pandas 'match' on non-ascii characters - regex

Question

I'm sure this is a very simple issue with regexps, but: Trying to use str.match in pandas to match a non-ASCII character (the times sign). I expect the first match call will match the first row of the DataFrame; the second match call will match the last row; and the third match will match the first and last rows. However, the first call does match but the second and third calls do not. Where am I going wrong?
Dataframe looks like (with x replacing the times sign, it actually prints as a ?):
Column
0 2x 32
1 42
2 64 x2
Pandas 0.20.3, python 2.7.13, OS X.
#!/usr/bin/env python
import pandas as pd
import re
html = '<table><thead><tr><th>Column</th></tr></thead><tbody><tr><td>2× 32</td></tr><tr><td>42</td></tr><tr><td>64 ×2</td></tr></tbody><table>'
df = pd.read_html(html)[0]
print df
print df[df['Column'].str.match(ur'^[2-9]\u00d7', re.UNICODE, na=False)]
print df[df['Column'].str.match(ur'\u00d7[2-9]$', re.UNICODE, na=False)]
print df[df['Column'].str.match(ur'\u00d7', re.UNICODE, na=False)]
Output I see (again with ? replaced with x):
Column
0 2x 32
Empty DataFrame
Columns: [Column]
Index: []
Empty DataFrame
Columns: [Column]
Index: []

Answer 1

Use contains():
df.Column.str.contains(r'^[2-9]\u00d7')
0 True
1 False
2 False
Name: Column, dtype: bool
df.Column.str.contains(r'\u00d7[2-9]$')
0 False
1 False
2 True
Name: Column, dtype: bool
df.Column.str.contains(r'\u00d7')
0 True
1 False
2 True
Name: Column, dtype: bool
Explanation: contains() uses re.search(), and match() uses re.match() (docs). Since re.match() only matches from the beginning of a string (docs), only your first case, which matches at the start (with ^) will work. Actually in that case you don't need both match and ^:
df.Column.str.match(r'[2-9]\u00d7')
0 True
1 False
2 False
Name: Column, dtype: bool