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Merged
ShahinAbdoulSoukour merged 3 commits into from
Jun 22, 2025
Merged

Dev #3

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bb6c1e6
fix: replace string 'null' with np.nan for proper null handling in merge
ShahinAbdoulSoukour Jun 22, 2025
988c9e9
test: fix merge test to expect np.nan instead of null strings
ShahinAbdoulSoukour Jun 22, 2025
204cd51
test: fix merge test to expect np.nan instead of null strings
ShahinAbdoulSoukour Jun 22, 2025
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12 changes: 6 additions & 6 deletions InteroperabilityEnabler/utils/merge_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,20 +5,20 @@
Maintainer: Shahin ABDOUL SOUKOUR - Inria
"""

import numpy as np
import pandas as pd


def merge_predicted_data(df_initial, predicted_df_with_metadata):
"""
Merge predicted data into the initial DataFrame by matching column names.
Add 'null' for missing columns in the predicted data.
Add `np.nan` (proper null) for missing columns in the predicted data.

Args:
df_initial: DataFrame containing the original selected columns.
predicted_df_with_metadata: DataFrame containing the predicted data with metadata (column names).
predicted_df_with_metadata: DataFrame containing the predicted data with metadata.

Returns:
A merged Pandas DataFrame with 'null' for missing columns.
A merged Pandas DataFrame with `np.nan` for missing columns.
"""
try:
# Get all unique columns from both DataFrames
Expand All @@ -29,9 +29,9 @@ def merge_predicted_data(df_initial, predicted_df_with_metadata):
# Ensure all columns are present in both DataFrames
for col in all_columns:
if col not in df_initial.columns:
df_initial[col] = "null"
df_initial[col] = np.nan # Use np.nan instead of "null"
if col not in predicted_df_with_metadata.columns:
predicted_df_with_metadata[col] = "null"
predicted_df_with_metadata[col] = np.nan # Use np.nan instead of "null"

# Reorder columns to match the initial DataFrame's order
df = df_initial[sorted(all_columns)]
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4 changes: 3 additions & 1 deletion tests/test_basic.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import pandas as pd
import numpy as np
import pytest
from InteroperabilityEnabler.utils.data_formatter import data_to_dataframe
from InteroperabilityEnabler.utils.annotation_dataset import add_quality_annotations_to_df
Expand Down Expand Up @@ -291,4 +292,5 @@ def test_merge_predicted_data_predicted_missing_column():
predicted_df = pd.DataFrame({"temperature": [-6.6, -6.1]}) # missing windSpeed
merged = merge_predicted_data(df_initial, predicted_df)
assert "windSpeed" in merged.columns
assert merged.iloc[2]["windSpeed"] == "null"
# Proper way to check for NaN values
assert pd.isna(merged.iloc[2]["windSpeed"]) # This checks if the value is NaN