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I have a Pandas DataFrame, suppose:
df = pd.DataFrame({'Column name':['0,5',600,700]})
I need to remove ,. The code is:
df_mod = df.stack().str.replace(',','').unstack()
As a result I get: [05, NaN, NaN]
Do you have any ideas why my expression replaces numbers with NaN and how to avoid it? Thanks a lot!
746
You can replace the missing value ( NaN ) in pandas. DataFrame and Series with any value using the fillna() method.
You can replace values of all or selected columns based on the condition of pandas DataFrame by using DataFrame. loc[ ] property. The loc[] is used to access a group of rows and columns by label(s) or a boolean array. It can access and can also manipulate the values of pandas DataFrame.
We can replace the NaN with an empty string using df. replace() function. This function will replace an empty string inplace of the NaN value.
Those numbers are treated as numeric values, which don't have str.replace methods, you can convert the column to string, remove the comma, and then convert the data type back:
df['Column name'].astype(str).str.replace(",", "").astype(int)
#0 5
#1 600
#2 700
#Name: Column name, dtype: int64
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