Regenerate integrations.js (#17487)

Co-authored-by: ilyam8 <22274335+ilyam8@users.noreply.github.com>

2 files changed, 2 insertions, 2 deletions

diff --git a/integrations/integrations.js b/integrations/integrations.js
index 49ade5a855..3cc2d22b30 100644
--- a/integrations/integrations.js
+++ b/integrations/integrations.js
@@ -18991,7 +18991,7 @@ export const integrations = [
             "most_popular": false
         },
         "overview": "# Pandas\n\nPlugin: python.d.plugin\nModule: pandas\n\n## Overview\n\n[Pandas](https://pandas.pydata.org/) is a de-facto standard in reading and processing most types of structured data in Python.\nIf you have metrics appearing in a CSV, JSON, XML, HTML, or [other supported format](https://pandas.pydata.org/docs/user_guide/io.html),\neither locally or via some HTTP endpoint, you can easily ingest and present those metrics in Netdata, by leveraging the Pandas collector.\n\nThis collector can be used to collect pretty much anything that can be read by Pandas, and then processed by Pandas.\n\n\nThe collector uses [pandas](https://pandas.pydata.org/) to pull data and do pandas-based preprocessing, before feeding to Netdata.\n\n\nThis collector is supported on all platforms.\n\nThis collector supports collecting metrics from multiple instances of this integration, including remote instances.\n\n\n### Default Behavior\n\n#### Auto-Detection\n\nThis integration doesn't support auto-detection.\n\n#### Limits\n\nThe default configuration for this integration does not impose any limits on data collection.\n\n#### Performance Impact\n\nThe default configuration for this integration is not expected to impose a significant performance impact on the system.\n",
-        "setup": "## Setup\n\n### Prerequisites\n\n#### Python Requirements\n\nThis collector depends on some Python (Python 3 only) packages that can usually be installed via `pip` or `pip3`.\n\n```bash\nsudo pip install pandas requests\n```\n\nNote: If you would like to use [`pandas.read_sql`](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html) to query a database, you will need to install the below packages as well.\n\n```bash\nsudo pip install 'sqlalchemy<2.0' psycopg2-binary\n```\n\n\n\n### Configuration\n\n#### File\n\nThe configuration file name for this integration is `python.d/pandas.conf`.\n\n\nYou can edit the configuration file using the `edit-config` script from the\nNetdata [config directory](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration.md#the-netdata-config-directory).\n\n```bash\ncd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata\nsudo ./edit-config python.d/pandas.conf\n```\n#### Options\n\nThere are 2 sections:\n\n* Global variables\n* One or more JOBS that can define multiple different instances to monitor.\n\nThe following options can be defined globally: priority, penalty, autodetection_retry, update_every, but can also be defined per JOB to override the global values.\n\nAdditionally, the following collapsed table contains all the options that can be configured inside a JOB definition.\n\nEvery configuration JOB starts with a `job_name` value which will appear in the dashboard, unless a `name` parameter is specified.\n\n\n{% details summary=\"Config options\" %}\n| Name | Description | Default | Required |\n|:----|:-----------|:-------|:--------:|\n| chart_configs | an array of chart configuration dictionaries | [] | yes |\n| chart_configs.name | name of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.title | title of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.family | [family](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/interact-new-charts.md#families) of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.context | [context](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/interact-new-charts.md#contexts) of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.type | the type of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.units | the units of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.df_steps | a series of pandas operations (one per line) that each returns a dataframe. | None | yes |\n| update_every | Sets the default data collection frequency. | 5 | no |\n| priority | Controls the order of charts at the netdata dashboard. | 60000 | no |\n| autodetection_retry | Sets the job re-check interval in seconds. | 0 | no |\n| penalty | Indicates whether to apply penalty to update_every in case of failures. | yes | no |\n| name | Job name. This value will overwrite the `job_name` value. 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. |  | no |\n\n{% /details %}\n#### Examples\n\n##### Temperature API Example\n\nexample pulling some hourly temperature data, a chart for today forecast (mean,min,max) and another chart for current.