area plot #116
area plot #116
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WalkthroughThis pull request introduces a new "Area Plot" feature. Documentation has been updated in two files to include descriptions and examples for creating area plots using the Changes
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🧹 Nitpick comments (4)
docs/analysis_modules.md (1)
69-69: Line exceeds recommended length limit.This line exceeds the 120 character limit recommended by the project's static analysis tools.
Consider breaking this line into multiple lines for better readability:
-Similar to line plots, **area plots** can display time-series data, but they emphasize the **area under the curve**, making them ideal for tracking proportions and cumulative metrics. +Similar to line plots, **area plots** can display time-series data, but they emphasize the +**area under the curve**, making them ideal for tracking proportions and cumulative metrics.🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
69-69: Line length
Expected: 120; Actual: 183(MD013, line-length)
tests/plots/test_area.py (2)
13-13: Style: Remove parentheses from empty pytest fixtures.According to pytest best practices, empty parentheses in fixture decorators should be removed.
Modify the fixture decorators:
-@pytest.fixture() +@pytest.fixtureAlso applies to: 29-29, 39-39
🧰 Tools
🪛 Ruff (0.8.2)
13-13: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
43-43: Missing test for the source_text parameter.The code coverage report indicates that line 126 in area.py is not covered, which is the source_text handling. Consider adding a test that verifies this functionality.
Add a test case specifically for the source_text parameter:
@pytest.mark.usefixtures("_mock_color_generators", "_mock_gu_functions") def test_plot_with_source_text(sample_data, mocker): """Test the plot function with source_text parameter.""" # Mock the add_source_text function to verify it's called mock_add_source_text = mocker.patch("pyretailscience.style.graph_utils.add_source_text") result_ax = area.plot( df=sample_data, value_col="Jeans", x_label="Transaction Date", y_label="Sales", title="Sales with Source Text", x_col="transaction_date", source_text="Test Source Text", ) assert isinstance(result_ax, Axes) # Verify that add_source_text was called with the correct parameters mock_add_source_text.assert_called_once_with(ax=result_ax, source_text="Test Source Text")pyretailscience/plots/area.py (1)
94-99: Complex DataFrame manipulation logic.The DataFrame manipulation logic is quite compact, making it harder to understand at first glance. This section handles both the case where group_col is None and when it's provided.
Consider breaking this into separate, more explicit code paths for better readability:
- if group_col is None: - pivot_df = df.set_index(x_col if x_col is not None else df.index)[ - [value_col] if isinstance(value_col, str) else value_col - ] - else: - pivot_df = df.pivot(index=x_col if x_col is not None else None, columns=group_col, values=value_col) + if group_col is None: + # Set the index to x_col or use the existing index + index_df = df.set_index(x_col) if x_col is not None else df + + # Select single column or multiple columns + if isinstance(value_col, str): + pivot_df = index_df[[value_col]] + else: + pivot_df = index_df[value_col] + else: + # Create a pivot table with group_col as columns + pivot_df = df.pivot( + index=x_col if x_col is not None else None, + columns=group_col, + values=value_col + )
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docs/analysis_modules.md
69-69: Line length
Expected: 120; Actual: 183
(MD013, line-length)
🪛 GitHub Check: codecov/patch
pyretailscience/plots/area.py
[warning] 126-126: pyretailscience/plots/area.py#L126
Added line #L126 was not covered by tests
🪛 Ruff (0.8.2)
tests/plots/test_area.py
13-13: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
29-29: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
39-39: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
🔇 Additional comments (9)
docs/analysis_modules.md (2)
56-100: Well-structured area plot documentation.The new Area Plot section follows the same structural pattern as the other plotting sections, making it consistent and easy to understand. The explanations about use cases and the provided example are helpful for users.
🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
69-69: Line length
Expected: 120; Actual: 183(MD013, line-length)
88-88:❓ Verification inconclusive
Potential issue with DataFrame pivoting.
