Jared Burgess jaredjburgess

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jaredjburgess / progress_bar.py

Last active March 17, 2016 18:25

Template for interactive progress bar.

	import progressbar

	pbar = progressbar.ProgressBar(maxval=len(object))
	pbar.start()

	for i in range(len(object)):
	pbar.update(i)

	pbar.finish()

jaredjburgess / pretty_print.py

Last active March 26, 2016 17:58

Pretty print in python

	from pprint
	import pprintpprint(example_json)

jaredjburgess / pg_create_fkey.sql

Created February 21, 2016 14:35

PostgreSQL syntax to create a foreign key on an existing table

alter table table_name add constraint table_name_column_name_fkey foreign key (column_name) references other_table_name (other_column_name) on delete cascade;

jaredjburgess / pg_dump_table.sql

Created February 21, 2016 14:36

PostgreSQL pg_dump syntax for a single table

pg_dump -Fc -v -U user_name -d db_name -t table_name > /path/to/file.bak

jaredjburgess / pg_array_overlap.sql

Created February 21, 2016 14:37

PostgreSQL syntax to condition on the overlap of an array column with a given array

where array_column && array['string1', 'string2']::text[]

jaredjburgess / pg_drop_col.sql

Created February 21, 2016 14:57

PostgreSQL syntax to drop a column from a table

alter table table_name drop column_name;

jaredjburgess / write_json.py

Created February 21, 2016 14:57

Write to a JSON file in Python

open(path_to_file, 'w').write(json.dumps(data))

jaredjburgess / read_json.py

Created February 21, 2016 14:58

Read a JSON file in Python

data = json.loads(open(path_to_file, 'r').read())

jaredjburgess / hist_plot.py

Last active February 23, 2016 19:38

Normalised histogram using matplotlib.pyplot.hist and numpy weights

	import numpy as np
	import matplotlib.pyplot as plt
	counts = [10, 8, 6, 14]
	weights = np.ones_like(counts) / float(len(counts))
	plt.figure(figsize=(10, 5))
	plt.hist(counts, bins=range(1, max(counts)+2), align='left', weights=weights)
	plt.xticks(range(1, max(counts)+1))
	plt.show()

jaredjburgess / tsne_vis.py

Created February 21, 2016 15:04

Visualise data using the t-SNE algorithm in Python

	# Visualise given word embeddings
	# words is a list of words
	# data is the vector representation of each word

	# Train the algorithm
	from sklearn.manifold import TSNE
	vis_algo = TSNE(random_state=0, verbose=10, init='pca', n_iter=200)
	vis = vis_algo.fit_transform(data)

	# Plot the resulting visualisation

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