Python从complex128转换为float64

Question

我遇到了一些可能与此1特定的.csv相关的错误。

Cannot cast array data from dtype('complex128') to dtype('float64') according to the rule 'safe' This fixed the complex128

这与home_score和away_score列一起使用。

'utf-8' codec can't decode byte 0xe9 in position 5: invalid continuation byte

另一个是电子急性错误MontrΘal Canadiens。

下面是脚本。我以前从未遇到过此错误，并且我已经使用此站点数据一段时间了。

想知道是否必须以其他方式导出此.csv以摆脱这两个错误。

import requests
from pprint import pprint as pp
import pandas as pd
import datetime
import pendulum

gamepk_list = []
list_of_outcomes = []


start = pendulum.datetime(2017, 10, 4)
end = pendulum.datetime(2017, 10, 8)
dates = pendulum.period(start, end)


for date in dates.range('days'):
    day = date.format('DD')
    month = date.format('MM')
    year = date.format('YYYY')
    the_date = str(year) + "-" + str(month) + "-" + str(day)

    try:
        schedule_api = requests.get('https://statsapi.web.nhl.com/api/v1/schedule?date=' + str(the_date) + '')
        game_id_data = schedule_api.json()

        game_id_dict = game_id_data['dates']
        for ids in game_id_dict:
            game = ids['games']
            for i in game:
                game_id = i['gamePk']
                if game_id < 2017021722:
                    #gamepk_list.append(game_id)

                    outcome = []
                    print(game_id)

                    url = requests.get('http://statsapi.web.nhl.com/api/v1/game/' + str(game_id) + '/feed/live')
                    pbp_dict = url.json()

                    linescore = pbp_dict['liveData']['linescore']

                    outcome.append(pbp_dict['gamePk'])
                    outcome.append(pbp_dict['gameData']['game']['type'])
                    outcome.append(pbp_dict['gameData']['game']['season'])
                    outcome.append(the_date)
                    outcome.append(pbp_dict['liveData']['linescore']['teams']['home']['team']['id'])
                    outcome.append(pbp_dict['liveData']['linescore']['teams']['home']['team']['name'])
                    outcome.append(pbp_dict['liveData']['linescore']['teams']['home']['team']['abbreviation'])
                    outcome.append(pbp_dict['liveData']['linescore']['teams']['home']['goals'])
                    outcome.append(pbp_dict['liveData']['linescore']['teams']['away']['team']['id'])
                    outcome.append(pbp_dict['liveData']['linescore']['teams']['away']['team']['name'])
                    outcome.append(pbp_dict['liveData']['linescore']['teams']['away']['team']['abbreviation'])
                    outcome.append(pbp_dict['liveData']['linescore']['teams']['away']['goals'])
                    if pbp_dict['liveData']['linescore']['currentPeriod'] == 4:
                        outcome.append(1)
                    else:
                        outcome.append(0)

                    if pbp_dict['liveData']['linescore']['currentPeriod'] == 5:
                        outcome.append(1)
                    else:
                        outcome.append(0)

                    if pbp_dict['liveData']['linescore']['currentPeriod'] == 4:
                        try:
                            game_end_time = pbp_dict['liveData']['plays']['currentPlay']['about']['periodTime'].split(':')
                            seconds = int(game_end_time[0]) * 60 + int(game_end_time[1])
                            outcome.append(seconds)
                        except KeyError:
                            outcome.append(0)

                    elif pbp_dict['liveData']['linescore']['currentPeriod'] == 5:
                        outcome.append(300)

                    else:
                        outcome.append(0)

                    # seconds_in_ot
                    if outcome[7] > outcome[11]:
                        outcome.append(1)
                    else:
                        outcome.append(0)
                    list_of_outcomes.append(outcome)


    except(KeyError,TypeError):
        pass

df = pd.DataFrame(list_of_outcomes)

df.columns = ["game_id", "game_type", "season", "game_date",
              "home_team_id", "home_team", "home_abbrev",
              "home_score", "away_team_id", "away_team",
              "away_abbrev", "away_score", "ot_flag",
              "shootout_flag", "seconds_in_ot",
              "home_win"]

df.to_csv('TEST_OUTCOME_error.csv', sep=',', float_format='%g', index = False)