Change Datatype in Pandas Dataframe Column

Question

I have a dataframe and one column Quantity has the below values

Col1
20,000
20
-10,000
-50

I want to convert this column to a float, as later I am doing a comparison with a floating number to filter some rows. However I get some or the other error: "Can only use .str accessor with string values!"

I think I found the issue, when it reads the value "-50", it reads it as a float, so no "str" accessor is valid. While loading the csv file the column is an object type series

my code looks like

df['Qty'] = np.where(~df['Qty'].str.contains(','),df['Qty'],df['Qty'].str.replace(",",""))
df['Qty'] = df['Qty'].astype(float)

How can I resolve this issue?

if I do to_numeric I get the error "ValueError: Unable to parse string "20,000" at position 0". @yatu — Suraj Sareen
– Suraj Sareen, Commented Feb 11, 2020 at 16:05
Can you share the code for loading the CSV? The dtype can be selected there. — AMC
– AMC, Commented Feb 11, 2020 at 16:12
Does this answer your question? Convert Pandas Dataframe to Float with commas and negative numbers — AMC
– AMC, Commented Feb 11, 2020 at 16:16

zipa · Accepted Answer · 2020-02-11 16:04:25Z

1

This will do the work:

pd.to_numeric(df['Col1'].astype(str).str.replace(',',''))

answered Feb 11, 2020 at 16:04

zipa

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