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From Micah Kornfield <emkornfi...@gmail.com>
Subject Re: pyarrow read_csv with different amount of columns per row
Date Tue, 19 Nov 2019 04:54:14 GMT
+dev@arrow to see if there is a more definitive answer, but I don't believe
this type of functionality is supported currently.




On Fri, Nov 15, 2019 at 1:42 AM Elisa Scandellari <
elisa.scandellari@gmail.com> wrote:

> Hi,
> I'm trying to improve the performance of my program that loads csv data
> and manipulates it.
> My CSV file contains 14 million rows and has a variable amount of columns.
> The first 27 columns will always be available, and a row can have up to 16
> more columns for a total of 43.
>
> Using vanilla pandas I've found this workaround:
> ```
>
>
>
>
>
>
>
>
>
>
> *largest_column_count = 0with open(data_file, 'r') as temp_f:    lines =
> temp_f.readlines()    for l in lines:        column_count =
> len(l.split(',')) + 1        largest_column_count = column_count if
> largest_column_count < column_count else
> largest_column_counttemp_f.close()column_names = [i for i in range(0,
> largest_column_count)]all_columns_df = pd.read_csv(file, header=None,
> delimiter=',', names=column_names, dtype='category').replace(pd.np.nan, '',
> regex=True)*```
> This will create the table with all my data plus empty cells where the
> data is not available.
> With a smaller file, this works perfectly well. With the complete file, my
> memory usage goes over the roof.
>
> I've been reading about Apache Arrow and, after a few attempts to load a
> structured csv file (same amount of columns for every row), I'm extremely
> impressed.
> I've tried to load my data file, using the same concept as above:
> ```
>
>
>
>
>
>
>
>
>
>
>
> *fixed_column_names = [str(i) for i in range(0, 27)]extra_column_names =
> [str(i) for i in range(len(fixed_column_names),
> largest_column_count)]total_columns =
> fixed_column_namestotal_columns.extend(extra_column_names)read_options =
> csv.ReadOptions(column_names=total_columns)convert_options =
> csv.ConvertOptions(include_columns=total_columns,
>            include_missing_columns=True,
>  strings_can_be_null=True)table = csv.read_csv(edr_filename,
> read_options=read_options, convert_options=convert_options)*
> ```
> but I get the following error
> ****Exception: CSV parse error: Expected 43 columns, got 32****
>
> I need to use the csv provided by pyarrow, if not I wouldn't be able to
> create the pyarrow table to then convert to pandas
> ```from pyarrow import csv```
>
> I guess that the csv library provided by pyarrow is more streamlined than
> the complete one.
>
> Is there any way I can load this file? Maybe using some ReadOptions and/or
> ConvertOptions?
> I'd be using pandas to manipulate the data after it's been loaded.
>
> Thank you in advance
>
>

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