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- from d3m import container
-
-
- class ContainersWithMetadata:
- params = [[True, False], [100, 300, 500, 700, 900]]
- param_names = ['compact', 'columns']
-
- def setup(self, compact, columns):
- # Compacting a DataFrame with more than 300 columns timeouts the benchmark and fails it.
- # By raising a "NotImplementedError exception such combinations are skipped.
- if compact and columns > 300:
- raise NotImplementedError
-
- def time_dataframe(self, compact, columns):
- df = container.DataFrame({str(i): [j for j in range(5)] for i in range(columns)}, columns=[str(i) for i in range(columns)], generate_metadata=False)
- df.metadata.generate(df, compact=compact)
-
- def time_columns(self, compact, columns):
- dfs = [
- container.DataFrame({str(i): [j for j in range(5, 10)]}, columns=[str(i)], generate_metadata=False)
- for i in range(int(columns / 2))
- ]
- for df in dfs:
- df.metadata.generate(df, compact=compact)
-
-
- class ContainersWithoutMetadata:
- params = [[100, 300, 500, 700, 900]]
- param_names = ['columns']
-
- def time_dataframe(self, columns):
- container.DataFrame({str(i): [j for j in range(5)] for i in range(columns)}, columns=[str(i) for i in range(columns)], generate_metadata=False)
-
- def time_columns(self, columns):
- [
- container.DataFrame({str(i): [j for j in range(5, 10)]}, columns=[str(i)], generate_metadata=False)
- for i in range(int(columns / 2))
- ]
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