OverflowError when loading uint64 from csv

[teto]

Hi,
I am trying to plot a set of values that are all uint64 with panda
The csv file is available here https://transfer.sh/Sy5DS/iperf-client-linux-2rtrs-f30b30-f30b30-w140k-lia-run1.csv and this is the command I used:

In [6]: df = pd.read_csv('/home/teto/ns3testing/iperf-client-linux_2rtrs_f30b30_f30b30_w140K_lia-run1.csv', sep='|', dtype={'dsn': np.uint64})
---------------------------------------------------------------------------
OverflowError                             Traceback (most recent call last)
<ipython-input-6-794bcaffa179> in <module>()
----> 1 df = pd.read_csv('/home/teto/ns3testing/iperf-client-linux_2rtrs_f30b30_f30b30_w140K_lia-run1.csv', sep='|', dtype={'dsn': np.uint64})

/usr/lib/python3/dist-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)
    461                     skip_blank_lines=skip_blank_lines)
    462 
--> 463         return _read(filepath_or_buffer, kwds)
    464 
    465     parser_f.__name__ = name

/usr/lib/python3/dist-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    247         return parser
    248 
--> 249     return parser.read()
    250 
    251 _parser_defaults = {

/usr/lib/python3/dist-packages/pandas/io/parsers.py in read(self, nrows)
    704                 raise ValueError('skip_footer not supported for iteration')
    705 
--> 706         ret = self._engine.read(nrows)
    707 
    708         if self.options.get('as_recarray'):

/usr/lib/python3/dist-packages/pandas/io/parsers.py in read(self, nrows)
   1148 
   1149         try:
-> 1150             data = self._reader.read(nrows)
   1151         except StopIteration:
   1152             if nrows is None:

/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader.read (pandas/parser.c:7287)()

/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader._read_low_memory (pandas/parser.c:7511)()

/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader._read_rows (pandas/parser.c:8336)()

/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader._convert_column_data (pandas/parser.c:9544)()

/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader._convert_tokens (pandas/parser.c:10106)()

/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader._convert_with_dtype (pandas/parser.c:10503)()

/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser._try_int64 (pandas/parser.c:18126)()

My version was the one with ubuntu's 15.04 repo
pd.version.version
Out[10]: '0.15.0'

I am trying to upgrade it, hoping it would solve this, would it ?
I believe the problem is related to:
#4471

I recently discovered panda and so far it's the best tool I found to plot/work with data (tried R/gnuplot etc...) thanks a lot for the work.

[teto]

I just tried with 0.17 and the error is now "Kerned died restarting" which translates from the interpreter into a segfault

Python 3.4.3 (default, Mar 26 2015, 22:03:40) 
[GCC 4.9.2] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.read_csv('/home/teto/ns3testing/iperf-client-linux_2rtrs_f30b30_f30b30_w140K_lia-run1.csv', sep='|', dtype={'dsn': np.uint64})
zsh: segmentation fault (core dumped)  python3

[Winterflower]

hi @teto,
I was trying to replicate, but it seems you have linked a .pcap file instead of the csv file you tried to create a DataFrame from.

[teto]

Indeed sorry, the CSV is generated from the pcap, my mistake. Here is the good file:
https://transfer.sh/Sy5DS/iperf-client-linux-2rtrs-f30b30-f30b30-w140k-lia-run1.csv

[Winterflower]

thanks @teto !
I think the problem here might be that values in your file are separated by vertical tabs instead of commas.

tcpstream|ipsrc|dss_length|dss_dsn|sport|packetid|ipdst|dss_rawack|dsn|time_delta|dport|tcpflags|dss_ssn|tcpseq|dack|subtype|mptcpstream|datafin|master|reltime
|||||1||||0.000000000||||||||||0.000000000
|||||2||||0.000000000||||||||||0.000000000
|||||3||||0.030046000||||||||||0.030046000

[teto]

All my TSV files use '|' as a separator, only with very long numbers I've this kind of crash (if you remove the 'dtype' parameter from the read_csv call it will load just fine).

[teto]

My bad, I forgot to say in my description that by default then the dsn column is considered as "object" but I want to plot it, that's why I then added the dtype=np.uint64

Thanks for your help :)

[Winterflower]

Can confirm that this happens in pandas 0.17.
I'm by far not an expert, but I think it might have something to do with some of the values for that column being NaN and if I recall correctly NaN is a value for float arrays, not integer arrays.

