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diff --git a/collectors/python.d.plugin/pandas/pandas.conf b/collectors/python.d.plugin/pandas/pandas.conf deleted file mode 100644 index 74a7da3e9..000000000 --- a/collectors/python.d.plugin/pandas/pandas.conf +++ /dev/null @@ -1,211 +0,0 @@ -# netdata python.d.plugin configuration for pandas -# -# This file is in YaML format. Generally the format is: -# -# name: value -# -# There are 2 sections: -# - global variables -# - one or more JOBS -# -# JOBS allow you to collect values from multiple sources. -# Each source will have its own set of charts. -# -# JOB parameters have to be indented (using spaces only, example below). - -# ---------------------------------------------------------------------- -# Global Variables -# These variables set the defaults for all JOBs, however each JOB -# may define its own, overriding the defaults. - -# update_every sets the default data collection frequency. -# If unset, the python.d.plugin default is used. -update_every: 5 - -# priority controls the order of charts at the netdata dashboard. -# Lower numbers move the charts towards the top of the page. -# If unset, the default for python.d.plugin is used. -# priority: 60000 - -# penalty indicates whether to apply penalty to update_every in case of failures. -# Penalty will increase every 5 failed updates in a row. Maximum penalty is 10 minutes. -# penalty: yes - -# autodetection_retry sets the job re-check interval in seconds. -# The job is not deleted if check fails. -# Attempts to start the job are made once every autodetection_retry. -# This feature is disabled by default. -# autodetection_retry: 0 - -# ---------------------------------------------------------------------- -# JOBS (data collection sources) -# -# The default JOBS share the same *name*. JOBS with the same name -# are mutually exclusive. Only one of them will be allowed running at -# any time. This allows autodetection to try several alternatives and -# pick the one that works. -# -# Any number of jobs is supported. -# -# All python.d.plugin JOBS (for all its modules) support a set of -# predefined parameters. These are: -# -# job_name: -# name: myname # the JOB's name as it will appear on the dashboard -# # dashboard (by default is the job_name) -# # JOBs sharing a name are mutually exclusive -# update_every: 1 # the JOB's data collection frequency -# priority: 60000 # the JOB's order on the dashboard -# penalty: yes # the JOB's penalty -# autodetection_retry: 0 # the JOB's re-check interval in seconds -# -# Additionally to the above, example also supports the following: -# -# chart_configs: [<dictionary>] # an array for chart config dictionaries. -# -# ---------------------------------------------------------------------- -# AUTO-DETECTION JOBS - -# Some example configurations, enable this collector, uncomment and example below and restart netdata to enable. - -# example pulling some hourly temperature data, a chart for today forecast (mean,min,max) and another chart for current. -# temperature: -# name: "temperature" -# update_every: 5 -# chart_configs: -# - name: "temperature_forecast_by_city" -# title: "Temperature By City - Today Forecast" -# family: "temperature.today" -# context: "pandas.temperature" -# type: "line" -# units: "Celsius" -# df_steps: > -# pd.DataFrame.from_dict( -# {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}&hourly=temperature_2m').json()['hourly']['temperature_2m'] -# for (city,lat,lng) -# in [ -# ('dublin', 53.3441, -6.2675), -# ('athens', 37.9792, 23.7166), -# ('london', 51.5002, -0.1262), -# ('berlin', 52.5235, 13.4115), -# ('paris', 48.8567, 2.3510), -# ('madrid', 40.4167, -3.7033), -# ('new_york', 40.71, -74.01), -# ('los_angeles', 34.05, -118.24), -# ] -# } -# ); -# df.describe(); # get aggregate stats for each city; -# df.transpose()[['mean', 'max', 'min']].reset_index(); # just take mean, min, max; -# df.rename(columns={'index':'city'}); # some column renaming; -# df.pivot(columns='city').mean().to_frame().reset_index(); # force to be one row per