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issue in handling CSV data


On Sat, Sep 7, 2019 at 8:28 PM Joel Goldstick <joel.goldstick at gmail.com> wrote:
>
> On Sat, Sep 7, 2019 at 8:21 PM Sharan Basappa <sharan.basappa at 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