Pandas:如果关键字出现在任何列中,请选择行

Question

我知道有一个关于在一列中搜索字符串的相关线程(这里)但是如何在所有列中使用pd.Series.str.contains(pattern)？

df = pd.DataFrame({'vals': [1, 2, 3, 4], 'ids': [u'aball', u'bball', u'cnut', u'fball'],
'id2': [u'uball', u'mball', u'pnut', u'zball']})


In [3]: df[df['ids'].str.contains("ball")]
Out[3]:
     ids  vals
0  aball     1
1  bball     2
3  fball     4

Answer 1

使用select_dtypes的唯一对象列(显然字符串)与applymap和in:

df = pd.DataFrame({'vals': [1, 2, 3, 4], 
                   'ids': [None, u'bball', u'cnut', u'fball'],
                   'id2': [u'uball', u'mball', u'pnut', u'zball']})
print (df)
   vals    ids    id2
0     1   None  uball
1     2  bball  mball
2     3   cnut   pnut
3     4  fball  zball

mask = df.select_dtypes(include=[object]).applymap(lambda x: 'ball' in x if pd.notnull(x) else False)
#if always non NaNs, no Nones
#mask = df.select_dtypes(include=[object]).applymap(lambda x: 'ball' in x)
print (mask)
     ids    id2
0  False   True
1   True   True
2  False  False
3   True   True

另一种解决方案是使用apply具有contains:

mask = df.select_dtypes(include=[object]).apply(lambda x: x.str.contains('ball', na=False))
#if always non NaNs, no Nones
#mask = df.select_dtypes(include=[object]).apply(lambda x: x.str.contains('ball'))
print (mask)
     ids    id2
0  False   True
1   True   True
2  False  False
3   True   True

然后过滤DataFrame.any用于检查True每行至少一行或DataFrame.all检查每行的所有值:

df1 = df[mask.any(axis=1)]
print (df1)
   vals    ids    id2
0     1   None  uball
1     2  bball  mball
3     4  fball  zball

df2 = df[mask.all(axis=1)]
print (df2)
   vals    ids    id2
1     2  bball  mball
3     4  fball  zball

Answer 2

stack

如果您只选择可能具有'ball'列的对象dtype object,那么您可以stack将结果数据帧转换为系列对象.此时,您可以执行pandas.Series.str.contains并将unstack结果返回到数据框中.

df.select_dtypes(include=[object]).stack().str.contains('ball').unstack()

     ids    id2
0   True   True
1   True   True
2  False  False
3   True   True