Netdata just got a Pandas collector.
Pandas is a de-facto standard in reading and processing most types of structured data in Python so if you have some csv/json/xml data, either locally or via some HTTP endpoint, containing metrics you’d like to monitor, chances are you can now easily do this by leveraging the Pandas collector without having to develop your own custom collector as you might have in the past.
Let’s take a look at a realistic example where we have some HTTP or API that returns json from which we would like to extract some metrics.
Monitoring weather data
We will use the awesome free api from Open-Meteo and the Pandas collector to pull some temperature forecasts for today across a range of cities and store the mean, min, and max for today in Netdata.
With the Pandas collector a user just needs to define a sequence of df_steps as part of their collector configuration. Below is the configuration used in this example. We will focus mostly on the df_steps parameter as that’s really where all the logic lives.
# example pulling some hourly temperature data
temperature:
name: "temperature"
update_every: 3
chart_configs:
- name: "temperature_by_city"
title: "Temperature By City"
family: "temperature.today"
context: "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),
]
}
); # use dictionary comprehension to make multiple requests;
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;
To make developing your own df_steps as easy as possible we have created this Google Colab notebook that lets you iterate and build up your code step by step, printing the output of each step along the way. There are some more examples in this notebook so feel free to duplicate it to work on your own use case.
Step by step
Each step needs to result in a Pandas DataFrame. This is a common pattern in data pipelining whereby we chain a series of transformations together, each step taking in a dataframe and outputting a transformed dataframe.
First we loop over a number of api calls to pull hourly temperature forecasts for each city in a starting dataframe.
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),
]
}
)
# =
# dublin athens london berlin paris
# 0 14.0 17.8 12.5 7.9 9.1
# 1 14.0 17.7 12.6 7.3 8.0
# 2 13.9 17.9 12.6 6.9 6.1
# 3 14.0 17.7 12.8 6.1 5.8
# 4 14.0 17.6 12.7 5.9 5.7
# .. ... ... ... ... ...
# 163 13.2 19.3 15.5 11.7 15.1
# 164 12.8 19.0 15.0 11.5 14.0
# 165 12.6 18.6 14.6 11.1 12.6
# 166 12.8 18.3 14.4 10.6 11.8
# 167 13.3 18.0 14.3 10.2 11.0
#
# [168 rows x 5 columns]
Next we aggregate this data to get summary statistics per city.
df.describe()
# =
# Dublin athens london berlin paris
# count 168.000000 168.000000 168.000000 168.000000 168.000000
# mean 12.008929 19.459524 12.513690 10.798214 12.059524
# std 2.442361 4.037315 3.044617 3.286672 4.046204
# min 6.600000 12.200000 5.200000 5.700000 4.800000
# 25% 10.675000 16.700000 9.775000 7.900000 8.475000
# 50% 12.800000 18.900000 12.550000 10.400000 11.750000
# 75% 13.900000 23.125000 14.900000 13.700000 15.825000
# max 15.300000 26.200000 18.900000 17.600000 19.300000
The next two steps filter to the metrics we want, reshape and rename some columns.
df.transpose()[['mean', 'max', 'min']].reset_index()
# =
# index mean max min
# 0 dublin 12.008929 15.3 6.6
# 1 athens 19.459524 26.2 12.2
# 2 London 12.513690 18.9 5.2
# 3 berlin 10.798214 17.6 5.7
# 4 paris 12.059524 19.3 4.8
df.rename(columns={'index':'city'})
# =
# city mean max min
# 0 dublin 12.008929 15.3 6.6
# 1 athens 19.459524 26.2 12.2
# 2 London 12.513690 18.9 5.2
# 3 berlin 10.798214 17.6 5.7
# 4 paris 12.059524 19.3 4.8
Now we have a table of data that’s what we want, the next steps are about reshaping this data to end up as one “wide” single row of data as that is what the collector expects to result from the last step.
