ValueError: could not convert string to float: id

This error is pretty verbose:

ValueError: could not convert string to float: id

Somewhere in your text file, a line has the word id in it, which can’t really be converted to a number.

Your test code works because the word id isn’t present in line 2.

If you want to catch that line, try this code. I cleaned your code up a tad:

#!/usr/bin/python

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

for index, line in enumerate(open('data2.txt', 'r').readlines()):
    w = line.split(' ')
    l1 = w[1:8]
    l2 = w[8:15]

    try:
        list1 = map(float, l1)
        list2 = map(float, l2)
    except ValueError:
        print 'Line {i} is corrupt!'.format(i = index)'
        break

    result = stats.ttest_ind(list1, list2)
    print result[1]

Your data may not be what you expect — it seems you’re expecting, but not getting, floats.

A simple solution to figuring out where this occurs would be to add a try/except to the for-loop:

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:
      # report the error in some way that is helpful -- maybe print out i
    result=stats.ttest_ind(list1,list2)
    print result[1]

My error was very simple: the text file containing the data had some space (so not visible) character on the last line.

As an output of grep, I had 45  instead of just 45. 

Perhaps your numbers aren’t actually numbers, but letters masquerading as numbers?

In my case, the font I was using meant that “l” and “1” looked very similar. I had a string like ‘l1919’ which I thought was ‘11919’ and that messed things up.

I solved the similar situation with basic technique using pandas. First load the csv or text file using pandas.It’s pretty simple

data=pd.read_excel('link to the file')

Then set the index of data to the respected column that needs to be changed. For example, if your data has ID as one attribute or column, then set index to ID.

 data = data.set_index("ID")

Then delete all the rows with “id” as the value instead of number using following command.

  data = data.drop("id", axis=0). 

Hope, this will help you.

For a Pandas dataframe with a column of numbers with commas, use this:

df["Numbers"] = [float(str(i).replace(",", "")) for i in df["Numbers"]]

So values like 4,200.42 would be converted to 4200.42 as a float.

Bonus 1: This is fast.

Bonus 2: More space efficient if saving that dataframe in something like Apache Parquet format.

Shortest way:

df["id"] = df['id'].str.replace(',', '').astype(float) – if ‘,’ is the problem

df["id"] = df['id'].str.replace(' ', '').astype(float) – if blank space is the problem

Update empty string values with 0.0 values:
if you know the possible non-float values then update it.

df.loc[df['score'] == '', 'score'] = 0.0


df['score']=df['score'].astype(float)