original_docstring: 
      Parameters
      ----------
      df: data frame 
          data frame containing at least 
          2 numeric variables to plot
      x: type string
          name of x variable to plot
      y: type string
          name of y variable to plot
          
      
docstring: Parameters
  df: data frame
  data frame containing at least
  2 numeric variables to plot
  x: type string
  name of x variable to plot
  y: type string
  name of y variable to plot
docstring_tokens: Parameters df : data frame data frame containing at least 2 numeric variables to plot x : type string name of x variable to plot y : type string name of y variable to plot
code: def scatterPlot(df, x, y, **kwargs):
      """
      Parameters
      ----------
      df: data frame 
          data frame containing at least 
          2 numeric variables to plot
      x: type string
          name of x variable to plot
      y: type string
          name of y variable to plot
          
      """
      import numpy as np
      import seaborn as sns
      import pandas as pd
      import matplotlib as mpl
      import matplotlib.pyplot as plt
      import matplotlib.cm as cm
      from math import pi
  
      pm10 = df.pm10
      o3 = df.o3
      ws = df.ws
      wd = df.wd
      nox = df.nox
      no2 = df.no2
  
      #########################################
      # converts wind data to randians
      df = pd.DataFrame({"speed": ws, "direction": wd})
      df["speed_x"] = df["speed"] * np.sin(df["direction"] * pi / 180.0)
      df["speed_y"] = df["speed"] * np.cos(df["direction"] * pi / 180.0)
      fig, ax = plt.subplots(figsize=(8, 8), dpi=80)
      x0, x1 = ax.get_xlim()
      y0, y1 = ax.get_ylim()
      ax.set_aspect("equal")
      _ = df.plot(kind="scatter", x="speed_x", y="speed_y", alpha=0.35, ax=ax)
  
      ####################################
      # simple seaborn plot that shows how given variables relate with one another
      if x == "nox":
          x = nox
      elif x == "no2":
          x = no2
      elif x == "o3":
          x = o3
      elif x == "pm10":
          x = pm10
      if y == "nox":
          y = nox
      elif y == "no2":
          y = no2
      elif y == "o3":
          y = o3
      elif y == "pm10":
          y = pm10
  
      sns.jointplot(x=x, y=y, kind="hex")
  
      plt.show()
code_tokens: def scatterPlot ( df , x , y , ** kwargs ) : import numpy as np import seaborn as sns import pandas as pd import matplotlib as mpl import matplotlib . pyplot as plt import matplotlib . cm as cm from math import pi pm10 = df . pm10 o3 = df . o3 ws = df . ws wd = df . wd nox = df . nox no2 = df . no2 df = pd . DataFrame ( { "speed" : ws , "direction" : wd } ) df [ "speed_x" ] = df [ "speed" ] * np . sin ( df [ "direction" ] * pi / 180.0 ) df [ "speed_y" ] = df [ "speed" ] * np . cos ( df [ "direction" ] * pi / 180.0 ) fig , ax = plt . subplots ( figsize = ( 8 , 8 ) , dpi = 80 ) x0 , x1 = ax . get_xlim ( ) y0 , y1 = ax . get_ylim ( ) ax . set_aspect ( "equal" ) _ = df . plot ( kind = "scatter" , x = "speed_x" , y = "speed_y" , alpha = 0.35 , ax = ax ) if x == "nox" : x = nox elif x == "no2" : x = no2 elif x == "o3" : x = o3 elif x == "pm10" : x = pm10 if y == "nox" : y = nox elif y == "no2" : y = no2 elif y == "o3" : y = o3 elif y == "pm10" : y = pm10 sns . jointplot ( x = x , y = y , kind = "hex" ) plt . show ( )
short_docstring: Parameters
  df: data frame
  data frame containing at least
  2 numeric variables to plot
  x: type string
  name of x variable to plot
  y: type string
  name of y variable to plot
short_docstring_tokens: Parameters df : data frame data frame containing at least 2 numeric variables to plot x : type string name of x variable to plot y : type string name of y variable to plot
comment: """
      Parameters
      ----------
      df: data frame 
          data frame containing at least 
          2 numeric variables to plot
      x: type string
          name of x variable to plot
      y: type string
          name of y variable to plot
          
      """$SEP$#########################################$SEP$# converts wind data to randians$SEP$####################################$SEP$# simple seaborn plot that shows how given variables relate with one another