ewm() Method in Pandas

What is the ewm() Method in Pandas?

Suppose we are keeping track of something that is changing according to the time. It can be the score in a game. As we know, sometimes the latest scores matter more than the older scores. So, Pandas provides a method called the ewm(). This helps to figure out the important information first. It does this by giving extra attention to recent information and not stressing too much about old information.

There is an important concept called Exponentially Weighted Moving Average(EWMA). This concept is also embraced by the ewm() method in Pandas. This concept is a type of moving average, and it gives more weight to the recent information and less weight to the past information. 

**As we have discussed, Pandas consist of series and DataFrame data structure. So, the ewm() method can be used with both of them. 

ewm() with Series

A Series in Pandas is a 1D labeled array. It can hold a variety of data types. It can hold numbers, strings, and more. We can also think of it as a column in a table or a list with labels for each element. Let us discuss the syntax of ewm() with series.

Syntax

The syntax of the ewm() method in Pandas with series is mentioned below:

series.ewm(alpha=alpha_value, span=span_value, halflife=halflife_value, adjust=True/False, min_periods=min_periods_value, ignore_na=False, axis=0/1)

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Let us understand the passed parameters under the method.

Parameters

There are several parameters that we need to pass under the ewm() method:

1. alpha: It is a smoothing factor between 0 and 1
    
2. span: It is a number of observations to consider for span-based calculations
    
3. halflife: it is a number of observations to consider for halflife-based calculations
    
4. adjust: It is a boolean value. It is used to adjust the weights to account for missing data, and its default value is true
    
5. min_periods: It shows the minimum number of observations needed for a valid calculation, and its default value is 0
    
6. ignore_na: It is a boolean value. It is used to exclude NaN (Not a Number) values from the calculations, and its default value is false
    
7. axis: It can be 0 or 1. It is 0 for the rows x and 1 for the columns. It is 0 by default
    

Let us understand what is the return type of the ewm() method with series.

Return Type

After performing the method ewm() with series, it will return a series.

Now you might be wondering how to implement this method with a series.

Example

Suppose we have a Pandas Series that represents the daily scores of ninjas for a week. We want to calculate the Exponentially Weighted Moving Average of these scores with an alpha value of 0.2. 

• Python

Python

#Importing Pandas library

import pandas as pd



# Scores of ninjas for a week

ninjas_scores = [400, 520, 930, 1000, 250, 190, 600]



# Generating a range of dates for the week

dates = pd.date_range('2023-08-15', periods=len(ninjas_scores))



# Creating a Pandas Series with ninjas scores and dates as index

ninjas_scores_series = pd.Series(ninjas_scores, index=dates)



# Displaying Series

print("Ninjas Scores Series:")

print(ninjas_scores_series)


# Calculating EWMA with an alpha value of 0.2

ewma = ninjas_scores_series.ewm(alpha=0.2).mean()



# Displaying the result

print("\nEWMA Result:")

print(ewma)

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Output:

Explanation

In this example, the ewm() method calculates the EWMA of the ninjas' scores using the provided alpha value. The result is shown in a new series. This new series has smoothed values based on exponential weighting.

**You can try the same example for your practice by passing other parameters into the ewm() method.

ewm() with DataFrame

A DataFrame is like a table of data. It's a 2D structure where we can store and organize information. We can also think of it as a spreadsheet or a database table. Each row represents a record, and each column represents a different attribute or feature. Let us discuss the syntax of ewm() with DataFrame.

Syntax

The syntax of the ewm() method in Pandas with DataFrame is mentioned below:

series.ewm(alpha=alpha_value, span=span_value, halflife=halflife_value, adjust=True/False, min_periods=min_periods_value, ignore_na=False, axis=0/1)

You can also try this code with Online Python Compiler

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Let us understand about the passed parameters under the method.

Parameters

There are several parameters that we need to pass under the ewm() method:

1. alpha: It is a smoothing factor between 0 and 1
    
2. span: It is a number of observations to consider for span-based calculations
    
3. halflife: it is a number of observations to consider for halflife-based calculations
    
4. adjust: It is a boolean value. It is used to adjust the weights to account for missing data, and its default value is true
    
5. min_periods: It shows the minimum number of observations needed for a valid calculation, and its default value is 0
    
6. ignore_na: It is a boolean value. It is used to exclude NaN (Not a Number) values from the calculations, and its default value is false
    
7. axis: It can be 0 or 1. It is 0 for the rows x and 1 for the columns. It is 0 by default
    

Let us understand what is the return type of ewm() method with series.

Return Type

After performing the method ewm() with DataFrame, it will return a DataFrame.

Now you might be wondering how to implement this method with a DataFrame.

Example

Suppose we have a DataFrame that represents the daily scores and the number of questions solved by ninjas for a week. We want to calculate the Exponentially Weighted Moving Average of both scores and the number of questions with an alpha value of 0.2 and adjust false. 

• Python

Python

# Importing Pandas library

import pandas as pd



# Creating a dictionary with date, scores, and number of questions solved

ninjasData = {'Date': pd.date_range('2023-08-15', periods=7),

        'Scores': [400, 520, 930, 1000, 250, 190, 600],

        'NumberOfQuestions': [20, 60, 40, 170, 30, 65, 32]}


# Creating a DataFrame using the ninjasData

dataframe = pd.DataFrame(ninjasData)


# Displaying the dataframe

print("Created DataFrame for Ninjas:")

print(dataframe)


# Calculating EWMA for 'Scores' and 'NumberOfQuestions' columns with an alpha value 0.2

ewma = dataframe[['Scores', 'NumberOfQuestions']].ewm(alpha=0.2,adjust=False).mean()


# Displaying the result

print("\n EWMA Result:")

print(ewma)

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Output:

Explanation

In this example, the ewm() method calculates the EWMA of the ninjas' scores and the number of questions solved using the provided alpha value and adjust as false. The result is shown in a new DataFrame. This new DataFrame has smoothed values based on exponential weighting.

**You can try the same example for your practice by passing other parameters into the ewm() method.

Frequently Asked Questions

What is the difference between rolling() and ewm()? 

rolling() and ewm() methods calculate moving averages. But the ewm() method gives more weight to recent data. This makes it more suitable for time-series analysis.

Can we customize the weightings in the ewm() method in Pandas? 

Yes, we can adjust parameters like span, center of mass, or halflife to control the weight given to different data points.

Is ewm() method in Pandas limited to finance data? 

No, it's versatile and applicable to any time-series data, such as temperature records, population growth, or social media trends.

Does the ewm() method in Pandas modify the original data? 

No, by default, it returns a new Series or DataFrame without altering the original data.

Conclusion

In this blog, we have discussed about the ewm() Method in Pandas. It can work as a detective tool for us to work with time-based data. It helps us to spot trends and changes by giving more attention to recent stuff. If you want to learn more about the Pandas in Python, then you can check out our blogs:

• How to Install Pandas in Python?
   
• Import Pandas as pd
   
• Pandas Read Excel
   
• Pandas dataframe.shift()
   
• Pandas Profiling in Python
   

We hope this blog helps you to get knowledge about the ewm() method in Pandas. You can refer to our guided paths on the Codestudio platform. You can check our course to learn more about DSA, DBMS, Competitive Programming, Python, Java, JavaScript, etc. 

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