Python – Pandas: Unable to safely convert passed int32 user dtype to float64

I’m having a problem loading my data into a Pandas dataframe using read_table(). Error TypeError: Cannot cast array from dtype('float64') to dtype('int32') according to the rule 'safe' and ValueError: cannot safely convert passed user dtype of int32 for float64 dtyped data in column 2

Test .py:

import numpy as np
import os
import pandas as pd

# put test.csv in same folder as script
mydir = os.path.dirname(os.path.abspath(__file__))
csv_path = os.path.join(mydir, "test.csv")

df = pd.read_table(csv_path, sep=' ',
                   comment='#',
                   header=None,
                   skip_blank_lines=True,
                   names=["A", "B", "C", "D", "E", "F", "G"],
                   dtype={"A": np.int32,
                       "B": np.int32,
                       "C": np.float64,
                       "D": np.float64,
                       "E": np.float64,
                       "F": np.float64,
                       "G": np.int32})

Test .csv:

2270433 3 21322.889 11924.667 5228.753 1.0 -1
2270432 3 21322.297 11924.667 5228.605 1.0 2270433


The problem is that I use spaces as delimiters and csv has trailing spaces. Removing trailing spaces solves this problem.

To remove all trailing spaces from every line of every file in the directory, I ran this command: find. -Name "*.csv" | xargs sed -i 's/[\t]*$//'