How to use ewm() in Pandas

How to use ewm() in Pandas

The ewm() function in Pandas is used to apply exponential weighting to a series or dataframe.

This is often used to compute an exponentially weighted moving average, which gives more weight to more recent values and less weight to older values.

Example

Here's an example of using the ewm() function in Pandas:

import pandas as pd

# create a sample series
s = pd.Series([1, 2, 3, 4, 5])

# compute the exponentially weighted moving average of the series
s_ewm = s.ewm(alpha=0.5).mean()

# display the result
print(s_ewm)

This will compute the exponentially weighted moving average of the series and return a new series with the same index as the original series.

The alpha parameter specifies the decay factor, which determines the weight of each value in the moving average.

The output will be:

0    1.000000
1    1.666667
2    2.428571
3    3.266667
4    4.161290
dtype: float64

Using ewm() on one or more columns

You can also use the ewm() function on a dataframe to compute the exponentially weighted moving average of one or more columns.

For example:

# create a sample dataframe
df = pd.DataFrame({"A": [1, 2, 3, 4, 5],
                   "B": [2, 3, 4, 5, 6]})

# compute the exponentially weighted moving average of the A and B columns
df_ewm = df[["A", "B"]].ewm(alpha=0.5).mean()

# display the result
print(df_ewm)

This will compute the exponentially weighted moving average of the A and B columns in the dataframe and return a new dataframe with the same index as the original dataframe.

The output will be:

          A         B
0  1.000000  2.000000
1  1.666667  2.666667
2  2.428571  3.428571
3  3.266667  4.266667
4  4.161290  5.161290

Conclusion

The ewm() function is useful for computing the exponentially weighted moving average of a series or dataframe, which can be useful for a variety of analysis and visualization tasks.