# Pandas
Plugin: python.d.plugin
Module: pandas
## 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.
## 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 |
|:------|:----------|:----|
## Alerts
There are no alerts configured by default for this integration.
## 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
#### File
The configuration file name for this integration is `python.d/pandas.conf`.
You can edit the configuration file using the `edit-config` script from the
Netdata [config directory](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md#the-netdata-config-directory).
```bash
cd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata
sudo ./edit-config python.d/pandas.conf
```
#### 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.
Config options
| Name | 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/cloud/visualize/interact-new-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/cloud/visualize/interact-new-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 |
Config
```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}¤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();
```
Config
```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;
```
Config
```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'});
```
Config
```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']]|
```
Config
```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')
);
```