Filter pandas data frame for col == None

Question

I have a data frame data_df with multiple columns, one of which is c which holds country names. How do I filter out the rows where c == None.

My first attempt was to do this:

countries_df = data_df[data_df.c != None]

However, that yielded 0 rows. This, however, worked:

countries_df = data_df[~data_df.c.isin([None])]

Can someone explain why? It seems that from the Pandas doc, the first should be able to filter correctly.

Some sample rows:

  _heartbeat_                           a                    al     c      cy     g
0   NaN Mozilla/5.0 (Linux; U; Android 4.1.2; en-us; H...   en-US   US  Anaheim 15r91
1   NaN Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ...   en-us   None    NaN ifIpBW
2   NaN Mozilla/5.0 (Windows NT 6.1; rv:21.0) Gecko/20...   en-US,en;q=0.5  US  Fort Huachuca   10DaxOu
3   NaN Mozilla/5.0 (Linux; U; Android 4.1.2; en-us; S...   en-US   US  Houston TysVFU
4   NaN Opera/9.80 (Android; Opera Mini/7.5.33286/29.3...   en  None    NaN 10IGW7m
5   NaN Mozilla/5.0 (compatible; MSIE 10.0; Windows NT...   en-US   US  Mishawaka   13GrCeP
6   NaN Mozilla/5.0 (Windows NT 6.1; WOW64; rv:20.0) G...   en-US,en;q=0.5  US  Hammond YmtpnZ
7   NaN Mozilla/5.0 (iPhone; U; CPU iPhone OS 4_3_5 li...   en-us   None    NaN 13oM0hV
8   NaN Mozilla/5.0 (iPhone; CPU iPhone OS 6_1_3 like ...   en-us   AU  Sydney  15r91
9   NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   en-US,en;q=0.8  None    NaN 109LtDc
10  NaN Mozilla/5.0 (iPhone; CPU iPhone OS 6_1_3 like ...   en-us   US  Middletown  109ar5F
11  NaN Mozilla/5.0 (iPhone; CPU iPhone OS 6_1_3 like ...   en-us   US  Germantown  107xZnW

Answer 1

It appears that pandas and Numpy treat None specially when comparing for equality. In pandas, None is supposed to be like NaN, representing a missing value. To find rows where the value is not None (or nan), you could do data_df[data_df.c.notnull()] (or data_df[~data_df.c.isnull()]).