I have 2 CSV files: ‘Data’ and ‘Mapping’:

• ‘Mapping’ file has 4 columns: Device_Name, GDN, Device_Type, and Device_OS. All four columns are populated.
• ‘Data’ file has these same columns, with Device_Name column populated and the other three columns blank.
• I want my Python code to open both files and for each Device_Name in the Data file, map its GDN, Device_Type, and Device_OS value from the Mapping file.

I know how to use dict when only 2 columns are present (1 is needed to be mapped) but I don’t know how to accomplish this when 3 columns need to be mapped.

Following is the code using which I tried to accomplish mapping of Device_Type:

x = dict([])
with open("Pricing Mapping_2013-04-22.csv", "rb") as in_file1:
    file_map = csv.reader(in_file1, delimiter=',')
    for row in file_map:
       typemap = [row[0],row[2]]
       x.append(typemap)

with open("Pricing_Updated_Cleaned.csv", "rb") as in_file2, open("Data Scraper_GDN.csv", "wb") as out_file:
    writer = csv.writer(out_file, delimiter=',')
    for row in csv.reader(in_file2, delimiter=','):
         try:
              row[27] = x[row[11]]
         except KeyError:
              row[27] = ""
         writer.writerow(row)

It returns Attribute Error.

After some researching, I think I need to create a nested dict, but I don’t have any idea how to do this.

A nested dict is a dictionary within a dictionary. A very simple thing.

>>> d = {}
>>> d['dict1'] = {}
>>> d['dict1']['innerkey'] = 'value'
>>> d
{'dict1': {'innerkey': 'value'}}

You can also use a defaultdict from the collections package to facilitate creating nested dictionaries.

>>> import collections
>>> d = collections.defaultdict(dict)
>>> d['dict1']['innerkey'] = 'value'
>>> d  # currently a defaultdict type
defaultdict(<type 'dict'>, {'dict1': {'innerkey': 'value'}})
>>> dict(d)  # but is exactly like a normal dictionary.
{'dict1': {'innerkey': 'value'}}

You can populate that however you want.

I would recommend in your code something like the following:

d = {}  # can use defaultdict(dict) instead

for row in file_map:
    # derive row key from something 
    # when using defaultdict, we can skip the next step creating a dictionary on row_key
    d[row_key] = {} 
    for idx, col in enumerate(row):
        d[row_key][idx] = col

According to your comment:

may be above code is confusing the question. My problem in nutshell: I have 2 files a.csv b.csv, a.csv has 4 columns i j k l, b.csv also has these columns. i is kind of key columns for these csvs’. j k l column is empty in a.csv but populated in b.csv. I want to map values of j k l columns using ‘i` as key column from b.csv to a.csv file

My suggestion would be something like this (without using defaultdict):

a_file = "path/to/a.csv"
b_file = "path/to/b.csv"

# read from file a.csv
with open(a_file) as f:
    # skip headers
    f.next()
    # get first colum as keys
    keys = (line.split(',')[0] for line in f) 

# create empty dictionary:
d = {}

# read from file b.csv
with open(b_file) as f:
    # gather headers except first key header
    headers = f.next().split(',')[1:]
    # iterate lines
    for line in f:
        # gather the colums
        cols = line.strip().split(',')
        # check to make sure this key should be mapped.
        if cols[0] not in keys:
            continue
        # add key to dict
        d[cols[0]] = dict(
            # inner keys are the header names, values are columns
            (headers[idx], v) for idx, v in enumerate(cols[1:]))

Please note though, that for parsing csv files there is a csv module.

UPDATE: For an arbitrary length of a nested dictionary, go to this answer.

Use the defaultdict function from the collections.

High performance: “if key not in dict” is very expensive when the data set is large.

Low maintenance: make the code more readable and can be easily extended.

from collections import defaultdict

target_dict = defaultdict(dict)
target_dict[key1][key2] = val

For arbitrary levels of nestedness:

In [2]: def nested_dict():
   ...:     return collections.defaultdict(nested_dict)
   ...:

In [3]: a = nested_dict()

In [4]: a
Out[4]: defaultdict(<function __main__.nested_dict>, {})

In [5]: a['a']['b']['c'] = 1

In [6]: a
Out[6]:
defaultdict(<function __main__.nested_dict>,
            {'a': defaultdict(<function __main__.nested_dict>,
                         {'b': defaultdict(<function __main__.nested_dict>,
                                      {'c': 1})})})

It is important to remember when using defaultdict and similar nested dict modules such as nested_dict, that looking up a nonexistent key may inadvertently create a new key entry in the dict and cause a lot of havoc.

Here is a Python3 example with nested_dict module:

import nested_dict as nd
nest = nd.nested_dict()
nest['outer1']['inner1'] = 'v11'
nest['outer1']['inner2'] = 'v12'
print('original nested dict: \n', nest)
try:
    nest['outer1']['wrong_key1']
except KeyError as e:
    print('exception missing key', e)
print('nested dict after lookup with missing key.  no exception raised:\n', nest)

# Instead, convert back to normal dict...
nest_d = nest.to_dict(nest)
try:
    print('converted to normal dict. Trying to lookup Wrong_key2')
    nest_d['outer1']['wrong_key2']
except KeyError as e:
    print('exception missing key', e)
else:
    print(' no exception raised:\n')

# ...or use dict.keys to check if key in nested dict
print('checking with dict.keys')
print(list(nest['outer1'].keys()))
if 'wrong_key3' in list(nest.keys()):

    print('found wrong_key3')
else:
    print(' did not find wrong_key3')

Output is:

original nested dict:   {"outer1": {"inner2": "v12", "inner1": "v11"}}

nested dict after lookup with missing key.  no exception raised:  
{"outer1": {"wrong_key1": {}, "inner2": "v12", "inner1": "v11"}} 

converted to normal dict. 
Trying to lookup Wrong_key2 

exception missing key 'wrong_key2' 

checking with dict.keys 

['wrong_key1', 'inner2', 'inner1']  
did not find wrong_key3