A clean syntax for this kind of "find and replace" uses a dict, as
df.Num_of_employees = df.Num_of_employees.replace({"10-Jan": "1-10",
"Nov-50": "11-50"})
Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.replace.html
pandas.DataFrame.replace — pandas 3.0.5 documentation
For a DataFrame a dict can specify that different values should be replaced in different columns. For example, {'a': 1, 'b': 'z'} looks for the value 1 in column ‘a’ and the value ‘z’ in column ‘b’ and replaces these values with whatever is specified in value.
python - Pandas replacing values on specific columns - Stack Overflow
I am aware of these two similar questions: Pandas replace values Pandas: Replacing column values in dataframe I used a different approach for substituting values from which I think it should be ... More on stackoverflow.com
Replacing certain values from entire columns of a pandas dataframe
Use the map method https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.map.html More on reddit.com
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June 28, 2021Best way to replace values in one column from another column in pandas?
No, you shouldn't be looping. You should generally avoid that in Pandas. You can assign directly with loc: df.loc[df['new_items'] != 0, 'old_items'] = df['new_items'] or use an apply function: df['old_items'] = df.apply(lambda row: row['new_items'] if row['new_items'] != 0 else row['old_items']) More on reddit.com
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October 20, 2022Re-Assign values in a specific dataframe column using .iloc[]
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September 16, 202313:45
Clean Your Data FAST with Pandas replace() - YouTube
06:59
Replace Values of pandas DataFrame in Python (Example) | Substitute ...
06:24
Replacing a value in a Column | Python Panda Tutorial - YouTube
05:21
Pandas Replace | pd.DataFrame.replace() - YouTube
11:32
How to Replace Values of Dataframes | Replace, Where, Mask, Update ...
03:18
How to Replace the Column Values in Pandas Dataframe - YouTube
GeeksforGeeks
geeksforgeeks.org › data analysis › how-to-replace-values-in-column-based-on-condition-in-pandas
How to Replace Values in Column Based on Condition in Pandas? - GeeksforGeeks
November 15, 2024 - In this article, we’ve explored four effective methods to replace values in a Pandas DataFrame column based on conditions: using loc[], np.where(), masking, and apply() with a lambda function.
Favtutor
favtutor.com › articles › pandas-replace-column-values
Pandas DataFrame: Replace Column Values (with code)
December 15, 2023 - The Pandas library provides the .replace() method in Python to replace columns in a DataFrame. The .replace() method is a versatile way to replace values in a Pandas DataFrame. It allows you to specify the column, the value to replace, and the ...
Data to Fish
datatofish.com › replace-values-pandas-dataframe
How to Replace Values in a pandas DataFrame
# replace one specific value in a column df['column_a'] = df['column_a'].replace("x", "y") # replace multiple values (x, y) with one value (z) in a column df['column_a'] = df['column_a'].replace(["x", "y"], "z") # replace values (w, x) with other values (y, z) in a column df['column_a'] = ...
Saturn Cloud
saturncloud.io › blog › how-to-replace-values-on-specific-columns-in-pandas
How to Replace Values on Specific Columns in Pandas | Saturn Cloud Blog
May 1, 2026 - Replacing missing or NaN values in a specific column is a common data preprocessing task. You can use the .fillna() method to replace NaN values with a specific value: import numpy as np import pandas as pd # Load data into a DataFrame data = { 'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Marie'], 'Age': [25, 30, 35, 40, np.nan], 'City': ['New York', 'Los Angeles', 'San Francisco', 'Chicago', 'Washington'] } df = pd.DataFrame(data) print(df)
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 ...
Thinking Neuron
thinkingneuron.com › home › finding and replacing values in a pandas data frame
Finding and replacing values in a pandas data frame - Thinking Neuron
September 21, 2020 - For example, replacing all negative values in a column with zero or replacing all the outlier values with a sensible value, etc. If you need to find and replace some specific values in columns then the replace() function.
Vultr Docs
docs.vultr.com › python › third-party › pandas › DataFrame › replace
Python Pandas DataFrame replace() - Replace Values | Vultr Docs
December 27, 2024 - The value 1 in column 'A' and 5 in column 'B' are replaced with 99. This method allows selective replacement within specific columns.
Saturn Cloud
saturncloud.io › blog › how-to-replace-column-values-in-a-pandas-dataframe
How to Replace Column Values in a Pandas DataFrame | Saturn Cloud Blog
May 1, 2026 - In this article, we explored three common methods: using the .replace() method, using Boolean indexing, and using the .map() method. By understanding these methods, you can choose the best approach for your specific ...
Top answer 1 of 2
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Here is the answer by one of the developers: https://github.com/pydata/pandas/issues/11984
This should ideally show a SettingWithCopyWarning, but I think this is quite difficult to detect.
