You can use defaultdict here. multi need not be sorted.

from collections import defaultdict
out=defaultdict(dict)
for v,k,vs in multi:
    out[k]={**out[k],**{v:vs[0]}}

Output

defaultdict(dict,
            {77766: {14: 2, 15: 1},
             88866: {70: 1, 71: 2, 72: 5, 73: 4, 74: 3},
             99966: {79: 1, 80: 2}})

EDIT:

Sorting the inner dictionaries.

out={k:dict(sorted(v.items(),key=lambda x:x[1])) for k,v in out.items()}

Output:

{77766: {15: 1, 14: 2},
 88866: {70: 1, 71: 2, 74: 3, 73: 4, 72: 5},
 99966: {79: 1, 80: 2}}

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

Copy>>> d = {}
>>> d['dict1'] = {}
>>> d['dict1']['innerkey'] = 'value'
>>> d['dict1']['innerkey2'] = 'value2'
>>> d
{'dict1': {'innerkey': 'value', 'innerkey2': 'value2'}}

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

Copy>>> 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:

Copyd = {}  # 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):

Copya_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.

if i understood you correctly, maybe iterating over the dataframe's rows and setting the values for the dictionary is a solution:

for index, row in df.iterrows():
    my_dict[row['name']] = {'name': row['name'],
                            row['country']: {'city 1':row['city 1'], 'city 2':row['city 2']},
                            'kids': row['kids'],
                            'date':row['date'], 
                            'features':[row['Feature 1'], row['Feature 2']]}

my_dict

Output:

{'jake': {'Russia': {'city 1': 'Moscow', 'city 2': 'St. Petersburg'},
  'date': '6/7/2021',
  'features': ['some', 'some2'],
  'kids': 5,
  'name': 'jake'},
 'justin': {'Australia': {'city 1': 'Sidney', 'city 2': 'Brisbane'},
  'date': '1/4/2011',
  'features': ['here', 'here2'],
  'kids': 1,
  'name': 'justin'},
 'martin': {'USA': {'city 1': 'New York', 'city 2': 'Los Angeles'},
  'date': '1/1/2031',
  'features': ['feature', 'feature2'],
  'kids': 3,
  'name': 'martin'}}