Selecting the row in a Pandas dataframe if the value is a float

Question

def categorizeMainUrl(url):
    category = "other"
    if "/special/" in url:
        category = "special"
    return category
df["category"] = df["main_URL"].apply(lambda url: categorizeMainUrl(url))

While running this part of the code, I keep the following exception.
"TypeError: argument of type 'float' is not iterable"
How can I select only the section of the dataframe with the float values?
(In this column, I would wait only string as a datatype)

Answer 1

Use Series.fillna to fill NaN values, then you can use Series.str.contains with np.where or Series.map to create a new serie:

df["category"] = np.where(df['main_URL'].fillna('').str.contains('/special/'),
                          "special", "other")

or

df["category"] = (df['main_URL'].fillna('')
                                .str.contains('/special/')
                                .map({True:"special",
                                      False:"other"}) 
                 )
#df['main_URL'].fillna('').str.contains('/special/').replace({True:"special",
#                                                              False:"other"})

---

I recommend you see: when should I want to use apply

Answer 2

The following command select only the rows containing on specific datatype (here float):
df[df["category"].apply(lambda x: isinstance(x, float))]