与使用熊猫的其他数据框相比，如何填充行间距？

Question

我想将 df1 与 df2 进行比较，只填充空白而不覆盖其他值。 我不知道如何在不覆盖或创建额外列的情况下实现这一目标。

我可以通过将 df2 转换为字典并使用 df1 映射来做到这一点吗？

df1 = pd.DataFrame({'players name':['ram', 'john', 'ismael', 'sam', 'karan'],
                   'hobbies':['jog','','photos','','studying'],
                   'sports':['cricket', 'basketball', 'chess', 'kabadi', 'volleyball']})

df1：

  players name   hobbies      sports
0          ram       jog     cricket
1         john            basketball
2       ismael    photos       chess
3          sam                kabadi
4        karan  studying  volleyball

而且，df，

df2 = pd.DataFrame({'players name':['jagan', 'mohan', 'john', 'sam', 'karan'],
                   'hobbies':['riding', 'tv', 'sliding', 'jumping', 'studying']})

df2：

  players name   hobbies
0        jagan    riding
1        mohan        tv
2         john   sliding
3          sam   jumping
4        karan  studying

我想要这样的输出：

Answer 1

尝试这个：

df1['hobbies'] = (df1['players name'].map(df2.set_index('players name')['hobbies'])
                                     .fillna(df1['hobbies']))
df1

输出：

  players name   hobbies      sports
0          ram       jog     cricket
1         john   sliding  basketball
2       ismael    photos       chess
3          sam   jumping      kabadi
4        karan  studying  volleyball

Answer 2

如果空格是NaN值

df1 = pd.DataFrame({"players name":["ram","john","ismael","sam","karan"],
"hobbies":["jog",pd.np.NaN,"photos",pd.np.NaN,"studying"],
"sports":["cricket","basketball","chess","kabadi","volleyball"]})

然后

dicts = df2.set_index("players name")['hobbies'].to_dict()
df1['hobbies'] = df1['hobbies'].fillna(df1['players name'].map(dicts))

输出：

  players name   hobbies      sports
0          ram       jog     cricket
1         john   sliding  basketball
2       ismael     photos       chess
3          sam   jumping      kabadi
4        karan  studying  volleyball