MachineLearningMastery
machinelearningmastery.com › home › blog › dealing with missing data strategically: advanced imputation techniques in pandas and scikit-learn
Dealing with Missing Data Strategically: Advanced Imputation Techniques in Pandas and Scikit-learn - MachineLearningMastery.com
June 7, 2025 - This article presents some advanced strategies to handle missing data, namely, imputation techniques made possible through a combined use of Pandas and Scikit-learn libraries in Python.
Medium
medium.com › @sanjushusanth › missing-value-imputation-techniques-in-python-62aeab65a6a6
Missing Value Imputation Techniques in Python :) | by Sanjushusanth | Medium
November 28, 2023 - In default tips dataset doesn’t have missing values in the dataset, i have added some nan values to some features manually. ... from sklearn.experimental import enable_iterative_imputer from sklearn.impute import IterativeImputer from sklearn.linear_model import LinearRegression
How to impute missing values in python - YouTube
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Handling Missing Data in Python: Simple Imputer in Python for Machine ...
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scikit-learn
scikit-learn.org › stable › modules › impute.html
8.4. Imputation of missing values — scikit-learn 1.9.1 documentation
However, this comes at the price of losing data which may be valuable (even though incomplete). A better strategy is to impute the missing values, i.e., to infer them from the known part of the data.
Kaggle
kaggle.com › code › parulpandey › a-guide-to-handling-missing-values-in-python
A Guide to Handling Missing values in Python
July 11, 2020 - Handling Missing Values in PythonTable of ContentsObjectiveDataLoading necessary libraries and datasetsReading in the datasetExamining the Target columnDetecting Missing valuesDetecting missing values numericallyDetecting missing data visually using Missingno libraryReasons for Missing ValuesFinding reason for missing data using matrix plotFinding reason for missing data using a HeatmapFinding reason for missing data using DendrogramTreating Missing valuesDeletionsImputations Techniques for non Time Series ProblemsImputations Techniques for Time Series ProblemsAdvanced Imputation TechniquesAlgorithms which handle missing valuesConclusionReferences and good resources
AskPython
askpython.com › python › examples › impute-missing-data-values
Impute missing data values in Python - 3 Easy Ways! - AskPython
February 16, 2023 - As clearly seen, the data variable ‘custAge’ contains 1804 missing values out of 7414 records. Further, we have used mean() function to impute all the null values with the mean of the column ‘custAge’.
MachineLearningMastery
machinelearningmastery.com › home › blog › how to handle missing data with python
How to Handle Missing Data with Python - MachineLearningMastery.com
November 27, 2023 - In this tutorial, you will learn how to handle missing data for machine learning with Python. Specifically, after completing this tutorial you will know: How to mark invalid or corrupt values as missing in your dataset. How to remove rows with missing data from your dataset. How to impute missing values with mean values in your dataset.
Medium
medium.com › @hassankhan2608 › missing-value-imputation-methods-using-python-f1b8796901ba
Missing Value Imputation Methods using Python | by Mohd Hassan Khan | Medium
January 15, 2024 - K-Nearest Neighbors (KNN) Imputation: Identifies ‘k’ samples in the dataset that are similar to the observation with missing data and imputes values based on the average (or majority) of these ‘k’ neighbours. For predictive imputation, let’s use k-nearest neighbours (KNN). We’ll use the KNNImputer from the scikit-learn library using Python:
Machine Learning Plus
machinelearningplus.com › blog › missing data imputation approaches | how to handle missing values in python
Missing Data Imputation Approaches | How to handle missing values in Python | MLPlus
February 28, 2023 - Interpolation is another useful strategy to impute missing values. But you need to be a bit careful when and how to apply. This is discussed in the following posts. Interpolation in Python – How to interpolate missing data in Python?
Analytics Vidhya
analyticsvidhya.com › home › how to handle missing data in python? [explained in 5 easy steps]
How to Handle Missing Data in Python? [Explained in 5 Easy Steps]
May 1, 2025 - This article taught us about the different ways of handling missing values in our dataset. If there are way too many missing values in a column then you can drop that column. Otherwise we can impute missing values with mean, median and mode.
GeeksforGeeks
geeksforgeeks.org › python › imputing-missing-values-before-building-an-estimator-in-scikit-learn
Imputing Missing Values Before Building an Estimator in Scikit Learn - GeeksforGeeks
July 23, 2025 - The following steps are required for imputing missing values before building an estimator in Scikit Learn: Import the required libraries: first You need to import the required libraries, including Scikit Learn and NumPy.
