Use regex (seperate the strings by |):

df['schoolname'] = df['schoolname'].str.replace('high|school', "")
Answer from Andy Hayden on Stack Overflow
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Reddit
reddit.com › r/learnpython › vectorized .str.replace() for multiple characters in pandas
r/learnpython on Reddit: Vectorized .str.replace() for multiple characters in pandas
June 9, 2022 -

I have a dataframe: 

 {'country': {0: 'Afghanistan?*', 1: 'Albania?*'},
 'region': {0: 'Asia', 1: 'Europe'},
 'subregion': {0: 'Southern Asia', 1: 'Southern Europe'},
 'rate_per_1000': {0: 6.7, 1: 2.1},
 'count': {0: '2,474', 1: '61'},
 'year': {0: 2018, 1: 2020},
 'source': {0: 'NSO', 1: 'NSO'}}

          country  region        subregion  rate_per_1000  count  year source
0   Afghanistan?*    Asia    Southern Asia            6.7  2,474  2018    NSO
1       Albania?*  Europe  Southern Europe            2.1     61  2020    NSO

There are multiple bad characters here that I want to get rid of. I made a short function for .apply() to get rid of them, however I am looping over a defined list of bad characters. This gives a bad code smell to me, I think this operation could be more vectorized in some way. This is what I've tried: 

bad_chars = ['?', '*', ',']

def string_cleaner(col):
    if col.dtype == 'object':
        for char in bad_chars:
            col = col.str.replace(f'{char}', '')
        return col

homicide_by_country = homicide_by_country.apply(string_cleaner)

homicide_by_country
        country  region        subregion rate_per_1000 count  year source
0   Afghanistan    Asia    Southern Asia          None  2474  None    NSO
1       Albania  Europe  Southern Europe          None    61  None    NSO

My desired outcome is a more pythonic/pandonic technique for accomplishing the same outcome.

You may notice for some reason my rate_per_1000 columns goes blank. I haven't troubleshot that problem yet but if you spot something obvious I'm all ears.

Top answer
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1
I think my count and year columns are breaking because of the if statement excluding those two columns.
2 of 3
1
You can use df.replace() with a regex . >>> df country region subregion rate_per_1000 count year source 0 Afghanistan?* Asia Southern Asia 6.7 2,474 2018 NSO 1 Albania?* Europe Southern Europe 2.1 61 2020 NSO >>> df.replace('[*?,]', '', regex=True) country region subregion rate_per_1000 count year source 0 Afghanistan Asia Southern Asia 6.7 2474 2018 NSO 1 Albania Europe Southern Europe 2.1 61 2020 NSO
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Quora
quora.com › How-can-I-replace-characters-in-a-multiple-column-name-in-pandas
How to replace characters in a multiple column name in pandas - Quora
To replace characters in multiple column names in pandas, use vectorized string methods on the DataFrame’s .columns (an Index) or rename with a mapping/func.
Discussions
python - pandas replace (erase) different characters from strings - Stack Overflow
As you can see, the years were entered using multiple formats (ugh!) with brackets and copyright symbols and lowercase c and uppercase C. Now I wanted to remove those unwanted characters and only have the years in four digits. Since it's an array, you also need to transform it into a string before using replace... More on stackoverflow.com
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python - Replace multiple characters across all columns pandas df - Stack Overflow
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GeeksforGeeks
geeksforgeeks.org › pandas › pandas-replace-multiple-values-in-python
Pandas Replace Multiple Values in Python - GeeksforGeeks
July 23, 2025 - Ways to Replace Multiple Values in Python Using Pandas are: ... One of Pandas most useful tools is the replace() method, which allows to substitute desired values with specified ones.
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Stack Overflow
stackoverflow.com › questions › 33157643 › pandas-replace-erase-different-characters-from-strings
python - pandas replace (erase) different characters from strings - Stack Overflow
Top answer
1 of 3
32