\n\n{% details summary=\"Config\" %}\n```yaml\ntemperature:\n    name: \"temperature\"\n    update_every: 5\n    chart_configs:\n      - name: \"temperature_forecast_by_city\"\n        title: \"Temperature By City - Today Forecast\"\n        family: \"temperature.today\"\n        context: \"pandas.temperature\"\n        type: \"line\"\n        units: \"Celsius\"\n        df_steps: >\n          pd.DataFrame.from_dict(\n            {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}&hourly=temperature_2m').json()['hourly']['temperature_2m']\n            for (city,lat,lng)\n            in [\n                ('dublin', 53.3441, -6.2675),\n                ('athens', 37.9792, 23.7166),\n                ('london', 51.5002, -0.1262),\n                ('berlin', 52.5235, 13.4115),\n                ('paris', 48.8567, 2.3510),\n                ('madrid', 40.4167, -3.7033),\n                ('new_york', 40.71, -74.01),\n                ('los_angeles', 34.05, -118.24),\n                ]\n            }\n            );\n          df.describe();                                               # get aggregate stats for each city;\n          df.transpose()[['mean', 'max', 'min']].reset_index();        # just take mean, min, max;\n          df.rename(columns={'index':'city'});                         # some column renaming;\n          df.pivot(columns='city').mean().to_frame().reset_index();    # force to be one row per city;\n          df.rename(columns={0:'degrees'});                            # some column renaming;\n          pd.concat([df, df['city']+'_'+df['level_0']], axis=1);       # add new column combining city and summary measurement label;\n          df.rename(columns={0:'measurement'});                        # some column renaming;\n          df[['measurement', 'degrees']].set_index('measurement');     # just take two columns we want;\n          df.sort_index();                                             # sort by city name;\n          df.transpose();                                              # transpose so its just one wide row;\n      - name: \"temperature_current_by_city\"\n        title: \"Temperature By City - Current\"\n        family: \"temperature.current\"\n        context: \"pandas.temperature\"\n        type: \"line\"\n        units: \"Celsius\"\n        df_steps: >\n          pd.DataFrame.from_dict(\n              {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}&current_weather=true').json()['current_weather']\n              for (city,lat,lng)\n              in [\n                  ('dublin', 53.3441, -6.2675),\n                  ('athens', 37.9792, 23.7166),\n                  ('london', 51.5002, -0.1262),\n                  ('berlin', 52.5235, 13.4115),\n                  ('paris', 48.8567, 2.3510),\n                  ('madrid', 40.4167, -3.7033),\n                  ('new_york', 40.71, -74.01),\n                  ('los_angeles', 34.05, -118.24),\n                  ]\n              }\n              );\n          df.transpose();\n          df[['temperature']];\n          df.transpose();\n\n```\n{% /details %}\n##### API CSV Example\n\nexample showing a read_csv from a url and some light pandas data wrangling.\n\n{% details summary=\"Config\" %}\n```yaml\nexample_csv:\n    name: \"example_csv\"\n    update_every: 2\n    chart_configs:\n      - name: \"london_system_cpu\"\n        title: \"London System CPU - Ratios\"\n        family: \"london_system_cpu\"\n        context: \"pandas\"\n        type: \"line\"\n        units: \"n\"\n        df_steps: >\n          pd.read_csv('https://london.my-netdata.io/api/v1/data?chart=system.cpu&format=csv&after=-60', storage_options={'User-Agent': 'netdata'});\n          df.drop('time', axis=1);\n          df.mean().to_frame().transpose();\n          df.apply(lambda row: (row.user / row.system), axis = 1).to_frame();\n          df.rename(columns={0:'average_user_system_ratio'});\n          df*100;\n\n```\n{% /details %}\n##### API JSON Example\n\nexample showing a read_json from a url and some light pandas data wrangling.\n\n{% details summary=\"Config\" %}\n```yaml\nexample_json:\n    name: \"example_json\"\n    update_every: 2\n    chart_configs:\n      - name: \"london_system_net\"\n        title: \"London System Net - Total Bandwidth\"\n        family: \"london_system_net\"\n        context: \"pandas\"\n        type: \"area\"\n        units: \"kilobits/s\"\n        df_steps: >\n          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']);\n          df.drop('time', axis=1);\n          abs(df);\n          df.sum(axis=1).to_frame();\n          df.rename(columns={0:'total_bandwidth'});\n\n```\n{% /details %}\n##### XML Example\n\nexample showing a read_xml from a url and some light pandas data wrangling.\n\n{% details summary=\"Config\" %}\n```yaml\nexample_xml:\n    name: \"example_xml\"\n    update_every: 2\n    line_sep: \"|\"\n    chart_configs:\n      - name: \"temperature_forcast\"\n        title: \"Temperature Forecast\"\n        family: \"temp\"\n        context: \"pandas.temp\"\n        type: \"line\"\n        units: \"celsius\"\n        df_steps: >\n          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')|\n          df.rename(columns={'value': 'dublin'})|\n          df[['dublin']]|\n\n```\n{% /details %}\n##### SQL Example\n\nexample showing a read_sql from a postgres database using sqlalchemy.\n\n{% details summary=\"Config\" %}\n```yaml\nsql:\n    name: \"sql\"\n    update_every: 5\n    chart_configs:\n      - name: \"sql\"\n        title: \"SQL Example\"\n        family: \"sql.example\"\n        context: \"example\"\n        type: \"line\"\n        units: \"percent\"\n        df_steps: >\n          pd.read_sql_query(\n            sql='\\\n                select \\\n                    random()*100 as metric_1, \\\n                    random()*100 as metric_2 \\\n              ',\n            con=create_engine('postgresql://localhost/postgres?user=netdata&password=netdata')\n            );\n\n```\n{% /details %}\n",
