Published:
If you use Jupyter on a regular basis, the steps to launch a notebook are probably second nature, but if you take a step back, it involves a lot of prior knowledge. A few times I’ve tried to bring brand new eager programmers into the glorious land of Python and Jupyter, but each time I found that the whole flow was really bogged down by this preamble that is pretty technical. I’ll give them an .ipynb file and then show them how to open it
Published:
If you use Jupyter on a regular basis, the steps to launch a notebook are probably second nature, but if you take a step back, it involves a lot of prior knowledge. A few times I’ve tried to bring brand new eager programmers into the glorious land of Python and Jupyter, but each time I found that the whole flow was really bogged down by this preamble that is pretty technical. I’ll give them an .ipynb file and then show them how to open it
Published:
I built a new tool! Code Crafter is a Python library designed for manipulating Python source code through Abstract Syntax Tree (AST) transformations. This tool simplifies the process of programmatically editing Python code, allowing developers to find and modify specific data structures such as lists, dictionaries, and sets within their code.
Published:
I built a new tool! Code Crafter is a Python library designed for manipulating Python source code through Abstract Syntax Tree (AST) transformations. This tool simplifies the process of programmatically editing Python code, allowing developers to find and modify specific data structures such as lists, dictionaries, and sets within their code.
Published:
I built a new tool! Code Crafter is a Python library designed for manipulating Python source code through Abstract Syntax Tree (AST) transformations. This tool simplifies the process of programmatically editing Python code, allowing developers to find and modify specific data structures such as lists, dictionaries, and sets within their code.
Published:
I built a new tool! Code Crafter is a Python library designed for manipulating Python source code through Abstract Syntax Tree (AST) transformations. This tool simplifies the process of programmatically editing Python code, allowing developers to find and modify specific data structures such as lists, dictionaries, and sets within their code.
Published:
Matplotlib provides a stackplot, which stacks area, and a step plot, which provides steps, but there is no stacked step plot. This is useful when you have some accumulating resource that have different types that begin and end all at once. I personally had a need for this when I wanted to visualize my man-month commitment to my company’s funded projects over the next 7 years.
Published:
I often have a need to plot a grouped bar plot. Matplotlib provides this example, which is helpful, but not quite generalizable enough for my needs, as it only shows how to group 2 categories together. Here is a generalization of that tutorial that was very helpful for me and I hope is helpful for others as well.
Published:
I recently faced a situation where I needed to assess the amount of work done by each member of a team on a project that has spanned over a year. That project has a git repo, and I could see when each person made a commit. I decided to break it down by weeks. Whenever a person submitted any commit to the repo on any branch, I counted them as working on the project for that week. Of course this is imperfect- someone could work a lot and make no commits for that week and someone could have submitted a commit but might have worked very little. Still, this seems like the most fair way to assess work I could think of.
Published:
Let’s use for example Google Trends results for the search term “gifts.” Google offers this plot:
Published:
Published:
I built a new tool! Code Crafter is a Python library designed for manipulating Python source code through Abstract Syntax Tree (AST) transformations. This tool simplifies the process of programmatically editing Python code, allowing developers to find and modify specific data structures such as lists, dictionaries, and sets within their code.
Published:
Matplotlib provides a stackplot, which stacks area, and a step plot, which provides steps, but there is no stacked step plot. This is useful when you have some accumulating resource that have different types that begin and end all at once. I personally had a need for this when I wanted to visualize my man-month commitment to my company’s funded projects over the next 7 years.
Published:
I often have a need to plot a grouped bar plot. Matplotlib provides this example, which is helpful, but not quite generalizable enough for my needs, as it only shows how to group 2 categories together. Here is a generalization of that tutorial that was very helpful for me and I hope is helpful for others as well.
Published:
I recently faced a situation where I needed to assess the amount of work done by each member of a team on a project that has spanned over a year. That project has a git repo, and I could see when each person made a commit. I decided to break it down by weeks. Whenever a person submitted any commit to the repo on any branch, I counted them as working on the project for that week. Of course this is imperfect- someone could work a lot and make no commits for that week and someone could have submitted a commit but might have worked very little. Still, this seems like the most fair way to assess work I could think of.
Published:
Let’s use for example Google Trends results for the search term “gifts.” Google offers this plot:
Published:
Published:
If you use Jupyter on a regular basis, the steps to launch a notebook are probably second nature, but if you take a step back, it involves a lot of prior knowledge. A few times I’ve tried to bring brand new eager programmers into the glorious land of Python and Jupyter, but each time I found that the whole flow was really bogged down by this preamble that is pretty technical. I’ll give them an .ipynb file and then show them how to open it
Published:
