Minor updates to README and utils.py

Author: Martin

README.md

@@ -10,7 +10,9 @@ We provide two options for integrating your CANedge data with Grafana dashboards
 
 The [CANedge Grafana Backend](https://github.com/CSS-Electronics/canedge-grafana-backend) app only processes data 'when needed' by an end user - and requires no database. It is ideal when you have large amounts of data - as you only process the data you need to visualize. 
 
-In contrast, the [CANedge InfluxDB Writer](https://github.com/CSS-Electronics/canedge-influxdb-writer) integration requires that you process relevant data in advance (e.g. periodically or on-file-upload) and write the decoded data to a database (e.g. InfluxDB). It is ideal if the dashboard loading speed is critical - but with the downside that large amounts of data is processed & stored (at a cost) without being used.
+The [CANedge InfluxDB Writer](https://github.com/CSS-Electronics/canedge-influxdb-writer) processes data in advance (e.g. periodically or on-file-upload) and writes the decoded data to a database. It is ideal if dashboard loading speed is critical - but with the downside that data is processed/stored even if it is not used.
+
+For details incl. 'pros & cons', see our [intro to telematics dashboards](https://www.csselectronics.com/pages/telematics-dashboard-open-source).
 
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canedge-influxdb-writer/utils.py

@@ -150,7 +150,7 @@ def extract_phys(self, df_raw):
         # remove duplicates in case multiple DBC files contain identical signals
         df_phys["datetime"] = df_phys.index
         df_phys = df_phys.drop_duplicates(keep="first")
-        df_phys = df_phys.drop("datetime", 1)
+        df_phys = df_phys.drop(labels="datetime", axis=1)
 
         # optionally filter and rebaseline the data
         df_phys = self.filter_signals(df_phys)