+        "setup": "## Setup\n\n### Prerequisites\n\n#### Python Requirements\n\nThis collector depends on some Python (Python 3 only) packages that can usually be installed via `pip` or `pip3`.\n\n```bash\nsudo pip install pandas requests\n```\n\nNote: If you would like to use [`pandas.read_sql`](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html) to query a database, you will need to install the below packages as well.\n\n```bash\nsudo pip install 'sqlalchemy<2.0' psycopg2-binary\n```\n\n\n\n### Configuration\n\n#### File\n\nThe configuration file name for this integration is `python.d/pandas.conf`.\n\n\nYou can edit the configuration file using the `edit-config` script from the\nNetdata [config directory](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration.md#the-netdata-config-directory).\n\n```bash\ncd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata\nsudo ./edit-config python.d/pandas.conf\n```\n#### Options\n\nThere are 2 sections:\n\n* Global variables\n* One or more JOBS that can define multiple different instances to monitor.\n\nThe following options can be defined globally: priority, penalty, autodetection_retry, update_every, but can also be defined per JOB to override the global values.\n\nAdditionally, the following collapsed table contains all the options that can be configured inside a JOB definition.\n\nEvery configuration JOB starts with a `job_name` value which will appear in the dashboard, unless a `name` parameter is specified.\n\n\n{% details summary=\"Config options\" %}\n| Name | Description | Default | Required |\n|:----|:-----------|:-------|:--------:|\n| chart_configs | an array of chart configuration dictionaries | [] | yes |\n| chart_configs.name | name of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.title | title of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.family | [family](https://github.com/netdata/netdata/blob/master/docs/dashboard/netdata-charts.md#families) of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.context | [context](https://github.com/netdata/netdata/blob/master/docs/dashboard/netdata-charts.md#contexts) of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.type | the type of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.units | the units of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.df_steps | a series of pandas operations (one per line) that each returns a dataframe. | None | yes |\n| update_every | Sets the default data collection frequency. | 5 | no |\n| priority | Controls the order of charts at the netdata dashboard. | 60000 | no |\n| autodetection_retry | Sets the job re-check interval in seconds. | 0 | no |\n| penalty | Indicates whether to apply penalty to update_every in case of failures. | yes | no |\n| name | Job name. This value will overwrite the `job_name` value. 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. |  | no |\n\n{% /details %}\n#### Examples\n\n##### Temperature API Example\n\nexample pulling some hourly temperature data, a chart for today forecast (mean,min,max) and another chart for current.\n\n{% details summary=\"Config\" %}\n```yaml\ntemperature:\n    name: \"temperature\"\n    update_every: 5\n    chart_configs:\n      - name: \"temperature_forecast_by_city\"\n        title: \"Temperature By City - Today Forecast\"\n        family: \"temperature.today\"\n        context: \"pandas.temperature\"\n        type: \"line\"\n        units: \"Celsius\"\n        df_steps: >\n          pd.DataFrame.from_dict(\n            {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}&hourly=temperature_2m').json()['hourly']['temperature_2m']\n            for (city,lat,lng)\n            in [\n                ('dublin', 53.3441, -6.2675),\n                ('athens', 37.9792, 23.7166),\n                ('london', 51.5002, -0.1262),\n                ('berlin', 52.5235, 13.4115),\n                ('paris', 48.8567, 2.3510),\n                ('madrid', 40.4167, -3.7033),\n                ('new_york', 40.71, -74.01),\n                ('los_angeles', 34.05, -118.24),\n                ]\n            }\n            );\n          df.describe();                                               # get aggregate stats for each city;\n          df.transpose()[['mean', 'max', 'min']].reset_index();        # just take mean, min, max;\n          df.rename(columns={'index':'city'});                         # some column renaming;\n          df.pivot(columns='city').mean().to_frame().reset_index();    # force to be one row per city;\n          df.rename(columns={0:'degrees'});                            # some column renaming;\n          pd.concat([df, df['city']+'_'+df['level_0']], axis=1);       # add new column combining city and summary measurement label;\n          df.rename(columns={0:'measurement'});                        # some column renaming;\n          df[['measurement', 'degrees']].set_index('measurement');     # just take two columns we want;\n          