The line
df_pivoted["category"] = df["category"]might cause issues as it will only copy the first 6 values of the category column (since the pivoted DataFrame has 6 rows). This could lead to unexpected results or errors when plotting.Consider removing this line or modifying it to ensure the categories are properly represented in the pivoted DataFrame.
🏁 Script executed:
#!/bin/bash # Check if this approach works in similar examples in the codebase rg --no-heading -A 2 -B 2 "pivot\(" pyretailscience/plots/Length of output: 1529
Attention: Verify DataFrame Alignment After Pivoting
It appears that directly assigning
df["category"]todf_pivoted["category"]may not correctly align the category values with the pivoted DataFrame’s rows—especially if the pivot operation reduces the row count (e.g., to 6 rows). In similar parts of the code (such as in the plotting modules), the pivoted DataFrames are constructed without this extra assignment, suggesting that a straightforward copy might lead to mismatches or unexpected NaN values during plotting.
- Location:
docs/analysis_modules.md, line 88- Issue: Assigning the complete
df["category"]to a pivoted DataFrame with fewer rows risks misalignment.- Recommendation:
- Consider removing this assignment if it isn’t essential.
- Alternatively, if the categorical information is needed, merge or reindex so that the category column correctly maps to the pivoted DataFrame’s rows.
Please verify that the intended behavior is preserved after making any changes.
tests/plots/test_area.py (2)
24-26: Potential inconsistency in sample data preparation.The line
df_pivoted["category"] = df["category"]adds categories back to the pivoted DataFrame, but this might cause issues since the pivoted DataFrame will have fewer rows than the original DataFrame. This is the same issue I noticed in the documentation example.Review this approach to ensure it works correctly in all test cases.
1-167: Comprehensive test coverage for the area plot functionality.The tests cover various use cases including single column plotting, series plotting, group-based plotting, and multiple column plotting. The tests also verify that appropriate warnings and errors are raised.
🧰 Tools
🪛 Ruff (0.8.2)
13-13: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
29-29: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
39-39: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
pyretailscience/plots/area.py (5)
1-29: Well-documented module with clear explanations.The module documentation provides excellent clarity about the purpose, features, use cases, and limitations of the area plot functionality. This helps users understand when and how to use this module effectively.
126-126: Add test coverage for source_text parameter.This line isn't covered by the current test suite according to code coverage reports.
Consider adding a specific test that verifies the source_text functionality works correctly as suggested in the test file review.
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🪛 GitHub Check: codecov/patch
[warning] 126-126: pyretailscience/plots/area.py#L126
Added line #L126 was not covered by tests
107-114: Well-implemented plotting logic.The plotting implementation is clean and effective, using pandas' built-in plot functionality with appropriate parameters. The code sets sensible defaults like alpha=0.7 for semi-transparency and correctly handles the legend based on whether multiple areas are being displayed.
75-78: Good input validation.The code properly validates inputs, converting Series to DataFrame when needed and raising a clear error message when there's ambiguity in the plotting configuration.
80-91: Helpful warnings for datetime data.The warnings about datetime data are well-implemented, guiding users to the more appropriate module for time-based visualizations. This demonstrates good UX design by steering users toward the right tool for their needs.
