whiledoing

// from: https://stackoverflow.com/questions/12753805/type-converting-slices-of-interfaces
// make any slice to interface{} slice
func InterfaceSlice(slice interface{}) []interface{} {
    s := reflect.ValueOf(slice)
    if s.Kind() != reflect.Slice {
        panic("InterfaceSlice() given a non-slice type")
    }

    ret := make([]interface{}, s.Len())

whiledoing

class Pipe(object):
    def __init__(self, func):
        self.func = func
 
    def __ror__(self, other):
        def generator():
            for obj in other:
                if obj is not None:
                    yield self.func(obj)
        return generator()

whiledoing

# from: https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb
import scipy.stats as stats
from matplotlib import pyplot as plt
import numpy as np

a = np.linspace(0, 4, 100)
expo = stats.expon
lambda_ = [0.5, 1]

for l, c in zip(lambda_, colours):

whiledoing

import numpy as np
import matplotlib.pyplot as plt
import ipywidgets as widget

from sklearn.datasets import make_blobs
from sklearn.svm import SVC

def plot_svc_decision_function(model, ax=None, plot_support=True):
    """Plot the decision function for a 2D SVC"""
    ax = ax or plt.gca()

whiledoing

# load an example dataset
from vega_datasets import data
cars = data.cars()

import altair as alt

interval = alt.selection_interval()

base = alt.Chart(cars).mark_point().encode(
  y='Miles_per_Gallon',

whiledoing

import numpy as np
from bokeh.plotting import figure, show
from bokeh.io import output_notebook

# Call once to configure Bokeh to display plots inline in the notebook.
output_notebook()

N = 4000
x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100

whiledoing

from sklearn.datasets import load_digits
digits = load_digits()

# split data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, shuffle=True)

# use Gaussian Naive Bayes
from sklearn.naive_bayes import GaussianNB
model = GaussianNB()

whiledoing

def f(x, y):
    return np.sin(x) ** 10 + np.cos(10+x*y) + np.cos(x)
  
x = np.linspace(0, 5, 50)
y = np.linspace(0, 5, 50)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)

# draw contour
contour = plt.contour(X, Y, Z, 3, colors='black')

whiledoing

from pandas_datareader import data
goog = data.DataReader('GOOG', start='2014', end='2020', data_source='yahoo')
goog.columns = goog.columns.str.lower()
goog = goog.asfreq('D', method='bfill')

whiledoing

from functools import wraps

def memoize(function):
    memo = {}
    @wraps(function)
    def wrapper(*args):
        try:
            return memo[args]
        except KeyError:
            rv = function(*args)