有没有办法强制镶木地板文件将pd.DataFrame
列编码为给定类型,即使该列的所有值都为空?parquet 在其架构中自动分配“null”的事实阻止我将许多文件加载到单个dask.dataframe
.
尝试使用 pandas 列df.column_name = df.column_name.astype(sometype)
无效。
为什么我要问这个
我想将许多镶木地板文件加载到一个dask.dataframe
. 所有文件都是从尽可能多的实例生成的pd.DataFrame
,使用df.to_parquet(filename)
. 所有数据框都有相同的列,但对于某些给定的列可能只包含空值。尝试将所有文件加载到dask.dataframe
(使用df = dd.read_parquet('*.parquet')
,我收到以下错误:
Schema in filename.parquet was different.
id: int64
text: string
[...]
some_column: double
vs
id: int64
text: string
[...]
some_column: null
重现我的问题的步骤
import pandas as pd
import dask.dataframe as dd
a = pd.DataFrame(['1', '1'], columns=('value',))
b = pd.DataFrame([None, None], columns=('value',))
a.to_parquet('a.parquet')
b.to_parquet('b.parquet')
df = dd.read_parquet('*.parquet') # Reads a and b
这给了我以下信息:
ValueError: Schema in path/to/b.parquet was different.
value: null
__index_level_0__: int64
metadata
--------
{b'pandas': b'{"index_columns": ["__index_level_0__"], "column_indexes": [{"na'
b'me": null, "field_name": null, "pandas_type": "unicode", "numpy_'
b'type": "object", "metadata": {"encoding": "UTF-8"}}], "columns":'
b' [{"name": "value", "field_name": "value", "pandas_type": "empty'
b'", "numpy_type": "object", "metadata": null}, {"name": null, "fi'
b'eld_name": "__index_level_0__", "pandas_type": "int64", "numpy_t'
b'ype": "int64", "metadata": null}], "pandas_version": "0.22.0"}'}
vs
value: string
__index_level_0__: int64
metadata
--------
{b'pandas': b'{"index_columns": ["__index_level_0__"], "column_indexes": [{"na'
b'me": null, "field_name": null, "pandas_type": "unicode", "numpy_'
b'type": "object", "metadata": {"encoding": "UTF-8"}}], "columns":'
b' [{"name": "value", "field_name": "value", "pandas_type": "unico'
b'de", "numpy_type": "object", "metadata": null}, {"name": null, "'
b'field_name": "__index_level_0__", "pandas_type": "int64", "numpy'
b'_type": "int64", "metadata": null}], "pandas_version": "0.22.0"}'}
请注意在一种情况下我们有"pandas_type": "unicode"
,而在另一种情况下我们有"pandas_type": "empty"
.
没有为我提供解决方案的相关问题