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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()

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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:

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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)

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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]

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# 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'))

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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))

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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_)

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Keybase proof

I hereby claim:

• I am ortsed on github.
• I am llewhinkes (https://keybase.io/llewhinkes) on keybase.
• I have a public key ASBcU6XcOnBo98h_BCPJ4a1w8HK6U3TXGKqLkle9emjSywo

To claim this, I am signing this object:

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# Cheat Sheet

import warnings
warnings.filterwarnings('ignore')

# Pandas
pd.to_numeric(df, coalesce="")
pd.to_datetime()
pd.to_timestamp()
pd.to_timedelta()

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These agencies received fewer than 50 FOIA requests in 2018. We can do better.

Administrative Conference of the United States

Advisory Council on Historic Preservation

American Battle Monuments Commission

Appraisal Subcommittee