CODEWITHSUNDEEP
pythonpandasdataframedask
najeem
najeem

Reputation: 1921

Concatenating a dask dataframe and a pandas dataframe

I have a dask dataframe (df) with around 250 million rows (from a 10Gb CSV file). I have another pandas dataframe (ndf) of 25,000 rows. I would like to add the first column of pandas dataframe to the dask dataframe by repeating every item 10,000 times each.

Here's the code that I tried. I have reduced the problem to a smaller size.

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

pd.DataFrame(np.random.rand(25000, 2)).to_csv("tempfile.csv")
df = dd.read_csv("tempfile.csv")
ndf = pd.DataFrame(np.random.randint(1000, 3500, size=2500))
df['Node'] = np.repeat(ndf[0], 10)

With this code, I end up with an error.

ValueError: Not all divisions are known, can't align partitions. Please use set_index to set the index.

I can perform a reset_index() followed by a set_index() to make df.known_divisions True for the dask dataframe. But it is a time consuming operation. Is there a better faster way to do what I am trying to do? Can I do this using pandas itself?

The end goal is to find rows from ndf where any of the corresponding rows from df matches some criteria.

Upvotes: 12

Views: 1665

Answers (2)

mdurant
mdurant

Reputation: 28673

Your basic algorithm is "I'd like the first 10 values of df['Node'] to be set to the first value of ndf, the next 10 values to the next value of ndf, and so on". The reason this is hard in Dask, is because it doesn't know how many rows are in each partition: you are reading from CSVs, and the number of rows you get in X bytes depends on exactly what the data are like in each part. Other formats give you more information...

You will, therefore, certainly need two passes through the data. You could work with the index, to figure out divisions and potentially do some sorting. To my mind, the easiest thing you can do is simply to measure the division lengths, and so get the offset of the start of each:

lengths = df.map_partitions(len).compute()
offsets = np.cumsum(lengths.values)
offsets -= offsets[0]

and now use custom delayed function to work on the parts

@dask.delayed
def add_node(part, offset, ndf):
    index = pd.Series(range(offset, offset + len(part)) // 10,
                      index=part.index)  # 10 is the repeat factor
    part['Node'] = index.map(ndf)
    return part

df2 = dd.from_delayed([add_node(d, off, ndf) 
                       for d, off in zip(df.to_delayed(), offsets)])

Upvotes: 1

rpanai
rpanai

Reputation: 13437

Using you same workflow you can manually set divisions as suggested here

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

pd.DataFrame(np.random.rand(25000, 2)).to_csv("tempfile.csv", index=False)
df = dd.read_csv("tempfile.csv")
ndf = pd.DataFrame(np.random.randint(1000, 3500, size=2500))


df.divisions = (0, len(df)-1)
df["Note"] = dd.from_array(np.repeat(ndf.values, 10))

I don't think using np.repeat is very efficient in particular for big df.

Upvotes: 0

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