Jupyter Notebooks are essential for data analysts working with Python.
Here’s how to make the most of this great tool:
1. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲 𝘄𝗶𝘁𝗵 𝗖𝗹𝗲𝗮𝗿 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲:
Break your notebook into logical sections using markdown headers. This helps you and your colleagues navigate the notebook easily and understand the flow of analysis. You could use headings (#, ##, ###) and bullet points to create a table of contents.
2. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀:
Add markdown cells to explain your methodology, code, and guidelines for the user. This Enhances the readability and makes your notebook a great reference for future projects. You might want to include links to relevant resources and detailed docs where necessary.
3. 𝗨𝘀𝗲 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗪𝗶𝗱𝗴𝗲𝘁𝘀:
Leverage ipywidgets to create interactive elements like sliders, dropdowns, and buttons. With those, you can make your analysis more dynamic and allow users to explore different scenarios without changing the code. Create widgets for parameter tuning and real-time data visualization.
𝟰. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗖𝗹𝗲𝗮𝗻 𝗮𝗻𝗱 𝗠𝗼𝗱𝘂𝗹𝗮𝗿:
Write reusable functions and classes instead of long, monolithic code blocks. This will improve the code maintainability and efficiency of your notebook. You should store frequently used functions in separate Python noscripts and import them when needed.
5. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆:
Utilize libraries like Matplotlib, Seaborn, and Plotly for your data visualizations. These clear and insightful visuals will help you to communicate your findings. Make sure to customize your plots with labels, noscripts, and legends to make them more informative.
6. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Jupyter Notebooks are great for exploration, but they often lack systematic version control. Use tools like Git and nbdime to track changes, collaborate effectively, and ensure that your work is reproducible.
7. 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Clean and secure your notebooks by removing sensitive information before sharing. This helps to prevent the leakage of private data. You should consider using environment variables for credentials.
Keeping these techniques in mind will help to transform your Jupyter Notebooks into great tools for analysis and communication.
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Here’s how to make the most of this great tool:
1. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲 𝘄𝗶𝘁𝗵 𝗖𝗹𝗲𝗮𝗿 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲:
Break your notebook into logical sections using markdown headers. This helps you and your colleagues navigate the notebook easily and understand the flow of analysis. You could use headings (#, ##, ###) and bullet points to create a table of contents.
2. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀:
Add markdown cells to explain your methodology, code, and guidelines for the user. This Enhances the readability and makes your notebook a great reference for future projects. You might want to include links to relevant resources and detailed docs where necessary.
3. 𝗨𝘀𝗲 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗪𝗶𝗱𝗴𝗲𝘁𝘀:
Leverage ipywidgets to create interactive elements like sliders, dropdowns, and buttons. With those, you can make your analysis more dynamic and allow users to explore different scenarios without changing the code. Create widgets for parameter tuning and real-time data visualization.
𝟰. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗖𝗹𝗲𝗮𝗻 𝗮𝗻𝗱 𝗠𝗼𝗱𝘂𝗹𝗮𝗿:
Write reusable functions and classes instead of long, monolithic code blocks. This will improve the code maintainability and efficiency of your notebook. You should store frequently used functions in separate Python noscripts and import them when needed.
5. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆:
Utilize libraries like Matplotlib, Seaborn, and Plotly for your data visualizations. These clear and insightful visuals will help you to communicate your findings. Make sure to customize your plots with labels, noscripts, and legends to make them more informative.
6. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Jupyter Notebooks are great for exploration, but they often lack systematic version control. Use tools like Git and nbdime to track changes, collaborate effectively, and ensure that your work is reproducible.
7. 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Clean and secure your notebooks by removing sensitive information before sharing. This helps to prevent the leakage of private data. You should consider using environment variables for credentials.
Keeping these techniques in mind will help to transform your Jupyter Notebooks into great tools for analysis and communication.
I have curated the best interview resources to crack Python Interviews 👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
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👍4🥰1
𝗧𝗼𝗽 𝟴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲
1. NumPy
→ Fundamental library for numerical computing.
