def Main():
#dset = preAnalysis()
#dset.columns()
#dset.describe()
#for c in dset.colTypes: print c, " ",dset.colTypes[c]
import numpy as np
p = []
for i in range(1,21):
p.append(i)
print p
print np.percentile(np.array(p),10)
csv = pycsv_reader("C:\\Users\\SingAn22\\Desktop\\hcsvreader\\test.csv")
d = csv.to_database(tabName='idiot', database='C:\\Users\\SingAn22\\Desktop\\hcsvreader\\tester.db',varTypes={'var':'NUMBER'})
#v = d.transform(resultTab = 'idiot2',resultType='TABLE',colTypes=d.analyze_columns())
d.describe()
print 'percentile: ',d.quantile('var',0.5)
def preAnalysis():
'''
Most of these things should be done, interactively.
They give a flavor of what data looks like.
'''
# ##Initialize the csv reader
csv = pycsv_reader("C:\\Users\\SingAn22\\Desktop\\hcsvreader\\533098.csv")
##Load the data into sqlite database, obtain pydset object.
data = csv.to_database(tabName='pydanTable_prelim',database ='C:\\Users\\SingAn22\\Desktop\\hcsvreader\\dset.db')
##Print the column names from data
data.columns()
##At the moment every element is a string, so analyze what datatypes can be associated with each string
#Missing values are represented as empty strings, and they dont effect this operation.
colTypes = data.analyze_columns()
for col in colTypes: print col,' ', colTypes[col]
##Let us print first few lines close to top...
##We saw that 'Dat' column is not in a format recognised by sqlite. So we need to fix it.
#####1. Come up with a format fixing function.
#####2. Update the Dat column by applying fixing function to each element.
def refine_date(element):
try:
myDate = element.split('-')
myMnth = '{:02d}'.format(int(month_enum(myDate[1])))
myDay = '{:02d}'.format(int(myDate[0]))
myYear = myDate[2]
newDate = '-'.join([myYear,myMnth,myDay])
return newDate
except Exception as err:
print_error(err,'pydan_test1.refine_date')
##Now time to apply this function to the col=Dat.
data.apply(colName='Dat', funcName='to_date', func=refine_date, numparams=1)
##Now see how the Dat column looks like.
data.head(5)
##Analyze the columns-types again.
colTypes = data.analyze_columns()
for col in colTypes: print col,' ', colTypes[col]
dset = data.transform(resultTab='pydanTable', resultType=' TABLE ', colTypes=colTypes, returnQueryOnly=False)
data.drop()
data = None
return dset
