MinMax Scaler in sklearn does not normalize values of column between 0 and 1
<p>I'm working on KNN algorithm in python and tried to normalise my data frames  with the MinMaxScaler to transform the data in a range between 0 to 1.</p>

<p>However when I return the output, I observe some column min / max the output exceeds 1. Am i using it wrongly?</p>

<p>Below is my a snippet of the min/max value returned:
<a href="https://i.stack.imgur.com/sdSjk.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/sdSjk.png" alt="enter image description here"></a></p>

<p>The code used was :</p>

<pre><code>kdd_data_10percent = pandas.read_csv("data/kdd_10pc", header=None, names = col_names)
features = kdd_data_10percent[num_features].astype(float)#num_features contain the specific column labels i wish to extract    
features.apply(lambda x: MinMaxScaler().fit_transform(x))
</code></pre>

<p>Features contain the dataframe containing the columns (e.g. wrong_fragment, urgent ...).</p>

<p>If i understand correctly, after the execution of the MinMaxScaler, the results returned will ensure each column values will be normalised to the range from 0 -1 only. Am i right?</p>