Predictive Hacks

# How to Simulate Data from Different Distributions

Let’s say that you want to simulate 10 observations from 3 normal distributions with different parameters mean and standard deviation. We can do that efficiently using the `purrr` package from `tidyverse` family. The 3 normal distributions are the following:

• Distribution A: mean=30 and sd=1
• Distribution B: mean=40 and sd=2
• Distribution C: mean=50 and sd=3
```library(tidyverse)

df<-tibble(Distribution=c("A","B","C"), Mean=c(30, 40, 50), StDev=c(1, 2, 3))
df
```

Let’s simulate the data using purrr and the map function:

```my_data<-map2(df\$Mean, df\$StDev, ~data.frame(Sims=rnorm(mean=.x, sd=.y, n=10)))

# set the name for each list element
my_data<-set_names(my_data, df\$Distribution)

my_data
```

We can get each element from the list by simply call it by index like `my_data[[1]]` or by name like `my_data[["A"]]`. If you have more than two arguments, let’s say `mean`, `sd` and `size` you can use the `pmap` function which takes multiple arguments.

### Get updates and learn from the best

Python

#### Image Captioning with HuggingFace

Image captioning with AI is a fascinating application of artificial intelligence (AI) that involves generating textual descriptions for images automatically.

Python

#### Intro to Chatbots with HuggingFace

In this tutorial, we will show you how to use the Transformers library from HuggingFace to build chatbot pipelines. Let’s