Pandas DataFrame: replace nan values with average of columns
Quickly fill NaN values in a pandas DataFrame
with column averages using:
This efficient line of code both computes column means and fills the missing data with those averages, modifying the DataFrame in place. (It's just that easy!)
Filling gaps with column averages
Why turn column averages into a dictionary?
The fillna
method is a versatile function that can take a scalar, a dict, or a Series as value
argument. To allow for selective behavior, consider turning your column averages into a dictionary:
This lets you pick and choose where to apply the averages, giving you granular control over column treatment.
Saving computational resources in large DataFrames
When wrestling with extensive DataFrames, selectively filling NaN values can give a boost to performance:
The code identifies columns with NaNs and addresses them specifically, saving quite a few computational cycles.
Working with lambda and apply()
For column-wise precision and control, lambda functions with apply()
function is a good choice:
This ensures each column is evaluated individually, applying fillna()
only when necessary. Coding ninjas will love this functionality!
Use cases and warnings
When average fits well
Columns with zero variance (all non-NaN values are the same), filling with the column mean makes sense as it aligns with the existing data. The context of the dataset will ultimately determine the appropriateness of such imputations.
A word of caution
While filling missing data with column average is convenient and prevalent, it's important to remember this can influence the results of your subsequent analysis. So, always tread into the world of statistical inference with caution.
Other available approaches
Pandas dataframe provides various other techniques, like mean
, median
, mode
, and more complex techniques for replacing missing data. Always consider what fits best for your data.
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