Upload normalization.py

Normalization process of our original dataset

normalization.py

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+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+# Define normalization parameters
+norm_params = {
+    'LAT': {'min_val': -66.817333, 'max_val': 51.055833},
+    'LON': {'min_val': -178.116667, 'max_val': 171.358333},
+    'ALTI': {'min_val': 0.0, 'max_val': 3845.0},
+    'AAAAMMJJHH': {'min_val': 1777010107, 'max_val': 2024030803},
+    'ANNEE': {'min_val': 1777, 'max_val': 2024},
+    'MOIS': {'min_val': 1, 'max_val': 12},
+    'JOUR': {'min_val': 1, 'max_val': 31},
+    'HEURE': {'min_val': 0, 'max_val': 23},
+}
+
+def normalize_column(column, min_val, max_val):
+    """Normalize pandas Series from [min_val, max_val] to [0, 1]."""
+    # Ensure column is treated as float for division to work properly.
+    return (column.astype('float64') - min_val) / (max_val - min_val)
+
+# Load the dataset in chunks
+dataset_path = 'C:/Users/View/Desktop/oetem/dataset/dataset.parquet'
+parquet_file = pq.ParquetFile(dataset_path)
+
+# Determine the output file path
+output_path = 'C:/Users/View/Desktop/oetem/dataset/dataset_normalized.parquet'
+
+# Initialize variables for writing
+writer = None
+schema = None
+
+# Process and normalize chunks
+for i in range(parquet_file.num_row_groups):
+    table = parquet_file.read_row_group(i, columns=list(norm_params.keys()) + [' T'])
+    chunk = table.to_pandas()
+
+    # Normalize the columns
+    for col, params in norm_params.items():
+        chunk[col] = normalize_column(chunk[col], min_val=params['min_val'], max_val=params['max_val'])
+
+    # Convert the DataFrame back to a PyArrow Table for writing
+    #table = pa.Table.from_pandas(chunk)
+    table = pa.Table.from_pandas(chunk, preserve_index=False)
+
+    # If first chunk, initialize the writer with the schema
+    if writer is None:
+        schema = table.schema
+        writer = pq.ParquetWriter(output_path, schema)
+
+    writer.write_table(table)
+
+# Close the writer to finalize the file
+if writer is not None:
+    writer.close()