On Sat, Sep 7, 2019 at 8:28 PM Joel Goldstick <joel.goldst...@gmail.com> wrote: > > On Sat, Sep 7, 2019 at 8:21 PM Sharan Basappa <sharan.basa...@gmail.com> > wrote: > > > > I am trying to read a log file that is in CSV format. > > > > The code snippet is below: > > > > ############################### > > import matplotlib.pyplot as plt > > import seaborn as sns; sns.set() > > import numpy as np > > import pandas as pd > > import os > > import csv > > from numpy import genfromtxt > > > > # read the CSV and get into X array > > os.chdir(r'D:\Users\sharanb\OneDrive - HCL Technologies > > Ltd\Projects\MyBackup\Projects\Initiatives\machine > > learning\programs\constraints') > > X = [] > > #with open("constraints.csv", 'rb') as csvfile: > > # reader = csv.reader(csvfile) > > # data_as_list = list(reader) > > #myarray = np.asarray(data_as_list) > > > > my_data = genfromtxt('constraints.csv', delimiter = ',', dtype=None) > > print (my_data) > > > > my_data_1 = np.delete(my_data, 0, axis=1) > > print (my_data_1) > > > > my_data_2 = np.delete(my_data_1, 0, axis=1) > > print (my_data_2) > > > > my_data_3 = my_data_2.astype(np.float) > > ################################ > > > > Here is how print (my_data_2) looks like: > > ############################## > > [['"\t"81' '"\t5c'] > > ['"\t"04' '"\t11'] > > ['"\t"e1' '"\t17'] > > ['"\t"6a' '"\t6c'] > > ['"\t"53' '"\t69'] > > ['"\t"98' '"\t87'] > > ['"\t"5c' '"\t4b'] > > ############################## > > > > Finally, I am trying to get rid of the strings and get array of numbers > > using Numpy's astype function. At this stage, I get an error. > > > > This is the error: > > my_data_3 = my_data_2.astype(np.float) > > could not convert string to float: " "81 > > > > As you can see, the string "\t"81 is causing the error. > > It seems to be due to char "\t". > > > > I don't know how to resolve this. > > > > Thanks for your help. > > > > -- > > https://mail.python.org/mailman/listinfo/python-list > > how about (strip(my_data_2).astype(np.float)) > > I haven't used numpy, but if your theory is correct, this will clean > up the string > oops, I think I was careless at looking at your data. so this doesn't seem like such a good idea > -- > Joel Goldstick > http://joelgoldstick.com/blog > http://cc-baseballstats.info/stats/birthdays
-- Joel Goldstick http://joelgoldstick.com/blog http://cc-baseballstats.info/stats/birthdays -- https://mail.python.org/mailman/listinfo/python-list