Issue with DataFrame Column Type and Recognition in Julia Scripts

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1

Hello Julia Community,

I am currently working on a project involving statistical modeling in Julia, where I am facing an issue with reading and processing a DataFrame. My project includes three main scripts:

  1. generate_synthetic_data.jl – Generates synthetic data and saves it as a CSV file.
  2. score_driven_model.jl – Contains functions for creating and training a score-driven model.
  3. main_script.jl – Loads the generated data, processes it, and runs the model.

The problem: Despite verifying that the data has the correct column names and data types, I am encountering an error indicating that required columns are missing or not recognized. The column value, which should be of type Float64, seems to be causing this issue. Here is a sample of the errors:

julia

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ERROR: LoadError: DataFrame is missing columns: Any[:time, :value]

Details of the issue:

  • The CSV file generated by generate_synthetic_data.jl includes a time column (Int64) and a value column (Float64).
  • I have ensured that the data is loaded with normalized column names and that the value column is converted to Float64 explicitly.
  • I am using DataFrames, CSV, and ScoreDrivenModels packages in my scripts.
  • Even after these checks, I receive the error stating that required columns are missing.

What I’ve tried:

  • Explicitly converting the column data types using Float64.(data.value).
  • Normalizing column names with Symbol.(lowercase.(String.(names(data)))).
  • Using both DataFrames.jl and DataFramesMeta.jl for better column handling.
  • Debugging with describe(data) to confirm that the data types are correct.

My questions to the community:

  1. What could be the reason behind Julia not recognizing columns that exist and have correct types in a DataFrame?
  2. Are there known issues or subtle behaviors in DataFrames.jl, CSV.jl, or column name handling that might be causing this problem?
  3. Could there be an environmental or package compatibility issue that I am overlooking?

I am attaching all three scripts (generate_synthetic_data.jl, score_driven_model.jl, and main_script.jl) for context.

Environment:

  • Julia version: 1.10.6 LTS (manually installed)
  • Packages: DataFrames, CSV, ScoreDrivenModels, DataFramesMeta
  • OS: Ubuntu Studio 24.04

Thank you in advance for any insights or solutions!

1. main_script.jl 


using CSV
using DataFrames
include("score_driven_model.jl")

# Load the data
file_path = "data/synthetic_data.csv"
data = CSV.read(file_path, DataFrame; normalizenames=true)

# Ensure the 'value' column is of type Float64
data.value = Float64.(data.value)

# Normalize column names to lowercase symbols
rename!(data, names(data) .=> Symbol.(lowercase.(String.(names(data)))))

# Print the current column names and the first few rows for verification
println("Current column names: ", names(data))
println("First 5 rows of data:")
println(first(data, 5))

# Ensure the DataFrame has the expected columns
required_columns = [:time, :value]
missing_cols = setdiff(required_columns, Symbol.(names(data)))
if !isempty(missing_cols)
    error("DataFrame is missing columns: $missing_cols")
end

# Extract the 'value' column as the time series data
y = data.value

# Fit and forecast using the score-driven model
model = create_score_driven_model(y)
println("Model training complete.")
println("Forecasts: ", generate_forecast(model, y, 10))

2. score_driven_model.jl


using ScoreDrivenModels

# Function to create and fit a score-driven model
function create_score_driven_model(y::Vector{Float64})
    # Define a score-driven model with a Gamma distribution
    model = Model([1], [1], Gamma, 1.0)

    # Fit the model to the data
    fit!(model, y)

    return model
end

# Function to forecast using the fitted model
function generate_forecast(model, y::Vector{Float64}, steps::Int)
    # Generate a forecast for the specified number of steps
    forecasts = forecast(model, y, steps)

    return forecasts
end


3. generate_synthetic_data.jl


using CSV
using DataFrames
using DataFramesMeta

# Generate synthetic data and save to CSV
function generate_data(file_path::String)
    time = 1:100
    value = 0.5 .* time .+ randn(100)
    data = @from t in DataFrame(time = time, value = value) select t
    @rename(data, old_col_name => :new_col_name)

    CSV.write(file_path, data)
    println("Data file generated at: $file_path")
end

# Specify the file path
file_path = "data/synthetic_data.csv"
generate_data(file_path)
1 Like
2

What is this line from? I’ve never any seen anything like it before

3

could you print the dataframe returned before the error? I can’t reproduce the error with this MWE:

using DataFrames
data = DataFrame(time = rand(Int,10), value = rand(10))

rename!(data, names(data) .=> Symbol.(lowercase.(String.(names(data)))))

# Ensure the DataFrame has the expected columns
required_columns = [:time, :value]
missing_cols = setdiff(required_columns, Symbol.(names(data)))
if !isempty(missing_cols)
    error("DataFrame is missing columns: $missing_cols")
end