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cnames = {
'aliceblue':            '#F0F8FF',
'antiquewhite':         '#FAEBD7',
'aqua':                 '#00FFFF',
'aquamarine':           '#7FFFD4',
'azure':                '#F0FFFF',
'beige':                '#F5F5DC',
'bisque':               '#FFE4C4',
'black':                '#000000',
'blanchedalmond':       '#FFEBCD',

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from geographiclib.geodesic import Geodesic

lat1, lon1 = 51.556021, -0.279519
lat2, lon2 = 51.595387,	-0.243415

geod = Geodesic.WGS84
g = geod.Inverse(lat1, lon1, lat2, lon2)

print("Distance = {:.2f} meters".format(g['s12']))

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# READING PARQUET FILES TO PANDAS
import pyarrow.parquet as pq
df = pq.read_table('<filename>').to_pandas()
# Only read a subset of the columns
df = pq.read_table('<filename>', columns=['A', 'B']).to_pandas()

# WRITING PARQUET FILES WITH PANDAS
import pyarrow as pa
import pyarrow.parquet as pq
table = pa.Table.from_pandas(data_frame, timestamps_to_ms=True)

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From a question on Stackoverflow: https://stackoverflow.com/questions/44106103/how-to-import-gitlab-repository-to-bitbucket-repository

1. Create a Bitbucket repo from web UI
2. Clone source GitLab repo with --bare option

git clone --bare GITLAB-URL

3. Add Bitbucket remote

cd REPO-NAME

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from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder, Imputer, LabelBinarizer

# We will use to sepate Pipelines for numerical and categorical attributes
num_attribs = list(housing_num)			# list of numerical attributes
cat_attribs = ["ocean_proximity"]		# list of categorical attributes

# Define Pipeline of numerical attributes as list of encoders and a name (arbitray)
num_pipeline = Pipeline([
        ('selector', DataFrameSelector(num_attribs)),

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# imput dataset
housing_num = pd.DataFrame(...)

# Dealing with missing values (replace with median for each attribute)
from sklearn.preprocessing import Imputer 				  # use the imputer estimator
imputer = Imputer(strategy="median")      				  # tell it which strategy to use
housing_num = housing.drop("ocean_proximity", axis=1)	  # remove categorical attributes
imputer.fit(housing_num)								  # train the estimator with data
imputer.statistics_										  # show statistics (check if ok)
X = imputer.transform(housing_num)						  # apply transf. (get numpy arr)

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points = points << hv.Spikes(points['y']) << hv.Spikes(points['x'])

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%%opts Histogram [invert_axes=True]

# note that this is different from invert_xaxis, which reverses an axes
# invert_axes, instead, will swap X and Y

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import holoviews as hv
import numpy as np
hv.extension('bokeh')

hv.DynamicMap(lambda i: hv.Curve(np.arange(i)), kdims=['i']).redim.range(i=((10, 20))).opts(norm=dict(framewise=True))

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hm = hv.HeatMap(((x_names, y_names, od_pivot)))
hm = hm.opts(plot={"xrotation": 90})

#Other axis options
# 'logy': True
# 'yaxis': None
# 'xrotation': 90}
# 'xticks': 3
# 'xticks': [0, 100, 300, 500]