ajpotts

Closes #3034 sparse sum partition helper This creates the function `arkouda.util.sparse_sum_partition_helper`. It attempts to optimize the sparse sum operation by first partitioning the indices into segments corresponding to matching ranges,...

Add a chapel helper function that takes two sorted distributed arrays, and segments each array such that the segments can be grouped by value range in a way that helps...

When we import functions from another library, such as numpy, in the __init__ files, users can access them through the arkouda api and therefore they should be in the docs....

Create a DataFrame.count function that replicates the capability in pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.count.html

Enhance the map functions (utils.map, Index.map, Series.map) to accept a dictionary with tuple values and generate multi-column/multi-index outputs.

Create a MultiIndex.map function to replicate the functionality in pandas: https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.MultiIndex.map.html

Make Series.fillna accept a dictionary as an argument, similar to the functionality of pandas: https://pandas.pydata.org/docs/reference/api/pandas.Series.fillna.html

The following example: ```python data = ak.Series([1, np.nan, 3, np.nan, 5]) fill_values = {1: 10, 3: 20} # Dictionary with keys as index positions and values as fill values filled_series...

dataframe.GroupBy.count() to align with pandas

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Right now dataframe.GroupBy.count() is an alias of dataframe.GroupBy.size(), but should align with the pandas api. More specifically, it should return a dataframe, where the count of each groupby value is...

Numpy Alignment

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Parent issue capturing the work needed to be done to better align arkouda pdarrays with numpy ndarrays. Feel free to edit this description or drop a comment with more information!...