Unique value on columns – pandas


Today is a short example on cases that have longer columns with spaces.

For example. I have a dataframe that has the following columns:

I have read in some sources that you can use the construction wine_new.[column name].unique() to filter the values.

If you have a one word column, it will work, but if the column is listed as multiple words, you can not use a construct like wine_new.’Page ID’.unique() because it will give a syntax error.

Good, so you try to rename it. why Page ID and not pageid? Ok, that should be easy

wine_new = wine_new.rename(columns={"Page ID": "pageid"}, errors="raise")

And it now looks “better”.

But if you need to keep the column name, you can just as easily use wine_new[‘Page ID’].unique() (If you want to count the number of unique values you can also use wine_new[‘Page ID’].nunique())

There are multiple resources on this topic but the approach is not explained using both of the versions on the majority of them.


cloud machine learning python

Prometheus metrics to Pandas data frame


We are trying to implement a decision tree algorithm in order to see if our resource usage can classify our servers in different categories.

First step in that process is querying Prometheus from Python and create some data frames with some basic information in order to get them aggregated.

To that purpose, you can also use the following lines of code:

import requests
import copy 

URL = "http://[node_hostname]:9090/api/v1/query?query=metric_to_be_quried[1d]"
r = requests.get(url = URL) 

data = r.json()

metric_list = []
for i in data['data']['result']:
    data_dict = copy.deepcopy(i['metric'])
    for j in i['values']:
        data_dict['time'] = j[0]
        data_dict['value'] = j[1]

df_metric = pd.DataFrame(metric_list)

Other pieces will follow.



Renice until cgroup implementation for process of Yahoo CMAK


We saw that ex Kafka Manager, now called Yahoo CMAK was using more than enough CPU in some cases, in general related to bad SSL client config.

It’s not really clear if the CPU usage was real or there was only wait time for resource like memory or I/O (I don’t have an example to post right now, but there are multiple fixes for this).

The easiest one is to change the nice value for usage. What I observed is that normally it starts with nice value of 0. I guess this is default. General check for this works with

ps ax -o ni,cmd | grep cmak | grep -v grep

In order to change this, you can add a crontab line with following command:

pid=`ps ax -o pid,cmd | grep cmak | grep -v grep |  awk {'print $1'}`; ni=`ps ax -o ni,cmd | grep cmak | grep -v grep |  awk {'print $1'}`; if [ "$ni" = "0" ]; then renice 10 $pid; fi

Or, even easier than that, add Nice value under [Service] in /etc/systemd/system/

It does the trick until further cgroup policies are applied.

cloud newtools puppet

Datadog and GCP are “friends” up to a point


Since in the last period I preferred to publish more on Medium, let me give you the link to the latest article.

There is an interesting case in which the combination of automation, Goggle Cloud Platform and Datadog didn’t go as we expected.

Hope you enjoy! I will get back with more also with interesting topics on this blog also.


cloud python

Optimizing VM costs in GCP


I recently published on Medium the hole story related to

You can find it at

Enjoy the read!

cloud puppet

Overriding OS fact with external one


Short notice article. We had a issue in which the traefik module code was not running because of a wrong os fact. Although the image is Ubuntu 14.04, facter returns it like:

  architecture => "amd64",
  family => "Debian",
  hardware => "x86_64",
  name => "Debian",
  release => {
    full => "jessie/sid",
    major => "jessie/sid"
  selinux => {
    enabled => false

I honestly don’t know why this happens since on rest of machines it works good, the way to fix it fast is by defining an external fact in /etc/facter/facts.d

Create a file named os_fact.json, for example, that will contain this content:

         "description":"Ubuntu 14.04.6 LTS",

And it’s fixed.


machine learning

Starting AIOps journey – first step

There is a learning program in our company focused on gaining knowledge for “AI era”

To that purpose we played a little bit with some performance data and came to some conclusions.

I invite you to take a look


Duplicate exported resources on puppet by mistake

We had a strange problem in our test environment the other day. There is a need to share an authorized key in order for the ssh connectivity to be available.

The way we shared the file resource was straight forward.

  @@file {"/home/kafka/.ssh/authorized_keys":
    ensure => present,
    mode => '0600',
    owner => 'kafka',
    group => 'kafka',
    content => "${::sharedkey}",
    tag => "${::tagvalue}",

The tag value variable was a fact unique to each Kafka cluster.

