python - 如何过滤以下数据框？

Question

你好，我想过滤一个数据框，我希望第一列的所有值都为“0”，问题是 mi csv 文件没有我试过的标题

import pandas as pd

df = pd.read_csv('big-cluster.csv',delimiter=',',encoding='ISO-8859-15')
print(df.iloc[:,0].isin([0]))

但是我得到了:

0       False
1       False
2       False
3       False
4       False
5       False
6       False
7       False
8       False
9       False
10      False
11      False
12      False
13      False
14      False
15      False
16      False
17      False
18      False
19      False
20      False
21      False
22      False
23      False

我的 csv 文件具有以下结构:

10,hello this is a test
4,just testing code
3,this is fun
4,you are good
10,this is very good

我想要得到的输出如下:

10,hello this is a test
10,this is very good

根据这里的反馈，我尝试了:

mask = df[0].astype(str).str.contains("0")
print(df[mask])

但是我得到了:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/usr/local/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2392             try:
-> 2393                 return self._engine.get_loc(key)
   2394             except KeyError:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5239)()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5085)()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20405)()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20359)()

KeyError: 0

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
<ipython-input-54-fe5bdde1bff6> in <module>()
     65 '''
     66 
---> 67 mask = df[0].astype(str).str.contains("0")
     68 print(df[mask])
     69 

/usr/local/lib/python3.6/site-packages/pandas/core/frame.py in __getitem__(self, key)
   2060             return self._getitem_multilevel(key)
   2061         else:
-> 2062             return self._getitem_column(key)
   2063 
   2064     def _getitem_column(self, key):

/usr/local/lib/python3.6/site-packages/pandas/core/frame.py in _getitem_column(self, key)
   2067         # get column
   2068         if self.columns.is_unique:
-> 2069             return self._get_item_cache(key)
   2070 
   2071         # duplicate columns & possible reduce dimensionality

/usr/local/lib/python3.6/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
   1532         res = cache.get(item)
   1533         if res is None:
-> 1534             values = self._data.get(item)
   1535             res = self._box_item_values(item, values)
   1536             cache[item] = res

/usr/local/lib/python3.6/site-packages/pandas/core/internals.py in get(self, item, fastpath)
   3588 
   3589             if not isnull(item):
-> 3590                 loc = self.items.get_loc(item)
   3591             else:
   3592                 indexer = np.arange(len(self.items))[isnull(self.items)]

/usr/local/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2393                 return self._engine.get_loc(key)
   2394             except KeyError:
-> 2395                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2396 
   2397         indexer = self.get_indexer([key], method=method, tolerance=tolerance)

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5239)()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5085)()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20405)()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20359)()

KeyError: 0

Answer 1

我认为您的问题是您的第一个字段被键入为 int 而不是字符串。因此，让我们使用“astype”和 .str.contains 来检查零。

让我们试试:

mask = df[0].astype(str).str.contains("0")
print(df[mask])

输出:

    0                     1
0  10  hello this is a test
4  10     this is very good