What are the different interpolation methods in Pandas DataFrame?
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What are the different interpolation methods in Pandas DataFrame?
Pandas offers several interpolation methods, including 'linear', 'time', 'index', 'nearest', 'zero', 'slinear', 'quadratic', and 'cubic'. Each method has its own use case depending on the data.
Can you explain how the 'linear' method works?
The 'linear' method fills missing values by performing linear interpolation between existing data points. It assumes a straight line between points.
What about the 'time' interpolation method?
The 'time' method is specifically for time series data. It uses the time index to interpolate values, ensuring that the interpolation respects the time intervals.
Are there any performance considerations when using these methods?
Yes, some methods like 'cubic' can be computationally intensive, especially on large datasets. It's best to choose a method that balances accuracy and performance.
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