Obviously some of your lines don't have valid float data, specifically some line have text id which can't be converted to float.

When you try it in interactive prompt you are trying only first line, so best way is to print the line where you are getting this error and you will know the wrong line e.g.

#!/usr/bin/python

import os,sys
from scipy import stats
import numpy as np

f=open('data2.txt', 'r').readlines()
N=len(f)-1
for i in range(0,N):
    w=f[i].split()
    l1=w[1:8]
    l2=w[8:15]
    try:
        list1=[float(x) for x in l1]
        list2=[float(x) for x in l2]
    except ValueError,e:
        print "error",e,"on line",i
    result=stats.ttest_ind(list1,list2)
    print result[1]

Though not the best solution, I found some success by converting it into pandas dataframe and working along.

code snippet

# convert X into dataframe
X_pd = pd.DataFrame(data=X)
# replace all instances of URC with 0 
X_replace = X_pd.replace('�',0, regex=True)
# convert it back to numpy array
X_np = X_replace.values
# set the object type as float
X_fa = X_np.astype(float)

input

array([['85', '0', '0', '1980', '0', '0'],
       ['233', '54', '27', '-1', '0', '0'],
       ['���', '�', '�����', '�', '��', '���']], dtype='<U5')

output

array([[ 8.50e+01,  0.00e+00,  0.00e+00,  1.98e+03,  0.00e+00,  0.00e+00],
       [ 2.33e+02,  5.40e+01,  2.70e+01, -1.00e+00,  0.00e+00,  0.00e+00],
       [ 0.00e+00,  0.00e+00,  0.00e+00,  0.00e+00,  0.00e+00,  0.00e+00]])

To cast that column of your data-frame as type float try:

k_train = train['cols_to_use'].astype(float)
target = train['final_status'].astype(float)

More documentation can be found here or you can cast them when loading the csv file sep=',' maybe usful assuming that your data is separated by a , in your CSV file

train = pd.read_csv("datasets/train2.csv", 
                    dtype={'cols_to_use': float , 'final_status': float})

Note that converting unix TimeStamp to readable date is very simple with the datetime library example:

import datetime 

datetime.datetime.fromtimestamp(1526972723).strftime('%Y-%m-%d %H:%M:%S')

I hope it's helpful.

Try to convert timestamp column as int:

df['timestamp'] = df['timestamp'].astype('datetime64').astype(int)

After replacing the ? with np.NaN, you will be able to convert the type of the BareNuc column from object to float (as np.NaN is considered a special floating-point value and cannot be converted to any other type than float). with that, you wouldn’t need to convert to a NumPy array.

df = df.astype({"BareNuc": float})

At the end, all columns will be used as float type, so that wouldn’t affect the resource allocation.