First, ensure that you have Python and ClickHouse installed on your machine. You will use Python to interact with PyPI, and ClickHouse for storing the data. Make sure that both are properly configured and accessible from your command line or terminal.

Use Python's `requests` library to fetch data from PyPI. You can target a specific package or use the PyPI JSON API to retrieve metadata. For example, to fetch details of a package, use:
```python
import requests

response = requests.get('https://pypi.org/pypi//json')
data = response.json()
```

Once you have retrieved the data, process it to fit the format required by ClickHouse. This might involve selecting specific fields, transforming data types, or removing unnecessary information. Use Python's built-in functions to clean and shape your data accordingly.

Before inserting data, create a table in ClickHouse to store the PyPI data. You can do this using the ClickHouse client with a SQL command, for example:
```sql
CREATE TABLE pypi_data (
package_name String,
version String,
summary String,
home_page String
) ENGINE = MergeTree() ORDER BY package_name;
```

Convert your processed data into a format suitable for insertion into ClickHouse. This typically involves preparing the data as a CSV or TSV format, which ClickHouse can readily ingest. Use Python to write the data to a file:
```python
import csv

with open('pypi_data.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow(['package_name', 'version', 'summary', 'home_page'])
writer.writerow([data['info']['name'], data['info']['version'], data['info']['summary'], data['info']['home_page']])
```

Use the ClickHouse client to load the data from the CSV file into the database. Execute the following command from your terminal:
```bash
clickhouse-client --query="INSERT INTO pypi_data FORMAT CSV" < pypi_data.csv
```

After loading the data, verify the transfer by querying the ClickHouse table to ensure that the data appears as expected:
```sql
SELECT FROM pypi_data LIMIT 10;
```
Check for accuracy and completeness to confirm that the data transfer was successful.

This guide outlines the process of moving data from PyPI to ClickHouse using Python and ClickHouse's native capabilities without relying on third-party tools.