old_contents: import matplotlib.mlab as mlab
  import numpy as np
  import pandas as pd
  import plotly.graph_objs as go
  import plotly.offline as offl
  
  
  def dist_plot(rating_df):
      x = np.linspace(0, 50, 500)
  
      data_dict = {}
      for row in rating_df.iterrows():
          label_name = (row[1]['first_name'] + ' ' + row[1]['last_name'][0] + '.')
          data_dict[label_name] = (x, mlab.normpdf(x, row[1]['rating'], row[1]['sigma']))
  
      final_df = pd.DataFrame()
  
      for k, v in data_dict.iteritems():
          final_df[k] = v[1]
  
      final_df['index'] = x
      final_df.set_index('index', inplace=True)
  
      trace_dict = dict()
  
      for n, col in enumerate(final_df.columns):
          trace_dict[n] = go.Scatter(
              x=final_df.index,
              y=final_df[col],
              name=col
          )
  
      data = trace_dict.values()
  
      # Edit the layout
      layout = dict(title='Individual Gaussian Skill Distribution',
                    xaxis=dict(title='Mu'),
                    yaxis=dict(title='Value'),
                    )
  
      return offl.plot(dict(data=data, layout=layout), output_type='div')
  
  
  def win_probability_matrix(matrix_df):
      'returns the win probability matrix plot as a plotly heatmap'
  
      trace = go.Heatmap(
          z=matrix_df.transpose().values.tolist(),
          x=matrix_df.columns[::-1],
          y=matrix_df.columns[::-1],
          colorscale='Viridis'
      )
  
      data = [trace]
  
      layout = go.Layout(
          title='Win Probability Matrix',
          xaxis=dict(title='Loser', ticks=''),
          yaxis=dict(title='Winner', ticks='')
      )
  
      return offl.plot(dict(data=data, layout=layout), output_type='div')
  
new_contents: import matplotlib.mlab as mlab
  import numpy as np
  import pandas as pd
  import plotly.graph_objs as go
  import plotly.offline as offl
  
  
  def dist_plot(rating_df):
      x = np.linspace(0, 50, 500)
  
      data_dict = {}
      for row in rating_df.iterrows():
          label_name = (row[1]['first_name'] + ' ' + row[1]['last_name'][0] + '.')
          data_dict[label_name] = (x, mlab.normpdf(x, row[1]['rating'], row[1]['sigma']))
  
      final_df = pd.DataFrame()
  
      for k, v in data_dict.iteritems():
          final_df[k] = v[1]
  
      final_df['index'] = x
      final_df.set_index('index', inplace=True)
  
      trace_dict = dict()
  
      for n, col in enumerate(final_df.columns):
          trace_dict[n] = go.Scatter(
              x=final_df.index,
              y=final_df[col],
              name=col
          )
  
      data = trace_dict.values()
  
      # Edit the layout
      layout = dict(title='Individual Gaussian Skill Distribution',
                    xaxis=dict(title='Mu'),
                    yaxis=dict(title='Value'),
                    height=750
                    )
  
      return offl.plot(dict(data=data, layout=layout), output_type='div')
  
  
  def win_probability_matrix(matrix_df):
      'returns the win probability matrix plot as a plotly heatmap'
  
      trace = go.Heatmap(
          z=matrix_df.transpose().values.tolist(),
          x=matrix_df.columns[::-1],
          y=matrix_df.columns[::-1],
          colorscale='Viridis'
      )
  
      data = [trace]
  
      layout = go.Layout(
          title='Win Probability Matrix',
          xaxis=dict(title='Loser', ticks=''),
          yaxis=dict(title='Winner', ticks=''),
          height=750
      )
  
      return offl.plot(dict(data=data, layout=layout), output_type='div')
  
subject: fix the height on the new plotly plots