df.sort_index();                                             # sort by city name;\n          df.transpose();                                              # transpose so its just one wide row;\n      - name: \"temperature_current_by_city\"\n        title: \"Temperature By City - Current\"\n        family: \"temperature.current\"\n        context: \"pandas.temperature\"\n        type: \"line\"\n        units: \"Celsius\"\n        df_steps: >\n          pd.DataFrame.from_dict(\n              {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}&current_weather=true').json()['current_weather']\n              for (city,lat,lng)\n              in [\n                  ('dublin', 53.3441, -6.2675),\n                  ('athens', 37.9792, 23.7166),\n                  ('london', 51.5002, -0.1262),\n                  ('berlin', 52.5235, 13.4115),\n                  ('paris', 48.8567, 2.3510),\n                  ('madrid', 40.4167, -3.7033),\n                  ('new_york', 40.71, -74.01),\n                  ('los_angeles', 34.05, -118.24),\n                  ]\n              }\n              );\n          df.transpose();\n          df[['temperature']];\n          df.transpose();\n\n```\n{% /details %}\n##### API CSV Example\n\nexample showing a read_csv from a url and some light pandas data wrangling.\n\n{% details summary=\"Config\" %}\n```yaml\nexample_csv:\n    name: \"example_csv\"\n    update_every: 2\n    chart_configs:\n      - name: \"london_system_cpu\"\n        title: \"London System CPU - Ratios\"\n        family: \"london_system_cpu\"\n        context: \"pandas\"\n        type: \"line\"\n        units: \"n\"\n        df_steps: >\n          pd.read_csv('https://london.my-netdata.io/api/v1/data?chart=system.cpu&format=csv&after=-60', storage_options={'User-Agent': 'netdata'});\n          df.drop('time', axis=1);\n          df.mean().to_frame().transpose();\n          df.apply(lambda row: (row.user / row.system), axis = 1).to_frame();\n          df.rename(columns={0:'average_user_system_ratio'});\n          df*100;\n\n```\n{% /details %}\n##### API JSON Example\n\nexample showing a read_json from a url and some light pandas data wrangling.\n\n{% details summary=\"Config\" %}\n```yaml\nexample_json:\n    name: \"example_json\"\n    update_every: 2\n    chart_configs:\n      - name: \"london_system_net\"\n        title: \"London System Net - Total Bandwidth\"\n        family: \"london_system_net\"\n        context: \"pandas\"\n        type: \"area\"\n        units: \"kilobits/s\"\n        df_steps: >\n          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']);\n          df.drop('time', axis=1);\n          abs(df);\n          df.sum(axis=1).to_frame();\n          df.rename(columns={0:'total_bandwidth'});\n\n```\n{% /details %}\n##### XML Example\n\nexample showing a read_xml from a url and some light pandas data wrangling.\n\n{% details summary=\"Config\" %}\n```yaml\nexample_xml:\n    name: \"example_xml\"\n    update_every: 2\n    line_sep: \"|\"\n    chart_configs:\n      - name: \"temperature_forcast\"\n        title: \"Temperature Forecast\"\n        family: \"temp\"\n        context: \"pandas.temp\"\n        type: \"line\"\n        units: \"celsius\"\n        df_steps: >\n          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')|\n          df.rename(columns={'value': 'dublin'})|\n          df[['dublin']]|\n\n```\n{% /details %}\n##### SQL Example\n\nexample showing a read_sql from a postgres database using sqlalchemy.\n\n{% details summary=\"Config\" %}\n```yaml\nsql:\n    name: \"sql\"\n    update_every: 5\n    chart_configs:\n      - name: \"sql\"\n        title: \"SQL Example\"\n        family: \"sql.example\"\n        context: \"example\"\n        type: \"line\"\n        units: \"percent\"\n        df_steps: >\n          pd.read_sql_query(\n            sql='\\\n                select \\\n                    random()*100 as metric_1, \\\n                    random()*100 as metric_2 \\\n              ',\n            con=create_engine('postgresql://localhost/postgres?user=netdata&password=netdata')\n            );\n\n```\n{% /details %}\n",
         "troubleshooting": "## Troubleshooting\n\n### Debug Mode\n\nTo troubleshoot issues with the `pandas` collector, run the `python.d.plugin` with the debug option enabled. The output\nshould give you clues as to why the collector isn't working.\n\n- Navigate to the `plugins.d` directory, usually at `/usr/libexec/netdata/plugins.d/`. If that's not the case on\n  your system, open `netdata.conf` and look for the `plugins` setting under `[directories]`.\n\n  ```bash\n  cd /usr/libexec/netdata/plugins.d/\n  ```\n\n- Switch to the `netdata` user.\n\n  ```bash\n  sudo -u netdata -s\n  ```\n\n- Run the `python.d.plugin` to debug the collector:\n\n  ```bash\n  ./python.d.plugin pandas debug trace\n  ```\n\n",
         "alerts": "## Alerts\n\nThere are no alerts configured by default for this integration.\n",
