问题

players_personal['awards'] = players_personal['awards'].apply(lambda x: json.loads(x))

# Create an empty DataFrame to store the unpacked awards
awards_df = pd.DataFrame()

# Loop through each player's awards dictionary
for index, row in players_personal.iterrows():
    awards_dict = row['awards']
    player_name = row['player_name']
    
    # Loop through the awards and their respective years
    for year, awards_list in awards_dict.items():
        for award in awards_list:
            # Create a column with the award name and set the year as its value
            awards_df.at[index, award.lower().replace(' ', '_')] = year

# Merge the unpacked awards DataFrame with the original player DataFrame
result_df = pd.merge(players_personal, awards_df, left_index=True, right_index=True)

# Drop the 'awards' column since it's no longer needed
result_df = result_df.drop(columns=['awards'])

This code will help you unpack the awards into separate columns in the DataFrame, where each award corresponds to the player's name and the year in which they received it.

答案1

得分: 0

这是一种方法。请注意，我使用了T来转置数据以在此处显示。您可以在最后将T去掉。

import pandas as pd

dat1 = [{'player_name': row.player_name, 'award': award, 'date': date} 
        for row in df.itertuples() 
           for date, awards in row.awards.items() for award in awards]

pd.DataFrame(dat1).pivot('player_name', 'award', 'date').T

玩家名称 Rafael Khakimov Sergei Belov
奖项
MHL全明星赛 2010-2011 NaN
MHL最佳GAA（1.79） 2010-2011 NaN
MHL最佳守门员 2010-2011 NaN
MHL本月最佳守门员（二月） 2010-2011 NaN
MHL本月最佳守门员（九月） 2010-2011 NaN
俄罗斯2银牌 NaN 2009-2010
VHL本月最佳守门员（十一月） 2021-2022 NaN
VHL季后赛最佳GAA（1.39） 2017-2018 NaN
VHL银牌 NaN 2013-2014

<details>
<summary>英文:</summary>

Here is one way. Note that I `T` transposed the data to make it visible here. You can get rid of the `T` at the very end

    import pandas as pd
    
    dat1 = [{&#39;player_name&#39;:row.player_name, &#39;award&#39;: award, &#39;date&#39;: date} 
                for row in df.itertuples() 
                   for date, awards in row.awards.items() for award in awards]
    
    pd.DataFrame(dat1).pivot(&#39;player_name&#39;, &#39;award&#39;, &#39;date&#39;).T

    player_name                             Rafael Khakimov Sergei Belov
    award                                                               
    MHL All-Star Game                             2010-2011          NaN
    MHL Best GAA (1.79)                           2010-2011          NaN
    MHL Best Goaltender                           2010-2011          NaN
    MHL Goaltender of the Month (February)        2010-2011          NaN
    MHL Goaltender of the Month (September)       2010-2011          NaN
    Russia2 Silver Medal                                NaN    2009-2010
    VHL Goaltender of the Month (November)        2021-2022          NaN
    VHL Playoffs Best GAA (1.39)                  2017-2018          NaN
    VHL Silver Medal                                    NaN    2013-2014

</details>



# 答案2
**得分**: 0

另一个版本：

```py
df['awards'] = df['awards'].apply(lambda x: {v[0]: k for k, v in x.items()})
df = pd.concat([df, df.pop('awards').apply(pd.Series)], axis=1)

print(df)

打印输出：

       player_name Russia2 Silver Medal VHL Silver Medal MHL All-Star Game VHL Playoffs Best GAA (1.39) VHL Goaltender of the Month (November)
0     Sergei Belov            2009-2010        2013-2014               NaN                          NaN                                    NaN
1  Rafael Khakimov                  NaN              NaN         2010-2011                    2017-2018                              2021-2022

df['awards'] = df['awards'].apply(lambda x: {v[0]: k for k, v in x.items()})
df = pd.concat([df, df.pop('awards').apply(pd.Series)], axis=1)

print(df)

       player_name Russia2 Silver Medal VHL Silver Medal MHL All-Star Game VHL Playoffs Best GAA (1.39) VHL Goaltender of the Month (November)
0     Sergei Belov            2009-2010        2013-2014               NaN                          NaN                                    NaN
1  Rafael Khakimov                  NaN              NaN         2010-2011                    2017-2018                              2021-2022