<p>将<a href="http://pandas.pydata.org/pandas-docs/stable/text.html#indexing-with-str" rel="nofollow noreferrer">indexing with str</a>创建的前3个值中的<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.map.html" rel="nofollow noreferrer">^{<cd1>}</a>与<code>dictionary</code>一起使用:</p>
<pre><code>d = {'AAA':'AAA0-9', 'CCC':'CCC5-9', 'BBB':'BBB0-5'}
#or generate dict from list
#L = ['AAA0-9', 'CCC5-9', 'BBB0-5']
#d = {x[:3]:x for x in L}
df['Code'] = df['Code'].str[:3].map(d)
print (df)
Code ID
0 AAA0-9 1
1 AAA0-9 2
2 AAA0-9 3
3 AAA0-9 4
4 AAA0-9 5
5 CCC5-9 6
6 AAA0-9 7
7 AAA0-9 8
8 BBB0-5 9
9 BBB0-5 10
</code></pre>
<p><strong>细节</strong>:</p>
<pre><code>print (df['Code'].str[:3])
0 AAA
1 AAA
2 AAA
3 AAA
4 AAA
5 CCC
6 AAA
7 AAA
8 BBB
9 BBB
Name: Code, dtype: object
</code></pre>
<p>编辑:</p>
<p>如果还需要展开值:</p>
<pre><code>a = df.Code.str.split()
b = np.repeat(df.ID.values, a.str.len())
c = np.concatenate(a.values)
d = {'AAA':'AAA0-9', 'CCC':'CCC5-9', 'BBB':'BBB0-5'}
df = pd.DataFrame({'Code':c, 'ID':b})
print (df)
Code ID
0 AAA1 1
1 AAA2 1
2 AAA3 1
3 AAA2 2
4 AAA3 3
5 AAA9 3
6 AAA4 4
7 AAA5 5
8 CCC2 6
9 CCC3 6
10 AAA7 7
11 AAA9 8
12 BBB1 9
13 BBB2 10
</code></pre>
<hr/>
<pre><code>df['Code'] = df['Code'].str[:3].map(d)
print (df)
Code ID
0 AAA0-9 1
1 AAA0-9 1
2 AAA0-9 1
3 AAA0-9 2
4 AAA0-9 3
5 AAA0-9 3
6 AAA0-9 4
7 AAA0-9 5
8 CCC5-9 6
9 CCC5-9 6
10 AAA0-9 7
11 AAA0-9 8
12 BBB0-5 9
13 BBB0-5 10
</code></pre>
<p>如果不需要更改格式:</p>
<pre><code>df = (df.set_index('ID')['Code']
.str.split(expand=True)
.stack()
.str[:3]
.map(d)
.groupby(level=0)
.apply(' '.join)
.reset_index(name='Code'))
print (df)
ID Code
0 1 AAA0-9 AAA0-9 AAA0-9
1 2 AAA0-9
2 3 AAA0-9 AAA0-9
3 4 AAA0-9
4 5 AAA0-9
5 6 CCC5-9 CCC5-9
6 7 AAA0-9
7 8 AAA0-9
8 9 BBB0-5
9 10 BBB0-5
</code></pre>
<p>编辑1:</p>
<p>如果需要按范围生成字典:</p>
<pre><code>L = ['AAA0-9', 'CCC2-9', 'BBB0-5']
d = (pd.Series(L, index=L)
.str.extract('(?P<a>\D+)(?P<b>\d)-(?P<c>\d+)', expand=True)
.set_index('a', append=True)
.astype(int)
.apply(lambda x: pd.Series(range(x.b, x.c + 1)), axis=1)
.stack()
.astype(int)
.astype(str)
.reset_index(name='d')
.assign(a=lambda x: x.a + x.d)
.rename(columns={'level_0':'e'})
.set_index('a')['e']
.to_dict()
)
print (d)
{'BBB1': 'BBB0-5', 'CCC6': 'CCC2-9', 'CCC2': 'CCC2-9',
'BBB4': 'BBB0-5', 'CCC5': 'CCC2-9', 'BBB2': 'BBB0-5',
'CCC4': 'CCC2-9', 'AAA4': 'AAA0-9', 'BBB0': 'BBB0-5',
'AAA9': 'AAA0-9', 'BBB3': 'BBB0-5', 'CCC3': 'CCC2-9',
'AAA0': 'AAA0-9', 'AAA3': 'AAA0-9', 'CCC9': 'CCC2-9',
'AAA2': 'AAA0-9', 'BBB5': 'BBB0-5', 'AAA1': 'AAA0-9',
'CCC8': 'CCC2-9', 'CCC7': 'CCC2-9', 'AAA8': 'AAA0-9',
'AAA7': 'AAA0-9', 'AAA5': 'AAA0-9', 'AAA6': 'AAA0-9'}
df['Code'] = df['Code'].map(d)
</code></pre>