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# If you want to filter rows by a certain number of columns with null values, you may use this:
df.iloc[df[(df.isnull().sum(axis=1) >= qty_of_nuls)].index]

# update each value in column while iterating over the dataframe row by row
for index_label, row_series in df.iterrows():
   # For each row update the 'Bonus' value to it's double
   df.at[index_label, 'Bonus'] = row_series['Bonus'] * 2
 
# select column by multiple range
df_10_2_rank.iloc[:, np.r_[0,16:31]]

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## THE CODE IS SELF EXPLANATORY AND COMMENTED 

## loading some dependencies
import gensim
from gensim.utils import simple_preprocess
from gensim.parsing.preprocessing import STOPWORDS
from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.stem.porter import *
import nltk
nltk.download('wordnet')

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# If you come from bash you might have to change your $PATH.
# export PATH=$HOME/bin:/usr/local/bin:$PATH

# Path to your oh-my-zsh installation.
export ZSH="/home/zhibo/.oh-my-zsh"

# Set name of the theme to load --- if set to "random", it will
# load a random theme each time oh-my-zsh is loaded, in which case,
# to know which specific one was loaded, run: echo $RANDOM_THEME
# See https://github.com/ohmyzsh/ohmyzsh/wiki/Themes

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set nocompatible " not vi compatible

syntax on           " enable syntax processing

" Spaces & Tabs
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
set tabstop=4       " number of visual spaces per TAB
set softtabstop=4   " number of spaces in tab when editing
set shiftwidth=4    " Insert 4 spaces on a tab
set expandtab       " tabs are spaces, mainly because of python

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w Go to the beginning of next word 
e Go to the end of current word 
b Go to the beginning of previous word 
0 Go to the starting of current line 
$ Go to the end of current line 
g_ Go to the last non blank character of current line

H Go to the first line of current screen. 
L Go to the last line of current screen 
gg Go to the top of the file 

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from tabulate import tabulate
print(tabulate(df, tablefmt="plain"))

'''
Supported table formats are:

"plain"
"simple"
"github"
"grid"

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python -m ipykernel install --user --name thisenv

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import tensorflow as tf
print(tf.test.is_built_with_cuda())

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

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force the remove of the image even if it is used by containers or having multiple tags

docker rmi -f

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# PyTorch device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
X = torch.randn(n_points, 2).to(device)