Given the following array, I want to replace commas with dots:
array(['0,140711', '0,140711', '0,0999', '0,0999', '0,001', '0,001',
'0,140711', '0,140711', '0,140711', '0,140711', '0,140711',
'0,140711', 0L, 0L, 0L, 0L, '0,140711', '0,140711', '0,140711',
'0,140711', '0,140711', '0,1125688', '0,140711', '0,1125688',
'0,140711', '0,1125688', '0,140711', '0,1125688', '0,140711',
'0,140711', '0,140711', '0,140711', '0,140711', '0,140711',
'0,140711', '0,140711', '0,140711', '0,140711', '0,140711',
'0,140711', '0,140711', '0,140711', '0,140711', '0,140711',
'0,140711', '0,140711', '0,140711', '0,140711'], dtype=object)
I’ve been trying different ways but I can’t figure out how to do this.
Also, I have imported it as a pandas DataFrame but can’t apply the function:
df
1-8 1-7
H0 0,140711 0,140711
H1 0,0999 0,0999
H2 0,001 0,001
H3 0,140711 0,140711
H4 0,140711 0,140711
H5 0,140711 0,140711
H6 0 0
H7 0 0
H8 0,140711 0,140711
H9 0,140711 0,140711
H10 0,140711 0,1125688
H11 0,140711 0,1125688
H12 0,140711 0,1125688
H13 0,140711 0,1125688
H14 0,140711 0,140711
H15 0,140711 0,140711
H16 0,140711 0,140711
H17 0,140711 0,140711
H18 0,140711 0,140711
H19 0,140711 0,140711
H20 0,140711 0,140711
H21 0,140711 0,140711
H22 0,140711 0,140711
H23 0,140711 0,140711
df.applymap(lambda x: str(x.replace(',','.')))
Any suggestions how to solve this?
Answers:
Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), leave a comment & I’ll get back to you as soon as possible.
Method 1
If you are reading in with read_csv, you can specify how it interprets decimals with the decimal parameter.
e.g.
your_df = pd.read_csv('/your_path/your_file.csv',sep=';',decimal=',')
From the man pages:
thousands : str, optional
Thousands separator.
decimal : str, default ‘.’
Character to recognize as decimal point
(e.g. use ‘,’ for European data).
Method 2
You need to assign the result of your operate back as the operation isn’t inplace, besides you can use apply or stack and unstack with vectorised str.replace to do this quicker:
In [5]:
df.apply(lambda x: x.str.replace(',','.'))
Out[5]:
1-8 1-7
H0 0.140711 0.140711
H1 0.0999 0.0999
H2 0.001 0.001
H3 0.140711 0.140711
H4 0.140711 0.140711
H5 0.140711 0.140711
H6 0 0
H7 0 0
H8 0.140711 0.140711
H9 0.140711 0.140711
H10 0.140711 0.1125688
H11 0.140711 0.1125688
H12 0.140711 0.1125688
H13 0.140711 0.1125688
H14 0.140711 0.140711
H15 0.140711 0.140711
H16 0.140711 0.140711
H17 0.140711 0.140711
H18 0.140711 0.140711
H19 0.140711 0.140711
H20 0.140711 0.140711
H21 0.140711 0.140711
H22 0.140711 0.140711
H23 0.140711 0.140711
In [4]:
df.stack().str.replace(',','.').unstack()
Out[4]:
1-8 1-7
H0 0.140711 0.140711
H1 0.0999 0.0999
H2 0.001 0.001
H3 0.140711 0.140711
H4 0.140711 0.140711
H5 0.140711 0.140711
H6 0 0
H7 0 0
H8 0.140711 0.140711
H9 0.140711 0.140711
H10 0.140711 0.1125688
H11 0.140711 0.1125688
H12 0.140711 0.1125688
H13 0.140711 0.1125688
H14 0.140711 0.140711
H15 0.140711 0.140711
H16 0.140711 0.140711
H17 0.140711 0.140711
H18 0.140711 0.140711
H19 0.140711 0.140711
H20 0.140711 0.140711
H21 0.140711 0.140711
H22 0.140711 0.140711
H23 0.140711 0.140711
the key thing here is to assign back the result:
df = df.stack().str.replace(',','.').unstack()
Method 3
If you need to replace commas with dots in particular columns, try
data["column_name"]=data["column_name"].str.replace(',','.')
to avoid ‘str’ object has no attribute ‘str’ error.
All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0