→ Used for array operations, linear algebra, and random number generation.
2. Pandas
→ Best for data manipulation and analysis.
→ Offers DataFrame and Series structures for handling tabular data.
3. Matplotlib
→ Creates static, animated, and interactive visualizations.
→ Ideal for line charts, scatter plots, and bar graphs.
4. Seaborn
→ Built on Matplotlib for statistical data visualization.
→ Supports heatmaps, violin plots, and pair plots for deeper insights.
5. Scikit-Learn
→ Essential for machine learning tasks.
→ Provides tools for regression, classification, clustering, and preprocessing.
6. TensorFlow
→ Used for deep learning and neural networks.
→ Supports distributed computing for large-scale models.
7. SciPy
→ Extends NumPy with advanced scientific computations.
→ Useful for optimization, signal processing, and integration.
8. Statsmodels
→ Designed for statistical modeling and hypothesis testing.
→ Great for linear models, time series analysis, and statistical tests.
𝗧𝗶𝗽: Start with NumPy and Pandas to build your foundation, then explore others as per your data science needs!
1. NumPy
→ Fundamental library for numerical computing.
→ Used for array operations, linear algebra, and random number generation.
2. Pandas
→ Best for data manipulation and analysis.
→ Offers DataFrame and Series structures for handling tabular data.
3. Matplotlib
→ Creates static, animated, and interactive visualizations.
→ Ideal for line charts, scatter plots, and bar graphs.
4. Seaborn
→ Built on Matplotlib for statistical data visualization.
→ Supports heatmaps, violin plots, and pair plots for deeper insights.
5. Scikit-Learn
→ Essential for machine learning tasks.
→ Provides tools for regression, classification, clustering, and preprocessing.
6. TensorFlow
→ Used for deep learning and neural networks.
→ Supports distributed computing for large-scale models.
7. SciPy
→ Extends NumPy with advanced scientific computations.
→ Useful for optimization, signal processing, and integration.
8. Statsmodels
→ Designed for statistical modeling and hypothesis testing.
→ Great for linear models, time series analysis, and statistical tests.
𝗧𝗶𝗽: Start with NumPy and Pandas to build your foundation, then explore others as per your data science needs!
🙏4
Python Roadmap for 2025: Complete Guide
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
👇 Python Interview 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
https://news.1rj.ru/str/dsabooks
📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://topmate.io/coding/914624
📙 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.me/getjobss
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
👇 Python Interview 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
https://news.1rj.ru/str/dsabooks
📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://topmate.io/coding/914624
📙 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.me/getjobss
👍10❤2👏1
𝐈𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
𝐋𝐨𝐚𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭:
df = pd.read_csv('your_dataset.csv')
𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧:
1- View the first few rows:
df.head()
2- Summary of the dataset:
df.info()
3- Statistical summary:
df.describe()
𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬:
1- Identify missing values:
df.isnull().sum()
2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()
𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧:
1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()
2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()
3- Pair plots:
sns.pairplot(df)
plt.show()
4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()
𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
Count plots for categorical features:
plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()
Python Interview Q&A: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Like for more ❤️
ENJOY LEARNING 👍👍
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
𝐋𝐨𝐚𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭:
df = pd.read_csv('your_dataset.csv')
𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧:
1- View the first few rows:
df.head()
2- Summary of the dataset:
df.info()
3- Statistical summary:
df.describe()
𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬:
1- Identify missing values:
df.isnull().sum()
2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()
𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧:
1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()
2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()
3- Pair plots:
sns.pairplot(df)
plt.show()
4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()
𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
Count plots for categorical features:
plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()
Python Interview Q&A: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Like for more ❤️
ENJOY LEARNING 👍👍
👍10❤2
🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐞𝐥𝐭 𝐢𝐦𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐚𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐛𝐮𝐭 𝐭𝐡𝐞𝐬𝐞 𝟗 𝐬𝐭𝐞𝐩𝐬 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠!
.
.
1️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬: Started with foundational Python concepts like variables, loops, functions, and conditional statements.
2️⃣ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐝 𝐄𝐚𝐬𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.
