Question

Which of the following correctly matches a function to its purpose?(1 point)
Responses

data["column"].value_counts()

displays the number of columns

data["column"].value_counts() displays the number of columns

data.describe()

displays the type of data in each column

data.describe() displays the type of data in each column

data.info()

displays the count, mean, min, max, standard deviation, and quartiles for all numerical data

data.info() displays the count, mean, min, max, standard deviation, and quartiles for all numerical data

data.shape()

displays the number of rows and columns in the data

Answer 1

Here’s the correct matching of functions to their purposes:

1. **data["column"].value_counts()** - Displays the count of unique values in the specified column, not the number of columns.

2. **data.describe()** - Displays count, mean, min, max, standard deviation, and quartiles for numerical data.

3. **data.info()** - Displays a summary of the DataFrame including the number of non-null entries in each column and the data types.

4. **data.shape** - Displays the number of rows and columns in the data.

From your provided options, the closest correct matches would be:

- **data["column"].value_counts()** does not correctly match the function to its purpose.
- **data.describe()** correctly matches with displaying the count, mean, min, max, standard deviation, and quartiles for all numerical data.
- **data.info()** does not display the count, mean, min, etc.—it provides an overview of the DataFrame.
- **data.shape** correctly matches with displaying the number of rows and columns in the data.

Based on this analysis, the only accurately stated option is:

**data.shape** - displays the number of rows and columns in the data.