<!--startmeta
custom_edit_url: "https://github.com/netdata/netdata/edit/master/src/collectors/python.d.plugin/pandas/README.md"
meta_yaml: "https://github.com/netdata/netdata/edit/master/src/collectors/python.d.plugin/pandas/metadata.yaml"
sidebar_label: "Pandas"
learn_status: "Published"
learn_rel_path: "Collecting Metrics/Collectors/Databases"
keywords: ['pandas', 'python']
message: "DO NOT EDIT THIS FILE DIRECTLY, IT IS GENERATED BY THE COLLECTOR'S metadata.yaml FILE"
endmeta-->

# Pandas


<img src="https://netdata.cloud/img/pandas.png" width="150"/>


Plugin: python.d.plugin
Module: pandas

<img src="https://img.shields.io/badge/maintained%20by-Netdata-%2300ab44" />

## Overview

[Pandas](https://pandas.pydata.org/) is a de-facto standard in reading and processing most types of structured data in Python.
If you have metrics appearing in a CSV, JSON, XML, HTML, or [other supported format](https://pandas.pydata.org/docs/user_guide/io.html),
either locally or via some HTTP endpoint, you can easily ingest and present those metrics in Netdata, by leveraging the Pandas collector.

This collector can be used to collect pretty much anything that can be read by Pandas, and then processed by Pandas.


The collector uses [pandas](https://pandas.pydata.org/) to pull data and do pandas-based preprocessing, before feeding to Netdata.


This collector is supported on all platforms.

This collector supports collecting metrics from multiple instances of this integration, including remote instances.


### Default Behavior

#### Auto-Detection

This integration doesn't support auto-detection.

#### Limits

The default configuration for this integration does not impose any limits on data collection.

#### Performance Impact

The default configuration for this integration is not expected to impose a significant performance impact on the system.

## Setup


### Prerequisites

#### Python Requirements

This collector depends on some Python (Python 3 only) packages that can usually be installed via `pip` or `pip3`.

```bash
sudo pip install pandas requests
```

Note: 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.

```bash
sudo pip install 'sqlalchemy<2.0' psycopg2-binary
```



### Configuration

#### Options

There are 2 sections:

* Global variables
* One or more JOBS that can define multiple different instances to monitor.

The following options can be defined globally: priority, penalty, autodetection_retry, update_every, but can also be defined per JOB to override the global values.

Additionally, the following collapsed table contains all the options that can be configured inside a JOB definition.

Every configuration JOB starts with a `job_name` value which will appear in the dashboard, unless a `name` parameter is specified.


<details open><summary>Config options</summary>



| Option | Description | Default | Required |
|:-----|:------------|:--------|:---------:|
| chart_configs | an array of chart configuration dictionaries | [] | yes |
| chart_configs.name | name of the chart to be displayed in the dashboard. | None | yes |
| chart_configs.title | title of the chart to be displayed in the dashboard. | None | yes |
| chart_configs.family | [family](https://github.com/netdata/netdata/blob/master/docs/dashboards-and-charts/netdata-charts.md#families) of the chart to be displayed in the dashboard. | None | yes |
| chart_configs.context | [context](https://github.com/netdata/netdata/blob/master/docs/dashboards-and-charts/netdata-charts.md#contexts) of the chart to be displayed in the dashboard. | None | yes |
| chart_configs.type | the type of the chart to be displayed in the dashboard. | None | yes |
| chart_configs.units | the units of the chart to be displayed in the dashboard. | None | yes |
| chart_configs.df_steps | a series of pandas operations (one per line) that each returns a dataframe. | None | yes |
| update_every | Sets the default data collection frequency. | 5 | no |
| priority | Controls the order of charts at the netdata dashboard. | 60000 | no |
| autodetection_retry | Sets the job re-check interval in seconds. | 0 | no |
| penalty | Indicates whether to apply penalty to update_every in case of failures. | yes | no |
| 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 |


</details>



#### via File

The configuration file name for this integration is `python.d/pandas.conf`.

The file format is YAML. Generally, the structure is:

```yaml
update_every: 1
autodetection_retry: 0

job_name:
  job_option1: some_value
  job_option2: some_other_vlaue
```
You can edit the configuration file using the [`edit-config`](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration/README.md#edit-configuration-files) script from the
Netdata [config directory](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration/README.md#locate-your-config-directory).

```bash
cd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata
sudo ./edit-config python.d/pandas.conf
```

##### Examples

###### Temperature API Example

example pulling some hourly temperature data, a chart for today forecast (mean,min,max) and another chart for current.

<details open><summary>Config</summary>

```yaml
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}&current_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();

```
</details>

###### API CSV Example

example showing a read_csv from a url and some light pandas data wrangling.

<details open><summary>Config</summary>

```yaml
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;

```
</details>

###### API JSON Example

example showing a read_json from a url and some light pandas data wrangling.

<details open><summary>Config</summary>

```yaml
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'});

```
</details>

###### XML Example

example showing a read_xml from a url and some light pandas data wrangling.

<details open><summary>Config</summary>

```yaml
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']]|

```
</details>

###### SQL Example

example showing a read_sql from a postgres database using sqlalchemy.

<details open><summary>Config</summary>

```yaml
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')
            );

```
</details>



## Alerts

There are no alerts configured by default for this integration.


## Metrics

Metrics grouped by *scope*.

The scope defines the instance that the metric belongs to. An instance is uniquely identified by a set of labels.

This collector is expecting one row in the final pandas DataFrame. It is that first row that will be taken
as the most recent values for each dimension on each chart using (`df.to_dict(orient='records')[0]`).
See [pd.to_dict()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_dict.html)."


### Per Pandas instance

These metrics refer to the entire monitored application.


This scope has no labels.

Metrics:

| Metric | Dimensions | Unit |
|:------|:----------|:----|



## Troubleshooting

### Debug Mode


To troubleshoot issues with the `pandas` collector, run the `python.d.plugin` with the debug option enabled. The output
should give you clues as to why the collector isn't working.

- Navigate to the `plugins.d` directory, usually at `/usr/libexec/netdata/plugins.d/`. If that's not the case on
  your system, open `netdata.conf` and look for the `plugins` setting under `[directories]`.

  ```bash
  cd /usr/libexec/netdata/plugins.d/
  ```

- Switch to the `netdata` user.

  ```bash
  sudo -u netdata -s
  ```

- Run the `python.d.plugin` to debug the collector:

  ```bash
  ./python.d.plugin pandas debug trace
  ```

### Getting Logs

If you're encountering problems with the `pandas` collector, follow these steps to retrieve logs and identify potential issues:

- **Run the command** specific to your system (systemd, non-systemd, or Docker container).
- **Examine the output** for any warnings or error messages that might indicate issues.  These messages should provide clues about the root cause of the problem.

#### System with systemd

Use the following command to view logs generated since the last Netdata service restart:

```bash
journalctl _SYSTEMD_INVOCATION_ID="$(systemctl show --value --property=InvocationID netdata)" --namespace=netdata --grep pandas
```

#### System without systemd

Locate the collector log file, typically at `/var/log/netdata/collector.log`, and use `grep` to filter for collector's name:

```bash
grep pandas /var/log/netdata/collector.log
```

**Note**: This method shows logs from all restarts. Focus on the **latest entries** for troubleshooting current issues.

#### Docker Container

If your Netdata runs in a Docker container named "netdata" (replace if different), use this command:

```bash
docker logs netdata 2>&1 | grep pandas
```


