A data layer is the application component that reads source data, applies user-driven filters, and prepares results for presentation. Separating it from the presentation layer keeps a proof of concept readable, reusable, and easier to change as requirements evolve. section 11.2.1
For a property-data app, begin by identifying the data operations the interface needs: load the DataFrame, populate dependent county, town, and street selectors, filter records, and create chart objects for display. section 11.2.1
A practical data layer can provide:
- A loader that reads property data into a DataFrame. section 11.2.1
- Selector helpers that derive towns from counties and streets from county–town selections. section 11.2.1
- Visualization functions that return charts for transaction counts, property-type distributions, median prices, or county price distributions. section 11.2.1
Property-data proof of concept
A user selects a county, after which the data layer returns only its towns. Selecting a town narrows the available streets, while chart functions receive the filtered data and return figures for the interface. section 11.2.1
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from ridgeplot import ridgeplot
def load_price_data():
return pd.read_parquet("./data/wales.parquet")
A minimal loader illustrates how the data layer isolates file access from the rest of the application. section 11.2.1
