unix and Macintosh etc.
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Pandas interview questions (for data analyst):
What are the basic data structures in pandas?
How do you create a DataFrame in pandas?
How do you read a CSV file in pandas?
How can you select specific columns from a DataFrame in pandas?
How do you filter rows in a DataFrame based on a condition in pandas?
How do you handle missing values in a DataFrame using pandas?
How do you merge two DataFrames in pandas?
How do you perform groupby operation in pandas?
How do you rename columns in a DataFrame using pandas?
How do you sort a DataFrame by a specific column in pandas?
How do you aggregate data using pandas?
How do you apply a function to each element in a DataFrame in pandas?
How do you perform data visualization using pandas?
How do you handle duplicate data in a DataFrame using pandas?
How do you calculate descriptive statistics for a DataFrame using pandas?
How do you set the index of a DataFrame using pandas?
How do you reset the index of a DataFrame in pandas?
How do you concatenate multiple DataFrames in pandas?
How do you pivot a DataFrame in pandas?
How do you melt a DataFrame in pandas?
How do you calculate the correlation between columns in a DataFrame using pandas?
How do you handle outliers in a DataFrame using pandas?
How do you extract unique values from a column in a DataFrame using pandas?
How do you calculate cumulative sum in a DataFrame using pandas?
How do you convert data types of columns in a DataFrame using pandas?
How do you handle datetime data in a DataFrame using pandas?
How do you resample time-series data in pandas?
How do you merge and append DataFrames with different column names in pandas?
How do you handle multi-level indexing in pandas?
How do you drop columns from a DataFrame in pandas?
How do you create a pivot table in pandas?
How do you calculate rolling statistics in pandas?
How do you concatenate strings in a DataFrame column using pandas?
How do you create a cross-tabulation in pandas?
How do you handle categorical data in pandas?
How do you calculate cumulative percentage in a DataFrame column using pandas?
How do you handle data imputation in pandas?
How do you calculate percentage change in a DataFrame column using pandas?
How do you calculate the rank of values in a DataFrame column using pandas?
How do you calculate the difference between consecutive values in a DataFrame column using pandas?
How do you drop duplicate rows based on a specific column in pandas?
How do you calculate the mean, median, and mode of a DataFrame column using pandas?
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
What are the basic data structures in pandas?
How do you create a DataFrame in pandas?
How do you read a CSV file in pandas?
How can you select specific columns from a DataFrame in pandas?
How do you filter rows in a DataFrame based on a condition in pandas?
How do you handle missing values in a DataFrame using pandas?
How do you merge two DataFrames in pandas?
How do you perform groupby operation in pandas?
How do you rename columns in a DataFrame using pandas?
How do you sort a DataFrame by a specific column in pandas?
How do you aggregate data using pandas?
How do you apply a function to each element in a DataFrame in pandas?
How do you perform data visualization using pandas?
How do you handle duplicate data in a DataFrame using pandas?
How do you calculate descriptive statistics for a DataFrame using pandas?
How do you set the index of a DataFrame using pandas?
How do you reset the index of a DataFrame in pandas?
How do you concatenate multiple DataFrames in pandas?
How do you pivot a DataFrame in pandas?
How do you melt a DataFrame in pandas?
How do you calculate the correlation between columns in a DataFrame using pandas?
How do you handle outliers in a DataFrame using pandas?
How do you extract unique values from a column in a DataFrame using pandas?
How do you calculate cumulative sum in a DataFrame using pandas?
How do you convert data types of columns in a DataFrame using pandas?
How do you handle datetime data in a DataFrame using pandas?
How do you resample time-series data in pandas?
How do you merge and append DataFrames with different column names in pandas?
How do you handle multi-level indexing in pandas?
How do you drop columns from a DataFrame in pandas?
How do you create a pivot table in pandas?
How do you calculate rolling statistics in pandas?
How do you concatenate strings in a DataFrame column using pandas?
