[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:# Group one-hot encoded variables of a category into one single variable<br><br>#names of all the columns<br>cols = dataset.columns<br><br>#number of rows=r , number of columns=c<br>r,c = dataset.shape<br><br>#Create a new dataframe with r rows, one column for each encoded category, and target in the end<br>data = pandas.DataFrame(index=numpy.arange(0, r),columns=[\'Wilderness_Area\',\'Soil_Type\',\'Cover_Type\'])<br><br>#Make an entry in \'data\' for each r as category_id, target value<br>for i in range(0,r):<br>    w=0;<br>    s=0;<br>    # Category1 range<br>    for j in range(10,14):<br>        if (dataset.iloc[i,j] == 1):<br>            w=j-9  #category class<br>            break<br>    # Category2 range        <br>    for k in range(14,54):<br>        if (dataset.iloc[i,k] == 1):<br>            s=k-13 #category class<br>            break<br>    #Make an entry in \'data\' for each r as category_id, target value        <br>    data.iloc[i]=[w,s,dataset.iloc[i,c-1]]<br><br>#Plot for Category1    <br>sns.countplot(x="Wilderness_Area", hue="Cover_Type", data=data)<br>plt.show()<br>#Plot for Category2<br>plt.rc("figure", figsize=(25, 10))<br>sns.countplot(x="Soil_Type", hue="Cover_Type", data=data)<br>plt.show()<br><br>#(right-click and open the image in a new window for larger size)<br>#WildernessArea_4 has a lot of presence for cover_type 4. Good class distinction<br>#WildernessArea_3 has not much class distinction<br>#SoilType 1-6,10-14,17, 22-23, 29-33,35,38-40 offer lot of class distinction as counts for some are very high'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]