Ignoring NaNs with str.contains

I want to find rows that contain a string, like so:

DF[DF.col.str.contains("foo")]

However, this fails because some elements are NaN:

ValueError: cannot index with vector containing NA / NaN values

So I resort to the obfuscated

DF[DF.col.notnull()][DF.col.dropna().str.contains("foo")]

Is there a better way?

There's a flag for that:

In [11]: df = pd.DataFrame([["foo1"], ["foo2"], ["bar"], [np.nan]], columns=['a'])

In [12]: df.a.str.contains("foo")
Out[12]:
0     True
1     True
2    False
3      NaN
Name: a, dtype: object

In [13]: df.a.str.contains("foo", na=False)
Out[13]:
0     True
1     True
2    False
3    False
Name: a, dtype: bool

See the str.replace docs:

na : default NaN, fill value for missing values.

So you can do the following:

In [21]: df.loc[df.a.str.contains("foo", na=False)]
Out[21]:
      a
0  foo1
1  foo2

In addition to the above answers, I would say for columns having no single word name, you may use:-

df[df['Product ID'].str.contains("foo") == True]

Hope this helps.

df[df.col.str.contains("foo").fillna(False)]