You can select your desired columns and do it by assignment:
df[['a', 'b']] = df[['a','b']].fillna(value=0)
The resulting output is as expected:
a b c
0 1.0 4.0 NaN
1 2.0 5.0 NaN
2 3.0 0.0 7.0
3 0.0 6.0 8.0
Top answer 1 of 10
441
You can select your desired columns and do it by assignment:
df[['a', 'b']] = df[['a','b']].fillna(value=0)
The resulting output is as expected:
a b c
0 1.0 4.0 NaN
1 2.0 5.0 NaN
2 3.0 0.0 7.0
3 0.0 6.0 8.0
2 of 10
215
You can using dict , fillna with different value for different column
df.fillna({'a':0,'b':0})
Out[829]:
a b c
0 1.0 4.0 NaN
1 2.0 5.0 NaN
2 3.0 0.0 7.0
3 0.0 6.0 8.0
After assign it back
df=df.fillna({'a':0,'b':0})
df
Out[831]:
a b c
0 1.0 4.0 NaN
1 2.0 5.0 NaN
2 3.0 0.0 7.0
3 0.0 6.0 8.0
Statology
statology.org › home › pandas: how to use fillna() with specific columns
Pandas: How to Use fillna() with Specific Columns
June 10, 2022 - This tutorial explains how to use the fillna() function in pandas to replace values in specific columns, including examples.
Pandas conditional fillna based on another column values
I finally solved in this way :
missing = train["Outlet_Size"].isna()
condlist = [train.loc[missing, "Outlet_Size"] & train.loc[missing,'Item_Outlet_Sales'] <= 1000,
train.loc[missing, "Outlet_Size"] & train.loc[missing,'Item_Outlet_Sales'] > 1000]
choicelist = ["Small", "Medium"]
train.loc[missing, 'Outlet_Size'] = np.select(condlist, choicelist)
3
2
July 28, 2020How do I fill NaN values of a column with its mean/median/mode? (Pandas DataFrame)
Hey, easiest way is to save the mean into a variable like col_mean=df['col'].mean() and then df['col']=df['col'].fillna(col_mean) - there are more elegant versions but this should do the work. More on reddit.com
1
2
October 15, 2020How can I replace all NaN values in my DF except the NaN values of some specific columns?
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.fillna.html Pass a dict to the values argument. More on reddit.com
5
1
January 13, 2023Pandas: Is it possible to use the fillna() method using a calculation between two columns of a specific row?
The way to do this would be to just calculate a series with all of the values, then pass the name of that series to fillna() as the first argument. Something like df["fill_value"] = df["Unit_Cost"] * df["Quantity"] df["Total_Cost"] = df["Total_Cost"].fillna(df["fill_value"]) More on reddit.com
1
0
February 23, 2025Videos
Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.fillna.html
pandas.DataFrame.fillna — pandas 3.0.2 documentation
Replace all NaN elements in column ‘A, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively. >>> values = {"A": 0, "B": 1, "C": 2, "D": 3} >>> df.fillna(value=values) A B C D 0 0.0 2.0 2.0 0.0 1 3.0 4.0 2.0 1.0 2 0.0 1.0 2.0 3.0 3 0.0 3.0 2.0 4.0
Linux find Examples
queirozf.com › entries › pandas-fillna-examples-filling-in-missing-data
Pandas Fillna Examples: Filling in Missing Data
May 29, 2023 - To fill nulls in multiple specific columns, pass a dict to fillna · import pandas as pd import numpy as np df = pd.DataFrame({ 'col1': [1.0, 2.0, 3.0, np.nan, None ], 'col2': [1, 2, 3, 4, 5 ], 'col3': ['foo', None, 'baz', 'quux', 'bax'], 'col4': ['xxx', None, None, None, 'xxx'], }) ...
