Note: This journey began with watching “CSV in Julia Programming Language” (https://youtu.be/u-v-N208EiA?si=oxLjRBXBOksfgHiJ) and following the sparse arrays tutorial.
📖 My Learning Story
What I Learned
I successfully completed a full workflow: creating data → saving to CSV → loading from CSV → building a sparse matrix → verifying it. Here’s everything I discovered, including all my struggles and how I fixed them.
My Complete Workflow
julia
# 1. Load required packages
using CSV, SparseArrays, DataFrames
# 2. Create data in coordinate format (row, col, value)
df = DataFrame(row=[1,1,2,2,3,3],
col=[1,2,1,2,1,2],
val=[1,2,3,4,5,6])
# 3. Save to CSV
CSV.write("test_matrix.csv", df)
# 4. Read from CSV
data = CSV.read("test_matrix.csv", DataFrame)
# 5. Extract coordinate vectors
rows = data.row
cols = data.col
vals = data.val
# 6. Build sparse matrix
A = sparse(rows, cols, vals)
# 7. Verify results
A # Display the matrix
size(A) # (3, 2) - dimensions
nnz(A) # 6 - number of non-zero entries
Array(A) # Convert to dense for verification
My Expected Output
text
3×2 SparseMatrixCSC{Int64, Int64} with 6 stored entries:
1 2
3 4
5 6
❌ My Mistakes & How I Fixed Them
Mistake 1: Typing julia> Inside the REPL
What I did:
julia
julia> julia> using CSV # ❌ I typed the prompt itself!
Why it happened: I thought I needed to include the prompt
How I fixed it: I realized the julia> prompt is already there – I just type the command
julia
julia> using CSV # ✅ Just the command
Mistake 2: Typos in Column Names
What I did:
julia
julia> vals = data.valvals # ❌ I added extra "vals" ERROR: ArgumentError: column name :valvals not found
Why it happened: I was typing too fast and didn’t check the column name
How I fixed it: I checked the column names with names(data) and used the correct one
julia
julia> vals = data.val # ✅ Correct column name
Mistake 3: Typing Output Instead of Code
What I did:
julia
julia> 3×2 SparseMatrixCSC{Int64, Int64} with 6 stored entries: # ❌ I typed the display output
ERROR: ParseError
Why it happened: I confused the output display with a command I needed to type
How I fixed it: I learned to just type the variable name and let Julia display it
julia
julia> A # ✅ Julia displays the matrix for me
Mistake 4: Using quit Instead of exit
What I did:
julia
julia> quit # ❌ Wrong command ERROR: UndefVarError: `quit` not defined
How I fixed it: I learned the correct exit commands
julia
julia> exit() # ✅ Correct # Or press Ctrl+D
Mistake 5: Package Not Installed
What happened:
julia
julia> using CSV # ❌ Package not found - it just hung
How I fixed it: I installed the package first from the command line
bash
julia -e 'using Pkg; Pkg.add("CSV")'
Mistake 6: Typing Bash Commands in Julia
What I did:
julia
julia> pkill julia # ❌ This is a bash command, not Julia! ERROR: ParseError
How I fixed it: I learned to use Julia’s exit commands or escape to bash
julia
julia> exit() # ✅ Exit Julia first # Then in bash: pkill julia
Mistake 7: Getting Stuck on using Statement
What happened:
text
julia> using CSV # ← The prompt just blinked forever
Why it happened: The package wasn’t installed or was compiling
How I fixed it: I installed the package first and was patient during compilation
bash
julia -e 'using Pkg; Pkg.add("CSV")'
🔧 My Troubleshooting Quick Reference
| My Problem | My Solution |
|---|---|
| Package not found | Install it: Pkg.add("PackageName") |
| REPL hangs | Press Ctrl+C to interrupt, then exit() |
| Can’t exit Julia | Press Ctrl+D or type exit() |
| Typo in column name | Check names with names(dataframe) |
| Typed output as code | Remember: just type the variable name |
| Julia frozen | From bash: pkill -9 julia |
📝 My Key Commands Reference
julia
# Installation (from bash)
julia -e 'using Pkg; Pkg.add("CSV")'
# Loading packages
using CSV, SparseArrays, DataFrames
# Create DataFrame
df = DataFrame(row=rows, col=cols, val=vals)
# Read/Write CSV
CSV.write("filename.csv", df)
data = CSV.read("filename.csv", DataFrame)
# Extract columns
rows = data.row
cols = data.col
vals = data.val
# Build sparse matrix
A = sparse(rows, cols, vals)
# Inspect matrix
A # Display
size(A) # Dimensions
nnz(A) # Number of non-zeros
Array(A) # Convert to dense
A.colptr # Column pointers
A.rowval # Row indices
A.nzval # Values
# Exit Julia
exit() # or Ctrl+D
🎓 What I’ve Mastered
- Package Management: Installing and loading packages like a pro
- DataFrames: Creating and manipulating tabular data
- CSV I/O: Reading and writing CSV files (thanks to the video tutorial!)
- Sparse Arrays: Building from coordinate format
- REPL Navigation: Proper usage of Julia’s interactive environment
- Error Handling: Common errors and their solutions
- Data Flow: Complete CSV → Sparse Matrix pipeline
📂 My CSV File Format
The CSV file I created looks like this:
csv
row,col,val 1,1,1 1,2,2 2,1,3 2,2,4 3,1,5 3,2,6
🚀 My Next Steps
What I want to explore next:
- Creating larger sparse matrices (10×10, 100×100)
- Adding zeros to see how sparsity works
- Matrix multiplication and operations
- Solving systems with
\ - Visualizing sparsity patterns with
spy()
💡 My Golden Rule
When I see the
julia>prompt, I just type my Julia code. I don’t type the prompt, I don’t type the output, I don’t type bash commands. I just type the code, press Enter, and let Julia show me the results!
📺 Where It All Began
My journey started with this video: “CSV in Julia Programming Language” (https://youtu.be/u-v-N208EiA?si=oxLjRBXBOksfgHiJ)
This taught me how to handle CSV files in Julia, which was the crucial first step before I could work with sparse matrices.
This is my complete journey from CSV to sparse matrix in Julia. I’ll save this as a reference for my future work! 🎉