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diff --git a/collectors/python.d.plugin/pandas/pandas.conf b/collectors/python.d.plugin/pandas/pandas.conf new file mode 100644 index 000000000..6684af9d5 --- /dev/null +++ b/collectors/python.d.plugin/pandas/pandas.conf @@ -0,0 +1,191 @@ +# 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: +# +# num_lines: 4 # the number of lines to create +# lower: 0 # the lower bound of numbers to randomly sample from +# upper: 100 # the upper bound of numbers to randomly sample from +# +# ---------------------------------------------------------------------- +# 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']]|
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