Begin by determining the specific data you need from PyPI. PyPI hosts Python packages, and you can access metadata or statistics. Decide whether you require package details, release statistics, or other metadata. Use PyPI’s JSON API, which provides package metadata in JSON format, as your data source.

Use Python scripts to fetch the data. You can use Python’s `requests` library to make HTTP GET requests to PyPI’s API endpoints. For example, to fetch metadata for a specific package, use:
```python
import requests
response = requests.get('https://pypi.org/pypi//json')
data = response.json()
```
Replace `` with the name of the package you are interested in.

Once you have fetched the data, process it to match the structure required by ClickHouse. ClickHouse supports various data types and structures, so ensure your data is formatted correctly. If necessary, clean and transform the JSON data into a tabular format, using Python libraries such as `pandas` to handle dataframes and data cleaning.

If not already installed, download and install ClickHouse from the official website. Follow the installation instructions for your operating system. Once installed, configure ClickHouse by setting up a database and tables that match the structure of your processed data. Use the ClickHouse client or ClickHouse SQL console to create tables with appropriate data types.

Convert your data into a format that ClickHouse can ingest, such as CSV or TSV. Use Python’s `pandas` to export the processed data:
```python
df.to_csv('data.csv', index=False, sep='\t')
```
Ensure the data types in your CSV/TSV file match those of the ClickHouse table.

Use the ClickHouse client to load the data from the CSV/TSV file into the ClickHouse database. You can execute a command like:
```shell
clickhouse-client --query="INSERT INTO .

FORMAT CSV" < data.csv
```
Replace `` and `` with your specific database and table names.

After loading the data, verify that the data in ClickHouse matches the original data from PyPI. Run queries to check row counts, sample data correctness, and data types. You can use simple SQL queries in ClickHouse to ensure the integrity and accuracy of your imported data. Adjust your data processing steps if discrepancies are found.

By following these steps, you can manually move data from PyPI to ClickHouse without relying on third-party connectors or integrations, ensuring a custom and controlled data migration process.