sunxd sunxd - 1 year ago 897
Python Question

Pandas read csv out of memory

I try to manipulate a large CSV file using Pandas, when I wrote this

df = pd.read_csv(strFileName,sep='\t',delimiter='\t')

it raises "pandas.parser.CParserError: Error tokenizing data. C error: out of memory"
wc -l indicate there are 13822117 lines, I need to aggregate on this csv file data frame, is there a way to handle this other then split the csv into several files and write codes to merge the results? Any suggestions on how to do that? Thanks

The input is like this:

0:'3M' '2345' '2345' '2014-10-5',3000
1:'3M' '2958' '2152' '2015-3-22',5000
2:'GE' '2183' '2183' '2012-12-31',515
3:'3M' '2958' '2958' '2015-3-10',395
4:'GE' '2183' '2285' '2015-4-19',1925
5:'GE' '2598' '2598' '2015-3-17',1915

And the desired output is like this:

columns=[ka,kb,errorNum,errorRate,totalNum of records]

if the data set is small, the below code could be used as provided by another

df2 = df.groupby(['ka','kb_1'])['isError'].agg({ 'errorNum': 'sum',
'recordNum': 'count' })

df2['errorRate'] = df2['errorNum'] / df2['recordNum']

ka kb_1 recordNum errorNum errorRate

3M 2345 1 0 0.0
2958 2 1 0.5
GE 2183 2 1 0.5
2598 1 0 0.0

(definition of error Record: when kb_1!=kb_2,the corresponding record is treated as abnormal record)

Answer Source

Based on your snippet in out of memory error when reading csv file in chunk, when reading line-by-line.

I assume that kb_2 is the error indicator,

with open("data/petaJoined.csv", "r") as large_file:
    for line in large_file:
        #assuming this structure: ka,kb_1,kb_2,timeofEvent,timeInterval
        if not (k in groups.keys())
            groups[k]={'record_count':0, 'error_sum': 0}
for k,v in groups.items:
    print ('{group}: {error_rate}'.format(group=k,error_rate=v['error_sum']/v['record_count']))

This code snippet stores all the groups in a dictionary, and calculates the error rate after reading the entire file.

It will encounter an out-of-memory exception, if there are too many combinations of groups.

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