Sangarshanan

To randomize a dataset: shuf=data.iloc[np.random.permutation(len(data))] 
sh = shuf.reset_index(drop=true)

TO KNOW NULL
data.isnull().sum()

TO DELETE FROM DICT
del dna_counts['e']

RAMDOM FOREST

Sangarshanan

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
set_session(tf.Session(config=config))


By default system uses max GPU and that seems to return an error while training

Sangarshanan

Threshold of 0.5 is used by default (for binary problems) to convert predicted probabilities into class predictions

Threshold can be adjusted to increase sensitivity or specificity

Sensitivity and specificity have an inverse relationship

Increasing one would always decrease the other

Adjusting the threshold should be one of the last step you do in the model-building process

Sangarshanan

When you wanna sort

10 2 5
7 1 0
9 9 9
1 23 12
6 5 9

with respect to element 1 (2,1,9,23,5)

Sangarshanan

--------------------------------
S = 'abcdef'
N = 3

print(list(zip(*[iter(S)] * N)))

will Print: [('a','b','c'),('d','e','f')]
--------------------------------
ROTATE AN LIST 

Sangarshanan

SALES WHERE SHOP_ID IS 26,27 OR 28
sales = sales[sales['shop_id'].isin([26, 27, 28])]

Sangarshanan

r'b[aeiou]{2}t'	  b followed by two vowels, followed by t
r'A\d{3,}'	      The letter A followed by 3 or more digits
r'[A-Z]{,5}'	    Zero to five capital letters
r'\w{3,7}'	      Three to seven “word” characters

Sangarshanan

import psycopg2
import random, string
hostname = 'localhost'
username = 'postgres'
password = '*****'
database = 'pgroute'
myConnection = psycopg2.connect(host=hostname, user=username, password=password, dbname=database )

# Simple routine to run a query on a database and print the results:
def doQuery( conn, query ) :

Sangarshanan

import pandas as pd
import urllib.request

### GRAPHOPPER API ###
urlStart = 'http://localhost:8989/route/?'
point = 'point='
urlEnd = '&type=gpx&instructions=false&vehicle=car'
separator = '%2C'

### SOURCE POINT ###

Sangarshanan

from yellowbrick.text import TSNEVisualizer,UMAPVisualizer
from sklearn.feature_extraction.text import TfidfVectorizer,CountVectorizer

def visualize(dim_reduction,encoding,corpus_data,corpus_target,labels = True,alpha=0.7,metric=None):
    if 'tfidf' in encoding.lower():
        encode  = TfidfVectorizer()
    if 'count' in encoding.lower():
        encode = CountVectorizer()
    docs   = encode.fit_transform(corpus_data)
    if labels is True: