rpgd60

# Preliminary investigation
# Shape of training data (num_rows, num_columns)
print(X_train.shape)

# Number of missing values in each column of training data
missing_val_count_by_column = (X_train.isnull().sum())
print(missing_val_count_by_column[missing_val_count_by_column > 0])

# Result
'''

rpgd60

# Source: https://jakevdp.github.io/PythonDataScienceHandbook/05.08-random-forests.html
#
def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):
    ax = ax or plt.gca()
    
    # Plot the training points
    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,
               clim=(y.min(), y.max()), zorder=3)
    ax.axis('tight')
    ax.axis('off')

rpgd60

# Typical / useful include
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
import pandas_profiling as pp
import plotly.express as px
import plotly.graph_objs as go
from plotly.offline import iplot
import plotly.express as px

rpgd60

import numpy as np; 
import pandas as pd
import matplotlib.pyplot as plt

np.random.seed(42)
# df from np.ndarray
df = pd.DataFrame(data = np.random.random(size=(4,4))*100, columns = ['A','B','C','D'])

df.plot(kind='box')
plt.show()

rpgd60

import numpy as np

positions = ['GK', 'M', 'A', 'D']
heights = [191, 184, 185, 180]

# Convert positions and heights to numpy arrays: np_positions, np_heights
np_heights = np.array(heights)
np_positions = np.array(positions)

# Heights of the goalkeepers: gk_heights

rpgd60

---
Parameters:
  ipBlockParam:
    Description:  IP cidr block for VPC
    Default: "172.16.0.0/16"
    Type: String
    MinLength: '9'
    MaxLength: '18'
    AllowedPattern: '(\d{1,3})\.(\d{1,3})\.(\d{1,3})\.(\d{1,3})/(\d{1,2})'
    ConstraintDescription: must be a valid IP CIDR range of the form x.x.x.x/x.

rpgd60

# Source: https://github.com/terraform-providers/terraform-provider-aws/blob/master/examples/eks-getting-started/workstation-external-ip.tf
#
# Workstation External IP
#
# This configuration is not required and is
# only provided as an example to easily fetch
# the external IP of your local workstation to
# configure inbound EC2 Security Group access
# to the Kubernetes cluster.
#

rpgd60

terraform {
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = ">= 4.6" # v3.38.0 minimal version to use default tags
    }
  }
}

provider "aws" {

rpgd60

data "aws_ami" "amazon_linux2_kernel_5" {
  most_recent = true
  owners      = ["amazon"]

  filter {
    name   = "name"
    values = ["amzn2-ami-kernel-5.10-hvm-2.0*x86_64-gp2"]
  }
  filter {

rpgd60

## Assume aws_instance resource called "flask1"
output "public_ip" {
  description = "Public IP of instance"
  value       = aws_instance.flask1.public_ip
}


output "key_name" {
  description = "SSH Key Name"
  value       = aws_instance.flask1.key_name