df.pivot(columns='city').mean().to_frame().reset_index()
# =
# level_0 city 0
# 0 mean Athens 19.459524
# 1 mean berlin 10.798214
# 2 mean Dublin 12.008929
# 3 mean London 12.513690
# 4 mean paris 12.059524
# 5 max Athens 26.200000
# 6 max berlin 17.600000
# 7 max dublin 15.300000
# 8 max London 18.900000
# 9 max paris 19.300000
# 10 min Athens 12.200000
# 11 min berlin 5.700000
# 12 min dublin 6.600000
# 13 min London 5.200000
# 14 min paris 4.800000
df.rename(columns={0:'degrees'})
# =
# level_0 city degrees
# 0 mean Athens 19.459524
# 1 mean berlin 10.798214
# 2 mean Dublin 12.008929
# 3 mean London 12.513690
# 4 mean paris 12.059524
# 5 max Athens 26.200000
# 6 max berlin 17.600000
# 7 max Dublin 15.300000
# 8 max London 18.900000
# 9 max paris 19.300000
# 10 min athens 12.200000
# 11 min berlin 5.700000
# 12 min Dublin 6.600000
# 13 min london 5.200000
# 14 min paris 4.800000
pd.concat([df, df['city']+'_'+df['level_0']], axis=1)
# =
# level_0 city degrees 0
# 0 mean Athens 19.459524 athens_mean
# 1 mean berlin 10.798214 berlin_mean
# 2 mean dublin 12.008929 dublin_mean
# 3 mean London 12.513690 london_mean
# 4 mean paris 12.059524 paris_mean
# 5 max Athens 26.200000 athens_max
# 6 max berlin 17.600000 berlin_max
# 7 max Dublin 15.300000 dublin_max
# 8 max london 18.900000 london_max
# 9 max paris 19.300000 paris_max
# 10 min Athens 12.200000 athens_min
# 11 min berlin 5.700000 berlin_min
# 12 min dublin 6.600000 dublin_min
# 13 min London 5.200000 london_min
# 14 min paris 4.800000 paris_min
df.rename(columns={0:'measurement'})
# =
# level_0 city degrees measurement
# 0 mean athens 19.459524 athens_mean
# 1 mean berlin 10.798214 berlin_mean
# 2 mean dublin 12.008929 dublin_mean
# 3 mean London 12.513690 london_mean
# 4 mean paris 12.059524 paris_mean
# 5 max athens 26.200000 athens_max
# 6 max berlin 17.600000 berlin_max
# 7 max Dublin 15.300000 dublin_max
# 8 max London 18.900000 london_max
# 9 max paris 19.300000 paris_max
# 10 min Athens 12.200000 athens_min
# 11 min berlin 5.700000 berlin_min
# 12 min Dublin 6.600000 dublin_min
# 13 min London 5.200000 london_min
# 14 min paris 4.800000 paris_min
df[['measurement', 'degrees']].set_index('measurement')
# =
# degrees
# measurement
# athens_mean 19.459524
# berlin_mean 10.798214
# dublin_mean 12.008929
# london_mean 12.513690
# paris_mean 12.059524
# athens_max 26.200000
# berlin_max 17.600000
# dublin_max 15.300000
# london_max 18.900000
# paris_max 19.300000
# athens_min 12.200000
# berlin_min 5.700000
# dublin_min 6.600000
# london_min 5.200000
# paris_min 4.800000
Next we sort the data.
df.sort_index()
# =
# degrees
# measurement
# athens_max 26.200000
# athens_mean 19.459524
# athens_min 12.200000
# berlin_max 17.600000
# berlin_mean 10.798214
# berlin_min 5.700000
# dublin_max 15.300000
# dublin_mean 12.008929
# dublin_min 6.600000
# london_max 18.900000
# london_mean 12.513690
# london_min 5.200000
# paris_max 19.300000
# paris_mean 12.059524
# paris_min 4.800000
And finally we do one last transpose to go from a long format to a wide format of one row where is column is a metric we want Netdata to collect.
df.transpose()
# =
# measurement athens_max athens_mean athens_min berlin_max berlin_mean \
# degrees 26.2 19.459524 12.2 17.6 10.798214
#
# measurement berlin_min dublin_max dublin_mean dublin_min london_max \
# degrees 5.7 15.3 12.008929 6.6 18.9
#
# measurement london_mean london_min paris_max paris_mean paris_min
# degrees 12.51369 5.2 19.3 12.059524 4.8
This row is then converted (by the collector internally) into a python dictionary of key value pairs.
{'athens_max': 26.2, 'athens_mean': 19.45952380952381, 'athens_min': 12.2, 'berlin_max': 17.6, 'berlin_mean': 10.798214285714286, 'berlin_min': 5.7, 'dublin_max': 15.3, 'dublin_mean': 12.008928571428571, 'dublin_min': 6.6, 'london_max': 18.9, 'london_mean': 12.513690476190478, 'london_min': 5.2, 'paris_max': 19.3, 'paris_mean': 12.059523809523808, 'paris_min': 4.8}
And that’s it, this should end up in a chart in Netdata like below.
Try it yourself
Pandas is a truly amazing library that can usually accomplish almost any data processing task, so if you have some custom data you would like to monitor with Netdata but do not quite feel ready yet to develop your own custom collector - give the Pandas collector a go!
If you haven’t already, sign up now for a free Netdata account!
We’d love to hear from you – if you have any questions, complaints or feedback please reach out to us on Discord or Github.