Simple way to Dask concatenate (horizontal, axis=1, columns)

Question

Action
Reading two csv (data.csv and label.csv) to a single dataframe.

df = dd.read_csv(data_files, delimiter=' ', header=None, names=['x', 'y', 'z', 'intensity', 'r', 'g', 'b'])
df_label = dd.read_csv(label_files, delimiter=' ', header=None, names=['label'])

Problem
Concatenation of columns requires known divisions. However setting an index will sort the data, which I explicitly do not want, because order of both files is their match.

df = dd.concat([df, df_label], axis=1)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-11-e6c2e1bdde55> in <module>()
----> 1 df = dd.concat([df, df_label], axis=1)

/uhome/hemmest/.local/lib/python3.5/site-packages/dask/dataframe/multi.py in concat(dfs, axis, join, interleave_partitions)
    573             return concat_unindexed_dataframes(dfs)
    574         else:
--> 575             raise ValueError('Unable to concatenate DataFrame with unknown '
    576                              'division specifying axis=1')
    577     else:

ValueError: Unable to concatenate DataFrame with unknown division specifying axis=1

Tried
Adding an 'id' column

df['id'] = pd.Series(range(len(df)))

However, the length of Dataframe results in a Series larger than memory.

Question
Apparently Dask knows both Dataframe have the same length:

In [15]:
df.index.compute()
Out[15]:
Int64Index([      0,       1,       2,       3,       4,       5,       6,
                  7,       8,       9,
            ...
            1120910, 1120911, 1120912, 1120913, 1120914, 1120915, 1120916,
            1120917, 1120918, 1120919],
           dtype='int64', length=280994776)
In [16]:
df_label.index.compute()
Out[16]:
Int64Index([1, 5, 5, 2, 2, 2, 2, 2, 2, 2,
            ...
            3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
           dtype='int64', length=280994776)

How to exploit this knowledge to simply concatenate?

Asked By: Tom Hemmes

||

Source

Answer 1

I had the same problem and solved it by making sure that both dataframes have the same number of partitions (since we know already that both have the same length):

df = df.repartition(npartitions=200)
df_label = df_label.repartition(npartitions=200)
df = dd.concat([df, df_label], axis=1)

Answered By: architectonic

Answer 2

The solution (from the comments by @Primer):

both repartitioning and resetting the index
use assign instead of concatenate

The final code;

import os
from pathlib import Path
import dask.dataframe as dd
import numpy as np
import pandas as pd



df = dd.read_csv(['data/untermaederbrunnen_station1_xyz_intensity_rgb.txt'], delimiter=' ', header=None, names=['x', 'y', 'z', 'intensity', 'r', 'g', 'b'])
df_label = dd.read_csv(['data/untermaederbrunnen_station1_xyz_intensity_rgb.labels'], header=None, names=['label'])
# len(df), len(df_label), df_label.label.isnull().sum().compute()

df = df.repartition(npartitions=200)
df = df.reset_index(drop=True)
df_label = df_label.repartition(npartitions=200)
df_label = df_label.reset_index(drop=True)

df = df.assign(label = df_label.label)
df.head()

Answered By: Tom Hemmes

Answer 3

I had similar problem and the solution was simply to compute the chunk sizes of each dask array that I was going to put into dataframe using .compute_chunk_sizes(). After that there was no issues to concatenate them into dataframe on axis=1.

Answered By: foxof

Answer 4

I have 5 dataframes and applied compute on one of them. After removing compute, the error is gone

Answered By: Talha Anwar

Simple way to Dask concatenate (horizontal, axis=1, columns)

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