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docs/analysis_modules.md (1)
69-69: Line length exceeds recommended limit
According to markdownlint (MD013), line 69 exceeds the recommended limit of 120 characters, potentially affecting readability.You can split it into two or more lines such as:
-Similar to line plots, **area plots** can display time-series data, but they emphasize the **area under the curve**, making them ideal for tracking proportions and cumulative metrics. +Similar to line plots, **area plots** can display time-series data. They emphasize the **area under the curve**, +making them ideal for tracking proportions and cumulative metrics.🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
69-69: Line length
Expected: 120; Actual: 183(MD013, line-length)
tests/plots/test_area.py (3)
13-13: Remove parentheses in the@pytest.fixturedecorator
Ruff (PT001) suggests using@pytest.fixtureover@pytest.fixture().-@pytest.fixture() +@pytest.fixture def sample_data(): ...🧰 Tools
🪛 Ruff (0.8.2)
13-13: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
29-29: Remove parentheses in the@pytest.fixturedecorator
Same suggestion here to comply with best practices.-@pytest.fixture() +@pytest.fixture def _mock_color_generators(mocker): ...🧰 Tools
🪛 Ruff (0.8.2)
29-29: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
39-39: Remove parentheses in the@pytest.fixturedecorator
Same recommendation for consistency.-@pytest.fixture() +@pytest.fixture def _mock_gu_functions(mocker): ...🧰 Tools
🪛 Ruff (0.8.2)
39-39: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
pyretailscience/plots/area.py (2)
39-42: Be mindful of Python version compatibility
Using the union operator (str | list[str]) requires Python 3.10 or higher. If you still support older versions (e.g., 3.9), consider fallback syntax likeUnion[str, List[str]].-from typing import Union ... -def plot( - df: pd.DataFrame, - value_col: str | list[str], +def plot( + df: pd.DataFrame, + value_col: Union[str, list[str]], ... ):
75-76: Document that a Series is automatically converted
Whendfis a Series, it’s converted to a DataFrame internally. To reduce confusion for users, mention this behavior in the docstring.
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docs/analysis_modules.md
69-69: Line length
Expected: 120; Actual: 183
(MD013, line-length)
🪛 Ruff (0.8.2)
tests/plots/test_area.py
13-13: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
29-29: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
39-39: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
🔇 Additional comments (5)
docs/analysis_modules.md (1)
76-78: Thank you for including NumPy import
This addresses a previous comment about missing NumPy in the code example. Looks good to go!pyretailscience/plots/area.py (4)
77-79: Clear and helpful error check
Raising aValueErrorwhen both a list of columns and agroup_colare specified prevents ambiguous plots.
80-91: Informative datetime warning
Issuing a warning for datetime-like x-axis data correctly nudges users towardplots.time_areafor time-based plots.
93-114: Pivot logic and color handling look good
The pivoting strategy and dynamic color map selection based on the number of columns or groups are straightforward and effective.
116-128: Consistent styling integration
Applyingstandard_graph_stylesandadd_source_textmaintains a unified look across plots. Great organization.
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docs/analysis_modules.md (1)
69-69: Consider splitting this long line for better readability.This line exceeds the recommended length of 120 characters (currently 183). Consider breaking it into multiple lines for better readability and to meet markdown style guidelines.
-Similar to line plots, **area plots** can display time-series data, but they emphasize the **area under the curve**, making them ideal for tracking proportions and cumulative metrics. +Similar to line plots, **area plots** can display time-series data, but they emphasize +the **area under the curve**, making them ideal for tracking proportions and cumulative metrics.🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
69-69: Line length
Expected: 120; Actual: 183(MD013, line-length)
tests/plots/test_area.py (3)
17-26: Use@pytest.fixturewithout parentheses.According to pytest best practices, it's recommended to use
@pytest.fixturewithout parentheses when no arguments are needed.-@pytest.fixture() +@pytest.fixture🧰 Tools
🪛 Ruff (0.8.2)
17-17: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
28-36: Use@pytest.fixturewithout parentheses.According to pytest best practices, it's recommended to use
@pytest.fixturewithout parentheses when no arguments are needed.-@pytest.fixture() +@pytest.fixture🧰 Tools
🪛 Ruff (0.8.2)
28-28: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
38-43: Use@pytest.fixturewithout parentheses.According to pytest best practices, it's recommended to use
@pytest.fixturewithout parentheses when no arguments are needed.-@pytest.fixture() +@pytest.fixture🧰 Tools
🪛 Ruff (0.8.2)
38-38: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
pyretailscience/plots/area.py (2)
52-52: Consider using proper type annotation forany.In the kwargs type annotation,
anyshould be capitalized asAnyand imported from the typing module for proper type hinting.- **kwargs: dict[str, any], + **kwargs: dict[str, Any],And add to imports:
import pandas as pd +from typing import Any from matplotlib.axes import Axes, SubplotBase
91-97: Consider simplifying the DataFrame preparation logic.The current DataFrame preparation logic is somewhat complex. It might be more readable if split into separate code paths with descriptive variable names.
