Python from scratch
by University of Waterloo
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
https://open.cs.uwaterloo.ca/python-from-scratch/
#python
by University of Waterloo
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
https://open.cs.uwaterloo.ca/python-from-scratch/
#python
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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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If you want to learn Python for data analysis, focus on these essentials
Don't aim for this:
NumPy - 100%
Pandas - 0%
Matplotlib - 0%
Seaborn - 0%
OS - 0%
Aim for this:
NumPy - 25%
Pandas - 25%
Matplotlib - 25%
Seaborn - 25%
OS - 25%
You don't need to master everything at once.
Focus on the essentials to build a strong foundation.
#python
Don't aim for this:
NumPy - 100%
Pandas - 0%
Matplotlib - 0%
Seaborn - 0%
OS - 0%
Aim for this:
NumPy - 25%
Pandas - 25%
Matplotlib - 25%
Seaborn - 25%
OS - 25%
You don't need to master everything at once.
Focus on the essentials to build a strong foundation.
#python
👍14👏4❤1
Python Interview Questions for data analyst interview
Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.
Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)
Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.
Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.
Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.
Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average
Hope you'll like it
Like this post if you need more resources like this 👍❤️
#Python
Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.
Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)
Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.
Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.
Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.
Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average
Hope you'll like it
Like this post if you need more resources like this 👍❤️
#Python
👍3❤1
Python is a popular programming language in the field of data analysis due to its versatility, ease of use, and extensive libraries for data manipulation, visualization, and analysis. Here are some key Python skills that are important for data analysts:
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
#Python
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
#Python
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🐍 Master Python for Data Analytics!
Python is a powerful tool for data analysis, automation, and visualization. Here’s the ultimate roadmap:
🔹 Basic Concepts:
➡️ Syntax, variables, and data types (integers, floats, strings, booleans)
➡️ Control structures (if-else, for and while loops)
➡️ Basic data structures (lists, dictionaries, sets, tuples)
➡️ Functions, lambda functions, and error handling (try-except)
➡️ Working with modules and packages
🔹 Pandas & NumPy:
➡️ Creating and manipulating DataFrames and arrays
➡️ Data filtering, aggregation, and reshaping
➡️ Handling missing values
➡️ Efficient data operations with NumPy
🔹 Data Visualization:
➡️ Creating visualizations using Matplotlib and Seaborn
➡️ Plotting line, bar, scatter, and heatmaps
#Python
Python is a powerful tool for data analysis, automation, and visualization. Here’s the ultimate roadmap:
🔹 Basic Concepts:
➡️ Syntax, variables, and data types (integers, floats, strings, booleans)
➡️ Control structures (if-else, for and while loops)
➡️ Basic data structures (lists, dictionaries, sets, tuples)
➡️ Functions, lambda functions, and error handling (try-except)
➡️ Working with modules and packages
🔹 Pandas & NumPy:
➡️ Creating and manipulating DataFrames and arrays
➡️ Data filtering, aggregation, and reshaping
➡️ Handling missing values
➡️ Efficient data operations with NumPy
🔹 Data Visualization:
➡️ Creating visualizations using Matplotlib and Seaborn
➡️ Plotting line, bar, scatter, and heatmaps
#Python
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Python (Pandas) interview questions for Data analyst role(entry level): ⬇️
1. What is Python Pandas and what is it used for?
2. Different types of Data Structures in Pandas?
3. Significant features of Pandas Library?
4. Time series in Pandas?
5. Reindexing in pandas along with its parameters?
6. Data Frames in Pandas?
7. MultiIndexing in Pandas?
8. Operation on Series in Pandas?
9. Different ways of creating Data Frames in Pandas?
10. Categorical Data in Pandas?
11. How to Read Text Files with Pandas?
12. How are iloc() and loc() different?
13. Difference between join() and merge() in Pandas?
14. How to add a row/column to a Pandas DataFrame?
15.GroupBy function in Pandas?
16.Use of pandas.Dataframe.aggregate() function?
17. Statistical functions in Python Pandas?
#Python
1. What is Python Pandas and what is it used for?
2. Different types of Data Structures in Pandas?
3. Significant features of Pandas Library?
4. Time series in Pandas?
5. Reindexing in pandas along with its parameters?
6. Data Frames in Pandas?
7. MultiIndexing in Pandas?
8. Operation on Series in Pandas?
9. Different ways of creating Data Frames in Pandas?
10. Categorical Data in Pandas?
11. How to Read Text Files with Pandas?
12. How are iloc() and loc() different?
13. Difference between join() and merge() in Pandas?
14. How to add a row/column to a Pandas DataFrame?
15.GroupBy function in Pandas?
16.Use of pandas.Dataframe.aggregate() function?
17. Statistical functions in Python Pandas?
#Python
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Python #Pandas Cheat Sheet 🐼
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
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Here are some most popular Python libraries for data visualization:
Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
#python
Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
#python
👍3
Most popular Python libraries for data visualization:
Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
#python
Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
#python
❤2
Forwarded from Python for Data Analysts
Python is a popular programming language in the field of data analysis due to its versatility, ease of use, and extensive libraries for data manipulation, visualization, and analysis. Here are some key Python skills that are important for data analysts:
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
#Python
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
#Python
❤1
Most popular Python libraries for data visualization:
Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
#python
Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
#python
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