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https://issues.apache.org/jira/browse/ARROW-1374?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Steven Anton closed ARROW-1374.
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Resolution: Not A Problem
> Compatibility with xgboost
> --------------------------
>
> Key: ARROW-1374
> URL: https://issues.apache.org/jira/browse/ARROW-1374
> Project: Apache Arrow
> Issue Type: Wish
> Reporter: Steven Anton
> Priority: Minor
>
> Traditionally I work with CSV's and really suffer with slow read/write times.
> Parquet and the Arrow project obviously give us huge speedups.
> One thing I've noticed, however, is that there is a serious bottleneck when
> converting a DataFrame read in through pyarrow to a DMatrix used by xgboost.
> For example, I'm building a model with about 180k rows and 6k float64
> columns. Reading into a pandas DataFrame takes about 20 seconds on my
> machine. However, converting that DataFrame to a DMatrix takes well over 10
> minutes.
> Interestingly, it takes about 10 minutes to read that same data from a CSV
> into a pandas DataFrame. Then, it takes less than a minute to convert to a
> DMatrix.
> I'm sure there's a good technical explanation for why this happens (e.g. row
> vs column storage). Still, I imagine this use case may occur to many and it
> would be great to improve these times, if possible.
> {code:none}
> import pandas as pd
> import pyarrow as pa
> import pyarrow.parquet as pq
> import xgboost as xgb
> # Reading from parquet:
> table = pq.read_table('/path/to/parquet/files') # 20 seconds
> variables = table.to_pandas() # 1 second
> dtrain = xgb.DMatrix(variables.drop(['tag'], axis=1), label=variables['tag'])
> # takes 10-15 minutes
> # Reading from CSV:
> variables = pd.read_csv('/path/to/file.csv', ...) # takes about 10 minutes
> dtrain = xgb.DMatrix(variables.drop(['tag'], axis=1), label=variables['tag'])
> # less than 1 minute
> {code}
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