You should NEVER do this type of chained inplace setting. It is simply bad practice.
idiomatic is:
In [7]: df[['A','B']] = df[['A','B']].replace([1, 3, 2], [3, 6, 7]) In [8]: df Out[8]: A B C 0 3 7 8 1 6 4 8 2 5 3 8(you can do with
df.loc[:,['A','B']]as well, but more clear as above.
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to_rep = dict(zip([1, 3, 2],[3, 6, 7]))
df.replace({'A':to_rep, 'B':to_rep}, inplace = True)
This will return:
A B C
0 3 7 8
1 6 4 8
2 5 3 8
Medium
medium.com › @whyamit101 › understanding-pandas-replace-function-c2b0b7709233
Understanding pandas replace() Function | by why amit | Medium
February 10, 2025 - Let’s take a look at how replace() works in different situations: ... If you want to replace one specific value with another, you can just pass those values directly into the function.
Statology
statology.org › home › how to replace values in a pandas dataframe (with examples)
How to Replace Values in a Pandas DataFrame (With Examples)
September 27, 2022 - The following code shows how to replace multiple values in an entire pandas DataFrame: #replace 'E' with 'East' and 'W' with 'West' df = df.replace(['E', 'W'],['East', 'West']) #view DataFrame print(df) team division rebounds 0 A East 11 1 A West 8 2 B East 7 3 B East 6 4 B West 6 5 C West 5 6 C East 12 · The following code shows how to replace a single value in a single column:
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas replace column value in dataframe
Pandas Replace Column Value in DataFrame - Spark By {Examples}
November 7, 2024 - In Pandas library there are several ways to replace or update the column value in DataFarame. Changing the column values is required to curate/clean the
Pandas
pandas.pydata.org › pandas-docs › version › 2.1 › reference › api › pandas.DataFrame.replace.html
pandas.DataFrame.replace — pandas 2.1.4 documentation
For a DataFrame a dict can specify that different values should be replaced in different columns. For example, {'a': 1, 'b': 'z'} looks for the value 1 in column ‘a’ and the value ‘z’ in column ‘b’ and replaces these values with whatever is specified in value.
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas dataframe replace() – by examples
Pandas DataFrame replace() - by Examples - Spark By {Examples}
October 10, 2024 - pandas.DataFrame.replace() function is used to replace values in columns (one value with another value on all columns). It is a powerful tool for data
Reddit
reddit.com › r/learnpython › replacing certain values from entire columns of a pandas dataframe
r/learnpython on Reddit: Replacing certain values from entire columns of a pandas dataframe
June 28, 2021 -
Hi, I created a pandas dataframe with one column called 'service' with 100+ rows. some of the values are 1, 2, and 3. i want to replace each with a word. so for example, whenever the value is 2, it instead prints as "Fun Pro". under the same column. thanks for any help
Top answer 1 of 2
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Use the map method https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.map.html
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You might want to look into map() (pd.series.map, not the built-in map) or alternatively applymap or assign. Create a function that maps each number to the right string, and then use map to apply it to each element of the column. Example inspired by your question: df = pd.DataFrame([{'a': 15, 'b': 15, 'c': 1}, {'a': 20, 'b': 10, 'c': 2}, {'a': 25, 'b': 30, 'c': 3}]) def namer (x): return ["first", "second", "third"][x-1] df['c'] = df['c'].map(namer) print(df)
Medium
medium.com › data-science › an-easy-way-to-replace-values-in-a-pandas-dataframe-2826bd34e59a
An Easy Way to Replace Values in a Pandas DataFrame | by Byron Dolon | TDS Archive | Medium
July 25, 2021 - To do this, Pandas provides a wide range of methods that you can use to work with columns of all data types in your DataFrames. In this piece, let’s take a look specifically at replacing values and sub-strings within columns in a DataFrame.
datagy
datagy.io › home › pandas tutorials › pandas dataframes › pandas replace() – replace values in pandas dataframe
Pandas replace() - Replace Values in Pandas Dataframe • datagy
March 2, 2023 - Learn how to use the Pandas replace method to replace values across columns and dataframes, including with regular expressions.
Reddit
reddit.com › r/learnpython › best way to replace values in one column from another column in pandas?
r/learnpython on Reddit: Best way to replace values in one column from another column in pandas?
October 20, 2022 -
Original range:
| old_items | new_items |
|---|---|
| item1 | item6 |
| item2 | 0 |
| item3 | item7 |
| item4 | 0 |
| item5 | item8 |
Desired output:
| old_items | new_items |
|---|---|
| item6 | item6 |
| item2 | 0 |
| item7 | item7 |
| item4 | 0 |
| item8 | item8 |
My stupid solution:
old_items = list(df['old_items'])
new_items = list(df['new_items'])
proper_items = []
for x in range(len(old_items)):
if new_items[x] != 0:
proper_items.append(new_items[x])
else:
proper_items.append(old_items[x])
df['old_items'] = proper_items