3️⃣ 𝐅𝐨𝐥𝐥𝐨𝐰𝐞𝐝 𝐏𝐲𝐭𝐡𝐨𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.
4️⃣ 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐊𝐞𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
5️⃣ 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.
6️⃣ 𝐖𝐚𝐭𝐜𝐡𝐞𝐝 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.
7️⃣ 𝐃𝐞𝐛𝐮𝐠𝐠𝐞𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲: Made it a habit to debug and analyze code to understand errors and optimize solutions.
8️⃣ 𝐉𝐨𝐢𝐧𝐞𝐝 𝐌𝐨𝐜𝐤 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Participated in coding challenges to simulate real-world problem-solving scenarios.
9️⃣ 𝐒𝐭𝐚𝐲𝐞𝐝 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.
I have curated the best interview resources to crack Python Interviews 👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this 👍❤️
#Python
.
.
1️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬: Started with foundational Python concepts like variables, loops, functions, and conditional statements.
2️⃣ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐝 𝐄𝐚𝐬𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.
3️⃣ 𝐅𝐨𝐥𝐥𝐨𝐰𝐞𝐝 𝐏𝐲𝐭𝐡𝐨𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.
4️⃣ 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐊𝐞𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
5️⃣ 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.
6️⃣ 𝐖𝐚𝐭𝐜𝐡𝐞𝐝 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.
7️⃣ 𝐃𝐞𝐛𝐮𝐠𝐠𝐞𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲: Made it a habit to debug and analyze code to understand errors and optimize solutions.
8️⃣ 𝐉𝐨𝐢𝐧𝐞𝐝 𝐌𝐨𝐜𝐤 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Participated in coding challenges to simulate real-world problem-solving scenarios.
9️⃣ 𝐒𝐭𝐚𝐲𝐞𝐝 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.
I have curated the best interview resources to crack Python Interviews 👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this 👍❤️
#Python
👍5
Python for Everything:
Python + Django = Web Development
Python + Matplotlib = Data Visualization
Python + Flask = Web Applications
Python + Pygame = Game Development
Python + PyQt = Desktop Applications
Python + TensorFlow = Machine Learning
Python + FastAPI = API Development
Python + Kivy = Mobile App Development
Python + Pandas = Data Analysis
Python + NumPy = Scientific Computing
Python + Django = Web Development
Python + Matplotlib = Data Visualization
Python + Flask = Web Applications
Python + Pygame = Game Development
Python + PyQt = Desktop Applications
Python + TensorFlow = Machine Learning
Python + FastAPI = API Development
Python + Kivy = Mobile App Development
Python + Pandas = Data Analysis
Python + NumPy = Scientific Computing
👍8❤4
How to master Python from scratch🚀
1. Setup and Basics 🏁
- Install Python 🖥️: Download Python and set it up.
- Hello, World! 🌍: Write your first Hello World program.
2. Basic Syntax 📜
- Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
- Control Structures 🔄: Understand if-else statements, for loops, and while loops.
- Functions 🛠️: Write reusable blocks of code.
3. Data Structures 📂
- Lists 📋: Manage collections of items.
- Dictionaries 📖: Store key-value pairs.
- Tuples 📦: Work with immutable sequences.
- Sets 🔢: Handle collections of unique items.
4. Modules and Packages 📦
- Standard Library 📚: Explore built-in modules.
- Third-Party Packages 🌐: Install and use packages with pip.
5. File Handling 📁
- Read and Write Files 📝
- CSV and JSON 📑
6. Object-Oriented Programming 🧩
- Classes and Objects 🏛️
- Inheritance and Polymorphism 👨👩👧
7. Web Development 🌐
- Flask 🍼: Start with a micro web framework.
- Django 🦄: Dive into a full-fledged web framework.
8. Data Science and Machine Learning 🧠
- NumPy 📊: Numerical operations.
- Pandas 🐼: Data manipulation and analysis.
- Matplotlib 📈 and Seaborn 📊: Data visualization.
- Scikit-learn 🤖: Machine learning.
