The correct answer is D. corr().
The corr() method calculates the correlation matrix for a DataFrame, showing the relationships between numerical columns. The correlation coefficient is a measure of the strength and direction of the linear relationship between two variables. A correlation coefficient of 1 indicates a perfect positive correlation, a correlation coefficient of -1 indicates a perfect negative correlation, and a correlation coefficient of 0 indicates no correlation.
The groupby() method groups DataFrame rows by a specified column or columns. The describe() method calculates descriptive statistics for a DataFrame, such as the mean, median, standard deviation, and count. The pivot_table() method creates a pivot table from a DataFrame. A pivot table is a data summarization tool that allows you to analyze data across multiple dimensions.
Here is an example of how to use the corr() method to calculate the correlation matrix for a DataFrame:
“`
import pandas as pd
df = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6], ‘C’: [7, 8, 9]})
df.corr()
“`
This will return a DataFrame with the following columns:
- A: The correlation coefficient between the A column and each other column in the DataFrame.
- B: The correlation coefficient between the B column and each other column in the DataFrame.
- C: The correlation coefficient between the C column and each other column in the DataFrame.
- A.B: The correlation coefficient between the A column and the B column.
- A.C: The correlation coefficient between the A column and the C column.
- B.C: The correlation coefficient between the B column and the C column.
The correlation matrix can be used to identify which columns are correlated with each other. For example, the correlation coefficient between the A column and the B column is 0.8, which indicates that there is a strong positive correlation between these two columns. This means that when the A column increases, the B column also tends to increase.