Pandas DataFrames are central to Data Analysis in Python. In this post, we introduce the itables
Python package that enhances how these DataFrames are displayed, by turning them into interactive HTML DataTables.
Using itables
is as simple as
""" | |
This is a script that concatenates mp4 files using the method recommended at | |
https://trac.ffmpeg.org/wiki/Concatenate, i.e. it does the equivalent of | |
ffmpeg -i input1.mp4 -c copy -bsf:v h264_mp4toannexb -f mpegts intermediate1.ts | |
ffmpeg -i input2.mp4 -c copy -bsf:v h264_mp4toannexb -f mpegts intermediate2.ts | |
ffmpeg -i "concat:intermediate1.ts|intermediate2.ts" -c copy -bsf:a aac_adtstoasc output.mp4 | |
To run this script, execute | |
python mp4concat input1.mp4 input2.mp4 ... --output output.mp4 | |
in a Python environment in which 'click' is installed (execute 'pip install click' otherwise) |
Pandas DataFrames are central to Data Analysis in Python. In this post, we introduce the itables
Python package that enhances how these DataFrames are displayed, by turning them into interactive HTML DataTables.
Using itables
is as simple as
"""This is a save hook for Jupyter that automatically exports your notebook to reveal slides, | |
every time you save it, if | |
- it has at least one cell with a "slideshow" metadata | |
- and if the notebook is not `Untitled...` | |
Add the following to your `.jupyter/jupyter_notebook_config.py` file: | |
(source: https://gist.github.com/mwouts/04a6dfa571bda5cc59fa1429d130998f ) | |
""" | |
import io |
The image was generated with the wordcloud
package - see this notebook.
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Did you ever had to debug some large cell in a Jupyter notebook? In the below I share my experience on the subject. We'll review the classical methods for debugging notebooks, and finally I'll show how to set breakpoints in PyCharm for code being execute in a jupyter notebook, and benefit of the comfort of a real Python IDE for debugging.
Before I actually describe what Pycharm can do, we quickly review the jupyter commands for debugging.