if group_col is None: - pivot_df = df.set_index(x_col if x_col is not None else df.index)[ - [value_col] if isinstance(value_col, str) else value_col - ] + # Prepare index + index_to_use = x_col if x_col is not None else df.index + # Prepare columns + columns_to_use = [value_col] if isinstance(value_col, str) else value_col + # Create pivot DataFrame + pivot_df = df.set_index(index_to_use)[columns_to_use] else: pivot_df = df.pivot(index=x_col if x_col is not None else None, columns=group_col, values=value_col)
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docs/analysis_modules.md
69-69: Line length
Expected: 120; Actual: 183
(MD013, line-length)
🪛 Ruff (0.8.2)
tests/plots/test_area.py
17-17: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
28-28: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
38-38: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
🔇 Additional comments (15)
docs/analysis_modules.md (2)
56-71: The Area Plot documentation section looks great!The documentation clearly explains the purpose and use cases for area plots, following the same structure as other plot modules in the document. The description is comprehensive and highlights how area plots differ from other visualization types.
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69-69: Line length
Expected: 120; Actual: 183(MD013, line-length)
76-99: LGTM! The example code is clear and correctly demonstrates the area plot feature.The example code demonstrates typical usage of the area plot functionality, with appropriate imports and clear variable names. This will be helpful for users to understand how to implement the feature in their own code.
tests/plots/test_area.py (8)
1-15: The test file setup is comprehensive with appropriate imports and test data.Good job setting up the test environment with proper imports, random seed configuration, and a consistent test periods variable. This ensures reliable and reproducible test results.
45-59: Good test for the basic functionality with a single column.This test effectively validates that the plot function works correctly with a single value column, which is a fundamental use case for the area plot feature.
61-76: Well-structured test for grouped area plots.This test properly validates the functionality of creating stacked area charts using a group column, which is an important use case for area plots in data visualization.
78-93: Good test for multiple columns scenario.This test correctly verifies that the area plot can handle multiple value columns, which allows for more complex visualizations.
95-114: Excellent warning test for datetime columns.This test properly checks that the appropriate warning is issued when using datetime columns, guiding users to the more appropriate module for time-based visualizations.
116-130: Good validation of error handling for conflicting parameters.This test ensures that the function correctly raises a ValueError when both a list for
value_coland agroup_colare provided, which helps prevent ambiguous plotting scenarios.
132-153: Thorough test for datetime index warning.This test properly verifies that a warning is issued when the DataFrame index is datetime-like, guiding users to the appropriate module for time-based visualizations.
155-171: Good test for source text functionality.This test ensures that the source text is properly added to the plot, which is important for attribution and documentation purposes.
pyretailscience/plots/area.py (5)
1-29: Excellent module documentation!The module docstring is comprehensive, clearly explaining the purpose, features, use cases, and limitations of the area plot functionality. This will be very helpful for users to understand when and how to use this module.
39-74: Well-structured function signature with comprehensive documentation.The
plotfunction is well-documented with clear parameter descriptions and return value information. The function signature provides good defaults and appropriate type hints.
75-90: Good input validation and helpful warning messages.The function properly validates input parameters and provides clear warning messages that guide users to more appropriate modules for specific use cases.
98-104: Good color handling logic.The function intelligently selects color generators based on the number of areas to be plotted, which ensures visually appealing results for both simple and complex plots.
105-126: Well-structured plotting and styling.The plot creation, styling, and finalization logic is clean and follows a logical sequence. The function properly handles source text and legend positioning.
