What Is the Key Data Structure in Pandas Called?

The key data structure in Pandas is called the DataFrame. It is a two-dimensional labeled data structure that consists of rows and columns, similar to a table or spreadsheet. The DataFrame is one of the most powerful and widely used features of Pandas, making it an essential tool for data manipulation and analysis in Python.

Creating a DataFrame:
To create a DataFrame in Pandas, you can start with a dictionary where the keys represent the column names and the values represent the data for each column. Here’s an example:

import pandas as pd

data = {'Name': ['John', 'Emma', 'Michael', 'Sophia'],
        'Age': [25, 28, 30, 27],
        'City': ['New York', 'London', 'Paris', 'Tokyo']}

df = pd.DataFrame(data)

In this example, we have created a DataFrame with three columns: “Name”, “Age”, and “City”. Each column contains corresponding data for each person.

Accessing Data in a DataFrame:
Once you have created a DataFrame, you can access and manipulate its data easily. Here are some common operations:

• df.head(): This method returns the first few rows of the DataFrame. By default, it returns the first five rows.
• df.tail(): This method returns the last few rows of the DataFrame.

  By default, it returns the last five rows.shape: This attribute returns a tuple representing the dimensions of the DataFrame (number of rows, number of columns).columns: This attribute returns a list of column names in the DataFrame.

• df[‘ColumnName’]: This syntax allows you to access a specific column in the DataFrame by its name. It returns a Series object.
• df[[‘Column1’, ‘Column2’]]: This syntax allows you to access multiple columns in the DataFrame by providing a list of column names. It returns a new DataFrame containing only the selected columns.

Manipulating Data in a DataFrame:
Pandas provides various methods and functions to manipulate data within a DataFrame. Some common operations include:

• df.drop(): This method allows you to drop rows or columns from the DataFrame based on specified labels.sort_values(): This method sorts the DataFrame by one or more columns.groupby(): This method groups the data in the DataFrame based on specified columns, allowing for aggregation and analysis of grouped data.merge(): This method combines two DataFrames based on common columns or indices.apply(): This method applies a function to each element or row/column of the DataFrame.

Conclusion:
In summary, the DataFrame is the key data structure in Pandas that enables efficient manipulation and analysis of tabular data. With its intuitive syntax and powerful functionality, it has become an indispensable tool for data scientists and analysts working with Python.

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