Before starting, ensure that your environment is properly set up. You need a Python environment with necessary libraries such as PyArrow for Parquet file handling and requests for HTTP operations. Install these using pip:
```bash
pip install pyarrow requests
```

Use PyArrow to read data from your Parquet file. PyArrow provides efficient tools to handle Parquet files in Python. Load the file and convert it into a pandas DataFrame for easy manipulation:
```python
import pyarrow.parquet as pq
import pandas as pd

parquet_file = 'your_data.parquet'
table = pq.read_table(parquet_file)
df = table.to_pandas()
```

In Weaviate, data is stored in classes with properties. Define the schema that matches your data. This involves specifying the class names and their properties. You can define the schema directly in Weaviate or via API. Here's an example for defining a schema using an API call:
```python
import requests

weaviate_url = 'http://localhost:8080' # Replace with your Weaviate instance URL
schema = {
"classes": [
{
"class": "YourClassName",
"properties": [
{
"name": "propertyName",
"dataType": ["string"]
},
# Add more properties as needed
]
}
]
}

response = requests.post(f"{weaviate_url}/v1/schema", json=schema)
if response.status_code == 200:
print("Schema created successfully")
else:
print("Failed to create schema:", response.text)
```

Convert the DataFrame into a JSON format that matches the Weaviate schema. Ensure that each data entry is formatted correctly based on the schema you defined:
```python
json_data = df.to_dict(orient='records')
```

For each record in the JSON data, prepare a batch import request to Weaviate. Weaviate's REST API allows for batch imports to efficiently upload data:
```python
batch_data = {
"objects": [
{
"class": "YourClassName",
"properties": record
}
for record in json_data
]
}
```

Use the requests library to send the batch data to Weaviate. Ensure your Weaviate instance is running and accessible:
```python
response = requests.post(f"{weaviate_url}/v1/batch/objects", json=batch_data)
if response.status_code == 200:
print("Data uploaded successfully")
else:
print("Failed to upload data:", response.text)
```

After uploading, verify that the data has been correctly uploaded to Weaviate. You can do this by querying the data using Weaviate's GraphQL endpoint to ensure it matches your expectations:
```python
query = """
{
Get {
YourClassName {
propertyName
# Add more properties as needed
}
}
}
"""
response = requests.post(f"{weaviate_url}/v1/graphql", json={"query": query})
print("Queried Data:", response.json())
```

This step-by-step guide will help you move data from a Parquet file into Weaviate without relying on third-party connectors or integrations. Adjust the schema and data formatting as necessary to fit your specific data structure and needs.