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docs/analysis_modules.md (1)
69-69: Line too long per markdownlint.This line exceeds the recommended limit of 120 characters. Consider breaking it for improved readability:
- Similar to line plots, **area plots** can display time-series data, but they emphasize the **area under the curve**, making them ideal for tracking proportions and cumulative metrics. + Similar to line plots, **area plots** can display time-series data, but they emphasize the **area under the curve**, + making them ideal for tracking proportions and cumulative metrics.🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
69-69: Line length
Expected: 120; Actual: 183(MD013, line-length)
tests/plots/test_area.py (3)
16-16: Use the consistent fixture declaration style.Ruff suggests removing parentheses for the fixture decorator:
-@pytest.fixture() +@pytest.fixture def sample_dataframe(): ...🧰 Tools
🪛 Ruff (0.8.2)
16-16: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
27-27: Use the consistent fixture declaration style.Likewise, remove parentheses for the fixture decorator:
-@pytest.fixture() +@pytest.fixture def _mock_color_generators(mocker): ...🧰 Tools
🪛 Ruff (0.8.2)
27-27: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
37-37: Use the consistent fixture declaration style.Again, remove parentheses for the fixture decorator:
-@pytest.fixture() +@pytest.fixture def _mock_gu_functions(mocker): ...🧰 Tools
🪛 Ruff (0.8.2)
37-37: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
pyretailscience/plots/area.py (1)
86-88: Make threshold for color generation configurable.Currently, the switch to multi-color generation happens when the number of areas is ≥4. This threshold might be too rigid for some use cases. Consider making it a parameter or applying a more sophisticated method for determining single vs. multi-color usage.
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docs/analysis_modules.md
69-69: Line length
Expected: 120; Actual: 183
(MD013, line-length)
🪛 Ruff (0.8.2)
tests/plots/test_area.py
16-16: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
27-27: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
37-37: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
🔇 Additional comments (1)
pyretailscience/plots/area.py (1)
38-113: Implementation looks robust.The function is well-structured, handles a variety of input scenarios, and includes concise, clear docstrings. The approach to handling alpha, source text, color maps, and pivoting logic is straightforward and aligns with typical pandas workflows.
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docs/analysis_modules.md (1)
62-70: Content Detail: Long Line in DescriptionThe text on line 69 exceeds the recommended line length (183 characters vs. a limit of 120). Consider breaking this sentence into shorter lines to improve readability and ensure adherence to markdown lint guidelines.
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69-69: Line length
Expected: 120; Actual: 183(MD013, line-length)
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docs/analysis_modules.md
69-69: Line length
Expected: 120; Actual: 183
(MD013, line-length)
🔇 Additional comments (2)
docs/analysis_modules.md (2)
56-61: New "Area Plot" Section: Clear IntroductionThe new Area Plot section is well structured and provides a concise overview of how area plots serve to visualize cumulative trends. The added image and description clearly communicate its purpose.
75-101: Example Code: Comprehensive and Correct ImplementationThe example code snippet is excellent—it clearly demonstrates the data preparation, pivoting, and usage of the
area.plotfunction with appropriate parameters. Notably, the necessary NumPy import is now included, addressing previous concerns regarding a missing import.
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It appears the legend got cut off in the graph. You might need to use bbox_inches="tight", ie
ax.get_figure().savefig("transaction_churn.svg", bbox_inches="tight")
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docs/analysis_modules.md (2)
69-69: Avoid exceeding the recommended line length.Line 69 exceeds the standard length limit recommended by Markdown lint rules (120 characters). Consider restructuring this sentence to fit within the limit.
- Similar to line plots, **area plots** can display time-series data, but they emphasize the **area under the curve**, making them ideal for tracking proportions and cumulative metrics. +Similar to line plots, **area plots** can display time-series data and emphasize the **area under the curve**. This design makes them ideal for tracking proportions and cumulative metrics.🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
69-69: Line length
Expected: 120; Actual: 183(MD013, line-length)
88-89: Consider handling missing categories in the pivot.If the DataFrame has missing categories (e.g., some categories are not present on certain dates), the pivot operation may introduce NaNs. You could fill those with zero to avoid gaps in the plot.
