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import geojson | |
import parse as p #shorter name for our parse program | |
def create_map(data_file): | |
"""csv file -> parse -> creates a geojson - javascript object notation - file """ | |
# define the type of geoJSON that we're creating | |
geo_map = {"type": "FeatureCollection"} #GeoJSON specification |
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import csv | |
MY_FILE = "../data/sample_sfpd_incident_all.csv" | |
#../ means go up one directory | |
#all caps means a constant variable | |
def parse(raw_file, delimiter): | |
""" csv file -> JSON-like object""" | |
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from collections import Counter #collections is a standard library, Counter is capitalized | |
import csv | |
import matplotlib.pyplot as plt #renames the pyplot function from third party library | |
import numpy.numarray as na # best practices: import in alphabetical order | |
MY_FILE = "../data/sample_sfpd_incident_all.csv" | |
def parse(raw_file, delimiter): | |
""" csv file -> JSON-like object""" |
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#Rosalind problem 1 | |
def ACGT_count(sequence): | |
''' | |
(str) -> (int, int, int, int) | |
Count the number of A, C, G and T in a string. | |
>>>ACGT_count('ATGCTTCAGAAAGGTCTTACG') | |
6 4 5 1 |
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def transcribe(s): | |
'''given a DNA string, replace each T with a U and write it back out as RNA | |
(str) -> (str) | |
>>>transcribe('ATGCCCGGGTTTAAA') | |
AUGCCCGGGUUUAAA | |
''' |
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def complementary(s): | |
''' | |
(str) -> (str) | |
Returns the reverse complementary strand of s. | |
>>>complementary('AAACCCTTTGGG') | |
CCCAAAGGGTTT | |
''' |
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def fizzbuzz(last): | |
list = range(1, (last + 1)) | |
for num in list: | |
if num % 15 == 0: | |
num = "fizzbuzz" | |
print str(num) | |
elif num % 3 == 0: | |
num = "fizz" |
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__author__= 'szeitlin' | |
import pandas | |
import vincent | |
MYFILE = "UNdata_Export_20140325_115007094.csv" | |
#read in a csv file | |
mydata = pandas.read_csv(MYFILE) |
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__author__ = 'szeitlin' | |
def return_duplicates(nums): | |
""" | |
(list) -> (sub-list) | |
look for duplicates and just return those | |
>>> return_duplicates([5,5,2,3,4,5,2]) | |
[2, 2, 5, 5, 5] |
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