GeeksforGeeks
geeksforgeeks.org › python › python-pandas-dataframe-fillna-to-replace-null-values-in-dataframe
Pandas DataFrame.fillna() | Python - GeeksforGeeks
February 23, 2026 - This example replaces missing values in the College column with a fixed text "No College" using fillna(). This is useful when you want to assign a default value to missing entries. ... import pandas as pd nba = pd.read_csv("nba.csv") nba["College"].fillna("No College", inplace = True) print(nba)
Reddit
reddit.com › r/learnpython › pandas conditional fillna based on another column values
r/learnpython on Reddit: Pandas conditional fillna based on another column values
July 28, 2020 -
Hello,
I am working on bigmart dataset and I would like to substitute missing values of a column based on the values of another column, practically:
| Outlet_Size | sales_bin |
|---|---|
| Medium | 3000-4000 |
| NaN | 0-1000 |
| Small | 0-1000 |
| .... | .... |
So if train[“Outlet_Size”] value is a NaN and train[“sales_bin”] is “0-1000”
train[“Outlet_Size”] value shoud become “Small”
else == Medium
But I really don’t know how to write it and all the information I found seems confusing to me
Is it possible to do it? How?
Many thanks
Top answer 1 of 2
2
I finally solved in this way :
missing = train["Outlet_Size"].isna()
condlist = [train.loc[missing, "Outlet_Size"] & train.loc[missing,'Item_Outlet_Sales'] <= 1000,
train.loc[missing, "Outlet_Size"] & train.loc[missing,'Item_Outlet_Sales'] > 1000]
choicelist = ["Small", "Medium"]
train.loc[missing, 'Outlet_Size'] = np.select(condlist, choicelist)
2 of 2
1
df.loc[(df.col1.isna()) & (df.col2 == '0-1000'), 'col1'] = 'small'.
Try something like this
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas.dataframe.fillna() – explained by examples
pandas.DataFrame.fillna() - Explained by Examples
June 26, 2025 - To fill NaN values with different values for different columns, you can use a dictionary with the .fillna() method. The dictionary specifies the replacement value for each column.
W3Schools
w3schools.com › python › pandas › ref_df_fillna.asp
Pandas DataFrame fillna() Method
Pandas HOME Pandas Intro Pandas ... Wrong Format Cleaning Wrong Data Removing Duplicates ... The fillna() method replaces the NULL values with a specified value....
IncludeHelp
includehelp.com › python › dataframe-fillna-only-some-columns-in-place.aspx
Pandas dataframe fillna() only some columns in place
September 22, 2023 - To apply this method to specific columns, we need to define the specific columns at time of function calling. ... # Importing pandas package import pandas as pd # Importing numpy package import numpy as np # Creating a dictionary d = { 'A':[1,2,3,np.NaN], 'B': [np.NaN,4,5,6], 'C':[7,8,np.NaN,9] ...
Apache
spark.apache.org › docs › latest › api › python › reference › pyspark.sql › api › pyspark.sql.DataFrame.fillna.html
pyspark.sql.DataFrame.fillna — PySpark 4.1.1 documentation
DataFrame.fillna() and DataFrameNaFunctions.fill() are aliases of each other. New in version 1.3.1. Changed in version 3.4.0: Supports Spark Connect. ... If the value is a dict, then subset is ignored and value must be a mapping from column name (string) to replacement value. The replacement value must be an int, float, boolean, or string. ... optional list of column names to consider. Columns specified ...
Reddit
reddit.com › r/learnpython › how do i fill nan values of a column with its mean/median/mode? (pandas dataframe)
r/learnpython on Reddit: How do I fill NaN values of a column with its mean/median/mode? (Pandas DataFrame)
October 15, 2020 -
Hello everyone, i'm trying to fill the missing values in a specific column of a dataframe, and i want to fill them using the mean, median or mode. How can i do it using pandas or numpy?
