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Replacing blank values (white space) with NaN in pandas Ask Question

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Replacing blank values (white space) with NaN in pandas Ask Question

I want to find all values in a Pandas dataframe that contain whitespace (any arbitrary amount) and replace those values with NaNs.

Any ideas how this can be improved?

Basically I want to turn this:

                   A    B    C
2000-01-01 -0.532681  foo    0
2000-01-02  1.490752  bar    1
2000-01-03 -1.387326  foo    2
2000-01-04  0.814772  baz     
2000-01-05 -0.222552         4
2000-01-06 -1.176781  qux

Into this:

                   A     B     C
2000-01-01 -0.532681   foo     0
2000-01-02  1.490752   bar     1
2000-01-03 -1.387326   foo     2
2000-01-04  0.814772   baz   NaN
2000-01-05 -0.222552   NaN     4
2000-01-06 -1.176781   qux   NaN

I've managed to do it with the code below, but man is it ugly. It's not Pythonic and I'm sure it's not the most efficient use of pandas either. I loop through each column and do boolean replacement against a column mask generated by applying a function that does a regex search of each value, matching on whitespace.

for i in df.columns:
    df[i][df[i].apply(lambda i: True if re.search('^\s*$', str(i)) else False)]=None

It could be optimized a bit by only iterating through fields that could contain empty strings:

if df[i].dtype == np.dtype('object')

But that's not much of an improvement

そして最後に、このコードはターゲット文字列を None に設定します。これは、 などの Pandas の関数で機能しますが、の代わりに を直接fillna()挿入できれば完全性を保つことができます。NaNNone

ベストアンサー1

pandas 0.13df.replace()以降では機能すると思います:

df = pd.DataFrame([
    [-0.532681, 'foo', 0],
    [1.490752, 'bar', 1],
    [-1.387326, 'foo', 2],
    [0.814772, 'baz', ' '],     
    [-0.222552, '   ', 4],
    [-1.176781,  'qux', '  '],         
], columns='A B C'.split(), index=pd.date_range('2000-01-01','2000-01-06'))

# replace field that's entirely space (or empty) with NaN
print(df.replace(r'^\s*$', np.nan, regex=True))

生産:

                   A    B   C
2000-01-01 -0.532681  foo   0
2000-01-02  1.490752  bar   1
2000-01-03 -1.387326  foo   2
2000-01-04  0.814772  baz NaN
2000-01-05 -0.222552  NaN   4
2000-01-06 -1.176781  qux NaN

Temak が指摘したように、df.replace(r'^\s+$', np.nan, regex=True)有効なデータに空白が含まれている場合に使用します。

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