Access nested dictionary items via a list of keys?

I have a complex dictionary structure which I would like to access via a list of keys to address the correct item.

dataDict = {
    "a":{
        "r": 1,
        "s": 2,
        "t": 3
        },
    "b":{
        "u": 1,
        "v": {
            "x": 1,
            "y": 2,
            "z": 3
        },
        "w": 3
        }
}    

maplist = ["a", "r"]

or

maplist = ["b", "v", "y"]

I have made the following code which works but I'm sure there is a better and more efficient way to do this if anyone has an idea.

# Get a given data from a dictionary with position provided as a list
def getFromDict(dataDict, mapList):    
    for k in mapList: dataDict = dataDict[k]
    return dataDict

# Set a given data in a dictionary with position provided as a list
def setInDict(dataDict, mapList, value): 
    for k in mapList[:-1]: dataDict = dataDict[k]
    dataDict[mapList[-1]] = value

Use reduce() to traverse the dictionary:

from functools import reduce  # forward compatibility for Python 3
import operator

def getFromDict(dataDict, mapList):
    return reduce(operator.getitem, mapList, dataDict)

and reuse getFromDict to find the location to store the value for setInDict():

def setInDict(dataDict, mapList, value):
    getFromDict(dataDict, mapList[:-1])[mapList[-1]] = value

All but the last element in mapList is needed to find the 'parent' dictionary to add the value to, then use the last element to set the value to the right key.

Demo:

>>> getFromDict(dataDict, ["a", "r"])
1
>>> getFromDict(dataDict, ["b", "v", "y"])
2
>>> setInDict(dataDict, ["b", "v", "w"], 4)
>>> import pprint
>>> pprint.pprint(dataDict)
{'a': {'r': 1, 's': 2, 't': 3},
 'b': {'u': 1, 'v': {'w': 4, 'x': 1, 'y': 2, 'z': 3}, 'w': 3}}

Note that the Python PEP8 style guide prescribes snake_case names for functions. The above works equally well for lists or a mix of dictionaries and lists, so the names should really be get_by_path() and set_by_path():

from functools import reduce  # forward compatibility for Python 3
import operator

def get_by_path(root, items):
    """Access a nested object in root by item sequence."""
    return reduce(operator.getitem, items, root)

def set_by_path(root, items, value):
    """Set a value in a nested object in root by item sequence."""
    get_by_path(root, items[:-1])[items[-1]] = value

And for completion's sake, a function to delete a key:

def del_by_path(root, items):
    """Delete a key-value in a nested object in root by item sequence."""
    del get_by_path(root, items[:-1])[items[-1]]

It seems more pythonic to use a for loop. See the quote from What’s New In Python 3.0.

Removed reduce(). Use functools.reduce() if you really need it; however, 99 percent of the time an explicit for loop is more readable.

def nested_get(dic, keys):    
    for key in keys:
        dic = dic[key]
    return dic

Note that the accepted solution doesn't set non-existing nested keys (it raises KeyError). Using the approach below will create non-existing nodes instead:

def nested_set(dic, keys, value):
    for key in keys[:-1]:
        dic = dic.setdefault(key, {})
    dic[keys[-1]] = value

The code works in both Python 2 and 3.

Using reduce is clever, but the OP's set method may have issues if the parent keys do not pre-exist in the nested dictionary. Since this is the first SO post I saw for this subject in my google search, I would like to make it slightly better.

The set method in ( Setting a value in a nested python dictionary given a list of indices and value ) seems more robust to missing parental keys. To copy it over:

def nested_set(dic, keys, value):
    for key in keys[:-1]:
        dic = dic.setdefault(key, {})
    dic[keys[-1]] = value

Also, it can be convenient to have a method that traverses the key tree and get all the absolute key paths, for which I have created:

def keysInDict(dataDict, parent=[]):
    if not isinstance(dataDict, dict):
        return [tuple(parent)]
    else:
        return reduce(list.__add__, 
            [keysInDict(v,parent+[k]) for k,v in dataDict.items()], [])

One use of it is to convert the nested tree to a pandas DataFrame, using the following code (assuming that all leafs in the nested dictionary have the same depth).

def dict_to_df(dataDict):
    ret = []
    for k in keysInDict(dataDict):
        v = np.array( getFromDict(dataDict, k), )
        v = pd.DataFrame(v)
        v.columns = pd.MultiIndex.from_product(list(k) + [v.columns])
        ret.append(v)
    return reduce(pd.DataFrame.join, ret)

This library may be helpful: https://github.com/akesterson/dpath-python

A python library for accessing and searching dictionaries via /slashed/paths ala xpath

Basically it lets you glob over a dictionary as if it were a filesystem.