How to categorize floating values in python using pandas library
<p>`    import pandas
    import numpy</p>

<pre><code>#importing the dataset
data=pandas.read_csv('gapminder.csv',low_memory=False)

#setting variables to be worked with to numeric
data['urbanrate']=data['urbanrate'].convert_objects(convert_numeric=True)
data['femaleemployrate']=data['femaleemployrate'].convert_objects(convert_numeric=True)
data['lifeexpectancy']=data['lifeexpectancy'].convert_objects(convert_numeric=True)

print("Count for urbanrate")
chk8=data['urbanrate'].value_counts(sort=False,dropna=False)
print(chk8)

sub1=data[['urbanrate','femaleemployrate','lifeexpectancy']]
print (sub1.head(25))

===========================================================================
def urban (row):
      if (row['urbanrate']&lt;25.0):
          return 1
      if (data[['urbanrate']&gt;=25.0 &amp; data['urbanrate']&lt;75.0]):
         return 2
      if (data['urbanrate']&gt;75.0):
         return 3
sub1['urban']=sub1.apply(lambda row: urban (row),axis=1)
print (sub1.head(25))
============================================================================`
</code></pre>

<p>I want to categorize all the values of <code>urbanrate</code> (which are all floating point numbers like <code>24.56</code>,<code>88.98</code>,<code>77.65</code>) into 3 different categories (one is values for <code>&lt;25</code>, another for <code>&gt;=25 and &lt;75</code> and last one for <code>&gt;=75</code>) and store those values in a new variable (say urban). How can I do this? I have given my code. I executed it and its giving me error as: </p>

<blockquote>
  <p>TypeError: ('cannot compare a dtyped [float64] array with a scalar of type [bool]', 'occurred at index 1'))</p>
</blockquote>

<p>Please help me </p>