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David Yerrington dyerrington

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from statsmodels.stats.power import tt_ind_solve_power
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
def test_ttest_power_diff(mean, std, sample1_size=None, alpha=0.05, desired_power=0.8, mean_diff_percentages=[0.1, 0.05]):
calculates the power function for a given mean and std. the function plots a graph showing the comparison between desired mean differences
:param mean: the desired mean
:param std: the std value
:param sample1_size: if None, it is assumed that both samples (first and second) will have same size. The function then will
dyerrington /
Created Nov 7, 2019
Basic implementation of a matplotlib polar plot using a basic observations with multiple variables.
from math import pi
from mpl_toolkits.axes_grid.inset_locator import inset_axes
# Set data
df = pd.DataFrame({
# 'group': ['A','B','C','D'],
'var1': [38, 1.5, 30, 4],
'var2': [29, 10, 9, 34],
'var3': [8, 39, 23, 24],
'var4': [7, 31, 33, 14]
dyerrington /
Created Sep 18, 2019
Python code that will create, essentially a pivot from a nested big query set. Based on the original method in the google big query documentation.
# fighting == most common event type
def build_udf_prototype(event_types):
null = "null" # default all types to null in the UDF function
PIVOT_FEATURES = str({"col_" + event_name.replace("-", "_"): null for event_name in event_types.tolist()}).replace("'null'", "null")
for event_type in event_types.tolist():
event_type = event_type.replace("-", "_")
SQL_RETURN += f"col_{event_type} INT64, "

Great Data Science Project Criteria:

  • Problem statement that defines a measurable, and/or falsifiable outcome. “Frequency of [specific event] is influential over [some outcome]”. “Users who use [some feature in app] are differentiable from users who less frequently use [some feature in app]”. etc. If you can’t frame a data problem properly, none of has it has purpose. The biggest challenge in data science is making sense and defining the gray area of business problems. This also comes with experience.
  • EDA EDA EDA. Define your scope. Report only what is necessary and relevant to your problem statement. If the model reports only 4-5 common variables as parameters (logistic regression for instance), focus on those when summarizing your work in terms of EDA.
  • How much data is necessary to make this analysis work? Are you sampling? Is a t-test necessary to gain assurance or a rank order test?
  • Explain which model makes the most sense to use. Are you trying to gain inference about a data problem?
View sf_slicing_apply_map.ipynb
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Parse Jupyter

This is a basic class that makes it convenient to parse notebooks. I built a larger version of this that was used for clustering documents to create symantic indeices that linked related content together for a personal project. You can use this to parse notebooks for doing things like NLP or preprocessing.


parser = ParseJupyter("./Untitled.ipynb")
parser.get_cells(source_only = True, source_as_string = True)
import tweepy
import wget
import os
oauth = {
"consumer_key": "",
"consumer_secret": ""
access = {
View sf_review.ipynb
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dyerrington /
Created Jul 16, 2018
As a point of comparison with the default Nietzsche example from the Keras repo, this little experiment swaps out the dataset with forum comments from My Little Pony subreddit.
'''Example script to generate text from Nietzsche's writings.
At least 20 epochs are required before the generated text
starts sounding coherent.
It is recommended to run this script on GPU, as recurrent
networks are quite computationally intensive.
If you try this script on new data, make sure your corpus
has at least ~100k characters. ~1M is better.
from __future__ import print_function
dyerrington /
Created Jul 12, 2018
Remove "n-grams" first, before stopwords with this handy class that extends the functionality of scikit-learn's CountVectorizer. Substitute the class extension for other types of vectorizers such as TfIDF in the class definition at the top.
# defines a custom vectorizer class
class CustomVectorizer(CountVectorizer):
stop_grams = []
def __init__(self, stop_grams = [], **opts):
self.stop_grams = stop_grams
def remove_ngrams(self, doc):
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