Tutorial

Setup

First, make sure you have XLSX.jl package installed.

julia> using Pkg

julia> Pkg.add("XLSX")

Getting Started

The basic usage is to read an Excel file and read values.

julia> import XLSX

julia> xf = XLSX.readxlsx("myfile.xlsx")
XLSXFile("myfile.xlsx") containing 3 Worksheets
            sheetname size          range
-------------------------------------------------
              mysheet 4x2           A1:B4
           othersheet 1x1           A1:A1
                named 1x1           B4:B4

julia> XLSX.sheetnames(xf)
3-element Array{String,1}:
 "mysheet"
 "othersheet"
 "named"

julia> sh = xf["mysheet"] # get a reference to a Worksheet
4×2 XLSX.Worksheet: ["mysheet"](A1:B4)

julia> sh["B2"] # From a sheet, you can access a cell value
"first"

julia> sh["A2:B4"] # or a cell range
3×2 Array{Any,2}:
 1  "first"
 2  "second"
 3  "third"

julia> XLSX.readdata("myfile.xlsx", "mysheet", "A2:B4") # shorthand for all above
3×2 Array{Any,2}:
 1  "first"
 2  "second"
 3  "third"

julia> sh[:] # all data inside worksheet's dimension
4×2 Array{Any,2}:
  "HeaderA"  "HeaderB"
 1           "first"
 2           "second"
 3           "third"

julia> xf["mysheet!A2:B4"] # you can also query values from a file reference
3×2 Array{Any,2}:
 1  "first"
 2  "second"
 3  "third"

julia> xf["NAMED_CELL"] # you can even read named ranges
"B4 is a named cell from sheet \"named\""

julia> xf["mysheet!A:B"] # Column ranges are also supported
4×2 Array{Any,2}:
  "HeaderA"  "HeaderB"
 1           "first"
 2           "second"
 3           "third"

To inspect the internal representation of each cell, use the getcell or getcellrange methods.

The example above used xf = XLSX.readxlsx(filename) to open a file, so all file contents are fetched at once from disk.

You can also use XLSX.openxlsx to read file contents as needed (see Reading Large Excel Files and Caching).

Read Tabular Data

The gettable method returns tabular data from a spreadsheet as a tuple (data, column_labels). You can use it to create a DataFrame from DataFrames.jl. Check the docstring for gettable method for more advanced options.

There's also a helper method readtable to read from file directly, as shown in the following example. In this case, the ... operator will splat the tuple (data, column_labels) into the constructor of DataFrame.

julia> using DataFrames, XLSX

julia> df = DataFrame(XLSX.readtable("myfile.xlsx", "mysheet")...)
3×2 DataFrames.DataFrame
│ Row │ HeaderA │ HeaderB  │
├─────┼─────────┼──────────┤
│ 1   │ 1       │ "first"  │
│ 2   │ 2       │ "second" │
│ 3   │ 3       │ "third"  │

Reading Large Excel Files and Caching

The method XLSX.openxlsx has a enable_cache option to control worksheet cells caching.

Cache is enabled by default, so if you read a worksheet cell twice it will use the cached value instead of reading from disk in the second time.

If enable_cache=false, worksheet cells will always be read from disk. This is useful when you want to read a spreadsheet that doesn't fit into memory.

The following example shows how you would read worksheet cells, one row at a time, where myfile.xlsx is a spreadsheet that doesn't fit into memory.

julia> XLSX.openxlsx("myfile.xlsx", enable_cache=false) do f
           sheet = f["mysheet"]
           for r in XLSX.eachrow(sheet)
              # r is a `SheetRow`, values are read using column references
              rn = XLSX.row_number(r) # `SheetRow` row number
              v1 = r[1]    # will read value at column 1
              v2 = r["B"]  # will read value at column 2

              println("v1=$v1, v2=$v2")
           end
      end
v1=HeaderA, v2=HeaderB
v1=1, v2=first
v1=2, v2=second
v1=3, v2=third

You could also stream tabular data using XLSX.eachtablerow(sheet), which is the underlying iterator in gettable method. Check docstrings for XLSX.eachtablerow for more advanced options.

