df = df.assign(title=df['homeTeamName'] + " vs " + df['awayTeamName'])
df

Query job e8a94ab7-7833-43ac-bf14-bfd4310260b9 is DONE. 582.8 kB processed. Open Job

Query job 8b1e4a6c-9f93-4588-9c34-ae324a42fd57 is DONE. 0 Bytes processed. Open Job

Query job 18db85e7-c94f-46ec-b981-5c582b5ce22a is DONE. 261.3 kB processed. Open Job

df = df.rename(columns={"title": "headline"})
df

Query job ef76c434-c4bc-4b4c-bb06-61521fc85b15 is DONE. 0 Bytes processed. Open Job

Query job d3e413ee-c0c3-49fe-a2ad-f61d671593eb is DONE. 0 Bytes processed. Open Job

[2431 rows x 5 columns in total]

[2431 rows x 5 columns in total]

df1 = df[["gameId", "homeTeamName"]]
df1

Query job 3c859587-582d-4b68-8b35-7072b9a42346 is DONE. 0 Bytes processed. Open Job

Query job 15bf1d87-152a-45a5-b000-e8e72ce6a982 is DONE. 152.8 kB processed. Open Job

df2 = df[["gameId", "awayTeamName"]].head(2)
df2

Query job 262a8d65-8eb7-4769-b26d-4a1d93f19950 is DONE. 152.8 kB processed. Open Job

Query job a18f6c86-dbff-4846-8d21-8f8c1d700a80 is DONE. 0 Bytes processed. Open Job

Query job 9cc89303-be7a-4c34-b4c0-d1d75837a1e4 is DONE. 126 Bytes processed. Open Job

df1.merge(df2, on="gameId", how="inner")

Query job 1edf3455-802d-4b93-900b-9677cb43955a is DONE. 133.5 kB processed. Open Job

Query job 98cccaa5-e630-4edf-bc15-2823e89aecb6 is DONE. 0 Bytes processed. Open Job

Query job 91af749e-6afa-488a-83da-1257667460f0 is DONE. 143 Bytes processed. Open Job

df1.merge(df2, on="gameId", how="outer")

Query job dfe4d1ec-9a3d-4877-ab39-bb6f1c38d070 is DONE. 133.5 kB processed. Open Job

Query job 6261a857-d256-4051-8af5-c6b04fb2795f is DONE. 0 Bytes processed. Open Job

Query job df400799-b054-4969-83a0-089fb2b25fdd is DONE. 152.9 kB processed. Open Job

df1.merge(df2, on="gameId", how="left")

Query job 9ae1e55b-36d0-4aef-ae39-67a3ad5fdb4d is DONE. 133.5 kB processed. Open Job

Query job 7a1822eb-7db9-4c54-abd5-74cb1cde6121 is DONE. 0 Bytes processed. Open Job

Query job 783d092c-5f79-4601-9365-633e48fac610 is DONE. 152.9 kB processed. Open Job

df1.merge(df2, on="gameId", how="right")

Query job ec1c442e-6ea1-461c-ada7-e3dd0454b0ca is DONE. 133.5 kB processed. Open Job

Query job 4ababa83-ad57-4520-b49d-e613256ae2f3 is DONE. 0 Bytes processed. Open Job

Query job f3db2d14-b877-46ea-8858-5cdb3706e26a is DONE. 143 Bytes processed. Open Job

[4862 rows x 5 columns in total]

0        Nationals
1             Reds
2          Orioles
3          Rockies
4            Twins
5        Blue Jays
6          Yankees
7          Red Sox
8        Cardinals
9             Rays
10            Rays
11          Royals
12          Astros
13         Dodgers
14          Tigers
15       Cardinals
16          Giants
17         Red Sox
18            Mets
19       Athletics
20           Twins
21           Twins
22       White Sox
23    Diamondbacks
24          Braves
Name: homeTeamName, dtype: string

print(df1.merge(df2, on="gameId", how="inner").sql)