         "metrics": "## Metrics\n\nMetrics grouped by *scope*.\n\nThe scope defines the instance that the metric belongs to. An instance is uniquely identified by a set of labels.\n\nThis collector is expecting one row in the final pandas DataFrame. It is that first row that will be taken\nas the most recent values for each dimension on each chart using (`df.to_dict(orient='records')[0]`).\nSee [pd.to_dict()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_dict.html).\"\n\n\n### Per Pandas instance\n\nThese metrics refer to the entire monitored application.\n\n\nThis scope has no labels.\n\nMetrics:\n\n| Metric | Dimensions | Unit |\n|:------|:----------|:----|\n\n",
diff --git a/integrations/integrations.json b/integrations/integrations.json
index 7f01b6e087..05730ea58e 100644
--- a/integrations/integrations.json
+++ b/integrations/integrations.json
@@ -18989,7 +18989,7 @@
                 "most_popular": false
             },
             "overview": "# Pandas\n\nPlugin: python.d.plugin\nModule: pandas\n\n## Overview\n\n[Pandas](https://pandas.pydata.org/) is a de-facto standard in reading and processing most types of structured data in Python.\nIf you have metrics appearing in a CSV, JSON, XML, HTML, or [other supported format](https://pandas.pydata.org/docs/user_guide/io.html),\neither locally or via some HTTP endpoint, you can easily ingest and present those metrics in Netdata, by leveraging the Pandas collector.\n\nThis collector can be used to collect pretty much anything that can be read by Pandas, and then processed by Pandas.\n\n\nThe collector uses [pandas](https://pandas.pydata.org/) to pull data and do pandas-based preprocessing, before feeding to Netdata.\n\n\nThis collector is supported on all platforms.\n\nThis collector supports collecting metrics from multiple instances of this integration, including remote instances.\n\n\n### Default Behavior\n\n#### Auto-Detection\n\nThis integration doesn't support auto-detection.\n\n#### Limits\n\nThe default configuration for this integration does not impose any limits on data collection.\n\n#### Performance Impact\n\nThe default configuration for this integration is not expected to impose a significant performance impact on the system.\n",
-            "setup": "## Setup\n\n### Prerequisites\n\n#### Python Requirements\n\nThis collector depends on some Python (Python 3 only) packages that can usually be installed via `pip` or `pip3`.\n\n```bash\nsudo pip install pandas requests\n```\n\nNote: If you would like to use [`pandas.read_sql`](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html) to query a database, you will need to install the below packages as well.\n\n```bash\nsudo pip install 'sqlalchemy<2.0' psycopg2-binary\n```\n\n\n\n### Configuration\n\n#### File\n\nThe configuration file name for this integration is `python.d/pandas.conf`.\n\n\nYou can edit the configuration file using the `edit-config` script from the\nNetdata [config directory](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration.md#the-netdata-config-directory).\n\n```bash\ncd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata\nsudo ./edit-config python.d/pandas.conf\n```\n#### Options\n\nThere are 2 sections:\n\n* Global variables\n* One or more JOBS that can define multiple different instances to monitor.\n\nThe following options can be defined globally: priority, penalty, autodetection_retry, update_every, but can also be defined per JOB to override the global values.\n\nAdditionally, the following collapsed table contains all the options that can be configured inside a JOB definition.\n\nEvery configuration JOB starts with a `job_name` value which will appear in the dashboard, unless a `name` parameter is specified.\n\n\n| Name | Description | Default | Required |\n|:----|:-----------|:-------|:--------:|\n| chart_configs | an array of chart configuration dictionaries | [] | yes |\n| chart_configs.name | name of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.title | title of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.family | [family](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/interact-new-charts.md#families) of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.context | [context](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/interact-new-charts.md#contexts) of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.type | the type of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.units | the units of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.df_steps | a series of pandas operations (one per line) that each returns a dataframe. | None | yes |\n| update_every | Sets the default data collection frequency. | 5 | no |\n| priority | Controls the order of charts at the netdata dashboard. | 60000 | no |\n| autodetection_retry | Sets the job re-check interval in seconds. | 0 | no |\n| penalty | Indicates whether to apply penalty to update_every in case of failures. | yes | no |\n| name | Job name. This value will overwrite the `job_name` value. 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. |  | no |\n\n#### Examples\n\n##### Temperature API Example\n\nexample pulling some hourly temperature data, a chart for today forecast (mean,min,max) and another chart for current.