data4sci

grep CRON /var/log/syslog

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crontab -l # LIST
crontab -e # EDIT

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grep --include='*.ipynb' --exclude-dir='.ipynb_checkpoints' -rliw . -e 'SEARCH QUERY ...'

# https://groups.google.com/forum/#!topic/jupyter/Qi9b7z_sgRU
# https://stackoverflow.com/questions/16956810/how-do-i-find-all-files-containing-specific-text-on-linux

# možno spustit i v integrovaném terminále

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# vypsat obsah seznamu (list) oddělený čárkami bez uvozovek
print('[%s]' % ', '.join(map(str, my_list)))

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%whos           # magic - vypíše alokované proměnné
del var1        # smaže proměnnou var1 

import gc
gc.collect()    # uvolní paměť po smazaných proměnných (okamžitě, jinak řeší Garbage Collector až když je potřeba...)

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# https://stackoverflow.com/questions/49280261/jupyter-lab-shortcuts

{
    // Move cell up
    "notebook:move-cell-up": {
      "selector": ".jp-Notebook:focus",
      "command": "notebook:move-cell-up",
      "keys": [
        "Ctrl ArrowUp"
      ]

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# změna velikosti jednoho obrázku 
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(15, 15))


# To change the default settings, and therefore all your plots:
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [15, 15]

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#one easy way by using Pandas: (here I want to use mean normalization)
normalized_df=(df-df.mean())/df.std()

#to use min-max normalization:
normalized_df=(df-df.min())/(df.max()-df.min())

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# jednotlivé procesy
smem -t -P chromium-browser

# celkový součet
smem -t -k -c pss -P chromium-browser | tail -n 1

# nebo 
ps aux | grep "chromium-browser" | awk '{print $5}' | awk '{sum += $1 } END { print sum }'

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# https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html
# https://towardsdatascience.com/style-pandas-dataframe-like-a-master-6b02bf6468b0
# https://stackoverflow.com/questions/41203959/conditionally-format-python-pandas-cell
# https://stackoverflow.com/questions/38931566/pandas-style-background-gradient-both-rows-and-columns
# https://kanoki.org/2019/01/02/pandas-trick-for-the-day-color-code-columns-rows-cells-of-dataframe/

df.style.background_gradient(cmap='coolwarm')