code: def calculate_trend(series):
      # numpy can't handle `Int64` values, so cast to float
      if series.dtype == "Int64":
          series = series.astype("float64")
      df = pd.DataFrame(
          {"x": series.index, "y": series.values}
      ).dropna()
      if df.shape[0] <= 2:
          return np.nan
      if isinstance(df["x"].iloc[0], (datetime, pd.Timestamp)):
          x = convert_datetime_to_floats(df["x"])
      else:
          x = df["x"].values
  
      if isinstance(df["y"].iloc[0], (datetime, pd.Timestamp)):
          y = convert_datetime_to_floats(df["y"])
      elif isinstance(df["y"].iloc[0], (timedelta, pd.Timedelta)):
          y = convert_timedelta_to_floats(df["y"])
      else:
          y = df["y"].values
  
      x = x - x.mean()
      y = y - y.mean()
  
      # prevent divide by zero error
      if len(np.unique(x)) == 1:
          return 0
  
      # consider scipy.stats.linregress for large n cases
      coefficients = np.polyfit(x, y, 1)
      return coefficients[0]
  
file_path: featuretools/featuretools/utils/time_utils.py
imports: from datetime import datetime, timedelta
  import numpy
  import pandas
label_desc: Use image node_id 6 for calling a global function with example usage: calculate_trend(series) and returns: coefficients, np, int