Python 使用 pandas 和 str.strip 崩溃
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时间：2020-09-13 21:36:28 来源:igfitidea点击:
Python crashes using pandas and str.strip
提问by Yariv
This minimal code crashes my Python. (Setting: pandas 0.13.0, python 2.7.3 AMD64, Win7.)
这个最小的代码使我的 Python 崩溃。（设置：pandas 0.13.0、python 2.7.3 AMD64、Win7。）
import pandas as pd
input_file = r"c3.csv"
input_df = pd.read_csv(input_file)
for col in input_df.columns: # strip whitespaces from string values
if input_df[col].dtype == object:
input_df[col] = input_df[col].apply(lambda x: x.strip())
print 'start'
for idx in range(len(input_df)):
input_df['LL'].iloc[idx] = 3
print idx
print 'finished'
Output:
输出：
start
0
Process finished with exit code -1073741819
What prevents the crash:
什么可以防止崩溃：
- Removing lines from c3.csv.
- Removing
.strip()from the code. - Changing c3.csv changes the amount of
foriterations until the crash in unexpected ways.
- 从 c3.csv 中删除行。
.strip()从代码中删除。- 更改 c3.csv 会更改
for迭代次数，直到以意想不到的方式崩溃。
Contents of c3.csv:
c3.csv 的内容：
Size , B/S , Symbol , Type , BN , Duration , VR , Time , SR ,LL,
0, xxxx , xxxx0 , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
00, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00-00:00:00 , 000000000 , 00-00:00:00 ,
回答by Jeff
You are doing a chained assignment which can behave in unexpected ways. see here: http://pandas.pydata.org/pandas-docs/dev/indexing.html#indexing-view-versus-copy. This is fixed in master and will work in 0.13.1 (coming soon). see here: https://github.com/pydata/pandas/pull/6031
您正在执行一个可能以意想不到的方式运行的链式作业。请参见此处：http: //pandas.pydata.org/pandas-docs/dev/indexing.html#indexing-view-versus-copy。这已在 master 中修复，并将在 0.13.1（即将推出）中起作用。见这里：https: //github.com/pydata/pandas/pull/6031
This is not correct to do:
这样做是不正确的：
input_df['LL'].iloc[idx] = 3
Instead do:
而是这样做：
input_df.ix[ix,'LL'] = 3
Or even better (as you are assigning ALL rows to 3)
甚至更好（因为您将所有行分配给 3）
input_df['LL'] = 3
If you are assigning just some of the rows (and have say an integer/boolean indexer)
如果您只分配一些行（并说一个整数/布尔索引器）
input_df.ix[indexer,'LL'] = 3
You should also just do this to strip the whitespace:
您也应该这样做以去除空格：
input_df[col] = input_df[col].str.strip()