WITH t0 AS (
  SELECT
    t9.`col_13`,
    t9.`col_14`,
    t9.`col_17`,
    t9.`bigframes_ordering_id`
  FROM `swast-scratch`._63cfa399614a54153cc386c27d6c0c6fdb249f9e.bqdf20240315_0f214503ed3e408abae057064ac2b4c2 AS t9
), t1 AS (
  SELECT
    t9.`col_13`,
    t9.`col_14`,
    t9.`col_16`,
    t9.`hidden_l_0`,
    t9.`hidden_r_0`
  FROM `swast-scratch`._63cfa399614a54153cc386c27d6c0c6fdb249f9e.bqdf20240315_22c51834c2b94bbc93da3d0ff27f980f AS t9
), t2 AS (
  SELECT
    t0.`col_13`,
    t0.`col_14`,
    t0.`col_17`,
    t0.`bigframes_ordering_id`
  FROM t0
), t3 AS (
  SELECT
    t1.`col_13`,
    t1.`col_14`,
    t1.`col_16`,
    t1.`hidden_l_0`,
    t1.`hidden_r_0`
  FROM t1
), t4 AS (
  SELECT
    t2.`col_13` AS `col_103`,
    t2.`col_14` AS `col_104`,
    t2.`col_17` AS `col_105`,
    t2.`bigframes_ordering_id` AS `hidden_r_0`
  FROM t2
), t5 AS (
  SELECT
    t3.`col_13` AS `col_100`,
    t3.`col_14` AS `col_101`,
    t3.`col_16` AS `col_102`,
    t3.`hidden_l_0`,
    t3.`hidden_r_0` AS `hidden_l_1`
  FROM t3
), t6 AS (
  SELECT
    coalesce(`col_101`, `col_104`) AS `col_106`,
    `col_102`,
    `col_105`,
    (
      row_number() OVER (ORDER BY `hidden_l_0` IS NULL ASC, `hidden_l_0` ASC, `hidden_r_0` IS NULL ASC, `hidden_r_0` ASC) - 1
    ) AS `bigframes_ordering_id`
  FROM t5
  INNER JOIN t4
    ON coalesce(t5.`col_101`, '$NULL_SENTINEL$') = coalesce(t4.`col_104`, '$NULL_SENTINEL$')
), t7 AS (
  SELECT
    t6.`col_106`,
    t6.`col_102`,
    t6.`col_105`,
    t6.`bigframes_ordering_id`
  FROM t6
)
SELECT
  t8.`col_106` AS `gameId`,
  t8.`col_102` AS `homeTeamName`,
  t8.`col_105` AS `awayTeamName`
FROM (
  SELECT
    t7.`col_106`,
    t7.`col_102`,
    t7.`col_105`
  FROM t7
) AS t8

df.rename(columns={"homeTeamName": "HOME    TEAM"})

Query job 9fcba646-219f-40ee-9792-d74af0ff7e22 is DONE. 0 Bytes processed. Open Job

Query job 657cdf62-f71e-482c-97f7-b66e4ac20e10 is DONE. 0 Bytes processed. Open Job

df.rename(columns={"homeTeamName": "homeTeam!@#$%col"})

Query job c90e0cd4-30e5-427c-8f5f-a0a8c778bc62 is DONE. 0 Bytes processed. Open Job

Query job 23bfdb6d-9411-484c-9766-93f76dfc1adc is DONE. 0 Bytes processed. Open Job

df3 = df.rename(columns={"homeTeamName": "team", "awayTeamName": "team"})
df3

Query job 63c2d27f-382c-4a43-8fc1-135d9fd66a54 is DONE. 0 Bytes processed. Open Job

Query job cc64185b-98e7-40d8-bf8e-2a3ea355ef67 is DONE. 0 Bytes processed. Open Job

[2431 rows x 2 columns in total]

df4 = df[["year", "duration_minutes"]]
df4

Query job d3b3cd83-d9cf-4c5b-9015-e3979e0857f3 is DONE. 0 Bytes processed. Open Job

Query job e4d99a58-738d-42af-863e-d1262a32c93c is DONE. 77.8 kB processed. Open Job

[2431 rows x 2 columns in total]

dfp = df4.to_pandas()
dfp

Query job b6495f3d-619c-429e-8904-5cdc4957d09f is DONE. 77.8 kB processed. Open Job