1. DW1 iris

# Import Libraries
import pandas as pd
from sklearn import preprocessing

# Load Dataset
path = "Iris.csv"
dt = pd.read_csv(path)

# Display Dataset
print(dt)

# First and Last Records
print(dt.head())
print(dt.tail())

# Shape of Dataset
print(dt.shape)

# Column Names
print(dt.columns)
print("-----------------------------")
# Data Types
print(dt.dtypes)
print("-----------------------------")
# Statistical Information
print(dt.describe())
print("-----------------------------")
# Complete Statistics
print(dt.describe(include='all'))
print("-----------------------------")
# Check Missing Values
print(dt.isnull())
print(dt.isnull().sum())
print("-----------------------------")
# Normalize Numerical Data
min_max_scaler = preprocessing.MinMaxScaler()

x = dt.iloc[:,1:5]

x_scaler = min_max_scaler.fit_transform(x)

dt_normalized = pd.DataFrame(x_scaler)

print(dt_normalized)

# Convert Categorical Data into Numeric
label_encoder = preprocessing.LabelEncoder()

dt['Species'] = label_encoder.fit_transform(dt['Species'])

print(dt['Species'].unique())