3 Files and Data Manipulation – Geoscientific Machine Learning

3.1 Reading and Writing Files

3.1.1 Text Files

# Writing to a file
open("example.txt", "w") do f
    write(f, "Hello, Julia!")
end

# Reading from a file
content = read("example.txt", String)
println(content)

Hello, Julia!

3.1.2 CSV Files

using CSV, DataFrames

# Read CSV
df = CSV.read("data.csv", DataFrame)

# Write CSV
CSV.write("output.csv", df)

3.2 DataFrames

DataFrames are tabular data structures similar to pandas in Python or data.frames in R.

3.2.1 Creating DataFrames

using DataFrames

# Create from columns
df = DataFrame(
    name = ["Alice", "Bob", "Charlie"],
    age = [25, 30, 35],
    city = ["Helsinki", "Espoo", "Tampere"]
)

3×3 DataFrame

Row	name	age	city
	String	Int64	String
1	Alice	25	Helsinki
2	Bob	30	Espoo
3	Charlie	35	Tampere

3.2.2 Basic Operations

# Select columns
df[:, :name]

3-element Vector{String}:
 "Alice"
 "Bob"
 "Charlie"

# Filter rows
filter(row -> row.age > 25, df)

2×3 DataFrame

Row	name	age	city
	String	Int64	String
1	Bob	30	Espoo
2	Charlie	35	Tampere

# Add new column
df.country = ["Finland", "Finland", "Finland"]
df

3×4 DataFrame

Row	name	age	city	country
	String	Int64	String	String
1	Alice	25	Helsinki	Finland
2	Bob	30	Espoo	Finland
3	Charlie	35	Tampere	Finland

3.2.3 Grouping and Aggregation

using Statistics

# Group by and summarize
gdf = groupby(df, :country)
combine(gdf, :age => mean => :avg_age)

1×2 DataFrame

Row	country	avg_age
	String	Float64
1	Finland	30.0

3.3 Working with Geoscientific Data

3.3.1 Loading Geophysical Data

using DelimitedFiles

# Read space-delimited data
data = readdlm("gravity_data.txt")

# Extract columns
x, y, gz = data[:, 1], data[:, 2], data[:, 3]

3.3.2 NetCDF Files

using NCDatasets

# Read NetCDF
ds = Dataset("climate_data.nc")
temp = ds["temperature"][:]
close(ds)