【发布时间】：2015-02-09 02:42:06
【问题描述】：
我正在尝试从 stats.nba.com 获取分数。所有数据都包含在json中。读取 json 并放入数据框后，我有以下示例数据：
Season Date Team PTS Game_id
2015 07/02/2015 Chicago 107 0021400758
2015 07/02/2015 New Orleans 72 0021400758
2015 07/02/2015 Brooklyn 77 0021400759
2015 07/02/2015 Washington 114 0021400759
我的目标是将具有相同 game_id 的行合并并重命名列，例如 PTS 为 HOME_PTS 和 AWAY_PTS。
直到现在我都使用字典来把所有东西都放在正确的形状中。
我的代码：
import requests
import json
import datetime
from dateutil.parser import parse
import pandas as pd
def read_json(json_url, retries=5):
headers = {'User-agent': 'Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.0.1) \
Gecko/2008071615 Fedora/3.0.1-1.fc9 Firefox/3.0.1'}
errors = 0
json_data = None
while errors <= retries:
r = requests.get(json_url, headers=headers)
json_data = r.json()
if json_data:
break
return json_data
def get_team_status(x):
global group_by_game_id
game_id = x["GAME_ID"]
team = x["TEAM_CITY_NAME"]
team_index = group_by_game_id.get_group(game_id)["TEAM_CITY_NAME"].tolist().index(team)
status_dict = {0: "Away", 1: "Home"}
team_status = status_dict[team_index]
return team_status
def parse_score(day):
global group_by_game_id
json_url = "http://stats.nba.com/stats/scoreboardV2?DayOffset=0&LeagueID=00&gameDate=" + day
json_data = read_json(json_url)
linescores = filter(lambda x: x["name"] == "LineScore", json_data["resultSets"])[0]
rowsets = linescores["rowSet"]
headers = linescores["headers"]
data_dict = map(lambda x: dict(zip(headers, x)), rowsets)
df = pd.DataFrame(data_dict)
# Find Home, Away
group_by_game_id = df.groupby("GAME_ID")
df["TEAM_STATUS"] = df.apply(get_team_status, axis=1)
# Fix date
df["GAME_DATE_EST"] = df.apply(lambda x: parse(x["GAME_DATE_EST"]).strftime("%d/%m/%Y"), axis=1)
# Create new columns based on TEAM_STATUS
columns = df.columns.tolist()
data = []
df_values = df.values.tolist()
for row in df_values:
new_columns = []
if "Home" in row:
for column in columns:
if column != "TEAM_STATUS":
if "TEAM" in column:
new_columns.append("HOME" + column)
elif "PTS" in column:
new_columns.append("HOME_" + column)
else:
new_columns.append(column)
elif "Away" in row:
for column in columns:
if column != "TEAM_STATUS":
if "TEAM" in column:
new_columns.append("AWAY" + column)
elif "PTS" in column:
new_columns.append("AWAY_" + column)
else:
new_columns.append(column)
row_dict = dict(zip(new_columns, row))
data.append(row_dict)
# Merge same matches on game_id
new_data = []
for index, row in enumerate(data):
game_id = row["GAME_ID"]
for index2, row2 in enumerate(data):
game_id2 = row2["GAME_ID"]
if game_id == game_id2 and index2 > index:
row.update(row2)
new_data.append(row)
df_data = pd.DataFrame(new_data)
return df_data
df_data = parse_score("02/07/2015")
【问题讨论】：
-
鉴于上面的示例数据框，您如何分辨哪些行代表主场点，哪些代表客场点？
-
当两行有相同的game_id时，第一行是客场，第二行是主场。