-df_pivoted = df.pivot(index="transaction_date", columns="category", values="unit_spend").reset_index() +df_pivoted = ( + df + .pivot(index="transaction_date", columns="category", values="unit_spend") + .fillna(0) + .reset_index() +)tests/plots/test_area.py (1)
16-16: Remove parentheses in the@pytest.fixturedecorator.The Ruff linter suggests removing unneeded parentheses for consistency, e.g.,
@pytest.fixtureinstead of@pytest.fixture().-@pytest.fixture() +@pytest.fixture def sample_dataframe(): ... -@pytest.fixture() +@pytest.fixture def _mock_color_generators(mocker): ... -@pytest.fixture() +@pytest.fixture def _mock_gu_functions(mocker): ...Also applies to: 27-27, 37-37
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16-16: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
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tests/plots/test_area.py
16-16: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
27-27: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
37-37: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
🔇 Additional comments (1)
pyretailscience/plots/area.py (1)
74-75: Good validation of conflicting parameters.Raising a
ValueErrorwhen both a list forvalue_coland agroup_colare supplied helps prevent ambiguous plotting behavior.
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docs/analysis_modules.md (1)
69-69: Consider breaking this line for better readability.The line exceeds the maximum recommended length of 120 characters (currently 183 characters).
-Similar to line plots, **area plots** can display time-series data, but they emphasize the **area under the curve**, making them ideal for tracking proportions and cumulative metrics. +Similar to line plots, **area plots** can display time-series data, but they emphasize +the **area under the curve**, making them ideal for tracking proportions and cumulative metrics.🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
69-69: Line length
Expected: 120; Actual: 183(MD013, line-length)
tests/plots/test_area.py (3)
16-16: Use@pytest.fixturewithout parentheses.Recommended to remove the empty parentheses for cleaner code and to follow pytest conventions.
-@pytest.fixture() +@pytest.fixture🧰 Tools
🪛 Ruff (0.8.2)
16-16: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
27-27: Use@pytest.fixturewithout parentheses.Recommended to remove the empty parentheses for cleaner code and to follow pytest conventions.
-@pytest.fixture() +@pytest.fixture🧰 Tools
🪛 Ruff (0.8.2)
27-27: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
37-37: Use@pytest.fixturewithout parentheses.Recommended to remove the empty parentheses for cleaner code and to follow pytest conventions.
-@pytest.fixture() +@pytest.fixture🧰 Tools
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37-37: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
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🪛 markdownlint-cli2 (0.17.2)
docs/analysis_modules.md
69-69: Line length
Expected: 120; Actual: 183
(MD013, line-length)
🪛 Ruff (0.8.2)
tests/plots/test_area.py
16-16: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
27-27: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
37-37: Use @pytest.fixture over @pytest.fixture()
Remove parentheses
(PT001)
🔇 Additional comments (2)
docs/analysis_modules.md (1)
76-101: Well-structured example with proper imports!The code example is clear, well-commented, and provides a good demonstration of the area plot functionality. All necessary imports are present, and the example shows realistic usage with proper data preparation.
tests/plots/test_area.py (1)
1-142: Excellent test coverage for the area plot functionality!The test suite comprehensively covers various use cases including:
- Single column plotting
- Group-based stacked area charts
- Multiple column plots
- Parameter validation
- Source text attribution
- Series-based plotting
Good use of fixtures for mocking dependencies, making the tests more isolated and reliable.
🧰 Tools
🪛 Ruff (0.8.2)
16-16: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
27-27: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
37-37: Use
@pytest.fixtureover@pytest.fixture()Remove parentheses
(PT001)
feat: Created area plot. Write test case of area plot. Updated md file
Summary by CodeRabbit
New Features
Documentation
Tests