from pydotplus import graphviz

# Example graph creation
graph = graphviz.Dot(graph_type='digraph')
node_a = graphviz.Node('A')
node_b = graphviz.Node('B')
graph.add_node(node_a)
graph.add_node(node_b)
graph.add_edge(graphviz.Edge(node_a, node_b))

# Save and visualize
graph.write_png("example_graph.png")
from IPython.display import Image
Image(graph.create_png())

import pylru

cache = pylru.lrucache(100)

cache

<pylru.lrucache at 0x112997fd0>

# add 
cache['name'] = 'One'

cache['name']

'One'

# add 
cache['city'] = 'Toronto'

'city' in cache # this doesn't affect the cache order

True

cache.keys()

<generator object lrucache …

a = 30
b = 6

c = a / b

print(c)

5.0

import sys
print(sys.path)

['', '/Users/rajacsp/anaconda3/envs/py36/lib/python36.zip', '/Users/rajacsp/anaconda3/envs/py36/lib/python3.6', '/Users/rajacsp/anaconda3/envs/py36/lib/python3.6/lib-dynload', '/Users/rajacsp/anaconda3/envs/py36/lib/python3.6 …

import os
import sys

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, ForeignKey, Integer, String

Base = declarative_base()

class City(Base):
    __tablename__ = 'city'
    # Here we …

import os
import sys

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, ForeignKey, Integer, String

Base = declarative_base()

class City(Base):
    __tablename__ = 'city'
    # Here we …

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x …

import matplotlib.pyplot as plt
import pandas as pd
from math import pi

df = pd.DataFrame({
    'group' : ['A', 'B', 'C', 'D'],

    'var1' : [10, 20, 30, 4],
    'var2' : [2, 88, 22, 2]
})

df

# copied from https://python-graph-gallery.com/390-basic-radar-chart/

# Libraries
import matplotlib.pyplot as plt
import pandas as pd
from math import pi

# Set data
df = pd.DataFrame({
'group': ['A','B','C','D'],

'java': [38, 1.5, 30, 4 …

import pandas as pd
from yellowbrick.features import RadViz

def load_yb_data(name = 'occupancy'):
    folder_path = name+'/'+name
    return pd.read_csv('/Users/rajacsp/datasets/yb_data/'+(folder_path)+'.csv')

# Load the classification data set
data = load_yb_data("occupancy")

# Specify the features …