Examples of common operations on dictionaries in Python.

preamble

Dictionary is a must-have and commonly used data structure in Python, this article combs commonly used dictionary operations, see this is enough to involve:

initialization
Combining Dictionaries
dictionary derivation
Collections Standard Library
Dictionary to JSON
Dictionary to Pandas

initialization

# It's most commonly used
my_object = {
  "a": 5,
  "b": 6
}
# If you don't like writing curly braces and double quotes:
my_object = dict(a=5, b=6)

Combining Dictionaries

a = { "a": 5, "b": 5 }
b = { "c": 5, "d": 5 }
c = { **a, **b } # The easiest way
assert c == { "a": 5, "b": 5, "c": 5, "d": 5 }

# And you have to modify it after the merger, so you can do that:
c = { **a, **b, "a": 10 }
assert c == { "a": 10, "b": 5, "c": 5, "d": 5 }
b["a"] = 10
c = { **a, **b }
assert c == { "a": 10, "b": 5, "c": 5, "d": 5 }

dictionary derivation

# Use dictionary derivatives to remove the key
a = dict(a=5, b=6, c=7, d=8)
remove = set(["c", "d"])
a = { k: v for k,v in () if k not in remove }
# a = { "a": 5, "b": 6 }

# Use dictionary derivatives to keep the key
a = dict(a=5, b=6, c=7, d=8)
keep = remove
a = { k: v for k,v in () if k in keep }
# a = { "c": 7, "d": 8 }

# Use dictionary derivatives to make all values plus 1
a = dict(a=5, b=6, c=7, d=8)
a = { k: v+1 for k,v in () }
# a = { "a": 6, "b": 7, "c": 8, "d": 9 }

Collections Standard Library

Collections is a built-in module in Python that has several useful dictionary subclasses that can greatly simplify Python code. Two of the classes I use a lot are defaultdict and Counter, and because it's a subclass of dict, it has standard methods like items(), keys(), values(), and so on.

from collections import Counter

counter = Counter()
#counter counts the frequency of elements in a list.
(['a','b','a']
# At this point counter = Counter({'a': 2, 'b': 1})

#Merge count
({ "a": 10000, "b": 1 })
# Counter({'a': 10002, 'b': 2})
counter["b"] += 100
# Counter({'a': 10002, 'b': 102})

print(counter.most_common())
#[('a', 10002), ('b', 102)]
print(counter.most_common(1)[0][0])
# => a

defaultdict is also a must-have for dict:

from collections import defaultdict

# If the value of the dictionary is the dictionary
a = defaultdict(dict)
assert a[5] == {}
a[5]["a"] = 5
assert a[5] == { "a": 5 }

# If the value of the dictionary is a list
a = defaultdict(list)
assert a[5] == []
a[5].append(3)
assert a[5] == [3]

# The default value of a dictionary's value can be a lambda expression
a = defaultdict(lambda: 10)
assert a[5] == 10
assert a[6] + 1 == 11

# Another dictionary inside a dictionary, how many initialization operations do you have to do without this?
a = defaultdict(lambda: defaultdict(dict))
assert a[5][5] == {}

Dictionary to JSON

By JSON we usually mean a JSON string, which is a character string.Dict can be converted to a string in JSON format.

import json

a = dict(a=5, b=6)

# Dictionary to JSON String
json_string = (a)
# json_string = '{"a": 5, "b": 6}'

# JSON string to dictionary
assert a == (json_string)

# Dictionary to JSON string saved in file
with open("", "w+") as f:
    (a, f)

# Recover dictionaries from JSON files
with open("", "r") as f:
    assert a == (f)

Dictionary to Pandas

import pandas as pd

# Dictionary to
df = ([
    { "a": 5, "b": 6 },
    { "a": 6, "b": 7 }
])
# df =
#    a  b
# 0  5  6
# 1  6  7

# DataFrame back to dictionary
a = df.to_dict(orient="records")
# a = [
#    { "a": 5, "b": 6 },
#    { "a": 6, "b": 7 }
# ]

# Dictionary to
srs = ({ "a": 5, "b": 6 })
# srs =
# a    5
# b    6
# dtype: int64

# Back to the dictionary
a = srs.to_dict()
# a = {'a': 5, 'b': 6}

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