Python 熊猫：按组ID逐行填充NaN值

我正在尝试根据组ID逐行填充NaN值

我尝试过使用fillNA，使用正向和反向填充选项，但fillNA函数不会逐行填充数据帧。此外，我希望确保在填写NaN值之前公司匹配。在这种情况下，使用正向填充将导致公司“Pear”中填充来自公司“Banana”的数据

追加=追加。排序值（按=['Company'，'Intro']，不按位置='last'）
追加=追加。重置索引（drop=True）
对于附加的.index中的i：
如果i==0：
通过
其他：
如果在[i，'Company']处追加，则==在[i-1，'Company']处追加：
追加.fillna（method='ffill'，inplace=True）
其他：
通过

附加数据帧

Company    Intro          Categories         Headquarters  Founded Date   Funding Stage

 Apple       xyz       Healthcare, Big Data     New York       2018           Series A

 Apple       NaN              NaN                NaN           NaN             NaN

 Apple       NaN              NaN                NaN           NaN             NaN

 Banana     Lier           Government           Europe        2010           Series B

 Pear        NaN              NaN                NaN           NaN             NaN

这是我希望达到的预期结果：

Expected Result

Company    Intro          Categories         Headquarters  Founded Date   Funding Stage

 Apple       xyz       Healthcare, Big Data     New York       2018           Series A

 Apple       xyz       Healthcare, Big Data     New York       2018           Series A

 Apple       xyz       Healthcare, Big Data     New York       2018           Series A

 Banana      Lier        Government             Europe        2010           Series B

 Pear         NaN              NaN                NaN           NaN             NaN

配合使用

---

NaaN只是NaN的错别字还是别的什么？oO@meissner_对不起，这是NaN的错别字。

df.groupby(['Company']).ffill()

  Company Intro            Categories Headquarters  Founded Date Funding Stage
0   Apple   xyz  Healthcare, Big Data     New York        2018.0      Series A
1   Apple   xyz  Healthcare, Big Data     New York        2018.0      Series A
2   Apple   xyz  Healthcare, Big Data     New York        2018.0      Series A
3  Banana  Lier            Government       Europe        2010.0      Series B
4    Pear   NaN                   NaN          NaN           NaN           NaN

import pandas as pd
from io import StringIO

# sample data
df = pd.read_fwf(StringIO("""
Company    Intro                 Categories   Headquarters  Founded_Date   Funding_Stage
 Apple       xyz       Healthcare, Big Data     New York       2018           Series A
 Apple       NaN              NaN                NaN           NaN             NaN
 Apple       NaN              NaN                NaN           NaN             NaN
 Banana     Lier           Government           Europe        2010           Series B
 Pear        NaN              NaN                NaN           NaN             NaN"""), header=1)


# Create the summary level - assumes repeat data comes first
df_summary = df.groupby("Company").head(1)

# Join the result
df_result = df[['Company']].merge(df_summary, on="Company")

#  Company Intro            Categories Headquarters  Founded_Date Funding_Stage
#0   Apple   xyz  Healthcare, Big Data     New York        2018.0      Series A
#1   Apple   xyz  Healthcare, Big Data     New York        2018.0      Series A
#2   Apple   xyz  Healthcare, Big Data     New York        2018.0      Series A
#3  Banana  Lier            Government       Europe        2010.0      Series B
#4    Pear   NaN                   NaN          NaN           NaN           NaN