How do you create a cross-tabulation in pandas?
How do you handle categorical data in pandas?
How do you calculate cumulative percentage in a DataFrame column using pandas?
How do you handle data imputation in pandas?
How do you calculate percentage change in a DataFrame column using pandas?
How do you calculate the rank of values in a DataFrame column using pandas?
How do you calculate the difference between consecutive values in a DataFrame column using pandas?
How do you drop duplicate rows based on a specific column in pandas?
How do you calculate the mean, median, and mode of a DataFrame column using pandas?
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
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If I were to learn Python for Data Analysis again I'd focus on:
- Python Programming fundamentals.
- Pandas, Numpy, and Matplotlib for data handling/visualisation.
- Seaborn for enhanced visualisation.
- Build projects with data from Kaggle/Google Datasets.
#python
- Python Programming fundamentals.
- Pandas, Numpy, and Matplotlib for data handling/visualisation.
- Seaborn for enhanced visualisation.
- Build projects with data from Kaggle/Google Datasets.
#python
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Essential Python Concepts 👇👇
https://medium.com/@data_analyst/must-know-differences-in-python-with-real-examples-1224227f8d0b
Like for more ❤️
https://medium.com/@data_analyst/must-know-differences-in-python-with-real-examples-1224227f8d0b
Like for more ❤️
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Official Python Docs
https://docs.python.org/3/
Tools:
https://docs.python-guide.org/en/latest/dev/virtualenvs/
https://www.pythonforbeginners.com/basics/python-pip-usage
Practice:
https://www.practicepython.org/
https://www.hackerrank.com
https://wiki.python.org/moin/PythonDecorators
Python GUI FAQ
https://docs.python.org/3/faq/gui.html
https://docs.python.org/3/
Tools:
https://docs.python-guide.org/en/latest/dev/virtualenvs/
https://www.pythonforbeginners.com/basics/python-pip-usage
Practice:
https://www.practicepython.org/
https://www.hackerrank.com
https://wiki.python.org/moin/PythonDecorators
Python GUI FAQ
https://docs.python.org/3/faq/gui.html
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Is Python Really Essential for Data Analysis as a Fresher?
Starting out in data analysis can be overwhelming, especially when everyone seems to say Python is a must-have. But here’s a fresher’s reality check: Python is not always required at the start!
💡 Why You Don’t Need to Worry About Python Right Away:
1️⃣ Excel, Power BI and SQL First! - Many entry-level roles prioritize skills in Excel and SQL. These tools alone can handle a lot of data tasks like cleaning, aggregating, and visualizing data.
2️⃣ Gradual Learning Path 📈 - Once you’re comfortable with the basics, Python is a powerful next step, especially for handling larger datasets or automating processes.
3️⃣ Value in Flexibility - Python’s libraries like Pandas and Matplotlib allow for more complex analysis, but they’re skills you can learn over time as you grow in your role.
🔑 Takeaway? Start with what’s essential—Excel, Power BI and SQL—and build your Python skills as you gain more experience.
Starting out in data analysis can be overwhelming, especially when everyone seems to say Python is a must-have. But here’s a fresher’s reality check: Python is not always required at the start!
💡 Why You Don’t Need to Worry About Python Right Away:
1️⃣ Excel, Power BI and SQL First! - Many entry-level roles prioritize skills in Excel and SQL. These tools alone can handle a lot of data tasks like cleaning, aggregating, and visualizing data.
2️⃣ Gradual Learning Path 📈 - Once you’re comfortable with the basics, Python is a powerful next step, especially for handling larger datasets or automating processes.
3️⃣ Value in Flexibility - Python’s libraries like Pandas and Matplotlib allow for more complex analysis, but they’re skills you can learn over time as you grow in your role.
🔑 Takeaway? Start with what’s essential—Excel, Power BI and SQL—and build your Python skills as you gain more experience.
