I am trying to change the datatype of column '2019' from object to int64 which also contains empty strings so I can perform mathematical operations on them, I tried using to_numeric but I keep on getting a value error of this
Traceback (most recent call last):
File "pandas\_libs\lib.pyx", line 2062, in
pandas._libs.lib.maybe_convert_numeric
ValueError: Unable to parse string " "
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\user\open csv.py", line 22, in <module>
df['2019']= pd.to_numeric(df['2019'])
File"C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\LocalCache\local-packages\Python37\site-packages\pandas\core\tools\numeric.py", line 155, in to_numeric
values, set(), coerce_numeric=coerce_numeric
File "pandas_libs\lib.pyx", line 2099, in pandas._libs.lib.maybe_convert_numeric ValueError: Unable to parse string " " at position 103
This is my code, please can anyone help me convert it and remove the empty strings too?
import pandas as pd
import numpy as np
df = pd.read_csv('seun.csv')
##df = df.astype(str)
##df.replace("-","")
##df.to_csv('seun.csv', index = False, header=True)
print(df.to_string())
a= df.loc[2, '2019']
b= df.loc[3, '2019']
print(df.to_numeric())
##f= (df[pd.to_numeric(df['2019'], errors='coerce').isnull()])
df['2019']= pd.to_numeric(df['2019'])
##df['2019'].replace('', np.nan, inplace=True)
##df.dropna(subset=['2019'], inplace=True)
print(df.dtypes)
df sample
S/NO HOUSE OWNERS NAME DATE 2019
1 MOBS INCORP 2019-04-22 100,000.00
2 SEUN HOUSES 2020-05-03 100,000.00
3 50,000.00
4 SEUN HOUSES 2020-05-03 50,000.00
5 100,000.00
6
7 50,000.00
8 CARSON VENTURE 2020-07-02
9 WONDERFUL LIMITED 2020-09-05
10 SEUN HOUSES 2020-05-03 50,0000.00
