title: Cleaning and grouping values
body: <pre><code>df = pd.read_csv(file, sep=',', encoding='ISO-8859-1') 
  
  column_names = list(df.columns)
  col_mapping = {'Sex ':'Sex', 'Fatal (Y/N)': 'Fatal', 'Species ' : 'Species'}
  df = df.rename(columns=col_mapping, copy=False)
  </code></pre>
  
  <p>I've transformed the data as that the values from df['Age'] column that contains letter or other symbols take the value NaN</p>
  
  <pre><code>df['Age'] = np.where(pd.to_numeric(df['Age'], 'coerce').notnull(), df['Age'], NaN)
  </code></pre>
  
  <p>I've tried to use <code>df.dropna(df.Age)</code> to clean the <code>NaN</code> values but it gives me: TypeError: 'Series' objects are mutable, thus they cannot be hashed</p>
  
  <p>I want to know how to set this problem and how to group and count the resulting values bu range (i.e 18 - 25 years : 215, 25 - 50 : 300) for future ploting  </p>