def boxplot()

in charts/builder.py [0:0]

• 22 lines of code
• 3 McCabe index (conditional complexity)

def boxplot(title, ylabel, xlabel, data, value_column, ax, operations, box_colors):
  """
  Create a box plot (box-and-whisker plot with mean line) to visualize the distribution of performance values
  for different products (Elasticsearch and OpenSearch) and compare their distributions.

  Parameters:
      title (str): Title for the plot.
      ylabel (str): Label for the y-axis of the plot.
      xlabel (str): Label for the x-axis of the plot.
      data (pandas.DataFrame): DataFrame containing performance data.
      value_column (str): The column in the DataFrame containing the performance values.
      ax (matplotlib.axes._subplots.AxesSubplot): Matplotlib AxesSubplot object to plot the box plot.
      operations (list): List of operations to include in the analysis.
      box_colors (list): List of colors for the boxes in the box plot.
  """

  data = data[(data['operation'].isin(operations))]

  grouped_data = data.groupby('user-tags.product')[value_column].apply(list).reset_index(name=value_column)
  
  bp = ax.boxplot(
      grouped_data[value_column], 
      labels=grouped_data['user-tags.product'], 
      patch_artist=True,
      showmeans=True,
      meanline=True,
      notch=False,
      medianprops = {'linestyle': '-', 'linewidth': 1, 'color': 'black'},
      flierprops={'marker': '.', 'markersize': 1},
      meanprops={'linestyle': ':', 'linewidth': 1, 'color': 'white'}
      )

  for box, color in zip(bp['boxes'], box_colors):
      box.set(facecolor=color)

  for median in bp['medians']:
    median.set_color('black')

  ax.set_title(title)
  ax.set_xlabel(xlabel)
  ax.set_ylabel(ylabel)
  ax.tick_params(axis='both', labelsize=8)