> ## Content Index > Fetch the complete content index at: https://datascientyst.com/llms.txt > Use this file to discover other available public pages before exploring further. # How to Convert Decimal Comma to Decimal Point in Pandas DataFrame - URL: https://datascientyst.com/convert-decimal-comma-to-decimal-point-pandas-dataframe/ - Published: 2022-08-25T20:57:37.000Z - Updated: 2022-08-25T20:57:37.000Z - Author: John D K - Tags: Convert In this quick tutorial, we're going to **convert decimal comma to decimal point in Pandas DataFrame** and vice versa. It will also show how to remove decimals from strings in Pandas columns. Different people in the world are using different decimal separator like: - decimal point - more often - decimal comma - in the Francophone area ## Setup Let's work with the following DataFrame: ```python import pandas as pd df = pd.DataFrame(data={'day': [1, 2, 3, 4, 5, 6, 7, 8], 'temp': [9, 8, 6, 13, 10, 15, 9, 10], 'humidity': [0.89, 0.86, 0.54, 0.73, 0.45, 0.63, 0.95, 0.67], 'humidity_eu': ['0,89', '0,86', '0,54', '0,73', '0,45', '0,63', '0,95', '0,67']}) ``` We have two columns with float data: - decimal comma - decimal point | | day | temp | humidity | humidity\_eu | | - | --- | ---- | -------- | ------------ | | 0 | 1 | 9 | 0.89 | 0,89 | | 1 | 2 | 8 | 0.86 | 0,86 | | 2 | 3 | 6 | 0.54 | 0,54 | | 3 | 4 | 13 | 0.73 | 0,73 | | 4 | 5 | 10 | 0.45 | 0,45 | ## 1: read\_csv - decimal point vs comma Let's start with the optimal solution - convert decimal comma to decimal point while reading CSV file in Pandas. Method `read_csv()` has parameter three parameters that can help: - `decimal` \- the decimal sign used in the CSV file - `delimiter` \- separator for the CSV file (tab, semi-colon etc) - `thousands` \- what is the symbol for thousands - if any To use them we can do: ```python df = pd.read_csv('file.csv', delimiter=";", decimal=",", thousands="`") ``` This will ensure that the correct decimal symbol is used for the DataFrame. ## 2: Convert comma to point If the DataFrame contains values with comma then we can convert them by `.str.replace()`: ```python df['humidity_eu'].str.replace(',', '.').astype(float) ``` result is: ``` 0 0.89 1 0.86 2 0.54 3 0.73 4 0.45 5 0.63 6 0.95 7 0.67 Name: humidity_eu, dtype: float64 ``` ## 3: Mixed decimal data - point and comma What can we do in case of mixed data in a given column? For this example we can use: list comprehensions and `pd.to_numeric()`. This can help us to identify the problematic values and keep the rest the same. For example we can do: ```python s = pd.Series(['0,89', '0,86', 0.54, 0.73, 0.45, '0,63', '0,95', '0,67']) mix = [x.replace(',', '.') if type(x) == str else x for x in s] ``` to replace the comma in all string records: ``` ['0.89', '0.86', 0.54, 0.73, 0.45, '0.63', '0.95', '0.67'] ``` Then we can convert the Series by: ```python pd.to_numeric(mix) ``` ``` array([0.89, 0.86, 0.54, 0.73, 0.45, 0.63, 0.95, 0.67]) ``` ## 4: Detect decimal comma in mixed column To detect which are the problematic values we can use: ```python s[pd.to_numeric(s, errors='coerce').isna() ] ``` the result of `to_numeric` is: ``` array([ nan, nan, 0.54, 0.73, 0.45, nan, nan, nan]) ``` while the final result is showing all values with decimal comma: ``` 0 0,89 1 0,86 5 0,63 6 0,95 7 0,67 dtype: object ``` ## 5: to\_csv - decimal point vs comma Finally if we like to write CSV file by method `to_csv` we can use parameters: - `decimal` - `sep` to control the decimal symbol. To convert CSV values from decimal comma to decimal point with Python and Pandas we can do : ```python df = pd.read_csv("file.csv", decimal=",") df.to_csv("test2.csv", sep=',', decimal='.') ``` ![](https://datascientyst.com/content/images/2022/08/convert-decimal-comma-to-decimal-point-pandas-dataframe.png) ## ValueError: could not convert string to float: '0,89' The error: "ValueError: could not convert string to float: '0,89'" is raised when we try to parse decimal comma to float. The error is given by method `s.astype(float)`: ```python s = pd.Series(['0,89', '0,86', 0.54, 0.73, 0.45, '0,63', '0,95', '0,67']) s.astype(float) ``` ## ValueError: Unable to parse string "0,89" at position 0 The error is the result of the `pd.to_numeric(s)` method - when a decimal comma is present in the input values. ```python s = pd.Series(['0,89', '0,86', 0.54, 0.73, 0.45, '0,63', '0,95', '0,67']) pd.to_numeric(s) ``` result: ``` ValueError: Unable to parse string "0,89" at position 0 ``` ## Conclusion In this article we saw how to replace, change and convert decimal symbols in Pandas. We saw how to detect problematic values in mixed columns - which have decimal commas and points simultaneously. Typical errors were explained. For further reference you can check also: - [How to Suppress and Format Scientific Notation in Pandas](https://datascientyst.com/suppress-format-scientific-notation-pandas/) - [How to Round Numbers in Pandas DataFrame](https://datascientyst.com/round-numbers-pandas-dataframe/) - [Solve - ValueError: could not convert string to float - Pandas](https://datascientyst.com/solve-valueerror-could-not-convert-string-to-float-pandas/)