Use regex (seperate the strings by |):

df['schoolname'] = df['schoolname'].str.replace('high|school', "")
2 of 3
5

You can create a dictionary and then .replace({}, regex=True) method:

replacements = {
   'schoolname': {
      r'(high|school)': ''}
}

df.replace(replacements, regex=True, inplace=True)
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Google Groups
groups.google.com › g › pydata › c › GVDNJCvjuXw
how to replace characters in multiple column name in pandas
June 9, 2018 - df_hld.columns = df_hld.columns.str.replace("hld", "pct") A similar question with helpful answers: https://stackoverflow.com/q/38299205 ... Either email addresses are anonymous for this group or you need the view member email addresses permission to view the original message ... but this will replace the characters in all of my columns names.
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Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.replace.html
pandas.DataFrame.replace — pandas 3.0.5 documentation
Map values of Series according to an input mapping or function. ... Simple string replacement. ... Regex substitution is performed under the hood with re.sub. The rules for substitution for re.sub are the same. Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched.
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Towards Data Science
towardsdatascience.com › home › latest › 2 different replace functions of python pandas
2 Different Replace Functions of Python Pandas | Towards Data Science
January 20, 2025 - The "eng" string in the profession column should be replaced with "engineer". Thanks to the flexibility of Pandas, we can do both replacements in a single operation. Each replacement is written as a key-value pair in the dictionary. ... Both "doc" and "eng" have been replaced. There is another way of replacing multiple values in a column, which is using Python lists to indicate values to be replaced and the new ones.
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Stack Overflow
stackoverflow.com › questions › 35457651 › how-to-replace-multiple-character-in-string-of-data-frame-in-pandas
python - How to replace multiple character in string of data frame in pandas? - Stack Overflow
Top answer
1 of 1
1

You can use apply method to run a function for every element:

import pandas as pd

s = pd.Series([
        "1 1 2 2 3 3 4 4 5 5 6 6 6",
        "3 4 2 1 2 3 4 4 5 5 4 34 2"
    ])

todel = set(["1", "3", "5"])
s.apply(lambda x:" ".join(v for v in x.strip().split() if v not in todel))

the output:

0       2 2 4 4 6 6 6
1    4 2 2 4 4 4 34 2
dtype: object
Find elsewhere
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Stack Overflow
stackoverflow.com › questions › 66973229 › replace-multiple-characters-across-all-columns-pandas-df
python - Replace multiple characters across all columns pandas df - Stack Overflow
Top answer
1 of 1
9

You can use

df.columns = df.columns.str.replace(r"[][() ]", "_", regex=True) 
df.columns = df.columns.str.replace(r"[][() ]+", "_", regex=True)

The first line will replace each separate ], [, (, ) and space with a _.

The second line will replace a chunk of subsequent ], [, (, ) and space chars with a single _.

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Stack Overflow
stackoverflow.com › questions › 72946937 › pandas-string-replace-multiple-character-in-a-cell › 72946960
python - pandas string replace multiple character in a cell - Stack Overflow
Top answer
1 of 1
3

Try .replace (not .str.replace) with option regex=True:

df['a'] = df['a'].replace({'1':'4', '2':'5', '3':'6'}, regex=True)

Output:

     a
0  456
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IncludeHelp
includehelp.com › python › pandas-replace-a-character-in-all-column-names.aspx
Python - Pandas replace a character in all column names
July 30, 2022 - To replace a character in all column names in pandas DataFrame, you can use the df.columns.str.replace() method by specifying the old and new character to be replaced as the parameters of the function.
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Note.nkmk.me
note.nkmk.me › home › python › pandas
pandas: Replace values in DataFrame and Series with replace() | note.nkmk.me
January 17, 2024 - In pandas, the replace() method allows you to replace values in DataFrame and Series. It is also possible to replace parts of strings using regular expressions (regex). pandas.DataFrame.replace — pan ...
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Stack Overflow
stackoverflow.com › questions › 61687030 › replace-multiple-characters-of-a-column-in-a-dataframe-con-python
Replace multiple characters of a column in a dataframe con python - Stack Overflow
Top answer
1 of 1
3

The string in str.replace is regex pattern, so you can take advantage of it:

df['Col1'] = df['Col1'].str.replace(r'C|D','W')

print(df)

Prints:

