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# To find out exactly which Python version you're using, you can use the --version flag
python --version

# Python 3 is usually available under the name python3
# python3 --version

# You can also figure out the version with the runtime configuration
# import sys
# print(sys.version_info)
# print(sys.version)

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"""Get geolocation information from a zipcode.

https://pypi.python.org/pypi/zipcode
"""
import zipcode


def location_from_zipcode(zip=None):
    return zipcode.isequal(zip).location_text

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import requests
from bs4 import BeautifulSoup
from time import sleep
from multiprocessing import Pool


def get_listing(url):
    headers = {
        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36'}
    html = None

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"""You can either use the response.raw file object, or iterate over the response.

To use the response.raw file-like object will not, by default, decode compressed responses (with GZIP or deflate). 
You can force it to decompress for you anyway by setting the decode_content attribute to True 
(requests sets it to False to control decoding itself). You can then use shutil.copyfileobj() 
to have Python stream the data to a file object:
"""
import requests
import shutil

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import requests
response = requests.get('https://website.com/id', headers={'Authorization': 'access_token myToken'})

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import dask.dataframe as dd
import blaze


df = dd.read_csv('csv/yellow_tripdata_2015-*.csv')    # '*' all files with the 'csv/yellow_tripdata_2015' prefix

df.to_parquet('yellow_tripdata.parquet')

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import itertools
import numpy as np
import matplotlib.pyplot as plt

from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix

# import some data to play with
iris = datasets.load_iris()

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from sklearn.metrics import confusion_matrix

def print_cm(cm, labels, hide_zeroes=False, hide_diagonal=False, hide_threshold=None):
    """pretty print for confusion matrixes"""
    columnwidth = max([len(x) for x in labels]+[5]) # 5 is value length
    empty_cell = " " * columnwidth
    # Print header
    print "    " + empty_cell,
    for label in labels: 
        print "%{0}s".format(columnwidth) % label,

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def plot_tree():
  """Make sure you have the correct imports.
  Imports:
      from sklearn import tree
      import pydotplus 
      from IPython.display import Image 
  
  Note: Make sure you already have a 'tree'.
  ...
  clf = tree.DecisionTreeClassifier()

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#  ---------------------------------------------------------------------------
#
#  Description:  This file holds all my BASH configurations and aliases
#
#  Sections:
#  1.   Environment Configuration
#  2.   Make Terminal Better (remapping defaults and adding functionality)
#  3.   File and Folder Management
#  4.   Searching
#  5.   Process Management