\n\n```yaml\ntemperature:\n    name: \"temperature\"\n    update_every: 5\n    chart_configs:\n      - name: \"temperature_forecast_by_city\"\n        title: \"Temperature By City - Today Forecast\"\n        family: \"temperature.today\"\n        context: \"pandas.temperature\"\n        type: \"line\"\n        units: \"Celsius\"\n        df_steps: >\n          pd.DataFrame.from_dict(\n            {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}&hourly=temperature_2m').json()['hourly']['temperature_2m']\n            for (city,lat,lng)\n            in [\n                ('dublin', 53.3441, -6.2675),\n                ('athens', 37.9792, 23.7166),\n                ('london', 51.5002, -0.1262),\n                ('berlin', 52.5235, 13.4115),\n                ('paris', 48.8567, 2.3510),\n                ('madrid', 40.4167, -3.7033),\n                ('new_york', 40.71, -74.01),\n                ('los_angeles', 34.05, -118.24),\n                ]\n            }\n            );\n          df.describe();                                               # get aggregate stats for each city;\n          df.transpose()[['mean', 'max', 'min']].reset_index();        # just take mean, min, max;\n          df.rename(columns={'index':'city'});                         # some column renaming;\n          df.pivot(columns='city').mean().to_frame().reset_index();    # force to be one row per city;\n          df.rename(columns={0:'degrees'});                            # some column renaming;\n          pd.concat([df, df['city']+'_'+df['level_0']], axis=1);       # add new column combining city and summary measurement label;\n          df.rename(columns={0:'measurement'});                        # some column renaming;\n          df[['measurement', 'degrees']].set_index('measurement');     # just take two columns we want;\n          df.sort_index();                                             # sort by city name;\n          df.transpose();                                              # transpose so its just one wide row;\n      - name: \"temperature_current_by_city\"\n        title: \"Temperature By City - Current\"\n        family: \"temperature.current\"\n        context: \"pandas.temperature\"\n        type: \"line\"\n        units: \"Celsius\"\n        df_steps: >\n          pd.DataFrame.from_dict(\n              {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}&current_weather=true').json()['current_weather']\n              for (city,lat,lng)\n              in [\n                  ('dublin', 53.3441, -6.2675),\n                  ('athens', 37.9792, 23.7166),\n                  ('london', 51.5002, -0.1262),\n                  ('berlin', 52.5235, 13.4115),\n                  ('paris', 48.8567, 2.3510),\n                  ('madrid', 40.4167, -3.7033),\n                  ('new_york', 40.71, -74.01),\n                  ('los_angeles', 34.05, -118.24),\n                  ]\n              }\n              );\n          df.transpose();\n          df[['temperature']];\n          df.transpose();\n\n```\n##### API CSV Example\n\nexample showing a read_csv from a url and some light pandas data wrangling.\n\n```yaml\nexample_csv:\n    name: \"example_csv\"\n    update_every: 2\n    chart_configs:\n      - name: \"london_system_cpu\"\n        title: \"London System CPU - Ratios\"\n        family: \"london_system_cpu\"\n        context: \"pandas\"\n        type: \"line\"\n        units: \"n\"\n        df_steps: >\n          pd.read_csv('https://london.my-netdata.io/api/v1/data?chart=system.cpu&format=csv&after=-60', storage_options={'User-Agent': 'netdata'});\n          df.drop('time', axis=1);\n          df.mean().to_frame().transpose();\n          df.apply(lambda row: (row.user / row.system), axis = 1).to_frame();\n          df.rename(columns={0:'average_user_system_ratio'});\n          df*100;\n\n```\n##### API JSON Example\n\nexample showing a read_json from a url and some light pandas data wrangling.\n\n```yaml\nexample_json:\n    name: \"example_json\"\n    update_every: 2\n    chart_configs:\n      - name: \"london_system_net\"\n        title: \"London System Net - Total Bandwidth\"\n        family: \"london_system_net\"\n        context: \"pandas\"\n        type: \"area\"\n        units: \"kilobits/s\"\n        df_steps: >\n          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']);\n          df.drop('time', axis=1);\n          abs(df);\n          df.sum(axis=1).to_frame();\n          df.rename(columns={0:'total_bandwidth'});\n\n```\n##### XML Example\n\nexample showing a read_xml from a url and some light pandas data wrangling.\n\n```yaml\nexample_xml:\n    name: \"example_xml\"\n    update_every: 2\n    line_sep: \"|\"\n    chart_configs:\n      - name: \"temperature_forcast\"\n        title: \"Temperature Forecast\"\n        family: \"temp\"\n        context: \"pandas.temp\"\n        type: \"line\"\n        units: \"celsius\"\n        df_steps: >\n          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')|\n          df.rename(columns={'value': 'dublin'})|\n          df[['dublin']]|\n\n```\n##### SQL Example\n\nexample showing a read_sql from a postgres database using sqlalchemy.\n\n```yaml\nsql:\n    name: \"sql\"\n    update_every: 5\n    chart_configs:\n      - name: \"sql\"\n        title: \"SQL Example\"\n        family: \"sql.example\"\n        context: \"example\"\n        type: \"line\"\n        units: \"percent\"\n        df_steps: >\n          pd.read_sql_query(\n            sql='\\\n                select \\\n                    random()*100 as metric_1, \\\n                    random()*100 as metric_2 \\\n              ',\n            con=create_engine('postgresql://localhost/postgres?user=netdata&password=netdata')\n            );\n\n```\n",
