pandas 替换为默认值 [英] Pandas replace with default value

问题描述

我有一个熊猫数据框，我想有条件地替换某些列.

I have a pandas dataframe I want to replace a certain column conditionally.

例如:

   col

 0 Mr
 1 Miss
 2 Mr
 3 Mrs
 4 Col.

我想将它们映射为

{'Mr': 0, 'Mrs': 1, 'Miss': 2}

如果dict中现在还有其他标题可用，那么我希望它们的默认值为3

If there are other titles now available in the dict then I want them to have a default value of 3

以上示例变为

   col

 0 0
 1 2
 2 0
 3 1
 4 3

我可以使用pandas.replace()而不使用正则表达式吗?

Can I do this with pandas.replace() without using regex ?

推荐答案

您可以使用

You can use map rather as replace, because faster, then fillna by 3 and cast to int by astype:

df['col'] = df.col.map({'Mr': 0, 'Mrs': 1, 'Miss': 2}).fillna(3).astype(int)

print (df)
   col
0    0
1    2
2    0
3    1
4    3

使用 numpy.where 并使用 isin 的条件:

Another solution with numpy.where and condition with isin:

d = {'Mr': 0, 'Mrs': 1, 'Miss': 2}
df['col'] = np.where(df.col.isin(d.keys()), df.col.map(d), 3).astype(int)
print (df)
   col
0    0
1    2
2    0
3    1
4    3

使用 replace 的解决方案:

Solution with replace:

d = {'Mr': 0, 'Mrs': 1, 'Miss': 2}
df['col'] = np.where(df.col.isin(d.keys()), df.col.replace(d), 3)
print (df)
   col
0    0
1    2
2    0
3    1
4    3

时间:

df = pd.concat([df]*10000).reset_index(drop=True)

d = {'Mr': 0, 'Mrs': 1, 'Miss': 2}
df['col0'] = df.col.map(d).fillna(3).astype(int)
df['col1'] = np.where(df.col.isin(d.keys()), df.col.replace(d), 3)
df['col2'] = np.where(df.col.isin(d.keys()), df.col.map(d), 3).astype(int)
print (df)

In [447]: %timeit df['col0'] = df.col.map(d).fillna(3).astype(int)
100 loops, best of 3: 4.93 ms per loop

In [448]: %timeit df['col1'] = np.where(df.col.isin(d.keys()), df.col.replace(d), 3)
100 loops, best of 3: 14.3 ms per loop

In [449]: %timeit df['col2'] = np.where(df.col.isin(d.keys()), df.col.map(d), 3).astype(int)
100 loops, best of 3: 7.68 ms per loop

In [450]: %timeit df['col3'] = df.col.map(lambda L: d.get(L, 3))
10 loops, best of 3: 36.2 ms per loop

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