EricSchles

def get_percent_empty(df):
    listing = list(df.isnull().sum()/df.shape[0] != 0)
    columns_of_interest = []
    cols = df.columns.tolist()
    for index, col in enumerate(cols):
        if listing[index]:
            columns_of_interest.append(col)
    return df[columns_of_interest].isnull().sum()/df.shape[0]

EricSchles

numpy
pandas
scipy
sklearn
tensorflow
keras
statsmodels
pomegranate
seaborn
matplotlib

EricSchles

$ python xor.py
Training:
  Epoch   0   MSE: 1.765
  Epoch 100   MSE: 0.015
  Epoch 200   MSE: 0.005
  * Target MSE reached *
Evaluating:
  1 XOR 0 = 1 ( 0.904)   Error: 0.096
  0 XOR 1 = 1 ( 0.908)   Error: 0.092
  1 XOR 1 = 0 (-0.008)   Error: 0.008

EricSchles

import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.model_selection import train_test_split

# In general for the way to do type hinting is very straight forward.
# simply don't instantiate the class to use it as a 'type'

# Here's a generic example:

EricSchles

python setup.py sdist bdist_wheel
python -m twine upload --repository-url https://test.pypi.org/legacy/ dist/*
python -m twine upload dist/*

EricSchles

from glob import glob
from json import load

def loc(nb):
    cells = load(open(nb))["cells"]
    return sum(len(c["source"]) for c in cells)

root_folder = "~"
summation = 0
for File in glob(root_folder+"/**/*.ipynb", recursive=True):

EricSchles

pyramid_sum = lambda n: (2*(n**2) - (2*n - 1))

EricSchles

import tensorflow as tf
import numpy as np
import pandas as pd

class ReluDense(tf.Module):
    def __init__(self, in_features, out_features, name=None):
        super().__init__(name=name)
        self.w = tf.Variable(
            tf.random.normal([out_features, out_features]), name='w'
        )

EricSchles

from tensorflow.keras import Model
import tensorflow as tf
import numpy as np
import pandas as pd
import random

class ReluDense(tf.Module):
    def __init__(self, in_features, out_features, name=None):
        super().__init__(name=name)
        self.w = tf.Variable(

EricSchles

from tensorflow.keras import Model
import tensorflow as tf
import numpy as np
import pandas as pd
import random

class ReluDense(tf.Module):
    def __init__(self, in_features, out_features, name=None):
        super().__init__(name=name)
        self.w = tf.Variable(