# netdata-pandas
> A helper library to pull data from netdata api into a pandas dataframe.
[](https://pypi.python.org/pypi/netdata-pandas/)
[](https://github.com/netdata/netdata-pandas/actions?query=workflow%3ACI)
## Install
`pip install netdata-pandas`
## Documentation
More detailed documentation can be found at https://netdata.github.io/netdata-pandas
## Quickstart
Get some data into a pandas dataframe.
```python
from netdata_pandas.data import get_data
df = get_data('london.my-netdata.io', ['system.cpu','system.load'], after=-60, before=0)
print(df.shape)
print(df.head())
```
(60, 12)
system.cpu|guest system.cpu|guest_nice system.cpu|iowait \
time_idx
1604928205 0.0 0.0 0.0
1604928206 0.0 0.0 0.0
1604928207 0.0 0.0 0.0
1604928208 0.0 0.0 0.0
1604928209 0.0 0.0 0.0
system.cpu|irq system.cpu|nice system.cpu|softirq \
time_idx
1604928205 0.0 0.0 0.0
1604928206 0.0 0.0 0.0
1604928207 0.0 0.0 0.0
1604928208 0.0 0.0 0.0
1604928209 0.0 0.0 0.0
system.cpu|steal system.cpu|system system.cpu|user \
time_idx
1604928205 0.000000 0.501253 0.501253
1604928206 0.000000 0.753769 0.502513
1604928207 0.000000 0.505050 0.505050
1604928208 0.000000 0.751880 0.501253
1604928209 0.251256 0.251256 0.502513
system.load|load1 system.load|load15 system.load|load5
time_idx
1604928205 0.03 0.0 0.04
1604928206 0.03 0.0 0.04
1604928207 0.03 0.0 0.04
1604928208 0.03 0.0 0.04
1604928209 0.03 0.0 0.04
An alternative way to call `get_data()` is to define what hosts and charts you want via the `host_charts_dict` param:
```python
# define list of charts for each host you want data for
host_charts_dict = {
"london.my-netdata.io" : ['system.io','system.ip'],
"newyork.my-netdata.io" : ['system.io','system.net'],
}
df = get_data(host_charts_dict=host_charts_dict, host_prefix=True)
print(df.shape)
print(df.head())
```
(61, 8)
london.my-netdata.io::system.io|in \
time_idx
1604928340 NaN
1604928341 0.0
1604928342 0.0
1604928343 0.0
1604928344 0.0
london.my-netdata.io::system.io|out \
time_idx
1604928340 NaN
1604928341 -53.89722
1604928342 -26.10278
1604928343 0.00000
1604928344 0.00000
london.my-netdata.io::system.ip|received \
time_idx
1604928340 NaN
1604928341 49.25227
1604928342 227.22840
1604928343 123.56787
1604928344 31.99060
london.my-netdata.io::system.ip|sent \
time_idx
1604928340 NaN
1604928341 -51.85469
1604928342 -85.22854
1604928343 -43.00154
1604928344 -19.55536
newyork.my-netdata.io::system.io|in \
time_idx
1604928340 0.0
1604928341 0.0
1604928342 0.0
1604928343 0.0
1604928344 0.0
newyork.my-netdata.io::system.io|out \
time_idx
1604928340 0.000000
1604928341 -6.545929
1604928342 -9.454071
1604928343 0.000000
1604928344 0.000000
newyork.my-netdata.io::system.net|received \
time_idx
1604928340 13.778033
1604928341 18.281470
1604928342 24.811770
1604928343 26.406000
1604928344 26.457510
newyork.my-netdata.io::system.net|sent
time_idx
1604928340 -16.97193
1604928341 -19.23857
1604928342 -76.86994
1604928343 -165.55492
1604928344 -115.83034
## Examples
You can find some more examples in the [examples](https://github.com/netdata/netdata-pandas/tree/master/examples) folder.
Or if you just want to play with it right now you can use [this Google Colab notebook](https://colab.research.google.com/drive/1SGF3Ij1r8gNJOwdk-3cVhCvyUGwGiTnc?usp=sharing) to quickly get started.
