i am trying to write python pandas dataframe but it showing errors

Title: Troubleshooting Common Errors in Pandas DataFrames
Introduction:
Python's Pandas library is a powerful tool for data manipulation and analysis, but like any other programming endeavor, you may encounter errors as you work with it. This tutorial will guide you through some common errors that you might encounter when working with Pandas DataFrames and provide solutions with code examples.
Before diving into specific error scenarios, make sure you have Pandas installed. You can install it using pip:
Let's get started with some common errors and how to fix them:
Importing Pandas:
Error: "ModuleNotFoundError: No module named 'pandas'"
Solution: This error occurs when Pandas is not installed. Make sure you've installed Pandas as described above.
Loading Data:
Creating DataFrames:
Error: "ValueError: Shape of passed values is (X, Y), indices imply (A, B)"
Solution: This error occurs when the data you're trying to create a DataFrame with doesn't match in dimensions. Ensure your data matches in rows and columns. Example:
Accessing Columns:
Error: "KeyError: 'Column_Name'"
Solution: Verify that the column name 'Column_Name' exists in your DataFrame. Check for typos or case sensitivity. Access columns using square brackets:
Indexing and Slicing:
Error: "TypeError: 'DataFrame' object is not subscriptable"
Solution: DataFrames are not subscriptable like lists or arrays. To access specific rows or columns, use iloc, loc, or simple slicing:
Missing Values:
Error: "AttributeError: 'DataFrame' object has no attribute 'fillna'"
Solution: This error occurs when trying to use a method that doesn't exist. Check the method name and use the correct one. To fill missing values, use fillna():
Data Types:
Error: "TypeError: data type 'int' not understood"
Solution: Pandas often struggles with mixed data types in a column. Ensure that each column has a consistent data type. You can use the astype() method to convert a column to a specific data type:
Conclusion:
Pandas is a versatile library, but it's common to encounter errors while working with DataFrames. Troubleshooting these errors is an essential skill for any data analyst or scientist. By understanding these common issues and how to resolve them, you'll be better equipped to work with da