将numpy数组转换为dask dataframe列？ [英] Converting numpy array into dask dataframe column?

问题描述

我有一个要在现有dask数据框中添加为列的numpy数组。

I have a numpy array that i want to add as a column in a existing dask dataframe.

enc = LabelEncoder()
nparr = enc.fit_transform(X[['url']])

I具有类型为dask dataframe的ddf。

I have ddf of type dask dataframe.

ddf['nurl'] = nparr   ???

有什么优雅的方法可以实现以上目标？

Any elegant way to achieve above please?

Python PANDAS：从pandas / numpy转换为dask数据框/数组这不能解决我的问题，因为我希望将numpy数组转换为现有的dask数据框。

Python PANDAS: Converting from pandas/numpy to dask dataframe/array This does not solve my issue as i want numpy array into existing dask dataframe.

推荐答案

您可以将numpy数组转换为dask Series对象，然后将其合并到数据框。您将需要使用Series对象的 .to_frame（）方法，因为它只支持将数据框与其他数据框合并。

You can convert the numpy array to a dask Series object, then merge it to the dataframe. You will need to use the .to_frame() method of the Series object since it dask only support merging dataframes with other dataframes.

import dask.dataframe as dd
import numpy as np
import pandas as pd

df = pd.DataFrame({'x': range(30), 'y': range(0,300, 10)})
arr = np.random.randint(0, 100, size=30)

# create dask frame and series
ddf = ddf = dd.from_pandas(df, npartitions=5)
darr = dd.from_array(arr)
# give it a name to use as a column head
darr.name = 'z'

ddf2 = ddf.merge(darr.to_frame())

ddf2
# returns:
Dask DataFrame Structure:
                   x      y      z
npartitions=5
0              int64  int64  int32
6                ...    ...    ...
...              ...    ...    ...
24               ...    ...    ...
29               ...    ...    ...
Dask Name: join-indexed, 33 tasks

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