The python empty dictionary list has different effects on the assignment.

The python empty dictionary list has different effects on the assignment.

Recently, Naive Bayes has encountered an interesting phenomenon (based on Python3.6.3)

I generate a list composed of 10 empty dictionaries:

FirstGeneration Method (most people may use this method, which is simple)

> A = [{}] * 10

However, a problem occurs when assigning values to the dictionary in.

> A [3] [3] = 32

>

[{3: 32 },
{3: 32 },
{3: 32 },
{3: 32 },
{3: 32 },
{3: 32 },
{3: 32 },
{3: 32 },
{3: 32 },
{3: 32 },
{3: 32 },
{3: 32}]

> A [4] [4] = 5

>

[{3: 32, 4: 5 },
{3: 32, 4: 5 },
{3: 32, 4: 5 },
{3: 32, 4: 5 },
{3: 32, 4: 5 },
{3: 32, 4: 5 },
{3: 32, 4: 5 },
{3: 32, 4: 5 },
{3: 32, 4: 5 },
{3: 32, 4: 5 },
{3: 32, 4: 5 },
{3: 32, 4: 5}]

We can find that for a [A] [B] = y, no matter what A is, this expression will add or modify field B for all dictionaries in.

HoweverSecondMethod (list generation)

> A = [{} for I in range (10)]

> A [3] [3] = 32

>

[{},{},{}, {3: 32 },{},{},{},{},{},{},{}]

> A [4] [4] = 5

>

[{},{},{}, {3: 32 },{ 4: 5 },{},{},{},{},{}]

We can find that for a [A] [B] = y, this expression can separately add or modify field B for the dictionary indexed as a in.

At that time, the empty dictionary list created in the first method was assigned a value and fell into the trap. The second problem is solved.

But I still don't know why there is such a difference. If you know anything, please leave a message below. Thank you !!!