def main():
d = Data('cars.csv')
print "Raw Headers"
print d.get_raw_headers()
print "\n\n"
print "Raw number of columns"
print d.get_raw_num_columns()
print "\n\n"
print "Raw number of rows"
print d.get_raw_num_rows()
print "\n\n"
print "13th row"
print d.get_raw_row(13)
print "\n\n"
print "Value at row 6, header 'Car'"
print d.get_raw_value(6, 'Car')
print "\n\n"
print "Matrix data"
print d.matrix_data
print "\n\n"
print "Headers"
print d.get_headers()
print "\n\n"
print "Number of cols"
print d.get_num_columns()
print "\n\n"
print "5th row"
print d.get_row(5)
print "\n\n"
print "Get value"
print d.get_value(5, 'Horsepower')
print "\n\n"
print "get_data function"
print d.get_data(['Origin', 'Horsepower'])
print "\n\n"
print "data range"
print analysis.data_range(d, ['Origin', 'Horsepower'])
print "\n\n"
print "mean of horsepower and origin"
print analysis.mean(d, ['Horsepower', 'Origin'])
print "\n\n"
print "standard deviation for horsepower and origin"
print analysis.stdev(d, ['Horsepower', 'Origin'])
print "\n"
print "normalized columns origin and horsepower"
print analysis.normalize_columns_separately(d, ['Origin', 'Horsepower'])
print "\n\n"
print "normalized together origin and horsepower"
print analysis.normalize_columns_together(d, ['Origin', 'Horsepower'])
print "\n\n"
print "median of columns origin, horspower and weight"
print analysis.median(d, ['Origin', 'Horsepower', 'Weight'])
print d.get_data(['Origin', 'Horsepower']).shape
def main(argv):
# test command line arguments
if len(argv) < 2:
print('Usage: python %s <csv filename>' % (argv[0]))
exit(0)
# create a data object, which reads in the data
dobj = Data(argv[1])
headers = dobj.get_headers()
#test the five analysis functions
print([headers[0], headers[2]])
print("Data range by column:",
analysis.data_range([headers[0], headers[2]], dobj))
print("Mean:", analysis.mean([headers[0], headers[2]], dobj))
print("Standard deviation:", analysis.stdev([headers[0], headers[2]],
dobj))
print(
"Normalize columns separately:",
analysis.normalize_columns_separately([headers[0], headers[2]], dobj))
print("Normalize columns together:",
analysis.normalize_columns_together([headers[0], headers[2]], dobj))
#Extension 1
print("Median:", analysis.median([headers[0], headers[2]], dobj))
#Extension 2
print("Median Separately:",
analysis.median_separately([headers[0], headers[2]], dobj))
#Extension 3
print("just few rows:", dobj.limit_rows())
#Extension 4
print(
"just a few columns. I changed the limit to 2 for demonstration purposes:",
dobj.limit_columns())
#Extension 5
print("Data range overall:",
analysis.data_range([headers[0], headers[2]], dobj, True))
#Extension 6
print(
"The next two print statements get the last row of data. I add a row of data in between,"
"so they are different.")
print(dobj.get_row(-1))
dobj.add_point([1, 2, 3])
print(dobj.get_row(-1))
# # Run multiple linear regression on the Data objects
# analysis.testRegression(dataClean)
# analysis.testRegression(dataGood)
# analysis.testRegression(dataNoisy)
data = Data(filename='GOOG-NASDAQ_TSLA.csv')
# print out some analyses
print("\n\nDescriptive statistics of Tesla's stock data (daily open and close prices and trading volume:")
print("Mean: ", analysis.mean(['Open', 'Close', 'Volume'], data))
print("Standard deviation: ", analysis.stdev(['Open', 'Close', 'Volume'], data))
print("Ranges: ", analysis.dataRange(['Open', 'Close', 'Volume'], data))
print("Normalized columns: ", analysis.normalizeColumnsSeparately(['Open', 'Close', 'Volume'], data))
print("Normalized globally: ", analysis.normalizeColumnsTogether(['Open', 'Close', 'Volume'], data))
print("Variance: ", analysis.variance(['Open', 'Close', 'Volume'], data))
print("Median: ", analysis.median(['Open', 'Close', 'Volume'], data))
print("Mode value: ", analysis.modeValue(['Open', 'Close', 'Volume'], data))
print("Mode frequency: ", analysis.modeFreq(['Open', 'Close', 'Volume'], data))
print("Range value: ", analysis.rangeDiff(['Open', 'Close', 'Volume'], data), "\n")
data.printData(20)
# manipulate the data to show their efficacy
data.set_value(0.0001, 5, 'Open')
data.set_column(data.get_column('Open'), 'Close')
data.add_column(data.get_column('Volume'), 'Volume2', 'numeric')
data.add_raw_row(['6/28/10', 2.0, 3.0, 4.0, 5.0, 1000.0, 3])
data.add_row([1.0, 2.0, 3.0, 4.0, 5.0, 6])
# Here I print out the whole data set to show its full five-year comprehensive glory
data.printData(20)
