Susensio

# Default way of handling groupby and mostfrequent is slow
df.groupby(groupby_column)[null_column].agg(lambda x: x.iat[0])

# if there are nan's in df:
df.groupby(groupby_column)[null_column].agg(lambda x: x.iat[0] if not x.isnull().all() else np.nan)

# faster way: use value_counts and keep first value
(df
 .groupby(groupby_column)[null_column]
 .value_counts(sort=True, dropna=False)

Susensio

#!/usr/bin/env bash
# Move ~/.bash* files to $XDG_CONFIG_HOME/bash/bash*
set -o nounset
set -o errexit
set -o pipefail

### ROOT SPACE

etc_bashrc_file="/etc/bash.bashrc"
etc_bashrcd_folder="/etc/bash/bashrc.d"

Susensio

How to cache slow functions with numpy.array as function parameter on Python

TL;DR

from numpy_lru_cache_decorator import np_cache

@np_cache()
def function(array):
 ...

Susensio

# Completions for the custom Script Directory (sd) script

# MODIFIED from https://gist.github.com/patricksjackson/5065e4a9d8e825dafc7824112f17a5e6
# Improvements:
# - compliant with SD_ROOT
# - better handling of empty folders

# These are based on the contents of the Script Directory, so we're reading info from the files.
# The description is taken either from the first line of the file $cmd.help,
# or the first non-shebang comment in the $cmd file.

Susensio

# Profile with cProfile python module and save to output
python -m cProfile -o output.cprof script.py

# Transform into tree format for QCacheGrind
pyprof2calltree -i output.cprof -o callgrind.cprof

# Open QCacheGrind and load callgrind.cprof file

Susensio

Cached Method Decorator in Python

This repository provides a Python decorator @cached_method that caches method results similar to functools.lru_cache, but with each instance having its own cache.

Overview

The cached_method decorator allows you to cache the results of an instance method call so that subsequent calls with the same arguments will return the cached result instead of performing the computation again.

This decorator provides two key benefits:

• The cache is cleared when the instance is garbage collected (by using weak references).

Susensio

Python @property inheritance the right way

Given a Parent class with value property, Child can inherit and overload the property while accessing Parent property getter and setter.

Although we could just reimplement the Child.value property logic completely without using Parent.value whatsover, this would violate the DRY principle and, more important, it wouldn't allow for proper multiple inheritance (as show in the example property_inheritance.py bellow).

Two options:

• Child redefines value property completely, both getter and setter.