title: Replacing Values in an Pandas Dataframe
body: <p>I assumed I understood the replace Function but seemingly I didnt. Please see my code below. I just want to replace all -999 values with NaN (or makes NULL more sense?) but the out put still contains -999 in all Dataframes. What am I missing?</p>
  
  <pre><code>          def SQLtoPandas(Connection,SQLString):
                  df =pd.read_sql(SQLString, con=Connection)
                  return df
  
              WeatherString = "select * FROM weather" 
      dfWeather = SQLtoPandas(Connection, WeatherString)
  
              RainkindsString = "select * FROM Rainkinds" 
      dfRainkinds = SQLtoPandas(Connection, RainkindsString)
  
              StationsString = "select * FROM Stations" 
      dfStations = SQLtoPandas(Connection, StationsString)
  
              #here is the important part. As stated, maybe replacing wiht NULL makesm ore sense? 
  dfWeather.replace(-999, 0)
  
              #---------------------------Output Data---------------------------------------- 
          def DatenAnalyse():    
                  pd.set_option('display.max_columns', None)  
  
                  print("\n --&gt; Zusammenfassung Wetterdaten &lt;-- \n" )
                  print(dfWeather.describe())
                  print("\n --&gt; Beispiel Wetterdaten &lt;-- \n" )
                  print(dfWeather.head(10))
  
                  print("\n ----------------------------------------------------------------")
                  print("\n \n --&gt; Zusammenfassung Regenarten &lt;-- \n" )
                  print(dfRainkinds.describe())
                  print("\n --&gt; Beispiel Regenarten &lt;-- \n" )
                  print(dfRainkinds.head(10))
  
                  print("\n ----------------------------------------------------------------")
                  print("\n \n --&gt; Zusammenfassung Stationen &lt;-- \n" )
                  print(dfStations.describe())
                  print("\n --&gt; Beispiel Stationen &lt;-- \n" )
                  print(dfStations.head(10))
  
              DatenAnalyse()
  </code></pre>