Spaarsh commented on PR #982:
URL:
https://github.com/apache/datafusion-python/pull/982#issuecomment-2708491224
Key Points:
1. ```|``` operator not supported for python < 3.10, anyone pulling the main
post merge will not be able to use ```SessionContext``` at all
2. ```global_ctx``` already exposed to python
Details:
The `|` operator being used in all the ```read_*``` functions is supported
only for python >=3.10. So in order to even import SessionContext, I had to
change all ```|``` operations with ```Union```. Until then, I was getting this
error:
```
$ python3
Python 3.9.7 (default, Oct 18 2021, 02:25:46)
[Clang 13.0.0 ] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> imp
KeyboardInterrupt
>>> from datafusion import SessionContext
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File
"/home/spaarsh/gsoc/df-py/datafusion-python/python/datafusion/__init__.py",
line 48, in <module>
from .io import read_avro, read_csv, read_json, read_parquet
File "/home/spaarsh/gsoc/df-py/datafusion-python/python/datafusion/io.py",
line 31, in <module>
path: str | pathlib.Path,
TypeError: unsupported operand type(s) for |: 'type' and 'type'
```
After replacing all ```|``` operations with ```Union```, it all works.
```global_ctx``` is already being exposed to python, unless I have
misunderstood something. I pulled the branch and tested it. It works.
```
$ python3 test.py
DataFrame()
+----+----------+-----+---------+------------+-----------+
| id | name | age | salary | start_date | is_active |
+----+----------+-----+---------+------------+-----------+
| 1 | John Doe | 32 | 75000.5 | 2020-01-15 | true |
+----+----------+-----+---------+------------+-----------+
DataFrame()
+----+----------+-----+---------+------------+-----------+
| id | name | age | salary | start_date | is_active |
+----+----------+-----+---------+------------+-----------+
| 1 | John Doe | 32 | 75000.5 | 2020-01-15 | true |
+----+----------+-----+---------+------------+-----------+
DataFrame()
+-----+----+-----------+----------+---------+------------+
| age | id | is_active | name | salary | start_date |
+-----+----+-----------+----------+---------+------------+
| 32 | 1 | true | John Doe | 75000.5 | 2020-01-15 |
+-----+----+-----------+----------+---------+------------+
DataFrame()
+----+----------+-----+---------+------------+-----------+
| id | name | age | salary | start_date | is_active |
+----+----------+-----+---------+------------+-----------+
| 1 | John Doe | 32 | 75000.5 | 2020-01-15 | true |
+----+----------+-----+---------+------------+-----------+
```
Just for reference, these are the scripts I used to generate and test the
functions:
<details>
```
####test.py
from datafusion import SessionContext
#### Create a new session
ctx = SessionContext()
#### Read different file formats
df1 = ctx.read_csv("data.csv") # Accepts str or Path
df2 = ctx.read_parquet("data.parquet")
df3 = ctx.read_json("data.json")
df4 = ctx.read_avro("data.avro")
print(df1)
print(df2)
print(df3)
print(df4)
```
```
####create.py - to create the data files
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
import json
import fastavro
#### Sample data as a dictionary
data = {
'id': 1,
'name': ['John Doe'],
'age': [32],
'salary': [75000.50],
'start_date': ['2020-01-15'],
'is_active': [True]
}
#### Create DataFrame
df = pd.DataFrame(data)
#### Save as Parquet
df.to_parquet('data.parquet')
#### Save as JSON (line-delimited)
with open('data.json', 'w') as f:
for _, row in df.iterrows():
json.dump(row.to_dict(), f)
f.write('\n')
#### Save as Avro
schema = {
'name': 'Employee',
'type': 'record',
'fields': [
{'name': 'id', 'type': 'int'},
{'name': 'name', 'type': 'string'},
{'name': 'age', 'type': 'int'},
{'name': 'salary', 'type': 'double'},
{'name': 'start_date', 'type': 'string'},
{'name': 'is_active', 'type': 'boolean'}
]
}
records = df.to_dict('records')
with open('data.avro', 'wb') as f:
fastavro.writer(f, schema, records)
```
</details>
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