Python-TypeError:'；数据帧'；对象不可调用

我试图在Spyder Python 3.7上运行以下Python脚本。我对python非常陌生，这是我第一次尝试运行如此复杂的程序：

  import pandas as pd
  import matplotlib.pyplot as plt
  import matplotlib.image as mpimg
  import numpy as np
  import statsmodels.api as sm
  import statsmodels.formula.api as smf
  import itertools
  matches = pd.read_csv( '/Input/international-football-results-from-1872-to-2017/results 3.csv')

matches.head(2)

matches = matches.astype({'date':'datetime64[ns]'})

print = ( "Then I create a dataframe with some statistics by team like the sum, count and mean of score for each team.")

# create two dataframe for the home and away teams
home = matches[['home_team', 'home_score']].rename(columns={'home_team':'team', 'home_score':'score'})
away = matches[['away_team', 'away_score']].rename(columns={'away_team':'team', 'away_score':'score'})
# merge it into one and removes any corrilation between away and home teams
team_score = home.append(away).reset_index(drop=True)
team_score.head(5)

# make an aggregation of the the score column group by the team
country_info = team_score.groupby('team')['score'].agg(['sum','count','mean']).reset_index()
country_info = country_info.rename(columns={'sum':'numb_goals', 'count':'numb_matches', 'mean':'goal_avg'})
del home, away

means = matches[['home_score','away_score']].mean()
means


def weight_from_tournament(tournament):
    if 'Cup' in tournament or 'Euro' in tournament or 'Copa del Rey' in tournament or 'UEFA' in tournament or 'Copa Libertadores' in tournament or 'Copa America' in tournament:
        return 1;
    else :
        return 100;
# Weight column based on type of tournament
matches.loc[:,'weight'] = matches['tournament'].apply(weight_from_tournament)

# Mpdify weight column based on the date
matches.loc[:,'weight'] = 1 / ((2019 - matches['date'].dt.year.astype('int64'))*matches['weight'])

# Create model data
matches_model_data = pd.concat([matches[['home_team','away_team','home_score','weight']].rename(
            columns={'home_team':'team', 'away_team':'opponent','home_score':'goals'}),
           matches[['away_team','home_team','away_score','weight']].rename(
            columns={'away_team':'team', 'home_team':'opponent','away_score':'goals'})])

#So here using the statsmodels librairies I create my model with a Poisson regression.

poisson_model = smf.glm(formula="goals ~ team + opponent", data=matches_model_data, 
                        family=sm.families.Poisson(), freq_weights=matches_model_data['weight'].values).fit()

print = ("Now it's time to make a function that can return the result of a game, there is some information that we need to know : ")

def get_proba_match(foot_model, team1, team2, max_goals=10):
    # Get the average goal for each team
    t1_goals_avg = foot_model.predict(pd.DataFrame.apply(data={'team': team1, 'opponent': team2}, index=[1])).values[0]
    t2_goals_avg = foot_model.predict(pd.DataFrame.apply(data={'team': team2, 'opponent': team1}, index=[1])).values[0]

    # Get probability of all possible score for each team
    team_pred = [[poisson.pmf(i, team_avg) for i in range(0, max_goals+1)] for team_avg in [t1_goals_avg, t2_goals_avg]]

    # Do the product of the 2 vectors to get the matrix of the match
    match = np.outer(np.array(team_pred[0]), np.array(team_pred[1]))

    # Get the proba for each possible outcome
    t1_wins = np.sum(np.tril(match, -1))
    draw = np.sum(np.diag(match))
    t2_wins = np.sum(np.triu(match, 1))
    result_proba = [t1_wins, draw, t2_wins]

    # Adjust the proba to sum to one
    result_proba =  np.array(result_proba)/ np.array(result_proba).sum(axis=0,keepdims=1)
    team_pred[0] = np.array(team_pred[0])/np.array(team_pred[0]).sum(axis=0,keepdims=1)
    team_pred[1] = np.array(team_pred[1])/np.array(team_pred[1]).sum(axis=0,keepdims=1)
    return result_proba, [np.array(team_pred[0]), np.array(team_pred[1])]


def get_match_result(foot_model, team1, team2, elimination=False, max_draw=50, max_goals=10):
        # Get the proba
        proba, score_proba = get_proba_match(foot_model, team1, team2, max_goals)

        # Get the result, if it's an elimination game we have to be sure the result is not draw
        results = pd.Series([np.random.choice([team1, 'draw', team2], p=proba) for i in range(0,max_draw)]).value_counts()
        result = results.index[0] if not elimination or (elimination and results.index[0] != 'draw') else results.index[1]

        # If the result is not a draw game then we calculate the score of the winner from 1 to the max_goals 
        # and the score of the looser from 0 to the score of the winner
        if (result != 'draw'): 
            i_win, i_loose = (0,1) if result == team1 else (1,0)
            score_proba[i_win] = score_proba[i_win][1:]/score_proba[i_win][1:].sum(axis=0,keepdims=1)
            winner_score = pd.Series([np.random.choice(range(1, max_goals+1), p=score_proba[i_win]) for i in range(0,max_draw)]).value_counts().index[0]
            score_proba[i_loose] = score_proba[i_loose][:winner_score]/score_proba[i_loose][:winner_score].sum(axis=0,keepdims=1)
            looser_score = pd.Series([np.random.choice(range(0, winner_score), p=score_proba[i_loose]) for i in range(0,max_draw)]).value_counts().index[0]
            score = [winner_score, looser_score]
        # If it's a draw then we calculate a score and repeat it twice
        else:
            score = np.repeat(pd.Series([np.random.choice(range(0, max_goals+1), p=score_proba[0]) for i in range(0,max_draw)]).value_counts().index[0],2)
        looser = team2 if result == team1 else team1 if result != 'draw' else 'draw'
        return result, looser, score


    print = ("Now let's try it !")

    print(get_match_result(poisson_model, 'Gabon', 'Togo'))
    print(get_match_result(poisson_model, 'France', 'Togo', elimination=True))
    print(get_match_result(poisson_model, 'Argentina', 'Germany'))
    print(get_match_result(poisson_model, 'Brazil', 'Vatican', max_goals=20))
    print(get_match_result(poisson_model, 'England', 'Morocco'))
    print(get_match_result(poisson_model, 'Iran', 'Japan'))

但是，我得到以下错误：

print(get_match_result(poisson_model, 'Gabon', 'Togo'))
Traceback (most recent call last):

  File "<ipython-input-72-3287e60bece4>", line 1, in <module>
    print(get_match_result(poisson_model, 'Gabon', 'Togo'))

TypeError: 'DataFrame' object is not callable

print（获取匹配结果（泊松模型，'Gabon'，'Togo'））
回溯（最近一次呼叫最后一次）：
文件“”，第1行，在
打印（获取匹配结果（泊松模型、“加蓬”、“多哥”））
TypeError:“DataFrame”对象不可调用

我修改了以下脚本：

如果叉上笔记本，使用Kaggle在线控制台，它运行正常

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如有任何建议，将不胜感激

这里的问题只在于语法

在Python中，您不希望表示

print=（'something'）

，因为这意味着您正在为名为print的变量赋值。相反，您必须执行

打印（'something'）

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我希望这有帮助

打印的语法是

print（“要打印的字符串”）

。当您编写

print=“String”

时，您将重新定义print以指向字符串，而不是print函数。此外，您也不会在return命令后面加上分号（你想要的是

return 1

，而不是

return 1；

。我会做出建议的更改。你们是对的，它现在运行得很顺利。谢谢！你们是对的，它现在运行得很顺利。谢谢！