julia> XLSX.openxlsx("myfile.xlsx", enable_cache=false) do f
           sheet = f["mysheet"]
           for r in XLSX.eachtablerow(sheet)
               # r is a `TableRow`, values are read using column labels or numbers
               rn = XLSX.row_number(r) # `TableRow` row number
               v1 = r[1] # will read value at table column 1
               v2 = r[:HeaderB] # will read value at column labeled `:HeaderB`

               println("v1=$v1, v2=$v2")
            end
       end
v1=1, v2=first
v1=2, v2=second
v1=3, v2=third

Writing Excel Files

Create New Files

Opening a file in write mode with XLSX.openxlsx will open a new (blank) Excel file for editing.

XLSX.openxlsx("my_new_file.xlsx", mode="w") do xf
    sheet = xf[1]
    XLSX.rename!(sheet, "new_sheet")
    sheet["A1"] = "this"
    sheet["A2"] = "is a"
    sheet["A3"] = "new file"
    sheet["A4"] = 100
end

Edit Existing Files

Opening a file in read-write mode with XLSX.openxlsx will open an existing Excel file for editing. This will preserve existing data in the original file.

XLSX.openxlsx("my_new_file.xlsx", mode="rw") do xf
    sheet = xf[1]
    sheet["B1"] = "new data"
end

Export Tabular Data

To export tabular data to Excel, use XLSX.writetable method.

julia> import DataFrames, XLSX

julia> df = DataFrames.DataFrame(integers=[1, 2, 3, 4], strings=["Hey", "You", "Out", "There"], floats=[10.2, 20.3, 30.4, 40.5], dates=[Date(2018,2,20), Date(2018,2,21), Date(2018,2,22), Date(2018,2,23)], times=[Dates.Time(19,10), Dates.Time(19,20), Dates.Time(19,30), Dates.Time(19,40)], datetimes=[Dates.DateTime(2018,5,20,19,10), Dates.DateTime(2018,5,20,19,20), Dates.DateTime(2018,5,20,19,30), Dates.DateTime(2018,5,20,19,40)])
4×6 DataFrames.DataFrame
│ Row │ integers │ strings │ floats │ dates      │ times    │ datetimes           │
├─────┼──────────┼─────────┼────────┼────────────┼──────────┼─────────────────────┤
│ 1   │ 1        │ Hey     │ 10.2   │ 2018-02-20 │ 19:10:00 │ 2018-05-20T19:10:00 │
│ 2   │ 2        │ You     │ 20.3   │ 2018-02-21 │ 19:20:00 │ 2018-05-20T19:20:00 │
│ 3   │ 3        │ Out     │ 30.4   │ 2018-02-22 │ 19:30:00 │ 2018-05-20T19:30:00 │
│ 4   │ 4        │ There   │ 40.5   │ 2018-02-23 │ 19:40:00 │ 2018-05-20T19:40:00 │

julia> XLSX.writetable("df.xlsx", DataFrames.columns(df), DataFrames.names(df))

You can also export multiple tables to Excel, each table in a separate worksheet.

julia> import DataFrames, XLSX

julia> df1 = DataFrames.DataFrame(COL1=[10,20,30], COL2=["Fist", "Sec", "Third"])
3×2 DataFrames.DataFrame
│ Row │ COL1 │ COL2  │
├─────┼──────┼───────┤
│ 1   │ 10   │ Fist  │
│ 2   │ 20   │ Sec   │
│ 3   │ 30   │ Third │

julia> df2 = DataFrames.DataFrame(AA=["aa", "bb"], AB=[10.1, 10.2])
2×2 DataFrames.DataFrame
│ Row │ AA │ AB   │
├─────┼────┼──────┤
│ 1   │ aa │ 10.1 │
│ 2   │ bb │ 10.2 │

julia> XLSX.writetable("report.xlsx", REPORT_A=( DataFrames.columns(df1), DataFrames.names(df1) ), REPORT_B=( DataFrames.columns(df2), DataFrames.names(df2) ))