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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?
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str. replace(',', '. ') replaces the comma for a dot.
Pandas DataFrame replace() MethodThe replace() method replaces the specified value with another specified value. The replace() method searches the entire DataFrame and replaces every case of the specified value.
pandas Convert String to FloatUse pandas DataFrame. astype() function to convert column from string/int to float, you can apply this on a specific column or on an entire DataFrame. To cast the data type to 54-bit signed float, you can use numpy. float64 , numpy.
In this post, you learned how to use the Pandas replace method to, well, replace values in a Pandas dataframe. The .replace () method is extremely powerful and lets you replace values across a single column, multiple columns, and an entire dataframe. The method also incorporates regular expressions to make complex replacements easier.
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. If you are reading in with read_csv, you can specify how it interprets decimals with the decimal parameter.
This is more of a logical approach in which we swap the symbols considering third variables. The replace method can also be used to replace the methods in strings. We can convert “, ” to a symbol then convert “.” to “, ” and the symbol to “.”. For more reference visit Python String Methods .
We can convert “, ” to a symbol then convert “.” to “, ” and the symbol to “.”. For more reference visit Python String Methods . str1 = str1.replace ('third', '.')
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()
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