code: def plot_information_table(ic_data):
      ic_summary_table = pd.DataFrame()
      ic_summary_table["IC Mean"] = ic_data.mean()
      ic_summary_table["IC Std."] = ic_data.std()
      ic_summary_table["Risk-Adjusted IC"] = (
          ic_data.mean() / ic_data.std()
      )
      t_stat, p_value = stats.ttest_1samp(ic_data, 0)
      ic_summary_table["t-stat(IC)"] = t_stat
      ic_summary_table["p-value(IC)"] = p_value
      ic_summary_table["IC Skew"] = stats.skew(ic_data)
      ic_summary_table["IC Kurtosis"] = stats.kurtosis(ic_data)
  
      print("Information Analysis")
      utils.print_table(ic_summary_table.apply(lambda x: x.round(3)).T)
  
imports: import numpy
  import pandas
  from scipy import stats
  import statsmodels.api
  import seaborn
  import matplotlib.cm
  import matplotlib.pyplot
  from matplotlib.ticker import ScalarFormatter
  from functools import wraps
  from .None import utils
  from .None import performance
label_desc: Use image node_id 6 for calling a global function with example usage: plot_information_table(ic_data) without return types