How to filter rows containing a string pattern from a Pandas dataframe

>>> mask = df['ids'].str.contains('ball')    
>>> mask
0     True
1     True
2    False
3     True
Name: ids, dtype: bool

>>> df[mask]
     ids  vals
0  aball     1
1  bball     2
3  fball     4

df[df['ids'].str.contains('ball', na = False)] # valid for (at least) pandas version 0.17.1

Step-by-step explanation (from inner to outer):

• df['ids'] selects the ids column of the data frame (technically, the object df['ids'] is of type pandas.Series)
• df['ids'].str allows us to apply vectorized string methods (e.g., lower, contains) to the Series
• df['ids'].str.contains('ball') checks each element of the Series as to whether the element value has the string 'ball' as a substring. The result is a Series of Booleans indicating True or False about the existence of a 'ball' substring.
• df[df['ids'].str.contains('ball')] applies the Boolean 'mask' to the dataframe and returns a view containing appropriate records.
• na = False removes NA / NaN values from consideration; otherwise a ValueError may be returned.

In [3]: df[df['ids'].str.contains("ball")]
Out[3]:
     ids  vals
0  aball     1
1  bball     2
3  fball     4