Data Handling Using | Class 12 Informatics Practices Notes

2.3.2 Operations on rows and columns in DataFrames

Pandas DataFrames support various operations on rows and columns including addition, deletion, and renaming. Adding a new column is straightforward by assigning a list of values to a new column label; if the label exists, values are updated. Similarly, new rows can be added using the DataFrame.loc[] method by specifying the row label and corresponding values. Adding a row with an existing label updates that row. Entire columns or rows can be set to a single value. Deletion of rows or columns is done using the DataFrame.drop() method, where the axis parameter specifies whether to drop rows (axis=0) or columns (axis=1). Multiple rows or columns can be dropped by passing a list of labels. If duplicate row labels exist, all matching rows are deleted. Renaming of row or column labels is done using the DataFrame.rename() method, specifying a dictionary of old and new labels and the axis ('index' for rows, 'columns' for columns). If a label to rename does not exist, it is ignored. These operations allow flexible manipulation of tabular data in DataFrames.

🧪 Activity: Think and Reflect: Write a program to count rows and columns in a DataFrame.

🔗 Connection: This section leads to accessing DataFrame elements through indexing.