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# How to get correlation coefficient

Correlation coefficient is a measure of the strength of the relationship between two variables. It ranges from -1 to 1, where -1 represents a perfect negative correlation, 0 represents no correlation, and 1 represents a perfect positive correlation. In other words, it tells us how much two variables change together.

There are various ways to calculate correlation coefficient in Python. One of the most common ways is to use the `pandas.DataFrame.corr()` function from the Pandas library. This function calculates the pairwise correlation of columns in a DataFrame and returns a DataFrame containing the correlation coefficients. The default method used by this function is Pearson correlation coefficient, which is the most commonly used method for continuous variables.

Another way to calculate the correlation coefficient in Python is to use the `numpy.corrcoef()` function from the Numpy library. This function calculates the correlation coefficient of two given arrays. The default method used by this function is also Pearson correlation coefficient.

You can also use the `scipy.stats.pearsonr()` function from the Scipy library, which calculates the Pearson correlation coefficient and the p-value for testing non-correlation.

In summary, Correlation coefficient is a measure of the strength of the relationship between two variables. It can be calculated in Python by using the `pandas.DataFrame.corr()` function from the Pandas library, which calculates the pairwise correlation of columns in a DataFrame, or the `numpy.corrcoef()` function from the Numpy library, which calculates the correlation coefficient of two given arrays or `scipy.stats.pearsonr()` function, which calculates the Pearson correlation coefficient and the p-value for testing non-correlation. The default method used by these functions is Pearson correlation coefficient, which is the most commonly used method for continuous variables.

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