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👍15❤5
Easy Python scenarios for everyday data tasks
Scenario 1: Data Cleaning
Question:
You have a DataFrame containing product prices with columns Product and Price. Some of the prices are stored as strings with a dollar sign, like $10. Write a Python function to convert the prices to float.
Answer:
import pandas as pd
data = {
'Product': ['A', 'B', 'C', 'D'],
'Price': ['$10', '$20', '$30', '$40']
}
df = pd.DataFrame(data)
def clean_prices(df):
df['Price'] = df['Price'].str.replace('$', '').astype(float)
return df
cleaned_df = clean_prices(df)
print(cleaned_df)
Scenario 2: Basic Aggregation
Question:
You have a DataFrame containing sales data with columns Region and Sales. Write a Python function to calculate the total sales for each region.
Answer:
import pandas as pd
data = {
'Region': ['North', 'South', 'East', 'West', 'North', 'South', 'East', 'West'],
'Sales': [100, 200, 150, 250, 300, 100, 200, 150]
}
df = pd.DataFrame(data)
def total_sales_per_region(df):
total_sales = df.groupby('Region')['Sales'].sum().reset_index()
return total_sales
total_sales = total_sales_per_region(df)
print(total_sales)
Scenario 3: Filtering Data
Question:
You have a DataFrame containing customer data with columns ‘CustomerID’, Name, and Age. Write a Python function to filter out customers who are younger than 18 years old.
Answer:
import pandas as pd
data = {
'CustomerID': [1, 2, 3, 4, 5],
'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
'Age': [17, 22, 15, 35, 40]
}
df = pd.DataFrame(data)
def filter_customers(df):
filtered_df = df[df['Age'] >= 18]
return filtered_df
filtered_customers = filter_customers(df)
print(filtered_customers)
Scenario 1: Data Cleaning
Question:
You have a DataFrame containing product prices with columns Product and Price. Some of the prices are stored as strings with a dollar sign, like $10. Write a Python function to convert the prices to float.
Answer:
import pandas as pd
data = {
'Product': ['A', 'B', 'C', 'D'],
'Price': ['$10', '$20', '$30', '$40']
}
df = pd.DataFrame(data)
def clean_prices(df):
df['Price'] = df['Price'].str.replace('$', '').astype(float)
return df
cleaned_df = clean_prices(df)
print(cleaned_df)
Scenario 2: Basic Aggregation
Question:
You have a DataFrame containing sales data with columns Region and Sales. Write a Python function to calculate the total sales for each region.
Answer:
import pandas as pd
data = {
'Region': ['North', 'South', 'East', 'West', 'North', 'South', 'East', 'West'],
'Sales': [100, 200, 150, 250, 300, 100, 200, 150]
}
df = pd.DataFrame(data)
def total_sales_per_region(df):
total_sales = df.groupby('Region')['Sales'].sum().reset_index()
return total_sales
total_sales = total_sales_per_region(df)
print(total_sales)
Scenario 3: Filtering Data
Question:
You have a DataFrame containing customer data with columns ‘CustomerID’, Name, and Age. Write a Python function to filter out customers who are younger than 18 years old.
Answer:
import pandas as pd
data = {
'CustomerID': [1, 2, 3, 4, 5],
'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
'Age': [17, 22, 15, 35, 40]
}
df = pd.DataFrame(data)
def filter_customers(df):
filtered_df = df[df['Age'] >= 18]
return filtered_df
filtered_customers = filter_customers(df)
print(filtered_customers)
👍20❤3
So as a beginner you may Python as first language.
To learn this language, I divided it into 5 parts.
1. Basics:
- Syntax
- Data types
- Variables
- Control structures
- Functions
2. Data Structures:
- Lists
- Tuples
- Dictionaries
- Sets
3. File Input/Output:
- Reading from files
- Writing to files
4. Modules and Packages:
- Importing modules
- Creating modules
5. Object-Oriented Programming:
- Classes
- Objects
- Inheritance
If anything left please comment 😀
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To learn this language, I divided it into 5 parts.
1. Basics:
- Syntax
- Data types
- Variables
- Control structures
- Functions
2. Data Structures:
- Lists
- Tuples
- Dictionaries
- Sets
3. File Input/Output:
- Reading from files
- Writing to files
4. Modules and Packages:
- Importing modules
- Creating modules
5. Object-Oriented Programming:
- Classes
- Objects
- Inheritance
If anything left please comment 😀
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Hope you'll like it
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👍19❤4
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👍19🤣2❤1
📚 9 must-have Python developer tools.