Edureka Community
edureka.co › home › community › categories › python › pandas - fillna with another column
Pandas - FillNa with another column | Edureka Community
July 5, 2019 - How do I fill the missing value in one column with the value of another column? I read that looping ... cat giraf 4 ant ant How do I resolve this?
KDnuggets
kdnuggets.com › 2023 › 02 › optimal-way-input-missing-data-pandas-fillna.html
The Optimal Way to Input Missing Data with Pandas fillna() - KDnuggets
To do that, we can use the Pandas function called fillna. Using the function is simple, but there are a few methods to optimally fill up our data, including replacing missing data in multiple columns, limiting the imputation, and using other rows to fill the data.
Saturn Cloud
saturncloud.io › blog › how-to-fill-missing-values-of-one-column-from-another-column-in-pandas
How to Fill Missing Values of One Column from Another Column in Pandas | Saturn Cloud Blog
November 10, 2023 - To fill missing values of one column from another column in pandas, we can use the fillna() method. This method allows us to replace missing values with a specified value or with values from another column.
IONOS
ionos.com › digital guide › websites › web development › python pandas: dataframe fillna()
What is Pandas fillna() and how to use it
June 26, 2025 - Since there was no preceding value in column B for row 0, the NaN value is retained: A B C 0 1.0 NaN 1.0 1 2.0 2.0 1.0 2 2.0 3.0 3.0 3 4.0 4.0 4.0 · NaN values can also be filled with succeeding values based on their row position. To do this, you need to use the bfill method and set the axis parameter to 1: df_bfill = df.fillna(method='bfill', axis=1) print(df_bfill)python
Pandas
pandas.pydata.org › docs › dev › reference › api › pandas.DataFrame.fillna.html
pandas.DataFrame.fillna — pandas 3.0.0rc2+8.g2b571cac91 documentation
Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively. >>> values = {"A": 0, "B": 1, "C": 2, "D": 3} >>> df.fillna(value=values) A B C D 0 0.0 2.0 2.0 0.0 1 3.0 4.0 2.0 1.0 2 0.0 1.0 2.0 3.0 3 0.0 3.0 2.0 4.0
Pandas
pandas.pydata.org › pandas-docs › version › 0.22 › generated › pandas.DataFrame.fillna.html
pandas.DataFrame.fillna — pandas 0.22.0 documentation
>>> df.fillna(method='ffill') A B C D 0 NaN 2.0 NaN 0 1 3.0 4.0 NaN 1 2 3.0 4.0 NaN 5 3 3.0 3.0 NaN 4 · Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively.
Bobby Hadz
bobbyhadz.com › blog › pandas-fillna-only-some-specific-columns-in-dataframe
Panda: Using fillna() with specific columns in a DataFrame | bobbyhadz
April 12, 2024 - You can also pass a dictionary to the fillna() method to only call the method on specific columns. ... Copied!import pandas as pd df = pd.DataFrame({ 'ID': [1, 1, None, 2, 2, None], 'Animal': ['Cat', 'Cat', None, 'Dog', 'Dog', None], 'Max Speed': ...
Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.html
pandas.DataFrame — pandas 3.0.2 documentation
>>> df2 = pd.DataFrame( ... np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=["a", "b", "c"] ...
Statology
statology.org › home › pandas: how to fill nan values with mean (3 examples)
Pandas: How to Fill NaN Values with Mean (3 Examples)
January 20, 2022 - #fill NaNs with column mean in 'rating' column df['rating'] = df['rating'].fillna(df['rating'].mean()) #view updated DataFrame df rating points assists rebounds 0 85.125 25.0 5.0 11 1 85.000 NaN 7.0 8 2 85.125 14.0 7.0 10 3 88.000 16.0 NaN 6 4 94.000 27.0 5.0 6 5 90.000 20.0 7.0 9 6 76.000 12.0 6.0 6 7 75.000 15.0 9.0 10 8 87.000 14.0 9.0 10 9 86.000 19.0 5.0 7