Python Tables Guide: Using Pandas DataFrames

In Python, a Table is a data structure used for storing and organizing data. Typically, a table consists of rows and columns, with each cell containing a data element. There are several libraries available in Python for creating and manipulating tables, with pandas being the most commonly used one.

The basic steps for creating and manipulating tables using the pandas library are as follows:

1. Import the pandas library as pd.
2. Create a table: You can use the DataFrame object in pandas to create a table. DataFrame objects can be created using dictionaries, lists, or other data structures.
3. For example, create a table using a dictionary.
4. Create a DataFrame using the given data with columns for Name, Age, and City containing the corresponding values.
5. This will create a table with three columns (‘Name’, ‘Age’, and ‘City’).
6. Performing operations on a table: One can manipulate tables by using various methods and functions provided by pandas, such as selecting specific rows and columns, adding or deleting rows and columns, filtering data, etc.
7. For example, selecting specific columns:
8. Select the column ‘Name’ from the dataframe.
9. Filter data:
10. df[df[‘Age’] is greater than 25] # Selecting rows with age greater than 25
11. Performing analysis and manipulation on tables: The table can be analyzed and manipulated using various functions and methods provided by pandas, such as calculating statistics, performing aggregation operations, and creating charts.
12. For example, calculating the average age:
13. Calculate the average age in the pandas dataframe.
14. Create a bar chart:
15. Create a bar plot with the data displayed using ‘Name’ and ‘Age’ as the x and y axes, respectively.

This is just the basic usage of working with tables using the pandas library, pandas also offers more advanced features and methods for further learning and usage based on specific needs.