9. Automation and Scripting 🤖
- Automate Tasks 🛠️: Use Python to automate repetitive tasks.
- APIs 🌐: Interact with web services.
10. Testing and Debugging 🐞
- Unit Testing 🧪: Write tests for your code.
- Debugging 🔍: Learn to debug efficiently.
11. Advanced Topics 🚀
- Concurrency and Parallelism 🕒
- Decorators 🌀 and Generators ⚙️
- Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects 💡
- Calculator 🧮
- To-Do List App 📋
- Weather App ☀️
- Personal Blog 📝
13. Community and Collaboration 🤝
- Contribute to Open Source 🌍
- Join Coding Communities 💬
- Participate in Hackathons 🏆
14. Keep Learning and Improving 📈
- Read Books 📖: Like "Automate the Boring Stuff with Python".
- Watch Tutorials 🎥: Follow video courses and tutorials.
- Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge 📢
- Write Blogs ✍️
- Create Video Tutorials 📹
- Mentor Others 👨🏫
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 👍❤️
1. Setup and Basics 🏁
- Install Python 🖥️: Download Python and set it up.
- Hello, World! 🌍: Write your first Hello World program.
2. Basic Syntax 📜
- Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
- Control Structures 🔄: Understand if-else statements, for loops, and while loops.
- Functions 🛠️: Write reusable blocks of code.
3. Data Structures 📂
- Lists 📋: Manage collections of items.
- Dictionaries 📖: Store key-value pairs.
- Tuples 📦: Work with immutable sequences.
- Sets 🔢: Handle collections of unique items.
4. Modules and Packages 📦
- Standard Library 📚: Explore built-in modules.
- Third-Party Packages 🌐: Install and use packages with pip.
5. File Handling 📁
- Read and Write Files 📝
- CSV and JSON 📑
6. Object-Oriented Programming 🧩
- Classes and Objects 🏛️
- Inheritance and Polymorphism 👨👩👧
7. Web Development 🌐
- Flask 🍼: Start with a micro web framework.
- Django 🦄: Dive into a full-fledged web framework.
8. Data Science and Machine Learning 🧠
- NumPy 📊: Numerical operations.
- Pandas 🐼: Data manipulation and analysis.
- Matplotlib 📈 and Seaborn 📊: Data visualization.
- Scikit-learn 🤖: Machine learning.
9. Automation and Scripting 🤖
- Automate Tasks 🛠️: Use Python to automate repetitive tasks.
- APIs 🌐: Interact with web services.
10. Testing and Debugging 🐞
- Unit Testing 🧪: Write tests for your code.
- Debugging 🔍: Learn to debug efficiently.
11. Advanced Topics 🚀
- Concurrency and Parallelism 🕒
- Decorators 🌀 and Generators ⚙️
- Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects 💡
- Calculator 🧮
- To-Do List App 📋
- Weather App ☀️
- Personal Blog 📝
13. Community and Collaboration 🤝
- Contribute to Open Source 🌍
- Join Coding Communities 💬
- Participate in Hackathons 🏆
14. Keep Learning and Improving 📈
- Read Books 📖: Like "Automate the Boring Stuff with Python".
- Watch Tutorials 🎥: Follow video courses and tutorials.
- Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge 📢
- Write Blogs ✍️
- Create Video Tutorials 📹
- Mentor Others 👨🏫
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 👍❤️
👍9❤4
How to convert image to pdf in Python
# Python3 program to convert image to pfd
# using img2pdf library
# importing necessary libraries
import img2pdf
from PIL import Image
import os
# storing image path
img_path = "Input.png"
# storing pdf path
pdf_path = "file_pdf.pdf"
# opening image
image = Image.open(img_path)
# converting into chunks using img2pdf
pdf_bytes = img2pdf.convert(image.filename)
# opening or creating pdf file
file = open(pdf_path, "wb")
# writing pdf files with chunks
file.write(pdf_bytes)
# closing image file
image.close()
# closing pdf file
file.close()
# output
print("Successfully made pdf file")
pip3 install pillow && pip3 install img2pdf👍11❤2