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# Credits to: http://stackoverflow.com/questions/11741876/getting-unique-values-from-a-list-of-dict
import ast
def unique_list_in_list_of_dictionaries(content_list: list())->list:
  """
  content_list = [{'A':1, 'B':1, 'C':1}, 
                  {'A':1, 'B':1, 'C':1},
                  {'A':2, 'B':2, 'C':2}
                  {'A':3, 'B':2, 'C':1}
                  {'A':3, 'B':2, 'C':1}]
  :returns:

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#!/usr/bin/env python

"""
Define and use custom step in Gremlin via pyorient.
"""

from pyorient.ogm import Graph, Config
from pyorient.ogm.declarative import declarative_node, declarative_relationship
from pyorient.ogm.property import String

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awk '{outfile=sprintf("fileName%02d.tsv",NR/100000+1);print > outfile}' fileName_of_really_big_textfile.tsv 

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Transfer docker image to non-internet-connected machine

See Docker save docs

Create a backup that can then be used with docker load

$ docker save busybox > busybox.tar

$ ls -sh busybox.tar

2.7M busybox.tar

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def calculate_initial_compass_bearing(pointA, pointB):
    """
    Calculates the bearing between two points.

    The formulae used is the following:
        θ = atan2(sin(Δlong).cos(lat2),
                  cos(lat1).sin(lat2) − sin(lat1).cos(lat2).cos(Δlong))

    :Parameters:
      - `pointA: The tuple representing the latitude/longitude for the

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# Real source heroes here: https://gis.stackexchange.com/questions/220997/pandas-to-geojson-multiples-points-features-with-python?rq=1
import json
import simplekml
import geojson
import pandas as pd

def data2kml(df):
    kml = simplekml.Kml()
    df.apply(lambda X: kml.newpoint(
            name=X["name"], 

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np.where(df.applymap(lambda x: x == ''))

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# Source: https://stackoverflow.com/questions/39635993/how-to-convert-pandas-dataframe-rows-into-columns-based-on-category
# convert the module variables into columns and group by the id. So something like:
# Example
ls = [{'count':5, 'module':'payroll', 'id':2}, {'count': 53, 'module': 'general','id':2}, {'id': 5,'count': 35, 'module': 'tax'}, ]
df = pd.DataFrame.from_dict(ls)

# Solution
# You can use groupby by columns which first create new index and last column. then need aggreagate some way - I use mean, then convert one column DataFrame to Series by DataFrame.squeeze (then is not necessary remove top level of Multiindex in columns) and reshape by unstack. Last add_suffix to column name
df = df.groupby(['id','module']).mean().squeeze().unstack().add_suffix('_count')

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VIM

:%norm A*

Description

%       = for every line
norm    = type the following commands
A*      = Append `*` the end of current line