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from datetime import datetime |
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import pandas as pd |
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from pandas_queries import utils |
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Q_NUM = 8 |
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def q(): |
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nation_ds = utils.get_nation_ds |
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customer_ds = utils.get_customer_ds |
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line_item_ds = utils.get_line_item_ds |
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orders_ds = utils.get_orders_ds |
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supplier_ds = utils.get_supplier_ds |
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nation_ds() |
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customer_ds() |
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line_item_ds() |
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orders_ds() |
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supplier_ds() |
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def query(): |
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nonlocal nation_ds |
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nonlocal customer_ds |
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nonlocal line_item_ds |
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nonlocal orders_ds |
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nonlocal supplier_ds |
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part_ds = utils.get_part_ds() |
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supplier_ds = supplier_ds() |
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lineitem_ds = line_item_ds() |
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orders_ds = orders_ds() |
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customer_ds = customer_ds() |
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nation_ds = nation_ds() |
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region_ds = utils.get_region_ds() |
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part_filtered = part_ds[(part_ds["p_type"] == "ECONOMY ANODIZED STEEL")] |
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part_filtered = part_filtered.loc[:, ["p_partkey"]] |
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lineitem_filtered = lineitem_ds.loc[:, ["l_partkey", "l_suppkey", "l_orderkey"]] |
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lineitem_filtered["volume"] = lineitem_ds["l_extendedprice"] * ( |
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1.0 - lineitem_ds["l_discount"] |
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) |
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total = part_filtered.merge( |
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lineitem_filtered, left_on="p_partkey", right_on="l_partkey", how="inner" |
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) |
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total = total.loc[:, ["l_suppkey", "l_orderkey", "volume"]] |
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supplier_filtered = supplier_ds.loc[:, ["s_suppkey", "s_nationkey"]] |
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total = total.merge( |
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supplier_filtered, left_on="l_suppkey", right_on="s_suppkey", how="inner" |
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) |
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total = total.loc[:, ["l_orderkey", "volume", "s_nationkey"]] |
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orders_filtered = orders_ds[ |
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(orders_ds["o_orderdate"] >= pd.Timestamp("1995-01-01")) |
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& (orders_ds["o_orderdate"] < pd.Timestamp("1997-01-01")) |
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] |
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orders_filtered["o_year"] = orders_filtered["o_orderdate"].dt.year |
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orders_filtered = orders_filtered.loc[:, ["o_orderkey", "o_custkey", "o_year"]] |
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total = total.merge( |
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orders_filtered, left_on="l_orderkey", right_on="o_orderkey", how="inner" |
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) |
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total = total.loc[:, ["volume", "s_nationkey", "o_custkey", "o_year"]] |
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customer_filtered = customer_ds.loc[:, ["c_custkey", "c_nationkey"]] |
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total = total.merge( |
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customer_filtered, left_on="o_custkey", right_on="c_custkey", how="inner" |
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) |
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total = total.loc[:, ["volume", "s_nationkey", "o_year", "c_nationkey"]] |
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n1_filtered = nation_ds.loc[:, ["n_nationkey", "n_regionkey"]] |
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n2_filtered = nation_ds.loc[:, ["n_nationkey", "n_name"]].rename( |
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columns={"n_name": "nation"} |
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) |
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total: pd.DataFrame = total.merge( |
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n1_filtered, left_on="c_nationkey", right_on="n_nationkey", how="inner" |
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) |
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total = total.loc[:, ["volume", "s_nationkey", "o_year", "n_regionkey"]] |
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total = total.merge( |
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n2_filtered, left_on="s_nationkey", right_on="n_nationkey", how="inner" |
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) |
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total = total.loc[:, ["volume", "o_year", "n_regionkey", "nation"]] |
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region_filtered = region_ds[(region_ds["r_name"] == "AMERICA")] |
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region_filtered = region_filtered.loc[:, ["r_regionkey"]] |
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total = total.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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total = total.loc[:, ["volume", "o_year", "nation"]] |
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def udf(df): |
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demonimator = df["volume"].sum() |
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df = df[df["nation"] == "BRAZIL"] |
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numerator = df["volume"].sum() |
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return round(numerator / demonimator, 2) |
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total = total.groupby("o_year", as_index=False).apply(udf) |
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total.columns = ["o_year", "mkt_share"] |
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total = total.sort_values(by=["o_year"], ascending=[True]) |
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return total |
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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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