# Calculate Exponential Moving Average Values in Pandas

This tutorial will discuss calculating the ewm (exponential moving average) in Pandas.

## Steps to Calculate Exponential Moving Average Values in Pandas

The following are the steps to find ewm values in Pandas.

### Import Pandas

We will need to import pandas to get started.

``````import pandas as pd
``````

### Create Pandas DataFrame

Let us now create a sample dataframe with column prices to calculate ewm.

``````data = {'prices':[22.27, 22.19, 22.08, 22.17, 22.18]}
df = pd.DataFrame(data)
``````

Let us take a look at our dataframe.

``````print(df)
``````

Output:

``````   prices
0   22.27
1   22.19
2   22.08
3   22.17
4   22.18
``````

### Use the `rolling()` Function to Divide DataFrame

We will now use the `rolling()` function to roll our dataframe with a defined span size and divide our dataframe into two dataframes.

``````span = 2
sma = df.rolling(window=span, min_periods=span).mean()[:span]
``````

We pass the window size and `min_periods` parameters to the `rolling()` function, a predefined variable.

Let us now insert the rest of our dataframe into a separate dataframe.

``````rest = df[span:]
``````

### Use the `pd.concat()` and `ewm()` Functions to Calculate the Exponential Moving Average

Now that we have successfully divided our default dataframe, we will use the `pd.concat()` and `ewm()` functions to calculate the exponential moving average in our dataframe column.

``````ewm1 = pd.concat([sma, rest]).ewm(span=span, adjust=False).mean()
``````

We calculated ewm using the `ewm()` function in the above code.

We passed the `span` parameter. Also, the `adjust` parameter is passed as False to prevent accounting for imbalance in relative weightings in beginning periods.

Let us now print the ewm values to see the output.

``````print(ewm1)
``````

Output:

``````      prices
0        NaN
1  22.230000
2  22.130000
3  22.156667
4  22.172222
``````

As seen in the above output, we have successfully calculated the ewm values for the sample dataframe. Thus, we can successfully find the ewm values in a Pandas dataframe.

Preet writes his thoughts about programming in a simplified manner to help others learn better. With thorough research, his articles offer descriptive and easy to understand solutions.