Grouped Forward Fill & Back Fill with Pandas

Python

Data Preprocessing

In this snippet all NAs in the latest_value column are filled but filling only happens within each category i.e. a latest value from one category won't be used to fill an NA from a different category

 1|  #Forward fill
 2|  df['latest_value_ffill'] = df.groupby('category')['latest_value'].transform(lambda x: x.fillna(method='ffill'))
 3|  
 4|  #Back fill
 5|  df['latest_value_bfill'] = df.groupby('category')['latest_value'].transform(lambda x: x.fillna(method='bfill'))

DETRO

1 Upvote

Tags: Groupby | Fillna

Did you find this snippet useful?

Sign up to bookmark this in your snippet library

Normalize Windowed Time Series

Python

Data Preprocessing

Scaler | Normalize | Scale | Min-max

4

Convert All Non-numeric Columns to Category Data Types

Python

Data Preprocessing

4

Creating a Running Total Column in Pandas

Python

Data Preprocessing

3

Pivoting Pandas Dataframes

Python

Data Preprocessing

3

Renaming Columns in a Pandas Dataframe Using Dictionaries & Lists

Python

Data Preprocessing

2

How to Remove Punctuation From Text with Python

Python

Data Preprocessing

Nlp | String | Punctuation

2

How To Set First Row of a Pandas Dataframe as Column Headers

Python

Data Preprocessing

2

How to Create Different Aggregations Using GroupBy, Agg & Dictionaries

Python

Data Preprocessing

2

Using Pandas GroupBy Transform Function

Python

Data Preprocessing

Transform | Groupby

2

Extract Date & Time Features From a Pandas Date Column

Python

Data Preprocessing

2