### Download DolphinDB Set Data

Source: https://github.com/dolphindb/api_python3/blob/master/README_CN_NEW/3_AdvancedOperations/3.1_DataTypeCasting/3.1.1_PROTOCOL_DDB.md

This example shows how to download a Set type from DolphinDB and its conversion to a Python set. Only specific DolphinDB types are supported for Set conversion.

```python
>>> re = s.run("set(1..5)")
>>> re
{1, 2, 3, 4, 5}
>>> type(re)
<class 'set'>
```

--------------------------------

### Download DolphinDB Matrix Data

Source: https://github.com/dolphindb/api_python3/blob/master/README_CN_NEW/3_AdvancedOperations/3.1_DataTypeCasting/3.1.1_PROTOCOL_DDB.md

This example demonstrates how to download a Matrix type from DolphinDB. The downloaded data includes row and column names if they exist; otherwise, None is used.

```python
>>> s.run("""
...     mtx = 1..12$4:3;
...     mtx.rename!(1 2 3 4, `c1`c2`c3);
...     mtx
... ")
[array([[ 1,  5,  9],
       [ 2,  6, 10],
       [ 3,  7, 11],
       [ 4,  8, 12]], dtype=int32), array([1, 2, 3, 4], dtype=int32), array(['c1', 'c2', 'c3'], dtype=object)]
```

--------------------------------

### Download DolphinDB Dict Data

Source: https://github.com/dolphindb/api_python3/blob/master/README_CN_NEW/3_AdvancedOperations/3.1_DataTypeCasting/3.1.1_PROTOCOL_DDB.md

This example demonstrates downloading a Dict type from DolphinDB and its conversion to a Python dictionary. Keys are converted as Scalars, and values are converted based on their data form and type.

```python
>>> re = s.run('''{"a": 123, "b": [1.1, 2.2]}''')
>>> re
{'b': array([1.1, 2.2]), 'a': 123}
>>> type(re)
<class 'dict'>
```

--------------------------------

### Enable PROTOCOL_PICKLE with Session and DBConnectionPool

Source: https://github.com/dolphindb/api_python3/blob/master/README_CN_NEW/3_AdvancedOperations/3.1_DataTypeCasting/3.1.2_PROTOCOL_PICKLE.md

Initialize a DolphinDB session or connection pool using the PROTOCOL_PICKLE. In the current version, PROTOCOL_DEFUALT is equivalent to PROTOCOL_PICKLE, making it the default serialization/deserialization protocol.

```python
import dolphindb as ddb
import dolphindb.settings as keys

s = ddb.session(protocol=keys.PROTOCOL_PICKLE)
s.connect("localhost", 8848, "admin", "123456")

pool = ddb.DBConnectionPool("localhost", 8848, "admin", "123456", 10, protocol=keys.PROTOCOL_PICKLE)
```

--------------------------------

### Deserialize Table Data with PROTOCOL_PICKLE

Source: https://github.com/dolphindb/api_python3/blob/master/README_CN_NEW/3_AdvancedOperations/3.1_DataTypeCasting/3.1.2_PROTOCOL_PICKLE.md

Download Table data using PROTOCOL_PICKLE. The dtypes for columns in the resulting pandas DataFrame are mapped from DolphinDB types. Note that certain DolphinDB types like BLOB, DECIMAL32/64, and specific Array Vectors are not supported.

```python
>>> re = s.run("table([1, NULL] as a, [2012.01.02, 2012.01.05] as b)")
>>> re
     a          b
0  1.0 2012-01-02
1  NaN 2012-01-05
>>> re['a'].dtype
float64
>>> re['b'].dtype
datetime64[ns]
```

--------------------------------

### Deserialize Matrix Data with PROTOCOL_PICKLE

Source: https://github.com/dolphindb/api_python3/blob/master/README_CN_NEW/3_AdvancedOperations/3.1_DataTypeCasting/3.1.2_PROTOCOL_PICKLE.md

When downloading Matrix data using PROTOCOL_PICKLE, the result is a list containing three elements: the actual data as a NumPy ndarray, and optionally row and column names. For time-related data types, the dtype is consistently datetime64[ns].

```python
>>> s.run("date([2012.01.02, 2012.02.03])$1:2")
[array([['2012-01-02T00:00:00.000000000', '2012-02-03T00:00:00.000000000']],
      dtype='datetime64[ns]'), None, None]
```

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