使用pyarrow对Pandas数据框进行分区并保存为parquet文件时，数据类型不被保留。

• • 案例1：保存分区数据集-不保留数据类型**

# Saving a Pandas Dataframe to Local as a partioned parquet file using pyarrow
import pandas as pd
df = pd.DataFrame({'age': [77,32,234],'name':['agan','bbobby','test'] })
path = 'test'
partition_cols=['age']
print('Datatypes before saving the dataset')
print(df.dtypes)
table = pa.Table.from_pandas(df)
pq.write_to_dataset(table, path, partition_cols=partition_cols, preserve_index=False)

# Loading a dataset partioned parquet dataset from local
df = pq.ParquetDataset(path, filesystem=None).read_pandas().to_pandas()
print('\nDatatypes after loading the dataset')
print(df.dtypes)

• • 输出：**

Datatypes before saving the dataset
age      int64
name    object
dtype: object

Datatypes after loading the dataset
name      object
age     category
dtype: object

• • 案例2：未分区数据集-保留数据类型**

import pandas as pd
print('Saving a Pandas Dataframe to Local as a parquet file without partitioning using pyarrow')
df = pd.DataFrame({'age': [77,32,234],'name':['agan','bbobby','test'] })
path = 'test_without_partition'
print('Datatypes before saving the dataset')
print(df.dtypes)
table = pa.Table.from_pandas(df)
pq.write_to_dataset(table, path, preserve_index=False)

# Loading a dataset partioned parquet dataset from local
df = pq.ParquetDataset(path, filesystem=None).read_pandas().to_pandas()
print('\nDatatypes after loading the dataset')
print(df.dtypes)

• • 输出**：

Saving a Pandas Dataframe to Local as a parquet file without partitioning using pyarrow
Datatypes before saving the dataset
age      int64
name    object
dtype: object

Datatypes after loading the dataset
age      int64
name    object
dtype: object