yvan

blue = [65,105,225]
green = [34,139,34]
beach = [238, 214, 175]
snow = [255, 250, 250]
mountain = [139, 137, 137]

def add_color(world):
    color_world = np.zeros(world.shape+(3,))
    for i in range(shape[0]):
        for j in range(shape[1]):

yvan

# keep track of losses
# from the generator and
# discriminator
generator_losses = []
discriminator_losses = []

# for some number of rounds (epochs)
for epoch in range(n_epochs):
    # track losses for this epoch
    gen_loss_epoch = 0

yvan

# in pandas
df[col1] = df[col1]*5

# in spark
df = df.withColumn(col1, F.col(col1)*5)

yvan

# pandas
df[col2] = np.where(df[col2] == 1, True, False)

# pyspark
df = df.withColumn(col2, F.when(F.col(col2) == 1, True).otherwise(False))

yvan

# pandas
row_ct = df.shape[0]
num_ct = pd.to_numeric(df[col3], errors='coerce').count() # coerce makes nan, count drops nan
# another check using regex
num_regex = r"^((-)?[0-9]+)(,[0-9]+)*(\.[0-9]+)?$|(^$)"
all_are_nums = all(df[col3].fillna('').astype(str).apply(lambda x: re.match(num_regex, x)))

if (num_ct == row_ct) or all_are_nums:
    df[col3] = pd.to_numeric(df[col3], errors='coerce')

yvan

# pandas 
df[col3] = df[col3].replace(regex=r"[^0-9\\.]", value="")
df[col3] = pd.to_numeric(df[col3], errors='coerce')

# pyspark
df = df.withColumn(col3, F.regexp_replace(F.col(col3), r"[^0-9\\.]", '').cast("double"))

yvan

# pandas 
df[col4] = df[col4].apply(lambda m: None if m in [None, float('nan'), np.nan, math.nan] else int(float(m)))


# pyspark
def floatint(x):
    return int(float(x))
int_udf = F.udf(lambda m: None if m is None else floatint(m))
df = df.withColumn(col4, F.when(F.col(col4).isNotNull(), int_udf(F.col(col4))).otherwise(None))

yvan

# pandas 
df[col5] = pd.to_datetime(df[col5], errors='coerce')

# pyspark
data_regex = r"\d{2,4}(\.|\-|\/|\\)+\d{2,4}(\.|\-|\/|\\)+\d{2,4}(\s)*(\d{2}\:\d{2}\:\d{2})?(\.\d{3})?|^$"
df = df.withColumn(col5, F.when(F.regexp_replace(F.col(col5), data_regex, '').isNotNull(),\
                                F.to_timestamp(F.col(col5), 'yyyy/MM/dd')).otherwise(None))

yvan

import noise
import numpy as np
from scipy.misc import toimage

shape = (1024,1024)
scale = 100.0
octaves = 6
persistence = 0.5
lacunarity = 2.0

yvan

import os
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils import data
from torchvision.datasets import ImageFolder
from torchvision import transforms, datasets