In today’s tutorial we will learn how to calculate the multiplication of multiple pandas series objects as shown below.

`series_3 = series_1 * series_2`

## Data Preparation

We will first import pandas and create two series of randomly created numbers:

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

The create two random Series objects, each consisting of 20 elements.

```
s_employees= pd.Series(random.choices(range(30,100), k=20))
s_working_days = pd.Series(random.choices(range (20,30), k=20))
```

**Expert Tip**: When trying to generate random list, you might have used the random.sample() function. If so, you might have received the following error message:

# ValueError: Sample larger than population or is negative

If so, make sure to use the random.choices() function as shown above.

## Multiplying you Series elements

We can now calculate the product of the two Series using the following vectorized operation

`s_total_working_days = s_employees * s_working_days`

## Multiply by a constant / scalar / float

You are able to multiply your series by an integer scalar:

`s_yearly_hours = s_total_working_days * 22`

Similarly, you can multiply your pandas column by a float value:

`s_yearly_hours = s_total_working_days * 22.545`

## Convert string series to numeric values and multiply

In case that you have a Series consisting of non numeric values, you won’t be able to apply arithmetic operations on it. Consider this example:

```
s1 = pd.Series([5,2,3,2,1])
s2 = pd.Series (["100", "200", "300", "400", "500"]) # series of strings
#we'll try to multiply the series objects:
s1*s2
```

This will render the following string series:

0 100100100100100 1 200200 2 300300300 3 400400 4 500 dtype: object

You can calculate the arithmetic multiplication by using the pandas pd.to_numeric() function:

`s1*pd.to_numeric(s2)`

This will render the right result

0 5000 1 4000 2 9000 3 8000 4 5000 dtype: int64

## Sum your multiplied Series

After multiplying your two or more series you can easily sum the total:

`print (f"The total number of working days was: {sum(s_total_working_days)}")`

This will return the following string (your result will be different as we are using random data).

The total number of working days was: 31640