我在Pandas中有一个大型DataFrame，当没有分配给任何值时，2列可以有值或NaN（Null）。
我想基于这些填充第3列2.当不是NaN时，它需要一些值。其工作原理如下：
In [16]: import pandas as pd
In [17]: import numpy as np
In [18]: df = pd.DataFrame([[np.NaN, np.NaN],['John', 'Malone'],[np.NaN, np.NaN]], columns = ['col1', 'col2'])
In [19]: df
Out[19]:
col1 col2
0 NaN NaN
1 John Malone
2 NaN NaN
In [20]: df['col3'] = np.NaN
In [21]: df.loc[df['col1'].notnull(),'col3'] = 'I am ' + df['col1']
In [22]: df
Out[22]:
col1 col2 col3
0 NaN NaN NaN
1 John Malone I am John
2 NaN NaN NaN
这也有效：
In [29]: df.loc[df['col1']== 'John','col3'] = 'I am ' + df['col2']
In [30]: df
Out[30]:
col1 col2 col3
0 NaN NaN NaN
1 John Malone I am Malone
2 NaN NaN NaN
但是，如果我没有使所有值NaN然后尝试最后一个loc，它会给我一个错误！
In [31]: df = pd.DataFrame([[np.NaN, np.NaN],[np.NaN, np.NaN],[np.NaN, np.NaN]], columns = ['col1', 'col2'])
In [32]: df
Out[32]:
col1 col2
0 NaN NaN
1 NaN NaN
2 NaN NaN
In [33]: df['col3'] = np.NaN
In [34]: df.loc[df['col1']== 'John','col3'] = 'I am ' + df['col2']
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
c:\python33\lib\site-packages\pandas\core\ops.py in na_op(x, y)
552 result = expressions.evaluate(op, str_rep, x, y,
--> 553 raise_on_error=True, **eval_kwargs)
554 except TypeError:
c:\python33\lib\site-packages\pandas\computation\expressions.py in evaluate(op, op_str, a, b, raise_on_error, use_numexpr, **eval_kwargs)
217 return _evaluate(op, op_str, a, b, raise_on_error=raise_on_error,
--> 218 **eval_kwargs)
219 return _evaluate_standard(op, op_str, a, b, raise_on_error=raise_on_error)
c:\python33\lib\site-packages\pandas\computation\expressions.py in _evaluate_standard(op, op_str, a, b, raise_on_error, **eval_kwargs)
70 _store_test_result(False)
---> 71 return op(a, b)
72
c:\python33\lib\site-packages\pandas\core\ops.py in _radd_compat(left, right)
805 try:
--> 806 output = radd(left, right)
807 except TypeError:
c:\python33\lib\site-packages\pandas\core\ops.py in <lambda>(x, y)
802 def _radd_compat(left, right):
--> 803 radd = lambda x, y: y + x
804 # GH #353, NumPy 1.5.1 workaround
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('<U32') dtype('<U32') dtype('<U32')
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-34-3b2873f8749b> in <module>()
----> 1 df.loc[df['col1']== 'John','col3'] = 'I am ' + df['col2']
c:\python33\lib\site-packages\pandas\core\ops.py in wrapper(left, right, name, na_op)
616 lvalues = lvalues.values
617
--> 618 return left._constructor(wrap_results(na_op(lvalues, rvalues)),
619 index=left.index, name=left.name,
620 dtype=dtype)
c:\python33\lib\site-packages\pandas\core\ops.py in na_op(x, y)
561 result = np.empty(len(x), dtype=x.dtype)
562 mask = notnull(x)
--> 563 result[mask] = op(x[mask], y)
564 else:
565 raise TypeError("{typ} cannot perform the operation {op}".format(typ=type(x).__name__,op=str_rep))
c:\python33\lib\site-packages\pandas\core\ops.py in _radd_compat(left, right)
804 # GH #353, NumPy 1.5.1 workaround
805 try:
--> 806 output = radd(left, right)
807 except TypeError:
808 raise
c:\python33\lib\site-packages\pandas\core\ops.py in <lambda>(x, y)
801
802 def _radd_compat(left, right):
--> 803 radd = lambda x, y: y + x
804 # GH #353, NumPy 1.5.1 workaround
805 try:
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('<U32') dtype('<U32') dtype('<U32')
如果Pandas不喜欢Column值==某些文本，如果所有值都是NaN ????
帮助！
答案 0 :(得分：1)
我认为，如果有任何非空的值，那么这一行所做的就是将字符串添加到第1列值。
df.loc[df['col1'].notnull(),'col3'] = 'I am ' + df['col1']
因此，您可以检查是否存在任何非空值，然后仅在执行以下操作时执行操作：
if df['col1'].notnull().any():
df['col3'] = 'I am ' + df['col1']
在以这种方式运行之前，您也不需要创建col3列。
答案 1 :(得分：0)
这里的问题是，如果整个列是np.nan，它可能存储为浮点数，而不是对象（文本）。
所以你可以这样做：
if not np.all(pandas.isnull(df['mycol'])):
df = my_string_operation(df)
您还可以将相关列强制转换为object类型。
df['mycol'] = df['mycol'].astype(object)
df = my_string_operation(df)