search
Search
Join our weekly DS/ML newsletter layers DS/ML Guides
menu
menu search toc more_vert
Robocat
Guest 0reps
Thanks for the thanks!
close
Comments
Log in or sign up
Cancel
Post
account_circle
Profile
exit_to_app
Sign out
help Ask a question
Share on Twitter
search
keyboard_voice
close
Searching Tips
Search for a recipe:
"Creating a table in MySQL"
Search for an API documentation: "@append"
Search for code: "!dataframe"
Apply a tag filter: "#python"
Useful Shortcuts
/ to open search panel
Esc to close search panel
to navigate between search results
d to clear all current filters
Enter to expand content preview
icon_star
Doc Search
icon_star
Code Search Beta
SORRY NOTHING FOUND!
mic
Start speaking...
Voice search is only supported in Safari and Chrome.
Navigate to
A
A
brightness_medium
share
arrow_backShare
Twitter
Facebook

Replacing values in a DataFrame in Pandas

Pandas
chevron_right
Cookbooks
chevron_right
DataFrame Cookbooks
chevron_right
Data Manipulation Cookbook
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
Tags

To replace values in a Pandas DataFrame, use the DataFrame's replace(~) method.

Consider the following DataFrame:

df = pd.DataFrame({"A":[2,3],"B":[2,"a"]})
df
A B
0 2 2
1 3 a

Replacing a value in entire DataFrame

To replace all 2s with "b":

df.replace(2, "b")
A B
0 b b
1 3 a

Note that replacement is not done in-place, meaning a new DataFrame is returned and the original df is left intact. We can modify df directly by passing in inplace=True.

Replacing a value in specific columns

To replace 2s in just column A with "b":

df.replace({"A":2}, "b")
A B
0 b 2
1 3 a

Notice how the 2 in column B was not replaced.

Replacing multiple values

To replace multiple values, supply them as a list like so:

df.replace([2,"a"], "b")
A B
0 b b
1 3 b

Here, we are replacing the values 2 and "a" with "b".

Replacing values based on condition

Consider the following DataFrame:

df = pd.DataFrame({"A":[2,3],"B":[4,5]})
df
A B
0 2 4
1 3 5

To replace values that are greater than 3 with 10:

df[df > 3] = 10
df
A B
0 2 10
1 3 10

Here, we are first creating a boolean mask (DataFrame) where True indicates the values that satisfy the condition:

df > 3
A B
0 False True
1 False True

We then fetch a reference to all the entries in df that correspond to True to in the mask:

df[df > 3]
A B
0 NaN 4
1 NaN 5

Finally, performing assignment will update the non-NaN values:

df[df > 3] = 10
df
A B
0 2 10
1 3 10
mail
Join our newsletter for updates on new DS/ML comprehensive guides (spam-free)
robocat
Published by Isshin Inada
Edited by 0 others
Did you find this page useful?
Ask a question or leave a feedback...
0
thumb_down
0
chat_bubble_outline
0
settings
Enjoy our search
Hit / to insta-search docs and recipes!