serhii.net

When saving seaborn images there was weirdness going on, with borders either cutting labels or benig too big.

Solution:

# bad: cut corners
ax.figure.savefig("inat_pnet_lorenz.png")
# good: no cut corners and OK bounding box
ax.figure.savefig("inat_pnet_lorenz.png", bbox_inches="tight")

Didn’t find this in the documentation, but:

gg = ds.groupby(by=["species"])
lg = next(gg.groups)
# lg is the group name tuple (in this case of one string)
group_df = gg.get_group(lg)

Looking for a way to have vertical/tree tabs, I found a mention of the zotero web version being really good.

Then you can have multiple papers open (with all annotations etc.) in different browser tabs that can be easily navigated using whatever standard thing one uses.

You can read annotations but not edit them. Quite useful nonetheless!

Shortcuts

Overleaf Keyboard Shortcuts - Overleaf, Online LaTeX Editor helpfully links to a PDF, screenshots here:

It seems to have cool multi-cursor functionality that might be worth learning sometime.

Kurz und gut

• Bold/italic work as expected, <C-b/i>. Same for copypaste etc.
• Advanced reference search: is cool.
• Comments:
  • <C-/> for adding %-style LaTex comments.
  • <C-S-c> for adding Overleaf comments

Templates

Overleaf has a lot of templates: Templates - Journals, CVs, Presentations, Reports and More - Overleaf, Online LaTeX Editor

If your conference’s is missing but it sends you a .zip, you can literally import it as-is in Overleaf, without even unpacking. Then you can “copy” it to somewhere else and start writing your paper.

• https://extract.bbbike.org/: you pick an area and click

Difference between %time and %%time in Jupyter Notebook - Stack Overflow

• when measuring execution time, %time refers to the line after it, %%time refers to the entire cell
• As we remember¹:
  • real/wall the ‘consensus reality’ time
  • user: the process CPU time
    • time it did stuff
  • sys: the operating system CPU time due to system calls from the process
    • interactions with CPU system r/w etc.

Format

• GBIF Infrastructure: Data processing has a detailed description of the flow
  • occurrences.txt is an improved/cleaned/formalized verbatim.txt
  • metadata
    • meta.xml has list of all colum data types etc.
      • for all files in the zip!
      • columns links lead to DCMI: DCMI Metadata Terms
    • metadata.xml is things like download doi, license, number of rows, etc.
• .zips are in Darwin format: FAQ
  • Because there are cases when both single and double quotes etc., and neither '/" as quotechar work.

  df = vx.read_csv(DS_LOCATION,convert="verbatim.hdf5",progress=True, sep="\t",quotechar=None,quoting=3,chunk_size=500_000)
  

Tools

• GBIF .zip parser lib:
  • BelgianBiodiversityPlatform/python-dwca-reader: 🐍 A Python package to read Darwin Core Archive (DwC-A) files.
  • Tried it, took a long time both for the zip and directory, so I gave up
• gbif/pygbif: GBIF Python client
  • API client, can also do graphs etc., neat!

Analysis

Things to try:

• limit number of columns through pd.read_csv.usecols()¹ to the ‘interesting’ ones
  • optionally take a smaller subset of the dataset and drop all NaNs
  • take column indexes from meta.xml
  • See if someone already did this:BelgianBiodiversityPlatform/python-dwca-reader: 🐍 A Python package to read Darwin Core Archive (DwC-A) files.

TIL that for readability, x = 100000000 can be written as x = 100_000_000 etc.! Works for all kinds of numbers - ints, floats, hex etc.!¹

I have a larger-than-usual text-based dataset, need to do analysis, pandas is slow (hell, even wc -l takes 50 seconds…)

Vaex: Pandas but 1000x faster - KDnuggets - that’s a way to catch one’s attention.

Reading files

I/O Kung-Fu: get your data in and out of Vaex — vaex 4.16.0 documentation

• vx.from_csv() reads a CSV in memory, kwargs get passed to pandas' read_csv()
• vx.open() reads stuff lazily, but I can’t find a way to tell it that my .txt file is a CSV, and more critically - how to pass params like sep etc
  • vx.from_ascii() has a parameter called sepe rator?! API documentation for vaex library — vaex 4.16.0 documentation
• the first two support convert= that converts stuff to things like HDFS, optionally chunk_size= is the chunk size in lines. It’ll create $N/chunk_size$ chunks and concat together at the end.
• Ways to limit stuff:
  • nrows= is the number of rows to read, works with convert etc.
  • usecols= limits to columns by name, id or callable, speeds up stuff too and by a lot

Writing files

• I can do df.export_hdf5() in vaex, but pandas can’t read that. It may be related to the opposite problem - vaex can’t open pandas HDF5 files directly, because one saves them as rows, other as columns. (See FAQ)
• When converting csv to hdf5, it breaks if one of the columns was detected as an object, in my case it was a boolean. Objects are not supported¹, and booleans are objects. Not trivial situation because converting that to, say, int, would have meant reading the entire file - which is just what I don’t want to do, I want to convert to hdf to make it manageable.

Doing stuff

Syntax is similar to pandas, but the documentation is somehow .. can’t put my finger on it, but I don’t enjoy it somehow.

Stupid way to find columns that are all NA

l_desc = df.describe()
# We find column names that have length_of_dataset NA values
not_empty_cols = list(l_desc.T[l_desc.T.NA!=df.count()].T.columns)
# Filter the description by them
interesting_desc = l_desc[not_empty_cols]

Use Virtual Environments Inside Jupyter Notebooks & Jupter Lab [Best Practices]

Create and activate it as usual, then:

python -m ipykernel install --user --name=myenv