However, each time we executed puppet, the following error the following error was present:

08:38:20 Error: Could not retrieve catalog from remote server: Error 500 on SERVER: Server Error: A duplicate resource was found while collecting exported resources, with the type and title File[/home/kafka/.ssh/authorized_keys] on node [node_name]

We had a couple of days at our disposal to play with the puppet DB, nothing relevant came from it

This behavior started after provisioning a second cluster named similar also with SSL enabled.

After taking a look on the official Puppet documentation ( – check the caution clause), it was clear that the naming of resource should not be the same.

The problem hadn’t appear on any of our clusters since now, so this was strange to say the least.

For whatever reason, the tag was not taken into consideration.

And we know that because resources shared on both nodes were put everywhere, there was no filtering.


Quick fix was done with following modifications.

  @@file {"/home/kafka/.ssh/authorized_keys_${::clusterid}":
    path => "/home/kafka/.ssh/authorized_keys",
    ensure => present,
    mode => '0600',
    owner => 'kafka',
    group => 'kafka',
    content => "${::sharedkey}",
    tag => "${::clusterid}",

So now there is an individual file per cluster, and we also have a tag that is recognized in order to filter the shared file that we need on our server.

Filtering will be done like File <<| tag == "${::clusterid}" |>>


cloud puppet python

Strange problem in puppet run for Ubuntu


Short sharing of a strange case.

We’ve written a small manifest in order to distribute some python scripts. You can find the reference here:

When you try to run it on Ubuntu 14.04, there is this very strange error:

Error: Failed to apply catalog: [nil, nil, nil, nil, nil, nil]

The cause for this is as follows:

Python 3.4.3 (default, Nov 12 2018, 22:25:49)
[GCC 4.8.4] on linux (and I believe this is the default max version on trusty)

In order to install the dependencies, you need python3-pip, so a short search returns following options:

apt search python3-pip
Sorting... Done
Full Text Search... Done
python3-pip/trusty-updates,now 1.5.4-1ubuntu4 all [installed]
  alternative Python package installer - Python 3 version of the package

python3-pipeline/trusty 0.1.3-3 all
  iterator pipelines for Python 3

If we want to list all the installed modules with pip3 list, guess what, it’s not working:

Traceback (most recent call last):
   File "/usr/bin/pip3", line 5, in 
     from pkg_resources import load_entry_point
   File "/usr/local/lib/python3.4/dist-packages/pkg_resources/", line 93, in 
     raise RuntimeError("Python 3.5 or later is required")
 RuntimeError: Python 3.5 or later is required

So, main conclusion is that it’s not related to puppet, just the incompatibility between version for this old distribution.



Small addition for ‘cat’ in Python


There was a issue on options that aggregate any other ones, like -A for my previous post

In my view the easiest way to solve it is by storing the options in a tuple.

Here is the snippet

run_options = []
    opts, args = getopt.gnu_getopt(sys.argv[1:-1], 'AbeEnstTv', ['show-all', 'number-nonblank', 'show-ends', 'number', 'show-blank', 'squeeze-blank' 'show-tabs', 'show-nonprinting', 'help', 'version'])
except getopt.GetoptError:
     print("Something went wrong")
for opt, arg in opts:
    if opt in ('-A','--show-all'):
    elif opt in ('-b', '--number-nonblank'):
    elif opt in ('-n', '--number'):
    elif opt in ('-E', '--show-ends'):
    elif opt in ('-s', '--squeeze-blank'):
    elif opt in ('-T', '--show-tabs'):
final_run_options = tuple(run_options)
for element in final_run_options:
    if element == 'b':
        content_list = number_nonempty_lines(content_list)
    elif element == 'n':
        content_list = number_all_lines(content_list)   
    elif element == 'E':
        content_list = display_endline(content_list)
    elif element == 's':
        content_list = squeeze_blanks(content_list)
    elif element == 'T':
        content_list = show_tabs(content_list)

So basically, you store the actual cases in a list which you convert to a tuple to eliminate duplicates. Once you have the final case, you parse it and change the actual content option by option.

I didn’t have the time to test it but there is no big reason why it should’t work.