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Create an env from scratch

conda create --name envname

Create from an env file

conda env create -f environment.yml

List all packages installed in this conda env

conda list

List all conda envs on this system

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%reload_ext autoreload
%autoreload 2

import os, sys
import pathlib

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

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# Assume our code has some function that is called later (possibly called multiple times). 
#   In the code below, we have a logging function, and we don't want to always use the logger.
# Instead of adding an `if disable_logging` line every time we log something with the logging function, 
#   we can instead just replace the logging function with a fake function that does nothing.

def mock_logger(*args, **kwargs):
     pass

if disable_logging:
    logger = mock_logger

himat

Google I'm Feeling Lucky search to select the first link on the search results page automatically

A lot of times for searches that have one main result, it will usually be the first result on the search results page, and you end up just clicking that first link. You can save time and mental capacity by using this more efficient method.
So use this if you pretty much know what you're looking for.

• You don't even need to have your browser open to do this!
• And this way, you don't need to remember URLs either, just need to know the description of the site!

With the I'm Feeling Lucky keyword set to l, just type in Alfred something like:

• l wiki ontology
• l github
• l pytorch docs

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test = {
383:    3.000000,
663:    1.000000,
726:    1.000000,
737:    9.000000,
833:    8.166667
}

pd.Series(test).where(lambda x : x!=1).dropna()

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# Doing `x in df["col"]` will always look for x in the *index* of the df, not the actual column values
# Thus, you have to use `x in df["col"].values` instead
# This can be especially insidious when you're checking if a number is in a col, but that number is also in the index.
#   If you don't use `.values` in this case, you will think the value is in the col when it actually isn't!

>>> df = pd.DataFrame({"a": [20, 40, 1], "b": [32, 42, 52]})
>>> df
    a   b
0  20  32
1  40  42