Top answer 1 of 2
4
As I said in the comment to the question, just replace (re-assign) the values in the dataframe with the data returned from the Imputer.
Lets say this is your dataframe:
import numpy as np
import pandas as pd
df = pd.DataFrame(data=[[1,2,3],
[3,4,4],
[3,5,np.nan],
[6,7,8],
[3,np.nan,1]],
columns=['A', 'B', 'C'])
Current df:
A B C
0 1 2.0 3.0
1 3 4.0 4.0
2 3 5.0 NaN
3 6 7.0 8.0
4 3 NaN 1.0
If you are sending whole the df to Imputer, just use this:
df[df.columns] = Imputer().fit_transform(df)
If you are sending only some columns, then use those columns only to assign the results:
columns_to_impute = ['B', 'C']
df[columns_to_impute] = Imputer().fit_transform(df[columns_to_impute])
Output:
A B C
0 1.0 2.0 3.0
1 3.0 4.0 4.0
2 3.0 5.0 4.0
3 6.0 7.0 8.0
4 3.0 4.5 1.0
2 of 2
0
To up to date @Vivek's answer:
import sklearn.preprocessing from Imputer was deprecated in scikit-learn v0.20.4 and is now completely removed in v0.22.2.
Use no the simpleImputer (refer to the documentation here):
from sklearn.impute import SimpleImputer
import numpy as np
imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')
GitHub
github.com › tarikbir › missing_data_imputation
GitHub - tarikbir/missing_data_imputation: Python code for applying missing data imputation methods.
December 30, 2022 - This is a basic python code to read a dataset, find missing data and apply imputation methods to recover data, with as less error as possible.
Starred by 14 users
Forked by 3 users
Languages: Python 100.0% | Python 100.0%
Journal of Statistical Software
jstatsoft.org › article › view › v108i04
gcimpute: A Package for Missing Data Imputation | Journal of Statistical Software
February 18, 2024 - This article introduces the Python package gcimpute for missing data imputation. Package gcimpute can impute missing data with many different variable types, including continuous, binary, ordinal, count, and truncated values, by modeling data as samples from a Gaussian copula model.
Medium
medium.com › analytics-vidhya › a-quick-guide-on-missing-data-imputation-techniques-in-python-2020-5410f3df1c1e
Master The Skills Of Missing Data Imputation Techniques In Python(2022) And Be Successful | by Mrinal Walia | Analytics Vidhya | Medium
December 29, 2021 - Data Imputation is a method in which the missing values in any variable or data frame(in Machine learning) are filled with numeric values for performing the task. By using this method, the sample size remains the same.
Towards Data Science
towardsdatascience.com › home › latest › how to handle missing values in python
How to Handle Missing Values in Python | Towards Data Science
January 30, 2025 - For example, if women really are ... of missing data on the weight variable is higher for women than men. Then we can conclude that weight is MAR. Replacing the missing values with the mean, median, or mode in a column is a very basic imputation ...
GitHub
github.com › MIDASverse › MIDASpy
GitHub - MIDASverse/MIDASpy: Python package for missing-data imputation with deep learning · GitHub
MIDASpy is a Python package for multiply imputing missing data using deep learning methods. The MIDASpy algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large ...
Starred by 158 users
Forked by 40 users
Languages: Python
Kaggle
kaggle.com › code › muhammadaammartufail › imputing-missing-values-in-python
Imputing Missing Values in Python | Kaggle
December 3, 2024 - Explore and run AI code with Kaggle Notebooks | Using data from No attached data sources
ProjectPro
projectpro.io › recipes › impute-missing-values-with-means-in-python
How to Impute Missing Values with Mean in Python? -
April 12, 2023 - Mean imputation is a simple method of imputation where missing values are replaced with the mean of the available data. This method assumes that the missing values are missing at random and that the data is normally distributed.
Medium
medium.com › @vayakakshay08 › missing-value-imputation-with-pandas-4efd5f68da23
Missing Value Imputation with Pandas | by Akshay V. | Medium
January 25, 2024 - import pandas as pd # Create a sample DataFrame with missing values data = {'A': [1, 2, None, 4, 5], 'B': [10, None, 30, 40, 50], 'C': [100, 200, 300, None, 500]} df = pd.DataFrame(data) # Display the original DataFrame print("Original DataFrame:") print(df) # Impute missing values with the mean of each column df_imputed = df.fillna(df.mean()) # Display the DataFrame after imputation print("\nDataFrame after mean imputation:") print(df_imputed)