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from datetime import datetime
import pandas as pd
from pandas_queries import utils
Q_NUM = 8
def q():
nation_ds = utils.get_nation_ds
customer_ds = utils.get_customer_ds
line_item_ds = utils.get_line_item_ds
orders_ds = utils.get_orders_ds
supplier_ds = utils.get_supplier_ds
# first call one time to cache in case we don't include the IO times
nation_ds()
customer_ds()
line_item_ds()
orders_ds()
supplier_ds()
def query():
nonlocal nation_ds
nonlocal customer_ds
nonlocal line_item_ds
nonlocal orders_ds
nonlocal supplier_ds
part_ds = utils.get_part_ds()
supplier_ds = supplier_ds()
lineitem_ds = line_item_ds()
orders_ds = orders_ds()
customer_ds = customer_ds()
nation_ds = nation_ds()
region_ds = utils.get_region_ds()
part_filtered = part_ds[(part_ds["p_type"] == "ECONOMY ANODIZED STEEL")]
part_filtered = part_filtered.loc[:, ["p_partkey"]]
lineitem_filtered = lineitem_ds.loc[:, ["l_partkey", "l_suppkey", "l_orderkey"]]
lineitem_filtered["volume"] = lineitem_ds["l_extendedprice"] * (
1.0 - lineitem_ds["l_discount"]
)
total = part_filtered.merge(
lineitem_filtered, left_on="p_partkey", right_on="l_partkey", how="inner"
)
total = total.loc[:, ["l_suppkey", "l_orderkey", "volume"]]
supplier_filtered = supplier_ds.loc[:, ["s_suppkey", "s_nationkey"]]
total = total.merge(
supplier_filtered, left_on="l_suppkey", right_on="s_suppkey", how="inner"
)
total = total.loc[:, ["l_orderkey", "volume", "s_nationkey"]]
orders_filtered = orders_ds[
(orders_ds["o_orderdate"] >= pd.Timestamp("1995-01-01"))
& (orders_ds["o_orderdate"] < pd.Timestamp("1997-01-01"))
]
orders_filtered["o_year"] = orders_filtered["o_orderdate"].dt.year
orders_filtered = orders_filtered.loc[:, ["o_orderkey", "o_custkey", "o_year"]]
total = total.merge(
orders_filtered, left_on="l_orderkey", right_on="o_orderkey", how="inner"
)
total = total.loc[:, ["volume", "s_nationkey", "o_custkey", "o_year"]]
customer_filtered = customer_ds.loc[:, ["c_custkey", "c_nationkey"]]
total = total.merge(
customer_filtered, left_on="o_custkey", right_on="c_custkey", how="inner"
)
total = total.loc[:, ["volume", "s_nationkey", "o_year", "c_nationkey"]]
n1_filtered = nation_ds.loc[:, ["n_nationkey", "n_regionkey"]]
n2_filtered = nation_ds.loc[:, ["n_nationkey", "n_name"]].rename(
columns={"n_name": "nation"}
)
total: pd.DataFrame = total.merge(
n1_filtered, left_on="c_nationkey", right_on="n_nationkey", how="inner"
)
total = total.loc[:, ["volume", "s_nationkey", "o_year", "n_regionkey"]]
total = total.merge(
n2_filtered, left_on="s_nationkey", right_on="n_nationkey", how="inner"
)
total = total.loc[:, ["volume", "o_year", "n_regionkey", "nation"]]
region_filtered = region_ds[(region_ds["r_name"] == "AMERICA")]
region_filtered = region_filtered.loc[:, ["r_regionkey"]]
total = total.merge(
region_filtered, left_on="n_regionkey", right_on="r_regionkey", how="inner"
)
total = total.loc[:, ["volume", "o_year", "nation"]]
def udf(df):
demonimator = df["volume"].sum()
df = df[df["nation"] == "BRAZIL"]
numerator = df["volume"].sum()
return round(numerator / demonimator, 2)
total = total.groupby("o_year", as_index=False).apply(udf)
total.columns = ["o_year", "mkt_share"]
total = total.sort_values(by=["o_year"], ascending=[True])
return total
utils.run_query(Q_NUM, query)
if __name__ == "__main__":
q()
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