# Getting Started

## Installation

Please close Excel and run the installer to get started. See the licensing page to access your installer.

## Excel Interface

When you start Excel, you'll have a new DataNitro tab: Editor
Launches a custom Idle editor. You can use it, or any other editor, to edit Python code. Once you're done, save it as a .py file and run it.
Python Shell
Launches a python shell you can use to interact with the spreadsheet directly.
import
Adds a script to the imported script list.
remove
Removes the script from the list.
Run
Runs the selected script.
Stop
Stops a running script.
Docs
This links to the documentation.
Add-ins
This links to our contact information.
Settings
Modify DataNitro's settings.

## Hello World

Hello world with DataNitro is short:

`Cell("A1").value = "Hello, World!"`
That's it!

## Calculating Pi

Let's approximate pi with a Monte Carlo simulation. We'll generate a random 2D point with x and y coordinates between 0 and 1, and see how far it is from the origin, specifically, if it's inside the unit circle. The total area of the points we're choosing from is 1, and the area of the points inside the circle and in our box is pi/4, the fraction of points that fall within our radius should be about pi/4.

We'll use Excel to keep track of our trials, and the variables within each trial. First, let's import random, and label a few cells so we know what's what:

```from random import random
Cell("A2").value, Cell("A3").value, Cell("A4").value = "Trial:","Hits:","pi is about:"
Cell("D1").value, Cell("E1").value, Cell("F1").value = "x","y","x^2 + y^2"
```
Next, we should decide on the number of trials. Let's allow the user to set a number by entering it in the first cell, and use 100 as a fallback.
```trials = Cell("A1").value
if (type(trials)!= int) or (trials < 1):
trials = 100
```
Now, we'll run the trials. We'll make a function for this, and keep track of the coordinates of our random points, as well as their distance from the origin, under the headings we made. We'll also keep track of the number of trials.
```def pi_calc(n):
while Cell("B2").value < n:
x, y = Cell("D2"), Cell("E2")
r = Cell("F2")
x.value, y.value = random(), random()
r.value = x.value**2 + y.value**2
```
We need to increment the number of trials each time, and also increment "Hits" if our point falls within the circle.
```        Cell("B2").value += 1
if r.value <= 1:
Cell("B3").value += 1
```
We can now approximate pi as 4 times the fraction of hits to trials:
```        Cell("B4").value = 4*float(Cell("B3").value)/Cell("B2").value
```
Let's add a small delay at the end of each run so we can watch the function update.
```from time import sleep
...
def pi_calc(n):
...
sleep(0.01)
```
Finally, let's run our function, making sure to reset the number of trials and number of hits beforehand:
```Cell("B2").value, Cell("B3").value = 0,0
pi_calc(trials)
```

We're done! Our finished script:

```# Generates approximations of pi.  Put the number of trials you want in A1. More
# trials will result in higher accuracy.

from random import random
from time import sleep

def pi_calc(n):
while Cell("B2").value < n:
x, y = Cell("D2"), Cell("E2")
r = Cell("F2")
x.value, y.value = random(), random()
r.value = x.value**2 + y.value**2
Cell("B2").value += 1
if r.value <= 1:
Cell("B3").value += 1
Cell("B4").value = 4*float(Cell("B3").value)/Cell("B2").value
sleep(0.01)

trials = Cell("A1").value
if (type(trials)!= int) or (trials < 1):
trials = 100

# sheet setup
Cell("A2").value, Cell("A3").value, Cell("A4").value = "Trial:","Hits:","pi is about:"
Cell("D1").value, Cell("E1").value, Cell("F1").value = "x","y","x^2 + y^2"
Cell("B2").value, Cell("B3").value = 0,0

pi_calc(trials)
```
More trials will give better accuracy. 1000 should be enough to approximate pi to two digits.

## Sample Scripts

DataNitro comes with two sample scripts in the example folder: hello_world.py, a hello world program, and pascal_triangle.py, which prints out Pascal's triangle. You can also download and play with pi.py, a spreadsheet and script for binomial option pricing, and the ystockquote script for pulling data from yahoo finance.

If you have any questions, contact us!
You can find more detailed documentation.