+            "setup": "## Setup\n\n### Prerequisites\n\n#### Python Requirements\n\nThis collector depends on some Python (Python 3 only) packages that can usually be installed via `pip` or `pip3`.\n\n```bash\nsudo pip install pandas requests\n```\n\nNote: If you would like to use [`pandas.read_sql`](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html) to query a database, you will need to install the below packages as well.\n\n```bash\nsudo pip install 'sqlalchemy<2.0' psycopg2-binary\n```\n\n\n\n### Configuration\n\n#### File\n\nThe configuration file name for this integration is `python.d/pandas.conf`.\n\n\nYou can edit the configuration file using the `edit-config` script from the\nNetdata [config directory](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration.md#the-netdata-config-directory).\n\n```bash\ncd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata\nsudo ./edit-config python.d/pandas.conf\n```\n#### Options\n\nThere are 2 sections:\n\n* Global variables\n* One or more JOBS that can define multiple different instances to monitor.\n\nThe following options can be defined globally: priority, penalty, autodetection_retry, update_every, but can also be defined per JOB to override the global values.\n\nAdditionally, the following collapsed table contains all the options that can be configured inside a JOB definition.\n\nEvery configuration JOB starts with a `job_name` value which will appear in the dashboard, unless a `name` parameter is specified.\n\n\n| Name | Description | Default | Required |\n|:----|:-----------|:-------|:--------:|\n| chart_configs | an array of chart configuration dictionaries | [] | yes |\n| chart_configs.name | name of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.title | title of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.family | [family](https://github.com/netdata/netdata/blob/master/docs/dashboard/netdata-charts.md#families) of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.context | [context](https://github.com/netdata/netdata/blob/master/docs/dashboard/netdata-charts.md#contexts) of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.type | the type of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.units | the units of the chart to be displayed in the dashboard. | None | yes |\n| chart_configs.df_steps | a series of pandas operations (one per line) that each returns a dataframe. | None | yes |\n| update_every | Sets the default data collection frequency. | 5 | no |\n| priority | Controls the order of charts at the netdata dashboard. | 60000 | no |\n| autodetection_retry | Sets the job re-check interval in seconds. | 0 | no |\n| penalty | Indicates whether to apply penalty to update_every in case of failures. | yes | no |\n| name | Job name. This value will overwrite the `job_name` value. 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. |  | no |\n\n#### Examples\n\n##### Temperature API Example\n\nexample pulling some hourly temperature data, a chart for today forecast (mean,min,max) and another chart for current.\n\n```yaml\ntemperature:\n    name: \"temperature\"\n    update_every: 5\n    chart_configs:\n      - name: \"temperature_forecast_by_city\"\n        title: \"Temperature By City - Today Forecast\"\n        family: \"temperature.today\"\n        context: \"pandas.temperature\"\n        type: \"line\"\n        units: \"Celsius\"\n        df_steps: >\n          pd.DataFrame.from_dict(\n            {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}&hourly=temperature_2m').json()['hourly']['temperature_2m']\n            for (city,lat,lng)\n            in [\n                ('dublin', 53.3441, -6.2675),\n                ('athens', 37.9792, 23.7166),\n                ('london', 51.5002, -0.1262),\n                ('berlin', 52.5235, 13.4115),\n                ('paris', 48.8567, 2.3510),\n                ('madrid', 40.4167, -3.7033),\n                ('new_york', 40.71, -74.01),\n                ('los_angeles', 34.05, -118.24),\n                ]\n            }\n            );\n          df.describe();                                               # get aggregate stats for each city;\n          df.transpose()[['mean', 'max', 'min']].reset_index();        # just take mean, min, max;\n          df.rename(columns={'index':'city'});                         # some column renaming;\n          df.pivot(columns='city').mean().to_frame().reset_index();    # force to be one row per city;\n          df.rename(columns={0:'degrees'});                            # some column renaming;\n          pd.concat([df, df['city']+'_'+df['level_0']], axis=1);       # add new column combining city and summary measurement label;\n          df.rename(columns={0:'measurement'});                        # some column renaming;\n          df[['measurement', 'degrees']].set_index('measurement');     # just take two columns we want;\n          df.sort_index();                                             # sort by city name;\n          df.transpose();                                              # transpose