  Col1 Col2
0    A    H
1    B    I
2  ABW    J
3    W    K
4   AB    L
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Pandas
pandas.pydata.org › pandas-docs › stable › generated › pandas.DataFrame.replace.html
pandas.DataFrame.replace — pandas 2.3.3 documentation
The page has been moved to this page
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Python Guides
pythonguides.com › pandas-replace-multiple-values
Replace Multiple Values In Pandas DataFrame Using Str.Replace()
May 22, 2025 - # Replace values across the entire DataFrame df_replaced = df.replace({ 'California': 'CA', 'New York': 'NY', 1200: 'Low Sales', 1500: 'High Sales' }) print("\nDataFrame after multiple replacements:") print(df_replaced) Check out Convert DataFrame To NumPy Array Without Index in Python · The loc[] method in Python allows you to replace values based on conditions, which gives you more flexibility. Here’s an example with sales data categorization: import pandas as pd # Sample US sales data data = { 'Product': ['Laptop', 'Smartphone', 'Tablet', 'Monitor', 'Keyboard'], 'Sales': [1200, 1800, 950
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Reddit
reddit.com › r/learnpython › pandas: is it possible to str.replace() multiple columns at once?
r/learnpython on Reddit: pandas: Is it possible to str.replace() multiple columns at once?
March 6, 2022 -

I'm trying to remove the special characters '$' and ','. Can the code below be shortened so I can perform the operation just once? 

df['ListPricePerUnit'] = df['ListPricePerUnit'].str.replace('$','').str.replace(',','')
df['PurchasePricePerUnit'] = df['PurchasePricePerUnit'].str.replace('$','').str.replace(',','')
Top answer
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2
You could put it into a loop? def df_string_replace(df, columns: list[str], find:str, replace:str): for column in columns: df[column] = df[column].str.replace(find, replace, regex=True) return df columns = ["ListPricePerUnit", "PurchacePricePerUnit"] df = df_string_replace(df, columns, find="$|,", replace="") This is untested, and you might need to enable regex to make it work. But it should give you an idea how to start =)
2 of 2
1
Found in stack overflow from decimal import Decimal money="$6,150,593.22" amount = Decimal("".join(d for d in money if d.isdigit() or d == '.')) amount
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Python Examples
pythonexamples.org › pandas-dataframe-replace-multiple-values
Pandas DataFrame - Replace Multiple Values
The syntax to replace multiple values in a column of DataFrame is · DataFrame.replace({'column_name' : { old_value_1 : new_value_1, old_value_2 : new_value_2}}) In the following example, we will use replace() method to replace 1 with 11 and 2 with 22 in column a. import pandas as pd df = ...
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Stack Overflow
stackoverflow.com › questions › 46623596 › pandas-dataframe-replace-string-in-multiple-columns-by-finding-substring
python 2.7 - Pandas dataframe replace string in multiple columns by finding substring - Stack Overflow
Top answer
1 of 1
10

You can use data frame replace method with regex=True, and use .*,.* to match strings that contain a comma (you can replace comma with other any other substring you want to detect):

str_cols = ['Answer']    # specify columns you want to replace
df[str_cols] = df[str_cols].replace('.*,.*', 'X', regex=True)
df
#Question   Answer
#0      1       A
#1      2       X
#2      3       C

or if you want to replace all string columns:

str_cols = df.select_dtypes(['object']).columns
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Stack Overflow
stackoverflow.com › questions › 72322680 › replacing-character-in-python
pandas - replacing character in python - Stack Overflow
Top answer
1 of 4
2

Try this:

data['q3'] = data['q3'].str.replace('[', '').replace(']','')
2 of 4
2

You can use the strip() as a possibility

data['q3'] = data['q3'].apply(lambda x : x.strip('[]'))
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CodeForGeek
codeforgeek.com › home › replace multiple values in a dataframe using pandas
Replace Multiple Values in a DataFrame Using Pandas | CodeForGeek
September 30, 2023 - In this article, we’ve explored three unique ways in which we can replace certain row and column values in a DataFrame using – the replace() function with dictionaries, using the loc attribute and using regex patterns for searching substrings.