robbertvdg

"loki" has been added to your repositories
"prometheus-community" has been added to your repositories
Hang tight while we grab the latest from your chart repositories...
...Successfully got an update from the "loki" chart repository
...Successfully got an update from the "prometheus-community" chart repository
Update Complete. ⎈ Happy Helming!⎈
[I 10-16 11:53:28.492 core:202] minio_version: updated from RELEASE.2020-09-17T04-49-20Z to RELEASE.2020-10-12T21-53-21Z
[I 10-16 11:53:30.221 core:202] prometheus_operator_chart_version: updated from 9.3.1 to 9.3.2
[I 10-16 11:53:30.222 core:202] loki_stack_version: updated from 0.40.0 to 0.41.2
[I 10-16 11:53:31.125 core:202] kubernetes_version: updated from v1.18.6 to v1.19.3

robbertvdg

helm repo add loki https://grafana.github.io/loki/charts
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
sed -e 's/:[^:\/\/]/ /g' -ne '/.* .*[0-9]\./p' -ne '/RELEASE/p' versions.yaml > current_versions.txt
nvchecker source.ini

robbertvdg

[__config__]
oldver = current_versions.txt
newver = new_versions.txt

[jupyterlab_version]
pypi = jupyterlab
use_pre_release = false

[kubernetes_version]
github = kubernetes/kubernetes

robbertvdg

jupyterlab_version 2.2.8
kubernetes_version v1.18.6
minio_version RELEASE.2020-09-17T04-49-20Z 
loki_stack_version 0.40.0
prometheus_operator_chart_version 9.3.1

robbertvdg

# versions used in the ansible playbook
jupyterlab_version: 2.2.8
kubernetes_version: v1.18.6
loki_stack_version: 0.40.0
minio_version: RELEASE.2020-09-17T04-49-20Z
prometheus_operator_chart_version: 9.3.1

robbertvdg

listings = listings.sample(frac=0.2, random_state=1337)

def optimized_merge(df1, df2, merge_column):
    df2 = df2[df2[merge_column].isin(df1[merge_column])]
    return df1.merge(df2, on=merge_column)

%%timeit
listings.merge(reviews, on='listing_id')
# 106 ms ± 2.46 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

robbertvdg

import pandas as pd
from typing import List


def optimize_floats(df: pd.DataFrame) -> pd.DataFrame:
    floats = df.select_dtypes(include=['float64']).columns.tolist()
    df[floats] = df[floats].apply(pd.to_numeric, downcast='float')
    return df

robbertvdg

listings = listings.set_index('listing_id', drop=False)

%%timeit
listings.loc[29844866, 'name']
# 10.1 µs ± 1.25 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)

%%timeit
listings.at[29844866, 'name']
# 5.34 µs ± 474 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

robbertvdg

%%timeit
listings.merge(reviews, on='listing_id')
# 439 ms ± 24.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
reviews_ = reviews.set_index('listing_id')
listings_ = listings.set_index('listing_id')
listings_.merge(reviews_, left_index=True, right_index=True)
# 393 ms ± 17.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

robbertvdg

%%timeit 
listings.loc[listings['room_type'] == 'Entire home/apt', 'room_type_score'] = 1
listings.loc[listings['room_type'] == 'Private room', 'room_type_score'] = 0.7
listings['room_type_score'].fillna(0.2, inplace=True)
listings.loc[listings['number_of_reviews'] > 50, 'review_score'] = 1
listings['review_score'].fillna(0.5, inplace=True)
listings['price_score'] = (100 - listings['price']) / 100
listings['score'] = listings['room_type_score'] * listings['price_score'] * listings['review_score']
listings.loc[(listings['availability_365'] == 0) | 
             (listings['price'] > 100), 'score'] = 0