city; -# df.rename(columns={0:'degrees'}); # some column renaming; -# pd.concat([df, df['city']+'_'+df['level_0']], axis=1); # add new column combining city and summary measurement label; -# df.rename(columns={0:'measurement'}); # some column renaming; -# df[['measurement', 'degrees']].set_index('measurement'); # just take two columns we want; -# df.sort_index(); # sort by city name; -# df.transpose(); # transpose so its just one wide row; -# - name: "temperature_current_by_city" -# title: "Temperature By City - Current" -# family: "temperature.current" -# context: "pandas.temperature" -# type: "line" -# units: "Celsius" -# df_steps: > -# pd.DataFrame.from_dict( -# {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}¤t_weather=true').json()['current_weather'] -# for (city,lat,lng) -# in [ -# ('dublin', 53.3441, -6.2675), -# ('athens', 37.9792, 23.7166), -# ('london', 51.5002, -0.1262), -# ('berlin', 52.5235, 13.4115), -# ('paris', 48.8567, 2.3510), -# ('madrid', 40.4167, -3.7033), -# ('new_york', 40.71, -74.01), -# ('los_angeles', 34.05, -118.24), -# ] -# } -# ); -# df.transpose(); -# df[['temperature']]; -# df.transpose(); - -# example showing a read_csv from a url and some light pandas data wrangling. -# pull data in csv format from london demo server and then ratio of user cpus over system cpu averaged over last 60 seconds. -# example_csv: -# name: "example_csv" -# update_every: 2 -# chart_configs: -# - name: "london_system_cpu" -# title: "London System CPU - Ratios" -# family: "london_system_cpu" -# context: "pandas" -# type: "line" -# units: "n" -# df_steps: > -# pd.read_csv('https://london.my-netdata.io/api/v1/data?chart=system.cpu&format=csv&after=-60', storage_options={'User-Agent': 'netdata'}); -# df.drop('time', axis=1); -# df.mean().to_frame().transpose(); -# df.apply(lambda row: (row.user / row.system), axis = 1).to_frame(); -# df.rename(columns={0:'average_user_system_ratio'}); -# df*100; - -# example showing a read_json from a url and some light pandas data wrangling. -# pull data in json format (using requests.get() if json data is too complex for pd.read_json() ) from london demo server and work out 'total_bandwidth'. -# example_json: -# name: "example_json" -# update_every: 2 -# chart_configs: -# - name: "london_system_net" -# title: "London System Net - Total Bandwidth" -# family: "london_system_net" -# context: "pandas" -# type: "area" -# units: "kilobits/s" -# df_steps: > -# pd.DataFrame(requests.get('https://london.my-netdata.io/api/v1/data?chart=system.net&format=json&after=-1').json()['data'], columns=requests.get('https://london.my-netdata.io/api/v1/data?chart=system.net&format=json&after=-1').json()['labels']); -# df.drop('time', axis=1); -# abs(df); -# df.sum(axis=1).to_frame(); -# df.rename(columns={0:'total_bandwidth'}); - -# example showing a read_xml from a url and some light pandas data wrangling. -# pull weather forecast data in xml format, use xpath to pull out temperature forecast. -# example_xml: -# name: "example_xml" -# update_every: 2 -# line_sep: "|" -# chart_configs: -# - name: "temperature_forcast" -# title: "Temperature Forecast" -# family: "temp" -# context: "pandas.temp" -# type: "line" -# units: "celsius" -# df_steps: > -# pd.read_xml('http://metwdb-openaccess.ichec.ie/metno-wdb2ts/locationforecast?lat=54.7210798611;long=-8.7237392806', xpath='./product/time[1]/location/temperature', parser='etree')| -# df.rename(columns={'value': 'dublin'})| -# df[['dublin']]| - -# example showing a read_sql from a postgres database using sqlalchemy. -# note: example assumes a running postgress db on localhost with a netdata users and password netdata. -# sql: -# name: "sql" -# update_every: 5 -# chart_configs: -# - name: "sql" -# title: "SQL Example" -# family: "sql.example" -# context: "example" -# type: "line" -# units: "percent" -# df_steps: > -# pd.read_sql_query( -# sql='\ -# select \ -# random()*100 as metric_1, \ -# random()*100 as metric_2 \ -# ', -# con=create_engine('postgresql://localhost/postgres?user=netdata&password=netdata') -# ); 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