The mean of each column in a dataframe can be calculated in different ways using Julia. In this article, we will explore three different approaches to solve this problem. We will provide sample codes for each solution and divide the solutions with different headings. Let’s get started!

## Approach 1: Using the `mean` function

One way to calculate the mean of each column in a dataframe is by using the `mean` function provided by Julia. This function calculates the arithmetic mean of a given array or iterable. We can apply this function to each column of the dataframe using a loop or a list comprehension.

```
# Sample dataframe
df = DataFrame(A = [1, 2, 3], B = [4, 5, 6], C = [7, 8, 9])
# Calculate the mean of each column using a loop
means = []
for col in names(df)
push!(means, mean(df[!, col]))
end
# Calculate the mean of each column using a list comprehension
means = [mean(df[!, col]) for col in names(df)]
```

## Approach 2: Using the `combine` function

Another way to calculate the mean of each column in a dataframe is by using the `combine` function provided by the DataFrames package in Julia. This function allows us to apply a function to each column or group of columns in a dataframe. We can use the `mean` function as the function argument to calculate the mean of each column.

```
using DataFrames
# Sample dataframe
df = DataFrame(A = [1, 2, 3], B = [4, 5, 6], C = [7, 8, 9])
# Calculate the mean of each column using the combine function
means = combine(df, names(df) .=> mean)
```

## Approach 3: Using the `by` function

The third approach involves using the `by` function provided by the DataFrames package in Julia. This function allows us to group a dataframe by one or more columns and apply a function to each group. In this case, we can group the dataframe by each column and calculate the mean for each group.

```
using DataFrames
# Sample dataframe
df = DataFrame(A = [1, 2, 3], B = [4, 5, 6], C = [7, 8, 9])
# Calculate the mean of each column using the by function
means = by(df, names(df)) do group
mean(group[!, :])
end
```

In conclusion, all three approaches provide a way to calculate the mean of each column in a dataframe. The choice of the best option depends on the specific requirements of your project. If you prefer a more concise and efficient solution, Approach 2 using the `combine` function is recommended. However, if you need more flexibility and want to perform additional operations on each group, Approach 3 using the `by` function might be a better choice.