Python – Extracts specific data from numpy ndarray

Newbie to Pandas, thanks for any help

def csv_reader(fileName):
    reqcols=['_id__$oid','payload','channel']
    io = pd.read_csv(fileName,sep=",",usecols=reqcols)
    print(io['payload'].values)
    return io  

Output line of io[‘payload’]:

{
    "destination_ip": "172.31.14.66",
    "date": "2014-10-19T01:32:36.669861",
    "classification": "Potentially Bad Traffic",
    "proto": "UDP",
    "source_ip": "172.31.0.2",
    "priority": "`2",
    "header": "1:2003195:5",
    "signature": "ET POLICY Unusual number of DNS No Such Name Responses ",
    "source_port": "53",
    "destination_port": "34638",
    "sensor": "5cda4a12-4730-11e4-9ee4-0a0b6e7c3e9e"
}

I’m trying to extract specific data from an ndarray object. What are the methods that can be used to extract from a dataframe

"destination_ip": "172.31.13.124",
"proto": "ICMP",
"source_ip": "201.158.32.1",
"date": "2014-09-28T14:49:43.391463",
"sensor": "139cfdf2-471e-11e4-9ee4-0a0b6e7c3e9e"

I think you need to first convert the string representation of dicts to dictionaries in each row or ast.literal_eval in the payload column via json.loads , then create a new DataFrame from the constructor, filter the columns by subset, and pass by if necessary concat to add the original column:

d = {'_id__$oid': ['542f8', '542f8', '542f8'], 'channel': ['snort_alert', 'snort_alert', 'snort_alert'], 'payload': ['{"destination_ip":"172.31.14.66","date": " 2014-10-19T01:32:36.669861","classification":"Potentially Bad Traffic","proto":"UDP","source_ip":"172.31.0.2","priority":"2","header":"1:2003195:5","signature":"ET POLICY Unusual number of DNS No Such Name Responses ","source_port":"53","destination_port":"34638","sensor":"5cda4a12-4730-11e4-9ee4-0a0b6e7c3e9e"}', '{"destination_ip":" 172.31.14.66","date": "2014-10-19T01:32:36.669861","classification":"Potentially Bad Traffic","proto":"UDP","source_ip":"172.31.0.2","priority":"2","header":"1:2003195:5 ","signature":"ET POLICY Unusual number of DNS No Such Name Responses ","source_port":"53","destination_port":"34638","sensor":"5cda4a12-4730-11e4-9ee4-0a0b6e7c3e9e"}', '{" destination_ip":"172.31.14.66","date": "2014-10-19T01:32:36.669861","classification":"Potentially Bad Traffic","proto":"UDP","source_ip":"172.31.0.2","priority":"2"," header":"1:2003195:5","signature":"ET POLICY Unusual number of DNS No Such Name Responses ","source_port":"53","destination_port":"34638","sensor": "5cda4a12-4730-11e4-9ee4-0a0b6e7c3e9e"}']}
reqcols=['_id__$oid','payload','channel']
df = pd. DataFrame(d)
print (df)
  _id__$oid      channel                                            payload
0     542f8  snort_alert  {"destination_ip":"172.31.14.66","date": "2014...
1     542f8  snort_alert  {"destination_ip":"172.31.14.66","date": "2014...
2     542f8  snort_alert  {"destination_ip":"172.31.14.66","date": "2014...

import json
import ast
df.payload = df.payload.apply(json.loads)
#another slowier solution
#df.payload = df.payload.apply(ast.literal_eval)

required = ["destination_ip", "proto", "source_ip", "date", "sensor"]
df1 = pd. DataFrame(df.payload.values.tolist())[required]
print (df1)
  destination_ip proto   source_ip                        date  \
0   172.31.14.66   UDP  172.31.0.2  2014-10-19T01:32:36.669861   
1   172.31.14.66   UDP  172.31.0.2  2014-10-19T01:32:36.669861   
2   172.31.14.66   UDP  172.31.0.2  2014-10-19T01:32:36.669861   

sensor  
0  5cda4a12-4730-11e4-9ee4-0a0b6e7c3e9e  
1  5cda4a12-4730-11e4-9ee4-0a0b6e7c3e9e  
2  5cda4a12-4730-11e4-9ee4-0a0b6e7c3e9e  

df2 = pd.concat([df[['_id__$oid','channel']], df1], axis=1)
print (df2)
  _id__$oid      channel destination_ip proto   source_ip  \
0     542f8  snort_alert   172.31.14.66   UDP  172.31.0.2   
1     542f8  snort_alert   172.31.14.66   UDP  172.31.0.2   
2     542f8  snort_alert   172.31.14.66   UDP  172.31.0.2   

date                                sensor  
0  2014-10-19T01:32:36.669861  5cda4a12-4730-11e4-9ee4-0a0b6e7c3e9e  
1  2014-10-19T01:32:36.669861  5cda4a12-4730-11e4-9ee4-0a0b6e7c3e9e  
2  2014-10-19T01:32:36.669861  5cda4a12-4730-11e4-9ee4-0a0b6e7c3e9e  

Time:

#[30000 rows x 3 columns]
df = pd.concat([df]*10000).reset_index(drop=True)
print (df)

In [38]: %timeit pd. DataFrame(df.payload.apply(json.loads).values.tolist())[required]
1 loop, best of 3: 379 ms per loop

In [39]: %timeit pd.read_json('[{}]'.format(df.payload.str.cat(sep=',')))[required]
1 loop, best of 3: 528 ms per loop

In [40]: %timeit pd. DataFrame(df.payload.apply(ast.literal_eval).values.tolist())[required]
1 loop, best of 3: 1.98 s per loop