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Refactor du filtre pour être plus résilient (et fonctionner sur les montants)

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Colin Maudry
2025-10-10 17:44:51 +02:00
parent 2eb984a95f
commit 3270abb7f6
1 changed files with 28 additions and 19 deletions
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src/utils.py
+26 -17
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@@ -211,21 +211,30 @@ def filter_table_data(lff: pl.LazyFrame, filter_query: str) -> pl.LazyFrame:
filtering_expressions = filter_query.split(" && ") filtering_expressions = filter_query.split(" && ")
for filter_part in filtering_expressions: for filter_part in filtering_expressions:
col_name, operator, filter_value = split_filter_part(filter_part) col_name, operator, filter_value = split_filter_part(filter_part)
col_type = str(schema[col_name]) col_type = str(schema[col_name])
if debug: if debug:
print("filter_value:", filter_value) print("filter_value:", filter_value)
print("filter_value_type:", type(filter_value)) print("filter_value_type:", type(filter_value))
print("operator:", operator)
print("col_type:", col_type) print("col_type:", col_type)
if col_type == "Date": lff = lff.filter(pl.col(col_name).is_not_null())
# Convertir la colonne en chaînes de caractères
lff = dates_to_strings(lff, col_name)
if operator in ("<", "<=", ">", ">="): if col_type == "Date":
lff = lff.filter( # Convertir la colonne date en chaînes de caractères
pl.col(col_name).is_not_null() & (pl.col(col_name) != pl.lit("")) lff = dates_to_strings(lff, col_name)
) col_type = "String"
if col_type == "String":
lff = lff.filter(pl.col(col_name) != pl.lit(""))
elif col_type.startswith("Int") or col_type.startswith("Float"):
try:
filter_value = int(filter_value)
except ValueError:
logger.error(f"Invalid numeric filter value: {filter_value}")
continue
if operator in ("contains", "<", "<=", ">", ">="):
if operator == "<": if operator == "<":
lff = lff.filter(pl.col(col_name) < filter_value) lff = lff.filter(pl.col(col_name) < filter_value)
elif operator == ">": elif operator == ">":
@@ -234,17 +243,17 @@ def filter_table_data(lff: pl.LazyFrame, filter_query: str) -> pl.LazyFrame:
lff = lff.filter(pl.col(col_name) >= filter_value) lff = lff.filter(pl.col(col_name) >= filter_value)
elif operator == "<=": elif operator == "<=":
lff = lff.filter(pl.col(col_name) <= filter_value) lff = lff.filter(pl.col(col_name) <= filter_value)
elif operator == "contains":
if col_type in ["String", "Date"]:
lff = lff.filter(
pl.col(col_name).str.contains("(?i)" + filter_value)
)
elif col_type.startswith("Int") or col_type.startswith("Float"): elif col_type.startswith("Int") or col_type.startswith("Float"):
try:
filter_value = int(filter_value)
except ValueError:
logger.error(f"Invalid numeric filter value: {filter_value}")
continue
lff = lff.filter(pl.col(col_name) == filter_value) lff = lff.filter(pl.col(col_name) == filter_value)
else:
elif operator == "contains" and col_type in ["String", "Date"]: logger.error(f"Invalid column type: {col_type}")
lff = lff.filter(pl.col(col_name).str.contains("(?i)" + filter_value)) else:
logger.error(f"Invalid operator: {operator}")
# elif operator == 'datestartswith': # elif operator == 'datestartswith':
# lff = lff.filter(pl.col(col_name).str.startswith(filter_value)") # lff = lff.filter(pl.col(col_name).str.startswith(filter_value)")