pandas 如何根据另一个数据框的条件创建新的数据框
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How to create a new data frame based on conditions from another data frame
提问by alwaysaskingquestions
Just getting into Python, so hopefully I'm not asking a stupid question here...
刚刚进入 Python，所以希望我不会在这里问一个愚蠢的问题......
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.
所以我有一个名为“df_complete”的 Pandas 数据框，假设有 100 行，包含名为：“type”、“writer”、“status”、“col a”、“col c”的列。我想创建/更新一个名为“temp_df”的新数据框，并根据使用“df_complete”值的条件创建它。
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']
However, when I do this, I got the following error message:
但是，当我这样做时，我收到以下错误消息：
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
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()
我阅读了这个线程并将我的“和”更改为“&”： 系列的真值不明确。使用 a.empty、a.bool()、a.item()、a.any() 或 a.all()
I also read this thread here to put everything in parenthesis: comparing dtyped [float64] array with a scalar of type [bool] in Pandas DataFrame
我还在这里阅读了此线程以将所有内容放在括号中：将 dtyped [float64] 数组与 Pandas DataFrame 中的 [bool] 类型标量进行比较
But the error is still present. What is causing this? and how can I fix it?
但是错误仍然存在。这是什么原因造成的？我该如何解决？
** follow up question ** Also, how can I obtain the index values of those rows that met the condition?
** 后续问题 ** 另外，如何获取满足条件的行的索引值？
采纳答案by jezrael
I think you need boolean indexingwith ixfor selecting only columns col aand col c:
我认为你需要boolean indexing使用ix仅选择列col a和col c：
temp_df = df_complete.ix[(df_complete['type'] == 'NDD') &
(df_complete['writer'] == 'Mary') &
(df_complete['status'] != '7'), ['col a','col c']]
#rename columns
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']]
Sample:
样本：
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
