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def gmap(lat,lon,zoom=14):

    from IPython.display import IFrame
    from IPython.core.display import display

    # Google Maps URL template for an iframe

    google_maps_url = "http://maps.google.com/maps?q={0}+{1}&" + \
      "ie=UTF8&t=h&z={2}&{0},{1}&output=embed".format(lat, lon,zoom)

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import pip
from subprocess import call

for dist in pip.get_installed_distributions():
    call("pip install --upgrade " + dist.project_name, shell=True)

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import math
 
def distance_on_unit_sphere(lat1, long1, lat2, long2):
 
    # Convert latitude and longitude to 
    # spherical coordinates in radians.
    degrees_to_radians = math.pi/180.0
         
    # phi = 90 - latitude
    phi1 = (90.0 - lat1)*degrees_to_radians

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def trim_par(line):
    outp = []
    comp=re.compile(r'\(+\S+\)', re.IGNORECASE)
    linesplit=line.split()
    for index in range(0, len(linesplit)):
        m = comp.search(linesplit[index])
        if m:
            continue
        else:
            outp.append(linesplit[index]

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#Reject entries containing non-alphanumeric chars
if re.match('^[a-zA-Z0-9_]+$', test1):
    #do stuff

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def res_clean(line):
    #set name entry equal to label if broken
    n = re.match('^[a-zA-Z0-9_]+$',line["name"])
    if line["name"]=="NULL" or not n:
        line["name"]=line["rdf-schema#label"]
    #if value is null set to none
    for key, value in line.items():
        if value=="NULL":
            line[key]=None
    #trim redundant parentheses from rdf-schema#label

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def get_IA_pop_dens_ZIP():
    #scrapes table from http://zipatlas.com/us/ia/zip-code-comparison/population-density.htm 
    #and http://zipatlas.com/us/ia/zip-code-comparison/population-density.?.htm
    url_base = "http://zipatlas.com/us/ia/zip-code-comparison/"
    page1 = "population-density."
    pnum = [i for i in range(1,11)]
    htm = "htm"
    tables_pages =[]
    for page in pnum:
        if page==1:

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#Took this function that makes nice correlation tables from here
#http://myowelt.blogspot.co.uk/2008/04/beautiful-correlation-tables-in-r.html
corstarsl <- function(x){ 
  require(Hmisc) 
  x <- as.matrix(x) 
  R <- rcorr(x)$r 
  p <- rcorr(x)$P 
  
  ## define notions for significance levels; spacing is important.
  mystars <- ifelse(p < .001, "***", ifelse(p < .01, "** ", ifelse(p < .05, "* ", " ")))

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#Adjusted from http://www.r-bloggers.com/how-to-make-a-histogram-with-ggplot2/
ggplot(data=data1, aes(x=`BOTTLE QTY`)) + 
  geom_histogram(aes(y =..density..), 
                 col="red", 
                 fill="green", 
                 alpha = .2, origin = -0.5, binwidth=1) + 
  labs(title="Histogram for Bottle Quantities") +
  labs(x="Bottles", y="Count") +  scale_x_continuous(limits=c(0,30), breaks=0:30)

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require("RSQLite")
# Set up database
drv <- dbDriver("SQLite")
tfile <- "Iowa.db"
con <- dbConnect(drv, dbname = "Iowa.db")
dbWriteTable(con, "data1", as.data.frame(data1))
dbDisconnect(con)