<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
What is Normalisation in DBMS?<|im_end|>
<|im_start|>assistant
Normalization is a process in database management systems (DBMS) that is used to minimize data redundancy and improve data integrity. Normalization is the process of organizing data in a database so that data is stored only once, and relationships between data elements are established through the use of foreign keys.

The goal of normalization is to reduce data redundancy and improve data consistency by ensuring that each data element is stored in only one place, and by ensuring that the relationships between data elements are properly defined. This helps to avoid data inconsistencies, such as multiple copies of the same data, and it makes it easier to maintain and update the database over time.

There are several levels of normalization, ranging from First Normal Form (1NF) to Fifth Normal Form (5NF). Each level of normalization imposes a set of rules and constraints on the data in the database to ensure that it is properly structured and optimized for efficient data retrieval and modification.

Normalization is an important process in database design, and it is used by many organizations to ensure that their databases are well-structured, consistent, and easy to maintain and update over time.<|im_end|>
<|im_start|>user
Here is an example code block for normalizing a database table in Python using the Pandas library:

sql

import pandas as pd

# Load the data into a pandas dataframe
df = pd.read_csv("data.csv")

# Drop any duplicates in the dataframe
df = df.drop_duplicates()

# Create a list of tables based on the dataframe
tables = [df]

# Function to normalize a table
def normalize_table(table):
  # Split the table into smaller tables based on the relationships between the columns
  tables = []
  for column in table.columns:
    # Create a new table for each unique value in the column
    unique_values = table[column].unique()
    for value in unique_values:
      new_table = table[table[column] == value]
      new_table = new_table.drop(column, axis=1)
      tables.append(new_table)

  return tables

# Normalize each table in the list
for table in tables:
  tables = tables + normalize_table(table)

# Write the normalized tables to separate CSV files
for i, table in enumerate(tables):
  table.to_csv(f"table_{i}.csv", index=False)<|im_end|>
<|im_start|>assistant
Yes, this code block demonstrates how to normalize a database table using the Python Pandas library. In this example, a dataframe is created by loading data from a CSV file and then removing any duplicate rows. Then, a function is defined to split the dataframe into smaller tables based on the relationships between the columns. Finally, the normalized tables are written to separate CSV files.<|im_end|>
<|im_start|>user
Can you give me an example of taking an unnormalised database and a normalised one?<|im_end|>
<|im_start|>assistant
Sure! Let's consider an example of a student database.

An unnormalized form of the database may look like this:



STUDENT_ID | NAME | AGE | COURSE | ADDRESS | PHONE_NO
--------------------------------------------------------------
1          | John | 20  | BTech  | ABC      | 1234567890
2          | Jane | 22  | BSc    | DEF      | 9876543210
3          | Joe  | 21  | BCom   | GHI      | 1111111111

This unnormalized form of the database has several problems:

    Data redundancy: The course and address information is repeated for each student.
    Inconsistent data: If the address of a student changes, it needs to be updated in multiple records.
    Data anomalies: Insertion, deletion, and update anomalies may occur. For example, if a new student is added without an address, the database will contain an inconsistent record.

A normalized form of the same database may look like this:


Table: STUDENT
-----------------
STUDENT_ID | NAME | AGE | COURSE_ID
-------------------------------------------------
1          | John | 20  | 1
2          | Jane | 22  | 2
3          | Joe  | 21  | 3

Table: COURSE
-----------------
COURSE_ID | COURSE_NAME
-----------------------
1         | BTech
2         | BSc
3         | BCom

Table: ADDRESS
-----------------
STUDENT_ID | ADDRESS
-----------------------
1          | ABC
2          | DEF
3          | GHI

Table: PHONE
-----------------
STUDENT_ID | PHONE_NO
-----------------------
1          | 1234567890
2          | 9876543210
3          | 1111111111

This normalized form of the database eliminates the problems of the unnormalized form:

    Data redundancy: The information is stored in separate tables, reducing the amount of duplicate data.
    Inconsistent data: If the address of a student changes, it only needs to be updated in one record.
    Data anomalies: Insertion, deletion, and update anomalies are reduced. For example, if a new student is added without an address, only one table will contain an inconsistent record.<|im_end|>