Filter Dictionary According to Existing List

Question

Still a Python novice so please go easy on me...

I've got a dictionary set up:

new_dict

I'd like to filter to return the keys, where any of the values attached to each key match the value in an existing list I have set up:

list(data.Mapped_gene)

Any ideas?

Edit: I still haven't been able to make this work.

The csv tables and keys are all strings if that helps.

Here is the full code to broaden understanding:

import csv    
new_dict = {}
with open(raw_input("Enter csv file (including path)"), 'rb') as f:
  reader = csv.reader(f)
  for row in reader:
    if row[0] in new_dict:
      new_dict[row[0]].append(row[1:])
    else:
      new_dict[row[0]] = row[1:]
print new_dict

#modified from: http://bit.ly/1iOS7Gu
import pandas
colnames = ['Date Added to Catalog',    'PUBMEDID', 'First Author', 'Date',     'Journal',  'Link', 'Study',    'DT',   'Initial Sample Size',  'Replication Sample Size',  'Region',   'Chr_id',   'Chr_pos',  'Reported Gene(s)', 'Mapped_gene',  'p-Value',  'Pvalue_mlog',  'p-Value (text)',   'OR or beta',   '95% CI (text)',    'Platform [SNPs passing QC]',   'CNV']
data = pandas.read_csv('C:\Users\Chris\Desktop\gwascatalog.csv', names=colnames)


my_list = list(data.Mapped_gene)
my_set = set(my_list)

[k for k, v in new_dict.items() if any(x in my_set for x in v)]

Error Message: "TypeError: unhashable type: 'list'"

Answer 1

Use any and a list comprehension:

my_list = list(data.Mapped_gene)
keys = [k for k, v in new_dict.items() if any(x in my_list for x in v)]

In case my_list is huge then convert it to a set first as it provides O(1) lookup.