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Python for Web3 and Smart Contracts Roadmap
Stage 1 – Python Basics (Syntax, OOP)
Stage 2 – Blockchain Fundamentals (Transactions, Ledgers)
Stage 3 – Web3(.)py and Ethereum Basics
Stage 4 – Smart Contracts with Solidity
Stage 5 – Decentralized Storage (IPFS)
Stage 6 – Integrate Wallets and MetaMask
Stage 7 – Decentralized Application (DApp) Development
Stage 8 – Deploy and Test Smart Contracts
🏆 – Python Web3 Developer
Stage 1 – Python Basics (Syntax, OOP)
Stage 2 – Blockchain Fundamentals (Transactions, Ledgers)
Stage 3 – Web3(.)py and Ethereum Basics
Stage 4 – Smart Contracts with Solidity
Stage 5 – Decentralized Storage (IPFS)
Stage 6 – Integrate Wallets and MetaMask
Stage 7 – Decentralized Application (DApp) Development
Stage 8 – Deploy and Test Smart Contracts
🏆 – Python Web3 Developer
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TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
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Many people pay too much to learn Python, but my mission is to break down barriers. I have shared complete learning series to learn Python from scratch.
Here are the links to the Python series
Complete Python Topics for Data Analyst: https://t.iss.one/sqlspecialist/548
Part-1: https://t.iss.one/sqlspecialist/562
Part-2: https://t.iss.one/sqlspecialist/564
Part-3: https://t.iss.one/sqlspecialist/565
Part-4: https://t.iss.one/sqlspecialist/566
Part-5: https://t.iss.one/sqlspecialist/568
Part-6: https://t.iss.one/sqlspecialist/570
Part-7: https://t.iss.one/sqlspecialist/571
Part-8: https://t.iss.one/sqlspecialist/572
Part-9: https://t.iss.one/sqlspecialist/578
Part-10: https://t.iss.one/sqlspecialist/577
Part-11: https://t.iss.one/sqlspecialist/578
Part-12:
https://t.iss.one/sqlspecialist/581
Part-13: https://t.iss.one/sqlspecialist/583
Part-14: https://t.iss.one/sqlspecialist/584
Part-15: https://t.iss.one/sqlspecialist/585
I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.
But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.
You can refer these amazing resources for Python Interview Preparation.
Complete SQL Topics for Data Analysts: https://t.iss.one/sqlspecialist/523
Complete Power BI Topics for Data Analysts: https://t.iss.one/sqlspecialist/588
I'll continue with learning series on Excel & Tableau.
Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.
Hope it helps :)
Here are the links to the Python series
Complete Python Topics for Data Analyst: https://t.iss.one/sqlspecialist/548
Part-1: https://t.iss.one/sqlspecialist/562
Part-2: https://t.iss.one/sqlspecialist/564
Part-3: https://t.iss.one/sqlspecialist/565
Part-4: https://t.iss.one/sqlspecialist/566
Part-5: https://t.iss.one/sqlspecialist/568
Part-6: https://t.iss.one/sqlspecialist/570
Part-7: https://t.iss.one/sqlspecialist/571
Part-8: https://t.iss.one/sqlspecialist/572
Part-9: https://t.iss.one/sqlspecialist/578
Part-10: https://t.iss.one/sqlspecialist/577
Part-11: https://t.iss.one/sqlspecialist/578
Part-12:
https://t.iss.one/sqlspecialist/581
Part-13: https://t.iss.one/sqlspecialist/583
Part-14: https://t.iss.one/sqlspecialist/584
Part-15: https://t.iss.one/sqlspecialist/585
I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.
But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.
You can refer these amazing resources for Python Interview Preparation.
Complete SQL Topics for Data Analysts: https://t.iss.one/sqlspecialist/523
Complete Power BI Topics for Data Analysts: https://t.iss.one/sqlspecialist/588
I'll continue with learning series on Excel & Tableau.
Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.
Hope it helps :)
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