Coding Ref

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.

Here's an example of using the `ewm()`

function in Pandas:

main.py

```
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:

output

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

`ewm()`

on one or more columnsYou can also use the `ewm()`

function on a dataframe to compute the exponentially weighted moving average of one or more columns.

For example:

main.py

```
# 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:

output

```
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
```

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.

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