python drop non-integer rows, convert to int

Question

Is there a simple way to drop rows containing a non-integer cell value, then/and convert strings to integers, then sort ascending? I have dataset (single column of what's supposed to be just record numbers) that has strings that I want to remove. This code seems to work, but then sorting seems to sort as if "float" is "string." For example, the record numbers are sorted like so:

0
1
2
200000000
201
3

Code:

import pandas

with open('GridExport.csv') as incsv:
    df1 = pandas.read_csv(incsv,  usecols=['Record Number'])
    cln = pandas.DataFrame()
    cln['Record Number'] = [x for x in df1['Record Number'] if x.isdigit()]
    cln.astype(float)
    print(cln.sort(['Record Number']))

Is there a way to do this without converting to float first? I'd like to drop the numbers that don't fit into int64

Answer 1

You may convert all string elements into float elements and conduct the following method for sorting

    def numeric_compare(x, y):
    return float(x)-float(y)

>>> sorted(['10.0','2000.0','30.0'],cmp=numeric_compare)
['10.0', '30.0', '2000.0']

Answer 2

The problem in your code is that the line

cln['Record Number'].astype(float)

does not modify the data frame. Consequently, it treats the column as of type string and sorts it accordingly. If you print cln['Record Number'].dtype after the statement, it should make it clear. If you would like to modify it, you should do the assignment

cln['Record Number'] = cln['Record Number'].astype(float)