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"description": "# netdata-pandas\r\n> A helper library to pull data from netdata api into a pandas dataframe.\r\n\r\n\r\n[](https://pypi.python.org/pypi/netdata-pandas/) \r\n[](https://github.com/netdata/netdata-pandas/actions?query=workflow%3ACI)\r\n\r\n## Install\r\n\r\n`pip install netdata-pandas`\r\n\r\n## Documentation\r\n\r\nMore detailed documentation can be found at https://netdata.github.io/netdata-pandas\r\n\r\n## Quickstart\r\n\r\nGet some data into a pandas dataframe.\r\n\r\n```python\r\nfrom netdata_pandas.data import get_data\r\n\r\ndf = get_data('london.my-netdata.io', ['system.cpu','system.load'], after=-60, before=0)\r\nprint(df.shape)\r\nprint(df.head())\r\n```\r\n\r\n (60, 12)\r\n system.cpu|guest system.cpu|guest_nice system.cpu|iowait \\\r\n time_idx \r\n 1604928205 0.0 0.0 0.0 \r\n 1604928206 0.0 0.0 0.0 \r\n 1604928207 0.0 0.0 0.0 \r\n 1604928208 0.0 0.0 0.0 \r\n 1604928209 0.0 0.0 0.0 \r\n \r\n system.cpu|irq system.cpu|nice system.cpu|softirq \\\r\n time_idx \r\n 1604928205 0.0 0.0 0.0 \r\n 1604928206 0.0 0.0 0.0 \r\n 1604928207 0.0 0.0 0.0 \r\n 1604928208 0.0 0.0 0.0 \r\n 1604928209 0.0 0.0 0.0 \r\n \r\n system.cpu|steal system.cpu|system system.cpu|user \\\r\n time_idx \r\n 1604928205 0.000000 0.501253 0.501253 \r\n 1604928206 0.000000 0.753769 0.502513 \r\n 1604928207 0.000000 0.505050 0.505050 \r\n 1604928208 0.000000 0.751880 0.501253 \r\n 1604928209 0.251256 0.251256 0.502513 \r\n \r\n system.load|load1 system.load|load15 system.load|load5 \r\n time_idx \r\n 1604928205 0.03 0.0 0.04 \r\n 1604928206 0.03 0.0 0.04 \r\n 1604928207 0.03 0.0 0.04 \r\n 1604928208 0.03 0.0 0.04 \r\n 1604928209 0.03 0.0 0.04 \r\n\r\n\r\nAn alternative way to call `get_data()` is to define what hosts and charts you want via the `host_charts_dict` param:\r\n\r\n```python\r\n# define list of charts for each host you want data for\r\nhost_charts_dict = {\r\n \"london.my-netdata.io\" : ['system.io','system.ip'],\r\n \"newyork.my-netdata.io\" : ['system.io','system.net'],\r\n}\r\ndf = get_data(host_charts_dict=host_charts_dict, host_prefix=True)\r\nprint(df.shape)\r\nprint(df.head())\r\n```\r\n\r\n (61, 8)\r\n london.my-netdata.io::system.io|in \\\r\n time_idx \r\n 1604928340 NaN \r\n 1604928341 0.0 \r\n 1604928342 0.0 \r\n 1604928343 0.0 \r\n 1604928344 0.0 \r\n \r\n london.my-netdata.io::system.io|out \\\r\n time_idx \r\n 1604928340 NaN \r\n 1604928341 -53.89722 \r\n 1604928342 -26.10278 \r\n 1604928343 0.00000 \r\n 1604928344 0.00000 \r\n \r\n london.my-netdata.io::system.ip|received \\\r\n time_idx \r\n 1604928340 NaN \r\n 1604928341 49.25227 \r\n 1604928342 227.22840 \r\n 1604928343 123.56787 \r\n 1604928344 31.99060 \r\n \r\n london.my-netdata.io::system.ip|sent \\\r\n time_idx \r\n 1604928340 NaN \r\n 1604928341 -51.85469 \r\n 1604928342 -85.22854 \r\n 1604928343 -43.00154 \r\n 1604928344 -19.55536 \r\n \r\n newyork.my-netdata.io::system.io|in \\\r\n time_idx \r\n 1604928340 0.0 \r\n 1604928341 0.0 \r\n 1604928342 0.0 \r\n 1604928343 0.0 \r\n 1604928344 0.0 \r\n \r\n newyork.my-netdata.io::system.io|out \\\r\n time_idx \r\n 1604928340 0.000000 \r\n 1604928341 -6.545929 \r\n 1604928342 -9.454071 \r\n 1604928343 0.000000 \r\n 1604928344 0.000000 \r\n \r\n newyork.my-netdata.io::system.net|received \\\r\n time_idx \r\n 1604928340 13.778033 \r\n 1604928341 18.281470 \r\n 1604928342 24.811770 \r\n 1604928343 26.406000 \r\n 1604928344 26.457510 \r\n \r\n newyork.my-netdata.io::system.net|sent \r\n time_idx \r\n 1604928340 -16.97193 \r\n 1604928341 -19.23857 \r\n 1604928342 -76.86994 \r\n 1604928343 -165.55492 \r\n 1604928344 -115.83034 \r\n\r\n\r\n## Examples\r\n\r\nYou can find some more examples in the [examples](https://github.com/netdata/netdata-pandas/tree/master/examples) folder. \r\n\r\nOr if you just want to play with it right now you can use [this Google Colab notebook](https://colab.research.google.com/drive/1SGF3Ij1r8gNJOwdk-3cVhCvyUGwGiTnc?usp=sharing) to quickly get started.\r\n",
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