Python Certification Onlie Training in Oxford UK

 

Python Data Cleansing by Pandas-Numpy | Python Data Operations:

1. Python Data Cleansing-Objective:

• • In our last Python tutorial, we studied Aggregation and Data Wrangling with Python. Today, we will discuss Python Data Cleansing tutorial, aims to deliver a brief introduction to the operations of data cleansing and how to carry your data in Python Programming. For this purpose, we will use two libraries- pandas and numpy. Moreover, we will discuss different ways to cleanse the missing data.

So, lets start the Python Data Cleansing.

• • Prerequisites
  • Operations on Data using NumPy
  • Operations on Date using Pandas
  • Python Data Cleansing
  • Ways to Cleans Missing Data in Python

2. Python Data Cleansing-Prerequisites

• • As mentioned earlier, we will need two libraries for Python Data Cleansing-Python pandas and Python numpy.

a. Pandas

• • Python pandas is an excellent software library for manipulating data and analyzing it. It will let us manipulate numerical tables and time series using data structures and operations.

b. Numpy

• • Python numpy is another library we will use here. It lets us handle arrays and matrices, especially those multidimensional. It also provides several high-level mathematical functions to help us operate on these.

3. Python Data Cleansing Operations on Data using NumPy

• • While dtype lets us tell the interpreter of the data type to use, admin, lets us define the minimum dimension.

4. Python Data Cleansing Operations on Data Using pandas

• • DataFrame-Pandas DataFrame is a data structure that holds data in two dimensions- as rows and columns.
  • Panel-Pandas panel holds data in three dimensions. Etymologically, the term panel data from one source for the name pandas.
  • Series-Pandas Series holds data in one dimension, in a labeled format. The index is the set of axis labels.

5. Python Data Cleansing

• • When some part of our data is missing, due to whichever reason, the accuracy of our predictions plummets. In our article on data wrangling and aggregation, we discussed missing data and how to drop it. Lets see how we can deal with this issue.
  • In real-time situations like the comment section of our website. The name and email are mandatory, but the input for website can be left empty. Some users may not run a website to be eligible to fill in this information. In ways like this and others, we may end up with missing data in some places. How should we go about with this? Let’s find out.
  • Python Pandas will depict a missing value as NaN, which is short for Not a Number. Simply using the reindex() method will fill in NaN for blank values.

6. Ways to Cleanse Missing Data in Python To perform a Python data cleansing, you can drop the missing values, replace them, replace each NaN with a scalar value, or fill forward or backward.

• • Dropping Missing Values
  • Replacing Missing Values
  • Replacing with a Scalar Value
  • Filling Forward or Backward

7. Python Data Cleansing-Other Operations While cleaning data, we may also need to find out more about it and manipulate it. Below, we make use of some of these operations.

• • Renaming Columns
  • Making Changes Stay

Relational Database with Python: 1. Python Relational Database In our last Python Database tutorial, we checked how to work with NoSQL databases with Python. In the same way today we talk about working of Relational Database with Python Programming Language. Moreover, we will read some important prerequisites of Relational Database and how to read a relation table. Along with this, we will get to know how to delete an entire table and a single row in relation database with Python.

• • Prerequisities
  • Reading a Relational Table
  • Inserting Values
  • Deleting Values

2. Prerequisites for Relational Database

• • SQLAlchemy
  • Pandas
  • A Sample CSV File

3. Reading a Relational Table

• • A relational table is an ordered set of columns. Because these columns are sequentially ordered, we can identify each one, and distinguish it from all the other columns of that table. In a database, we use column names to identify each column.

4. Insert Values in Relational Database with Python

• • Connect to the MySQL database server by creating a new MySQLConnection object.
  • Initiate a MySQLCursor object from the MySQLConnection object.
  • Execute the INSERT statement to insert data into the table.
  • Close the database connection.

5. Delete Values in Relational Database with Python

• Deleting the Entire Table
• Deleting a Single Row