Data Class API Documentation

The Data class is the core data container for termgraph charts. It handles data validation, normalization, and provides methods for working with both flat and nested data structures.

Constructor

from termgraph import Data

# Basic usage
data = Data(data=[10, 20, 30, 40], labels=["Q1", "Q2", "Q3", "Q4"])

# With categories for multi-series data
data = Data(
    data=[[10, 15], [20, 25], [30, 35], [40, 45]],
    labels=["Q1", "Q2", "Q3", "Q4"],
    categories=["Product A", "Product B"]
)

Parameters

• data (list): Required. The numeric data to be graphed. Can be:
  • Flat list: [10, 20, 30, 40]
  • Nested list: [[10, 15], [20, 25], [30, 35]]
• labels (list[str]): Required. Labels for each data point/row
• categories (list[str], optional): Category names for multi-series data

Validation

The constructor validates that:

• Both data and labels are provided
• Data and labels have the same length
• For nested data, all inner lists have consistent dimensions

Methods

Data Statistics

find_min() -> Union[int, float]

Returns the minimum value in the dataset.

# Flat data
data = Data([10, 5, 30, 15], ["A", "B", "C", "D"])
print(data.find_min())  # Output: 5

# Nested data
data = Data([[10, 5], [30, 15], [20, 25]], ["X", "Y", "Z"])
print(data.find_min())  # Output: 5

find_max() -> Union[int, float]

Returns the maximum value in the dataset.

# Flat data
data = Data([10, 5, 30, 15], ["A", "B", "C", "D"])
print(data.find_max())  # Output: 30

# Nested data
data = Data([[10, 5], [30, 15], [20, 25]], ["X", "Y", "Z"])
print(data.find_max())  # Output: 30

Label Methods

find_min_label_length() -> int

Returns the length of the shortest label.

data = Data([10, 20, 30], ["A", "BB", "CCC"])
print(data.find_min_label_length())  # Output: 1

find_max_label_length() -> int

Returns the length of the longest label.

data = Data([10, 20, 30], ["A", "BB", "CCC"])
print(data.find_max_label_length())  # Output: 3

Data Normalization

normalize(width: int) -> list

Normalizes data values to fit within the specified width for chart rendering.

# Flat data normalization
data = Data([10, 20, 30, 40], ["Q1", "Q2", "Q3", "Q4"])
normalized = data.normalize(50)  # Normalize to 50 character width
print(normalized)  # [12.5, 25.0, 37.5, 50.0]

# Nested data normalization
data = Data([[10, 15], [20, 25], [30, 35]], ["X", "Y", "Z"])
normalized = data.normalize(40)
print(normalized)  # [[11.43, 17.14], [22.86, 28.57], [34.29, 40.0]]

The normalize method:

• Handles negative values by offsetting all data
• Scales values proportionally to the target width
• Preserves relative relationships between data points
• Works with both flat and nested data structures

Properties

data

The raw data provided during initialization.

labels

The labels for each data row/point.

categories

Category names for multi-series data (empty list if not provided).

dims

Tuple representing the dimensions of the data structure.

# Flat data
data = Data([10, 20, 30], ["A", "B", "C"])
print(data.dims)  # (3,)

# Nested data
data = Data([[10, 15], [20, 25]], ["X", "Y"])
print(data.dims)  # (2, 2)

Examples

Basic Flat Data

from termgraph import Data

# Simple sales data
sales_data = Data(
    data=[150, 230, 180, 290, 210],
    labels=["Jan", "Feb", "Mar", "Apr", "May"]
)

print(f"Min sales: {sales_data.find_min()}")  # Min sales: 150
print(f"Max sales: {sales_data.find_max()}")  # Max sales: 290
print(f"Data: {sales_data}")

Multi-Series Data with Categories

from termgraph import Data

# Quarterly sales for two products
quarterly_data = Data(
    data=[
        [120, 80],   # Q1: Product A, Product B
        [150, 95],   # Q2: Product A, Product B
        [180, 110],  # Q3: Product A, Product B
        [200, 125]   # Q4: Product A, Product B
    ],
    labels=["Q1", "Q2", "Q3", "Q4"],
    categories=["Product A", "Product B"]
)

print(f"Dimensions: {quarterly_data.dims}")  # (4, 2)
print(f"Categories: {quarterly_data.categories}")

Working with Negative Values

from termgraph import Data

# Profit/Loss data with negative values
pnl_data = Data(
    data=[-50, 30, -20, 80, 45],
    labels=["Jan", "Feb", "Mar", "Apr", "May"]
)

# Normalization handles negative values automatically
normalized = pnl_data.normalize(40)
print(f"Normalized data: {normalized}")

Data Validation Examples

from termgraph import Data

# These will raise exceptions:
try:
    # Missing labels
    Data([10, 20, 30])
except Exception as e:
    print(f"Error: {e}")

try:
    # Mismatched dimensions
    Data([10, 20], ["A", "B", "C"])
except Exception as e:
    print(f"Error: {e}")

try:
    # Inconsistent nested structure
    Data([[10, 20], [30]], ["A", "B"])
except Exception as e:
    print(f"Error: {e}")

String Representation

The Data class provides a tabular string representation:

data = Data(
    data=[[100, 150], [200, 250], [300, 350]],
    labels=["Product 1", "Product 2", "Product 3"],
    categories=["Sales", "Revenue"]
)

print(data)

Output:

    Labels | Data
-----------|---------------
 Product 1 | (Sales) 100
           | (Revenue) 150
           |
 Product 2 | (Sales) 200
           | (Revenue) 250
           |
 Product 3 | (Sales) 300
           | (Revenue) 350
           |

Integration with Charts

The Data class is designed to work seamlessly with all chart types:

from termgraph import Data, BarChart, Args

# Create data
data = Data([45, 32, 78, 56, 23], ["A", "B", "C", "D", "E"])

# Create chart with custom arguments
args = Args(width=60, colors=["red"])
chart = BarChart(data, args)

# Draw the chart
chart.draw()

For more examples of using Data with different chart types, see the Chart Classes Documentation.