Martina Pugliese martinapugliese

A collection of little libraries that help workflow

Shows progress bar in a notebook's cell.

for i in tqdm(range(10), 'wasting time', unit='iterations wasted'):
    sleep(0.5)

Those things that I always forget how to do.

import pyplot as plt

vm_stat is the command, this makes output user friendly, thanks to this.

vm_stat | perl -ne '/page size of (\d+)/ and $size=$1; /Pages\s+([^:]+)[^\d]+(\d+)/ and printf("%-16s % 16.2f Mi\n", "$1:", $2 * $size / 1048576);'

df is a DataFrame.

grouped_df = df.groupby(['colA', 'colB']) \
    .agg(
 {

See Difference in for key in d.keys() and for key in d
On underscores in Python
Assigning more vars to same value
Various ways, different in speed and in preserving the order, to uniquify a list: here and a followup with another suggestion (f7) here
Various ways, with time benchmarks, to check if key is in dict, here

	# Imports
	import pandas as pd
	import numpy as np
	from scipy.stats import entropy

	from sklearn.ensemble import RandomForestClassifier
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import classification_report

	from matplotlib import pyplot as plt

	# Copyright (C) 2016 Martina Pugliese


	def run_methods():

	print '\n'

	print '* Count occurrences of substring in string'
	print 'Martina'.count('art')
	print 'Martina'.count('a')

	# Copyright (C) 2016 Martina Pugliese


	def plot_freqdist_freq(fd,
	max_num=None,
	cumulative=False,
	title='Frequency plot',
	linewidth=2):
	"""
	As of NLTK version 3.2.1, FreqDist.plot() plots the counts and has no kwarg for normalising to frequency. Work this around here.

	# Copyright (C) 2016 Martina Pugliese

	# Imports
	from datetime import datetime


	# #################### ANSI Escape codes for terminal #########################

	codes_dict = {