so its just one wide row;\n      - name: \"temperature_current_by_city\"\n        title: \"Temperature By City - Current\"\n        family: \"temperature.current\"\n        context: \"pandas.temperature\"\n        type: \"line\"\n        units: \"Celsius\"\n        df_steps: >\n          pd.DataFrame.from_dict(\n              {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}&current_weather=true').json()['current_weather']\n              for (city,lat,lng)\n              in [\n                  ('dublin', 53.3441, -6.2675),\n                  ('athens', 37.9792, 23.7166),\n                  ('london', 51.5002, -0.1262),\n                  ('berlin', 52.5235, 13.4115),\n                  ('paris', 48.8567, 2.3510),\n                  ('madrid', 40.4167, -3.7033),\n                  ('new_york', 40.71, -74.01),\n                  ('los_angeles', 34.05, -118.24),\n                  ]\n              }\n              );\n          df.transpose();\n          df[['temperature']];\n          df.transpose();\n\n```\n##### API CSV Example\n\nexample showing a read_csv from a url and some light pandas data wrangling.\n\n```yaml\nexample_csv:\n    name: \"example_csv\"\n    update_every: 2\n    chart_configs:\n      - name: \"london_system_cpu\"\n        title: \"London System CPU - Ratios\"\n        family: \"london_system_cpu\"\n        context: \"pandas\"\n        type: \"line\"\n        units: \"n\"\n        df_steps: >\n          pd.read_csv('https://london.my-netdata.io/api/v1/data?chart=system.cpu&format=csv&after=-60', storage_options={'User-Agent': 'netdata'});\n          df.drop('time', axis=1);\n          df.mean().to_frame().transpose();\n          df.apply(lambda row: (row.user / row.system), axis = 1).to_frame();\n          df.rename(columns={0:'average_user_system_ratio'});\n          df*100;\n\n```\n##### API JSON Example\n\nexample showing a read_json from a url and some light pandas data wrangling.\n\n```yaml\nexample_json:\n    name: \"example_json\"\n    update_every: 2\n    chart_configs:\n      - name: \"london_system_net\"\n        title: \"London System Net - Total Bandwidth\"\n        family: \"london_system_net\"\n        context: \"pandas\"\n        type: \"area\"\n        units: \"kilobits/s\"\n        df_steps: >\n          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']);\n          df.drop('time', axis=1);\n          abs(df);\n          df.sum(axis=1).to_frame();\n          df.rename(columns={0:'total_bandwidth'});\n\n```\n##### XML Example\n\nexample showing a read_xml from a url and some light pandas data wrangling.\n\n```yaml\nexample_xml:\n    name: \"example_xml\"\n    update_every: 2\n    line_sep: \"|\"\n    chart_configs:\n      - name: \"temperature_forcast\"\n        title: \"Temperature Forecast\"\n        family: \"temp\"\n        context: \"pandas.temp\"\n        type: \"line\"\n        units: \"celsius\"\n        df_steps: >\n          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')|\n          df.rename(columns={'value': 'dublin'})|\n          df[['dublin']]|\n\n```\n##### SQL Example\n\nexample showing a read_sql from a postgres database using sqlalchemy.\n\n```yaml\nsql:\n    name: \"sql\"\n    update_every: 5\n    chart_configs:\n      - name: \"sql\"\n        title: \"SQL Example\"\n        family: \"sql.example\"\n        context: \"example\"\n        type: \"line\"\n        units: \"percent\"\n        df_steps: >\n          pd.read_sql_query(\n            sql='\\\n                select \\\n                    random()*100 as metric_1, \\\n                    random()*100 as metric_2 \\\n              ',\n            con=create_engine('postgresql://localhost/postgres?user=netdata&password=netdata')\n            );\n\n```\n",
             "troubleshooting": "## Troubleshooting\n\n### Debug Mode\n\nTo troubleshoot issues with the `pandas` collector, run the `python.d.plugin` with the debug option enabled. The output\nshould give you clues as to why the collector isn't working.\n\n- Navigate to the `plugins.d` directory, usually at `/usr/libexec/netdata/plugins.d/`. If that's not the case on\n  your system, open `netdata.conf` and look for the `plugins` setting under `[directories]`.\n\n  ```bash\n  cd /usr/libexec/netdata/plugins.d/\n  ```\n\n- Switch to the `netdata` user.\n\n  ```bash\n  sudo -u netdata -s\n  ```\n\n- Run the `python.d.plugin` to debug the collector:\n\n  ```bash\n  ./python.d.plugin pandas debug trace\n  ```\n\n",
             "alerts": "## Alerts\n\nThere are no alerts configured by default for this integration.\n",
             "metrics": "## Metrics\n\nMetrics grouped by *scope*.\n\nThe scope defines the instance that the metric belongs to. An instance is uniquely identified by a set of labels.\n\nThis collector is expecting one row in the final pandas DataFrame. It is that first row that will be taken\nas the most recent values for each dimension on each chart using (`df.to_dict(orient='records')[0]`).\nSee [pd.to_dict()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_dict.html).\"\n\n\n### Per Pandas instance\n\nThese metrics refer to the entire monitored application.\n\n\nThis scope has no labels.\n\nMetrics:\n\n| Metric | Dimensions | Unit |\n|:------|:----------|:----|\n\n",