CODEWITHSUNDEEP
pythonmultidimensional-array
mitch2k
mitch2k

Reputation: 526

How to create an associative array from a delimited list?

I'm processing data from a readout from a storage device in this format:

id:name:UPS_serial_number:WWNN:status:IO_group_id:IO_group_name:config_node:UPS_unique_id:hardware:iscsi_name:iscsi_alias:panel_name:enclosure_id:canister_id:enclosure_serial_number:site_id:site_name
10:node_A::00A550:online:0:io_grp0:yes::SV1:iqn.1986-03.com:2145.test.nodeA::A:::::
15:node_B::00A548:online:0:io_grp0:no::SV1:iqn.1986-03.com.:2145.test.nodeB::B:::::

How can I read that data as a 2D array, like datarray['15']['status']?

I tried this way:

# Create array
datarray = []
try:
    # Loop trough list
    for i, x in enumerate(lis):
        # Split on the delimter
        linesplit = x.split(":")
        row = []
        for lsi,lsx in enumerate(linesplit):
            row.append([lsi,lsx])
        datarray.append(row)

But that seems to slice the the data wrong:

[[[0, u'id'], [1, u'name'], [2, u'UPS_serial_number'], [3, u'WWNN'], [4, u'status'], [5, u'IO_group_id'], [6, u'IO_group_name'], [7, u'config_node'], [8, u'UPS_unique_id'], [9, u'hardware'], [10, u'iscsi_name'], [11, u'iscsi_alias'], [12, u'panel_name'], [13, u'enclosure_id'],

Upvotes: 2

Views: 716

Answers (2)

Leo_28
Leo_28

Reputation: 26

What I can make out of the data is that it is colon(:) separated data and first line has header. If that is the case you can load it to pandas dataframe as you load a csv file with separator = ':'. And then convert that dataframe to numpy array.

import pandas as pd
import os
os.chdir('/Users/Downloads/')
df = pd.read_csv('train.txt',sep=':')
df

id  name    UPS_serial_number   WWNN    status  IO_group_id IO_group_name   config_node UPS_unique_id   hardware    iscsi_name  iscsi_alias panel_name  enclosure_id    canister_id enclosure_serial_number site_id site_name
10  node_A  NaN 00A550  online  0   io_grp0 yes NaN SV1 iqn.1986-03.com 2145.test.nodeA NaN A   NaN NaN NaN NaN NaN
15  node_B  NaN 00A548  online  0   io_grp0 no  NaN SV1 iqn.1986-03.com.    2145.test.nodeB NaN B   NaN NaN NaN NaN NaN

df.as_matrix()

array([['node_A', nan, '00A550', 'online', 0, 'io_grp0', 'yes', nan,
        'SV1', 'iqn.1986-03.com', '2145.test.nodeA', nan, 'A', nan, nan,
        nan, nan, nan],
       ['node_B', nan, '00A548', 'online', 0, 'io_grp0', 'no', nan,
        'SV1', 'iqn.1986-03.com.', '2145.test.nodeB', nan, 'B', nan, nan,
        nan, nan, nan]], dtype=object)

Upvotes: 1

tobias_k
tobias_k

Reputation: 82929

Use a csv.DictReader to read the individual lines as dictionaries and then use a dictionary comprehention to create the "outer" dict mapping the ID attribute to the inner dicts with that ID.

raw = """id:name:UPS_serial_number:WWNN:status:IO_group_id:IO_group_name:config_node:UPS_unique_id:hardware:iscsi_name:iscsi_alias:panel_name:enclosure_id:canister_id:enclosure_serial_number:site_id:site_name
10:node_A::00A550:online:0:io_grp0:yes::SV1:iqn.1986-03.com:2145.test.nodeA::A:::::
15:node_B::00A548:online:0:io_grp0:no::SV1:iqn.1986-03.com.:2145.test.nodeB::B:::::"""

reader = csv.DictReader(raw.splitlines(), delimiter=":")
result = {line["id"]: line for line in reader}
print(result["15"]["status"])  # 'online'

Note that this is not a 2D array but a dictionary of dictionaries (with dictionaries being associative arrays). As a simple 2D array, a query like result["15"]["status"] would not work.

Upvotes: 1

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