1. PyCharm IDE
2. Jupyter notebook
3. Keras
4. Pip Package
5. Python Anywhere
6. Scikit-Learn
7. Sphinx
8. Selenium
9. Sublime Text
1. PyCharm IDE
2. Jupyter notebook
3. Keras
4. Pip Package
5. Python Anywhere
6. Scikit-Learn
7. Sphinx
8. Selenium
9. Sublime Text
👍13❤4
15 Best Project Ideas for Python : 🐍
🚀 Beginner Level:
1. Simple Calculator
2. To-Do List
3. Number Guessing Game
4. Dice Rolling Simulator
5. Word Counter
🌟 Intermediate Level:
6. Weather App
7. URL Shortener
8. Movie Recommender System
9. Chatbot
10. Image Caption Generator
🌌 Advanced Level:
11. Stock Market Analysis
12. Autonomous Drone Control
13. Music Genre Classification
14. Real-Time Object Detection
15. Natural Language Processing (NLP) Sentiment Analysis
🚀 Beginner Level:
1. Simple Calculator
2. To-Do List
3. Number Guessing Game
4. Dice Rolling Simulator
5. Word Counter
🌟 Intermediate Level:
6. Weather App
7. URL Shortener
8. Movie Recommender System
9. Chatbot
10. Image Caption Generator
🌌 Advanced Level:
11. Stock Market Analysis
12. Autonomous Drone Control
13. Music Genre Classification
14. Real-Time Object Detection
15. Natural Language Processing (NLP) Sentiment Analysis
👍27❤4🔥4😁1
10 Ways to Speed Up Your Python Code
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python noscript, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python noscript, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.
👍14❤9
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
👍22🫡2
⌨️ Asynchronous code
Asynchronous code is an approach to writing code that allows multiple tasks to be performed simultaneously within a single process. This is achieved through the use of asynchronous functions and coroutines. Unlike synchronous code, which executes each task sequentially, asynchronous code can run multiple tasks “in parallel” and organize their execution using iterations and callback calls.
👍8❤3🤔1
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👍16❤4🔥1🙏1
PYTHON INTERVIEW QUESTIONS
1 what is python ?
2 why python ?
3 what are advantage of python ?
4 what is pep 8 ?
5. What do you mean by literal ?
6 explain python function ?
7 what is use of break statement ?
8 what is tuple ?
9 python libraries /module ?
10. What is an in operator in python ?
11 why python interpreted ?
12 how is memory managed in python ?
13 python decorator ?
14 global variable / local variable ?
15 what is iterators in python ?
16 what is slicing in python ?
17 what is a dictionary in python ?
18 what is pass in python ?
19 what isinit ?
20 what is self in python ?
Most important for Technical round interview.
1 what is python ?
2 why python ?
3 what are advantage of python ?
4 what is pep 8 ?
5. What do you mean by literal ?
6 explain python function ?
7 what is use of break statement ?
8 what is tuple ?
9 python libraries /module ?
10. What is an in operator in python ?
11 why python interpreted ?
12 how is memory managed in python ?
13 python decorator ?
14 global variable / local variable ?
15 what is iterators in python ?
16 what is slicing in python ?
17 what is a dictionary in python ?
18 what is pass in python ?
19 what isinit ?
20 what is self in python ?
Most important for Technical round interview.
👍18😁1
11. Python + BeautifulSoup = Web Scraping
12. Python + Scrapy = Web Scraping and Crawling
13. Python + PySpark = Big Data Processing
14. Python + OpenCV = Computer Vision
15. Python + PyTorch = Deep Learning
16. Python + FastAPI = Web Development (high-performance APIs)
17. Python + SQLAlchemy = Database Management
18. Python + Jupyter Notebook = Interactive Computing and Data Analysis
19. Python + Celery = Distributed Task Queue
20. Python + Pygame = Game Development
#python
12. Python + Scrapy = Web Scraping and Crawling
13. Python + PySpark = Big Data Processing
14. Python + OpenCV = Computer Vision
15. Python + PyTorch = Deep Learning
16. Python + FastAPI = Web Development (high-performance APIs)
17. Python + SQLAlchemy = Database Management
18. Python + Jupyter Notebook = Interactive Computing and Data Analysis
19. Python + Celery = Distributed Task Queue
20. Python + Pygame = Game Development
#python
👍20❤5🔥1
21. Python + Ansible = IT Automation and Configuration Management
22. Python + Fabric = Automation and Deployment
23. Python + NLTK = NLP
24. Python + spaCy = Industrial-Strength Natural Language Processing
25. Python + Bokeh = Interactive Web Visualization
26. Python + Dash = Web-Based Data Visualization
27. Python + Scikit-learn = ML
28. Python + NetworkX = Network Analysis and Graph Theory
29. Python + Twisted = Network Programming
30. Python + PyQt = GUI Application Development
#python
22. Python + Fabric = Automation and Deployment
23. Python + NLTK = NLP
24. Python + spaCy = Industrial-Strength Natural Language Processing
25. Python + Bokeh = Interactive Web Visualization
26. Python + Dash = Web-Based Data Visualization
27. Python + Scikit-learn = ML
28. Python + NetworkX = Network Analysis and Graph Theory
29. Python + Twisted = Network Programming
30. Python + PyQt = GUI Application Development
#python
👍15🤔1
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