I tried this with your datafile:

>>> pd.read_csv("iperf.csv", sep="|", dtype={'dsn':np.float64}).head()
   tcpstream  ipsrc  dss_length  dss_dsn  sport  packetid  ipdst  dss_rawack  \
0        NaN    NaN         NaN      NaN    NaN         1    NaN         NaN   
1        NaN    NaN         NaN      NaN    NaN         2    NaN         NaN   
2        NaN    NaN         NaN      NaN    NaN         3    NaN         NaN   
3        NaN    NaN         NaN      NaN    NaN         4    NaN         NaN   
4        NaN    NaN         NaN      NaN    NaN         5    NaN         NaN   

   dsn  time_delta  dport tcpflags  dss_ssn  tcpseq dack subtype  mptcpstream  \
0  NaN    0.000000    NaN      NaN      NaN     NaN  NaN     NaN          NaN   
1  NaN    0.000000    NaN      NaN      NaN     NaN  NaN     NaN          NaN   
2  NaN    0.030046    NaN      NaN      NaN     NaN  NaN     NaN          NaN   
3  NaN    0.000000    NaN      NaN      NaN     NaN  NaN     NaN          NaN   
4  NaN    3.969954    NaN      NaN      NaN     NaN  NaN     NaN          NaN   

   datafin  master   reltime  
0      NaN     NaN  0.000000  
1      NaN     NaN  0.000000  
2      NaN     NaN  0.030046  
3      NaN     NaN  0.030046  
4      NaN     NaN  4.000000 

and it seems to work, which would suggest that the np.uint64 is not liking the NaNs. I'll let someone more experienced weigh in, though.

Can you still make your plot when the datatype is float or do you need np.uint64 for something in particular?
(sorry about all the spam in this thread....)

[jreback]

@teto so the way to do this is to just pass dtype={'dsn' : object}. instead of trying to convert these to an integer type.

[teto]

They are by default loaded as object but then, I can't plot the column, hence I force the conversion to uint64.

[jreback]

In [1]: data = """tcpstream|ipsrc|dss_length|dss_dsn|sport|packetid|ipdst|dss_rawack|dsn|time_delta|dport|tcpflags|dss_ssn|tcpseq|dack|subtype|mptcpstream|datafin|master|reltime
   ...: |||||1||||0.000000000||||||||||0.000000000
   ...: |||||2||||0.000000000||||||||||0.000000000
   ...: |||||3||||0.030046000||||||||||0.030046000"""


In [3]: pd.read_csv(StringIO(data),sep='|',dtype={'dsn' : object})
Out[3]: 
   tcpstream  ipsrc  dss_length  dss_dsn  sport  packetid  ipdst  dss_rawack  dsn  time_delta  dport  tcpflags  dss_ssn  tcpseq  dack  subtype  mptcpstream  datafin  master   reltime
0        NaN    NaN         NaN      NaN    NaN         1    NaN         NaN  NaN    0.000000    NaN       NaN      NaN     NaN   NaN      NaN          NaN      NaN     NaN  0.000000
1        NaN    NaN         NaN      NaN    NaN         2    NaN         NaN  NaN    0.000000    NaN       NaN      NaN     NaN   NaN      NaN          NaN      NaN     NaN  0.000000
2        NaN    NaN         NaN      NaN    NaN         3    NaN         NaN  NaN    0.030046    NaN       NaN      NaN     NaN   NaN      NaN          NaN      NaN     NaN  0.030046

In [4]: pd.read_csv(StringIO(data),sep='|',dtype={'dsn' : object}).dtypes
Out[4]: 
tcpstream      float64
ipsrc          float64
dss_length     float64
dss_dsn        float64
sport          float64
packetid         int64
ipdst          float64
dss_rawack     float64
dsn             object
time_delta     float64
dport          float64
tcpflags       float64
dss_ssn        float64
tcpseq         float64
dack           float64
subtype        float64
mptcpstream    float64
datafin        float64
master         float64
reltime        float64
dtype: object

[jreback]

can you show what the values actually are? (in a small copy-pastable example)?

[jreback]

fyi this is a manifestation of #4471

[teto]

I guessed so but the mentioned branch (https://github.com/jtratner/pandas/tree/GH4471_fix_uint64_maybe_convert_objects) looks out of tree since it was scheduled for 0.14 and is not yet merged upstream, is that correct ?

[jreback]

that's correct. its still an open bug.

[jreback]

closing as dupe.

thanks for the report.