如何基于来自另一个数据框的条件创建一个新的数据框 [英] How to create a new data frame based on conditions from another data frame

问题描述

只是进入Python，所以希望我在这里不问一个愚蠢的问题...

Just getting into Python, so hopefully I'm not asking a stupid question here...

所以我有一个名为"df_complete"的熊猫数据框，假设它有100行，并且包含名为"type"，"writer"，"status"，"col a"，"col c"的列. /更新一个名为"temp_df"的新数据框，并根据条件使用"df_complete"值进行创建.

So I have a pandas dataframe named "df_complete' with let's say 100 rows, and containing columns named: "type", "writer", "status", 'col a', 'col c'. I want to create/update a new dataframe named "temp_df" and create it based on conditions using "df_complete" values.

temp_df = pandas.DataFrame()

if ((df_complete['type'] == 'NDD') & (df_complete['writer'] == 'Mary') & (df_complete['status'] != '7')):
    temp_df['col A'] = df_complete['col a']
    temp_df['col B'] = 'good'
    temp_df['col C'] = df_complete['col c']

但是，当我这样做时，出现以下错误消息:

However, when I do this, I got the following error message:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

我阅读了该线程，并将我的和"更改为&":

I read this thread and changed my "and" to "&": Truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

我还在这里阅读了此主题，以将所有内容括在括号中:

I also read this thread here to put everything in parenthesis: comparing dtyped [float64] array with a scalar of type [bool] in Pandas DataFrame

但是错误仍然存​​在.是什么原因造成的?以及我该如何解决?

But the error is still present. What is causing this? and how can I fix it?

**后续问题** 另外，如何获取满足条件的行的索引值?

** follow up question ** Also, how can I obtain the index values of those rows that met the condition?

推荐答案

我认为您需要 boolean indexing 与示例:

df_complete = pd.DataFrame({'type':  ['NDD','NDD','NT'],
                            'writer':['Mary','Mary','John'],
                            'status':['4','5','6'],
                            'col a': [1,3,5],
                            'col b': [5,3,6],
                            'col c': [7,4,3]}, index=[3,4,5])

print (df_complete)
   col a  col b  col c status type writer
3      1      5      7      4  NDD   Mary
4      3      3      4      5  NDD   Mary
5      5      6      3      6   NT   John

temp_df = df_complete.ix[(df_complete['type'] == 'NDD') & 
                         (df_complete['writer'] == 'Mary') & 
                         (df_complete['status'] != '7'), ['col a','col c']]

print (temp_df)  
   col a  col c
3      1      7
4      3      4

temp_df = temp_df.rename(columns={'col a':'col A','col c':'col C'})
#add new column 
temp_df['col B'] = 'good'
#reorder columns
temp_df = temp_df[['col A','col B','col C']]
print (temp_df)  
   col A col B  col C
3      1  good      7
4      3  good      4

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