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from pandas_queries import utils |
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Q_NUM = 2 |
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def q(): |
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var1 = 15 |
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var2 = "BRASS" |
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var3 = "EUROPE" |
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region_ds = utils.get_region_ds |
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nation_ds = utils.get_nation_ds |
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supplier_ds = utils.get_supplier_ds |
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part_ds = utils.get_part_ds |
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part_supp_ds = utils.get_part_supp_ds |
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region_ds() |
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nation_ds() |
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supplier_ds() |
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part_ds() |
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part_supp_ds() |
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def query(): |
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nonlocal region_ds |
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nonlocal nation_ds |
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nonlocal supplier_ds |
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nonlocal part_ds |
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nonlocal part_supp_ds |
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region_ds = region_ds() |
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nation_ds = nation_ds() |
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supplier_ds = supplier_ds() |
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part_ds = part_ds() |
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part_supp_ds = part_supp_ds() |
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nation_filtered = nation_ds.loc[:, ["n_nationkey", "n_name", "n_regionkey"]] |
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region_filtered = region_ds[(region_ds["r_name"] == var3)] |
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region_filtered = region_filtered.loc[:, ["r_regionkey"]] |
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r_n_merged = nation_filtered.merge( |
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region_filtered, left_on="n_regionkey", right_on="r_regionkey", how="inner" |
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) |
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r_n_merged = r_n_merged.loc[:, ["n_nationkey", "n_name"]] |
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supplier_filtered = supplier_ds.loc[ |
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:, |
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[ |
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"s_suppkey", |
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"s_name", |
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"s_address", |
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"s_nationkey", |
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"s_phone", |
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"s_acctbal", |
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"s_comment", |
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], |
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] |
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s_r_n_merged = r_n_merged.merge( |
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supplier_filtered, |
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left_on="n_nationkey", |
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right_on="s_nationkey", |
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how="inner", |
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) |
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s_r_n_merged = s_r_n_merged.loc[ |
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:, |
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[ |
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"n_name", |
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"s_suppkey", |
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"s_name", |
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"s_address", |
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"s_phone", |
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"s_acctbal", |
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"s_comment", |
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], |
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] |
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partsupp_filtered = part_supp_ds.loc[ |
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:, ["ps_partkey", "ps_suppkey", "ps_supplycost"] |
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] |
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ps_s_r_n_merged = s_r_n_merged.merge( |
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partsupp_filtered, left_on="s_suppkey", right_on="ps_suppkey", how="inner" |
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) |
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ps_s_r_n_merged = ps_s_r_n_merged.loc[ |
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:, |
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[ |
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"n_name", |
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"s_name", |
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"s_address", |
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"s_phone", |
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"s_acctbal", |
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"s_comment", |
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"ps_partkey", |
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"ps_supplycost", |
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], |
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] |
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part_filtered = part_ds.loc[:, ["p_partkey", "p_mfgr", "p_size", "p_type"]] |
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part_filtered = part_filtered[ |
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(part_filtered["p_size"] == var1) |
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& (part_filtered["p_type"].str.endswith(var2)) |
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] |
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part_filtered = part_filtered.loc[:, ["p_partkey", "p_mfgr"]] |
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merged_df = part_filtered.merge( |
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ps_s_r_n_merged, left_on="p_partkey", right_on="ps_partkey", how="inner" |
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) |
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merged_df = merged_df.loc[ |
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:, |
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[ |
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"n_name", |
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"s_name", |
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"s_address", |
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"s_phone", |
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"s_acctbal", |
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"s_comment", |
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"ps_supplycost", |
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"p_partkey", |
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"p_mfgr", |
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], |
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] |
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min_values = merged_df.groupby("p_partkey", as_index=False)[ |
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"ps_supplycost" |
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].min() |
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min_values.columns = ["P_PARTKEY_CPY", "MIN_SUPPLYCOST"] |
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merged_df = merged_df.merge( |
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min_values, |
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left_on=["p_partkey", "ps_supplycost"], |
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right_on=["P_PARTKEY_CPY", "MIN_SUPPLYCOST"], |
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how="inner", |
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) |
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result_df = merged_df.loc[ |
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:, |
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[ |
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"s_acctbal", |
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"s_name", |
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"n_name", |
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"p_partkey", |
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"p_mfgr", |
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"s_address", |
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"s_phone", |
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"s_comment", |
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], |
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] |
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result_df = result_df.sort_values( |
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by=[ |
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"s_acctbal", |
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"n_name", |
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"s_name", |
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"p_partkey", |
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], |
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ascending=[ |
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False, |
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True, |
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True, |
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True, |
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], |
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)[:100] |
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return result_df |
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utils.run_query(Q_NUM, query) |
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if __name__ == "__main__": |
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q() |
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