Julia for Excel users

[technocrat]

If you are coming to Julia from office software, the transition is not as difficult as you may think. Try this.

# ==============================================================================
# Julia Starter Template for Excel Users
# Mission: Load a CSV, clean it, and run a "Pivot Table" summary.
# ==============================================================================

using DataFrames
using Statistics
using XLSX

# 1. LOAD DATA 
# In Excel: File > Open
# In Julia: We read the file into a 'DataFrame' (think of it as a virtual sheet)
file_path = "your_data_file.csv" 

# Note: Check if file exists before loading
if isfile(file_path)
    df = DataFrame(XLSX.readtable(file_path, 1))
    println("Successfully loaded $(nrow(df)) rows.")
else
    # Create dummy data for testing if no file is present
    df = DataFrame(
        Region = ["East", "West", "East", "North", "West"],
        Sales = [1200.50, 850.00, 2100.00, 400.25, 1500.75],
        Category = ["Tech", "Office", "Tech", "Furniture", "Office"]
    )
    println("Using sample data for demonstration...")
end

# 2. THE "CALCULATED COLUMN"
# adjust to your vareiable names
# In Excel: =B2 * 0.08 (Tax Calculation)
# In Julia: Use the '.' for element-wise math (applying to every row at once)
df.Tax = df.Sales .* 0.08

# 3. FILTERING
# In Excel: Data > Filter > "Tech"
# In Julia: Create a subset
tech_sales = subset(df, :Category => x -> x .== "Tech")

# 4. THE "PIVOT TABLE"
# In Excel: Insert > Pivot Table (Rows: Region, Values: Sum of Sales)
# In Julia: GroupBy and Combine
pivot_summary = combine(groupby(df, :Region), 
    :Sales => sum => :Total_Sales,
    :Sales => mean => :Average_Ticket
)

# 5. EXPORT
# In Excel: File > Save As
CSV.write("julia_report_output.csv", pivot_summary)

println("--- Summary Report ---")
show(pivot_summary)

which will output

       # Julia Starter Template for Excel Users
       # Mission: Load a CSV, clean it, and run a "Pivot Table" summary.
       # ==============================================================================

       using DataFrames

julia> using CSV

julia> using Statistics

julia> # 1. LOAD DATA 
       # In Excel: File > Open
       # In Julia: We read the file into a 'DataFrame' (think of it as a virtual sheet)
       file_path = "your_data_file.csv"
"your_data_file.csv"

julia> # Note: Check if file exists before loading
       if isfile(file_path)
           df = CSV.read(file_path, DataFrame)
           println("Successfully loaded $(nrow(df)) rows.")
       else
           # Create dummy data for testing if no file is present
           df = DataFrame(
               Region = ["East", "West", "East", "North", "West"],
               Sales = [1200.50, 850.00, 2100.00, 400.25, 1500.75],
               Category = ["Tech", "Office", "Tech", "Furniture", "Office"]
           )
           println("Using sample data for demonstration...")
       end
Using sample data for demonstration...

julia> # 2. THE "CALCULATED COLUMN"
       # In Excel: =B2 * 0.08 (Tax Calculation)
       # In Julia: Use the '.' for element-wise math (applying to every row at once)
       df.Tax = df.Sales .* 0.08
5-element Vector{Float64}:
  96.04
  68.0
 168.0
  32.02
 120.06

julia> # 3. FILTERING
       # In Excel: Data > Filter > "Tech"
       # In Julia: Create a subset
       tech_sales = subset(df, :Category => x -> x .== "Tech")
2×4 DataFrame
 Row │ Region  Sales    Category  Tax     
     │ String  Float64  String    Float64 
─────┼────────────────────────────────────
   1 │ East     1200.5  Tech        96.04
   2 │ East     2100.0  Tech       168.0

julia> # 4. THE "PIVOT TABLE"
       # In Excel: Insert > Pivot Table (Rows: Region, Values: Sum of Sales)
       # In Julia: GroupBy and Combine
       pivot_summary = combine(groupby(df, :Region), 
           :Sales => sum => :Total_Sales,
           :Sales => mean => :Average_Ticket
       )
3×3 DataFrame
 Row │ Region  Total_Sales  Average_Ticket 
     │ String  Float64      Float64        
─────┼─────────────────────────────────────
   1 │ East        3300.5          1650.25
   2 │ West        2350.75         1175.38
   3 │ North        400.25          400.25

julia> # 5. EXPORT
       # In Excel: File > Save As
       CSV.write("julia_report_output.csv", pivot_summary)
"julia_report_output.csv"

julia> println("--- Summary Report ---")
--- Summary Report ---

julia> show(pivot_summary)
3×3 DataFrame
 Row │ Region  Total_Sales  Average_Ticket 
     │ String  Float64      Float64        
─────┼─────────────────────────────────────
   1 │ East        3300.5          1650.25
   2 │ West        2350.75         1175.38
   3 │ North        400.25          400.25
julia>