How to Identify Periodicity and Correlation?

Introduction

Before learning how we can identify periodicity and correlation, let us first get a brief introduction to the Pandas module.

Pandas is an open-source package (or library) that provides us with highly optimized data structures and data analysis tools. Pandas library is very fast and comes with a lot of handy tools which makes it very useful in terms of high performance and productivity.

Let us now learn how we can identify periodicity and correlation.

What is Periodicity?

Periodicity refers to the tendency of occurring of a certain element at a certain interval in a large data set or DataFrames. Now, to get the periodicity of an element in Pandas, we can use the Pandas.infer_freq() function. Let us now learn about the Pandas.infer_freq() function in the next section in detail with example(s).

How to Find Periodicity in A Time-Series Column?

To find the periodicity in a time-series column, we can use the Pandas.infer_freq() function. The Pandas.infer_freq() function tells us the most likely frequency from the provided input index.

The Pandas.infer_freq() function takes two parameters namely:

• index: The index parameter is used to denote the DatetimeIndex or TimedeltaIndex. We can even pass a series and if a series is passed then the value of the series is used in place of the index.
• warn: It is the second parameter that is deprecated since version 1.5.0 and its default value is set to True.

The Pandas.infer_freq() returns a string or a None value. None is returned when there is no occurrence of discernible frequency.

The Pandas.infer_freq() function also raises two types of exceptions when the syntax is wrong.

• TypeError: The exception is raised if the provided index is not a dataTime-like index.
• ValueError: The exception is raised if there are fewer values than three values.

Using Infer_freq()

Let us see the working of the Pandas.infer_freq() function with an example.

Output:

What is Correlation?

Correlation means summarizing the strength and the direction of the linear association between the two variables (they are quantitative type variables). The correlation is denoted by the symbol r and the range of correlation is from -1 to +1.

The positive value of r depicts that the association is positive while the negative value of r depicts a negative association. In Python, we have the corr() function that helps to calculate the correlation in DataFrame. The correlation is calculated between two columns of the DataFrame.

Perfect Correlation

The perfect correlation is when the r value comes out to be 1.000000. The perfect correlation depicts that each column is always having a perfect relationship with itself. So, the r value makes sense.

Good Correlation

If the r value is near to 1 it means that the correlation is a good relation. We can take an example of anything like calories and can see if we got some greater value in calories than we have done a good workout.

Bad Correlation

If the r value is far from 1 it means that the correlation is a bad relationship. We can take an example of anything like pulse rate and can see if we got some lesser value then we have done a bad workout.

How to Find Correlation in A Pandas Data Frame?

To find the correlation in a Pandas DataFrame, we can use the corr() function. The syntax of the corr() function is:

The corr() function computes the pairwise correlation between the columns. This function does not include the null values or the NA values.

The corr() function takes various parameters which some are an option as well. The parameters of the corr() function are as follows:

• method: This parameter can be the Pearson correlation coefficient, or Kendall correlation coefficient, or Spearman rank correlation. The method parameter can also take a callable (having the input of two 1-D arrays) as a value.
• min_periods: The min_periods parameter is an optional parameter that is of integer type and it depicts the minimum number of observations that are required for a pair of columns to get a valid result or output. This parameter is only available for the Pearson and the Spearman correlation.
• numeric_only: This parameter is of the boolean type whose default value is True and it depicts the what type of value to be included like only boolean value or only integer values or only floating point value.

The corr() function returns a DataFrame of the correlational matrix after calculating the correlation.

Using the Corr() Function

Let us take an example to understand the working of the corr() function in more detail.

Output:

In the above code, we have tried to correlate two different entities. Since a cat is completely identical to a cat and the same goes for a dog. So, we can see in the output matrix that for the dog-dog relation we are getting the r value 1.0. But for the dog-cat relation, we are getting NaN are they both are not related. The same thing goes for the next column.