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As such, we would like to combine two Options objects where missing values in one DataFrame are conditionally filled with like-labeled values from 101 other DataFrame. Each also takes an optional level parameter which applies only if the object has a hierarchical index. The appropriate method to use depends on whether your function expects to operate on an entire DataFrame or Seriesrow- or column-wise, or elementwise. This function takes the floor division and modulo operation at the same time returning a two-tuple of the same type as the left hand binary options strategy side. However, if the function needs to be called in a chain, consider using the pipe method. Refer to there for details about accepted inputs. I could be convinced to make the axis argument in the DataFrame methods match the broadcasting behavior of Panel. What if the function you wish to apply takes its data as, say, the second argument? The implementation of pipe here is quite clean and feels right at home in python. DataFrames and Series can of course just be passed into functions. In the example above, the functions fgand h each expected the DataFrame as the first positional argument. The section 101 GroupBy demonstrates related, binary functionality for grouping by some criterion, applying, and combining the results into a Series, DataFrame, etc. For example, we can fit a regression using statsmodels. Their API expects a formula first and a DataFrame as the second argument, data. Since not all functions can be vectorized accept NumPy arrays and binary options strategy return another basics or valuethe methods applymap on DataFrame and analogously map on Series accept basics Python function taking basics single value and returning a single value. If the applied function returns a Seriesthe result of the application will be a Panel. 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For exploratory analysis you will hardly notice the difference because reindex has been heavily optimizedbut when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact. If the applied function reduces to a scalar, the result of the application will be a DataFrame. It is used to implement nearly all other features relying on label-alignment functionality. The limit and tolerance arguments provide additional control over filling while reindexing. The behavior basics basic iteration over pandas objects depends on the type. A method closely related to reindex is the drop function. When iterating over a Series, it is regarded as array-like, and basic iteration produces the values. Binary many cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches: binary options strategy You should never modify something you are iterating over. Iterating through pandas objects is generally slow. 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