Question:
__________ method can also be used to change the data values of a row to a particular value.
__________ method can also be used to change the data values of a row to a particular value.
Show Hint
The ".loc[]" indexer is label-based, while ".iloc[]" is integer-location based.
Both are powerful tools for updating data values dynamically in a DataFrame.
Both are powerful tools for updating data values dynamically in a DataFrame.
Updated On: Jun 11, 2026
- DataFrame.values[]
- DataFrame.change[]
- DataFrame.loc[]
- DataFrame.datavalues[]
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The Correct Option is C
Solution and Explanation
•
Step 1: Understanding the Question:
We need to identify which method in Pandas is used to modify or update data values of a specific row or elements in a DataFrame.
•
Step 2: Key Formula or Approach:
In Pandas, indexers like .loc[] and .iloc[] are used for selecting, slicing, and modifying data.
Specifically, label-based indexing is performed using .loc[], which allows direct assignment of values to specific rows or columns.
•
Step 3: Detailed Explanation:
The .loc[] indexer is primarily label-based. It allows us to access a group of rows and columns by labels or a boolean array.
When we want to change the values of a specific row, we can index that row using df.loc[row_label] and assign a new value to it.
For example, df.loc['Row_1'] = 10 will change all values in the row with label 'Row_1' to 10.
Let's analyze the other options:
- DataFrame.values is an attribute that returns the raw NumPy representation of the DataFrame; it is not a method used for index assignment in this manner.
- DataFrame.change[] and DataFrame.datavalues[] do not exist in the Pandas library.
Therefore, DataFrame.loc[] is the correct and standard choice for accessing and modifying rows in a DataFrame.
•
Step 4: Final Answer:
The correct option is (C), which is DataFrame.loc[].
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