You can parse JSON files using the json module in Python. This module parses the json and puts it in a dict. You can then get the values from this like a normal dict. For example, if you have a json with the following content{    "id": "file",    "value": "File",    "popup": {       "menuitem": [          {"value": "New", "onclick": "CreateNewDoc()"},          {"value": "Open", "onclick": "OpenDoc()"},          {"value": "Close", "onclick": "CloseDoc()"}       ]    } }You can load it in your python program and loop over ... Read More

If you have the exact key you want to find, then you can simply use the [] operator or get the function to get the value associated with this key. For example,Examplea = {    'foo': 45,    'bar': 22 } print(a['foo']) print(a.get('foo'))OutputThis will give the output:45 45ExampleIf you have a substring that you want to search in the dict, you can use substring search on the keys list and if you find it, use the value. For example,a = {    'foo': 45,    'bar': 22 } for key in a.keys():    if key.find('oo') > -1:       print(a[key])OutputThis will give the output45

dicts in python are heavily optimized. Creating a dict from N keys or key/value pairs is O(N), fetching is O(1), putting is amortized O(1), and so forth. You don't need to optimize them explicitly. You can be sure of this as python under the hood implements its own classes using dicts.Don't compare lists/tuples to dicts/sets though as they solve different problems.

You can pretty print a dict in python using the pprint library. The pprint module provides a capability to “pretty-print” arbitrary Python data structures in a form which can be used as input to the interpreter. You can use it as followsExamplea = {    'bar': 22,    'foo': 45 } pprint.pprint(a, width=10)OutputThis will give the output:{'bar': 22, 'foo': 45}As you can see that even this can be unreadable. You can use the json module to actually print it better. For example,Exampleimport json a = {    'bar': 22,    'foo': 45 } print(json.dumps(a, indent=4))OutputThis will give the output:{    "bar": 22,    "foo": 45 }

You can use a list comprehension to truncate keys in a python dict. Iterate over the keys in the dict, and create a new dict with the truncated keys. exampledef truncate_keys(a, length):    return dict((k[:length], v) for k, v in a.items()) a = {'foo': 125, 'bar': 'hello'} b = truncate_keys(a, 2) print(b)OutputThis will give the output{'fo': 125, 'ba': 'hello'}You need to vary about the name collision though. This is because if 2 strings have the same prefix, they will override the values.

You can do this by merging the other dictionary to the first dictionary. In Python 3.5+, you can use the ** operator to unpack a dictionary and combine multiple dictionaries using the following syntax −Syntaxa = {'foo': 125} b = {'bar': "hello"} c = {**a, **b} print(c)OutputThis will give the output −{'foo': 125, 'bar': 'hello'}This is not supported in older versions. You can however replace it using the following similar syntax −Syntaxa = {'foo': 125} b = {'bar': "hello"} c = dict(a, **b) print(c)OutputThis will give the output −{'foo': 125, 'bar': 'hello'}Another thing you can do is using copy and ... Read More

Dicts are hash tables. No tree searching is used. Looking up a key is a nearly constant time(Amortized constant) operation, regardless of the size of the dict. It creates the hash of the key, then proceeds to find the location associated with the hashed value. If a collision listed address is encountered, it starts the collision resolution algorithm to find the actual value.This causes dictionaries to take up more space as they are sparse.

You can get all the values using a call to dict.values(). Then you can call ", ".join on the values to concatenate just the values in the dict separated by commas. examplea = {'foo': "Hello", 'bar': "World"} vals = a.values() concat = ", ".join(vals) print(concat)OutputThis will give the output −Hello, World

Dicts are hash tables. No tree searching is used. Looking up a key is a nearly constant time(Amortized constant) operation, regardless of the size of the dict. It creates the hash of the key, then proceeds to find the location associated with the hashed value. If a collision listed address is encountered, it starts the collision resolution algorithm to find the actual value.This causes dictionaries to take up more space as they are sparse.

The easiest way to convert a spreadsheet to Python dictionary is to use an external library like pandas. This provides very helpful features like to_dict on excel objects. You can use these like −Examplefrom pandas import * xls = ExcelFile('my_file.xls') data = xls.parse(xls.sheet_names[0]) print(data.to_dict())OutputThis will give the output −{'id': 10, 'name': "John"}