Edward Brennan EdMan1022

💭

working

Physicist turned data scientist. Mostly, I know Python. My main programming interests are in Machine-Learning techniques for statistical analysis.

EdMan1022 / ajax_trouble_view.py

Created November 27, 2017 18:38

	from flask import Blueprint, request

	from ..helper_functions import cluster_analysis_function

	analytics = Blueprint('analytics', __name__, template_folder='templates')


	@analytics.route('/api/analytics/cluster_analysis', methods=['GET'])
	def cluster_analysis_view():
	"""

EdMan1022 / scratch.jinja2

Created November 27, 2017 18:29

Ajax trouble

	{% extends 'utilities/base.html' %}
	{% block title %}{{ analysis_group.name }}{% endblock %}
	{% block content %}
	<div class="row justify-content-center">
	<div class="col-8">
	<div class="card border-primary mb-3">
	<div class="card-header">
	<h3>{{ analysis_group.name }}</h3>
	<h4>{{ analysis_group.description }}</h4>
	</div>

EdMan1022 / Flask-blueprint-with-imported-routes

Created November 7, 2017 18:02 — forked from Jaza/Flask-blueprint-with-imported-routes

Example of how to split a Flask blueprint into multiple files, with routes in each file, and of how to register all those routes.

EdMan1022 / df_to_txt.py

Created September 22, 2017 15:52

Pandas dataframe to txt file with comma delimiter test

	import pandas as pd

	# Pandas dataframe to txt file with comma delimiter test

	df = pd.DataFrame(index = [0,1,2,3,4])

	df.loc[:, 'a'] = 'a'
	df.loc[:, 'b'] = 2
	df.loc[:, 'c'] = ' '
	df.loc[:, 'd'] = None

EdMan1022 / null_categorical_variables.py

Created September 1, 2017 21:24

Create a dataframe with null variable columns indicating indices with null values for each column

	import pandas as pd
	import numpy as np

	df = pd.DataFrame(data=[[3., 1., np.NaN], [np.NaN, 3., 2.], [4., 1., 3.]], index=[0, 1, 2],
	columns=['apple', 'carrot', 'pear'])


	def null_only_categorical_func(data):
	"""
	Take a series, and return values of 1. where the series is null

EdMan1022 / scratch_9.py

Created August 11, 2017 20:06

sklearn pca example

	from sklearn import decomposition
	import numpy as np

	pca = decomposition.PCA(n_components=2)

	X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])

	x = pca.fit_transform(X)