ortsed

def model_summary(model, X, y, columns=[]):
	"""
	Takes a sklearn model and outputs basic stats,
	based on input features (X) and target (y)
	"""
	import pandas as pd
	from scipy import stats
	import numpy as np
	lm = model
	params = np.append(lm.intercept_,lm.coef_)

ortsed

import numpy as np

def find_term_derivative(term):
    constant = term[0]*term[1]
    exponent = term[1] - 1
    return (constant, exponent)

def find_derivative(function_terms):
    derivative_terms = list(map(lambda term: find_term_derivative(term),function_terms))
    return list(filter(lambda derivative_term: derivative_term[0] != 0, derivative_terms))

ortsed

# keras https://keras.io/
from keras.models import Sequential
from keras import models
from keras import layers
from keras import optimizers

model = Sequential()
model.add(layers.Dense(50, activation='relu', input_shape=(2000,)))
model.add(layers.Dense(1, activation='relu'))

ortsed

from nltk.corpus import stopwords
stopwords_list = stopwords.words('english')
from string import punctuation
stopwords_list += list(punctuation)


from nltk import word_tokenize

tokens = word_tokenize(some_text_data)
stopped_tokens = [w.lower() for w in tokens if w not in stopwords_list]

ortsed

import pandas as pd
def merge_similar(files=[], encoding=None):
""" 
Concats datasets with similar but not necessarily the same columns
by creating empty columns for each dataframe missing a column found in the others 
"""

	merged = []
	for file in files:
		df = pd.read_csv(file, encoding=encoding)

ortsed

def strs_in_float(series):
  """ 
  Extracts the strings in what would otherwise be a Pandas Series of floats 
  For data cleaning.
  """
	def is_float(x):
		try:
			float(x)
			return False
		except:

ortsed

Python
from sklearn import datasets
import seaborn as sn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
 
#import the digits dataset
digits = datasets.load_digits()

ortsed

def multivariateGaussian(X, mu, sigma):
    k = len(mu)
    sigma=np.diag(sigma)
    X = X - mu.T
    p = 1/((2*np.pi)**(k/2)*(np.linalg.det(sigma)**0.5))* np.exp(-0.5* np.sum(X @ np.linalg.pinv(sigma) * X,axis=1))
    return p
p = multivariateGaussian(X, mu, sigma)

ortsed

import numpy as np


class LPSolution(object):
    def __init__(self):
        self.iterations = None
        self.tolerance = None
        self.intermediates = []
        self.solution = None
        self.solution_string = None

ortsed

#!/usr/bin/env python
import fileinput
import csv
import sys

#https://github.com/jamesmishra/mysqldump-to-csv

# This prevents prematurely closed pipes from raising
# an exception in Python
from signal import signal, SIGPIPE, SIG_DFL