code: def top_correlation_to_name(stocks, column_name, searchstring, top=5):
      """
      ####################################################################################
      This function draws a correlation chart of the top "x" rows of a data frame that are highly
      correlated to a selected row in the dataframe. You can think of the rows of the input
      dataframe as containing stock prices or fund flows or product sales and the columns should
      contain time series data of prices or flows or sales over multiple time periods.
      Now this program will allow you to select the top 5 or 10 rows that are highly correlated
      to a given row selected by the column: column_name and using a search string "searchstring".
      The program will search for the search string in that column column_name and return a list
      of 5 or 10 rows that are the most correlated to that selected row. If you give "top" as
      a float ratio then it will use the ratio as the cut off point in the correlation
      coefficient to select rows.
      ####################################################################################
      """
      #### First increment top by 1 since you are asking for top X names in addition to the one you have, top += 1
      incl = [x for x in list(stocks) if x not in column_name]
      ### First drop all NA rows since they will mess up your correlations, stocks.dropna(inplace=True)
      if stocks.empty:
          print("After dropping NaNs, the data frame has become empty.")
          return
      ### Now find the highest correlated rows to the selected row ###
      try:
          index_val = search_string(
              stocks, column_name, searchstring
          ).index[0]
      except:
          print("Not able to find the search string in the column.")
          return
      ### Bring that selected Row to the top of the Data Frame
      df = stocks[:]
      # TODO: Undefined variable 'l'
      df["new"] = range(l, len(df) + l)
      df.loc[index_val, "new"] = 0
      stocks = df.sort_values("new").drop("new", axis=1)
      stocks.reset_index(inplace=True, drop=True)
      ##### Now calculate the correlation coefficients of other rows with the Top row
      try:
          cordf = pd.DataFrame(
              stocks[incl].T.corr().sort_values(0, ascending=False)
          )
      except:
          print(
              "Cannot calculate Correlations since Dataframe contains string values or objects."
          )
          return
      try:
          cordf = stocks[column_name].join(cordf)
      except:
          cordf = pd.concat((stocks[column_name], cordf), axis=1)
      #### Visualizing the top 5 or 10 or whatever cut-off they have given for Corr Coeff
      if top >= 1:
          top10index = (
              cordf.sort_values(0, ascending=False).iloc[:top, :3].index
          )
          top10names = cordf.sort_values(0, ascending=False).iloc[
              :top, :3
          ][column_name]
          top10values = cordf.sort_values(0, ascending=False)[0].values[
              :top
          ]
      else:
          top10index = cordf.sort_values(0, ascending=False)[
              cordf.sort_values(0, ascending=False)[0].values >= top
          ].index
          top10names = cordf.sort_values(0, ascending=False)[
              cordf.sort_values(0, ascending=False)[0].values >= top
          ][column_name]
          top10alues = cordf.sort_values(0, ascending=False)[
              cordf.sort_values(0, ascending=False)[0].values >= top
          ][0]
      print(top10names, top10values)
      #### Now plot the top rows that are highly correlated based on condition above
      stocksloc = stocks.iloc[top10index]
      #### Visualizing using Matplotlib ###
      stocksloc = stocksloc.T
      stocksloc = stocksloc.reset_index(drop=True)
      stocksloc.columns = stocksloc.iloc[0].values.tolist()
      stocksloc.drop(0).plot(
          subplots=True,
          figsize=(15, 10),
          legend=False,
          title="Top %s Correlations to %s" % (top, searchstring),
      )
      [ax.legend(loc=1) for ax in plt.gcf().axes]
      plt.tight_layout()
      plt.show(block=False)
  
imports: from itertools import cycle
  import numpy
  import pandas
  import matplotlib.dates
  from itertools import cycle
  import matplotlib.pyplot
  import seaborn
  import statsmodels.tsa.api
  import pdb
  import copy
  import dask
  import dask.dataframe
  from .colors import colorful
  import copy
label_desc: Use image node_id 3 for calling a global function with example usage: top_correlation_to_name(stocks, column_name, searchstring, top) without return types