> ## 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 Replace Text in a Pandas DataFrame Or Column - URL: https://datascientyst.com/replace-text-pandas-dataframe-column/ - Published: 2021-11-02T11:52:03.000Z - Updated: 2021-11-02T11:53:35.000Z - Author: John D K - Tags: replace() **Replace text** is one of the most popular operation in **Pandas DataFrames and columns.** In this post we will see how to replace text in a Pandas. The short answer of this questions is: **(1) Replace character in Pandas column** ```python df['Depth'].str.replace('.',',') ``` **(2) Replace text in the whole Pandas DataFrame** ```python df.replace('\.',',', regex=True) ``` We will see several practical examples on how to replace text in Pandas columns and DataFrames. Suppose we have DataFrame like: | Date | Time | Latitude | Longitude | Depth | Magnitude Type | | ---------- | -------- | -------- | ---------- | ----- | -------------- | | 12/27/2016 | 23:20:56 | 45.7192 | 26.5230 | 97.0 | MWW | | 12/28/2016 | 08:18:01 | 38.3754 | \-118.8977 | 10.8 | ML | | 12/28/2016 | 08:22:12 | 38.3917 | \-118.8941 | 12.3 | ML | | 12/28/2016 | 09:13:47 | 38.3777 | \-118.8957 | 8.8 | ML | | 12/28/2016 | 12:38:51 | 36.9179 | 140.4262 | 10.0 | MWW | ## Replace single character in Pandas Column with .str.replace First let's start with the most simple example - replacing a single character in a single column. We are going to use the string method - `replace`: ```python df['Depth'].str.replace('.',',') ``` A warning message might be shown - for this one you can check the section below: > FutureWarning: The default value of regex will change from True to False in a future version. If you are using regex than you can specify it by: ```python df['Depth'].str.replace('.',',', regex= True) ``` result: ``` 23405 97 23406 10,8 23407 12,3 ``` ## Replace regex pattern in Pandas Column Let say that you would like to use a **regex in order to replace specific text patterns in Pandas.** For example let's change the date format of you Pandas DataFrame from: - mm/dd/yyyy to - yyyy-mm-dd This can be done by using regex flag and using regex groups like: ```python df['Date'].str.replace(r'(\d{2})/(\d{2})/(\d{4})', r"\3-\2-\1", regex=True) ``` result: ``` 23405 2016-27-12 23406 2016-28-12 23407 2016-28-12 ``` **How does the regex replace in Pandas work?** . So this one is translated as `(\d{2})`: - 1st capturing group `(\d{2})` - `\d` \- matches a digit (equivalent to \[0-9\]) - {2} matches the previous token exactly 2 times Then in the replacement part we are replaced by the group numbers - 1st group is `\1`. So you can try a simple exercise - to change the format to: dd mm 'yy . The answer is below: ```python df['Date'].str.replace(r'(\d{2})/(\d{2})/\d{2}(\d{2})', r"\2 \1 '\3", regex=True) ``` result: ``` 23405 27 12 '16 23406 28 12 '16 23407 28 12 '16 ``` ## Replace text in whole DataFrame If you like to replace values in all columns in your Pandas DataFrame then you can use syntax like: ```python df.replace('\.',',', regex=True) ``` If you don't specify the columns then the replace operation will be done over all columns and rows. ## Replace text with conditions in Pandas with lambda and .apply/.applymap **`.applymap` is another option to replace text and string in Pandas.** This one is useful if you have additional conditions - sometimes the regex might be too complex or to not work. In this case the **replacement can be done by lambda and `.apply` for columns**: ```python df['Date'].apply(lambda x: x.replace('/', '-')) ``` result: | Date | Time | Latitude | Longitude | Depth | Magnitude Type | | ---------- | -------- | -------- | ---------- | ----- | -------------- | | 12/27/2016 | 23:20:56 | 45.7192 | 26.523 | 97 | MWW | | 12/28/2016 | 08:18:01 | 38.3754 | \-118.8977 | 10.8 | ML | and **`.applymap` for the whole DataFrame**: ```python df.applymap(lambda x: x.replace('/', '-')) ``` result: | Date | Time | Latitude | Longitude | Depth | Magnitude Type | | ---------- | -------- | -------- | ---------- | ----- | -------------- | | 12/27/2016 | 23:20:56 | 45,7192 | 26,523 | 97 | MWW | | 12/28/2016 | 08:18:01 | 38,3754 | \-118,8977 | 10,8 | ML | Note that this solution might be slower than the others. ### Conditional replace in Pandas If you like to apply condition to your replacement in Pandas you can use syntax like: ```python df['Date'].apply(lambda x: x.replace('/', '-') if '20' in x else x) ``` result: ``` 23405 12-27-2016 23406 12-28-2016 23407 12-28-2016 ``` ## Warning: FutureWarning: The default value of regex will change from True to False in a future version. If you get an warning like: > FutureWarning: The default value of regex will change from True to False in a future version. It means that you will need to explicitly set the `regex` parameter for `replace` method: ```python df.replace('\.',',', regex=True) ``` The official documentation of method `replace` is: ``` `regex` -** bool, default True** Determines if the passed-in pattern is a regular expression: * If True, assumes the passed-in pattern is a regular expression. * If False, treats the pattern as a literal string Cannot be set to False if pat is a compiled regex or repl is a callable. ``` So the difference is how the passed pattern will be parsed - as a regex expression or not. So in a practical example: ```python df.replace('.',',') ``` this will not change the values of the DataFrame: ``` 45.7192 ``` While the regex expression - `'\.'` and `regex=True`: ```python df.replace('\.',',', regex=True) ``` will change them: ``` 45,7192 ``` ## Resources - [Notebook](https://github.com/softhints/Pandas-Tutorials/blob/master/replace/replace-text-pandas-dataframe-column.ipynb?ref=datascientyst.com) - [pandas.Series.str.replace](https://pandas.pydata.org/docs/reference/api/pandas.Series.str.replace.html?ref=datascientyst.com) - [pandas.Series.replace](https://pandas.pydata.org/docs/reference/api/pandas.Series.replace.html?ref=datascientyst.com) - [pandas.Series.apply](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html?ref=datascientyst.com) - [pandas.DataFrame.applymap](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.applymap.html?ref=datascientyst.com)