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use std::collections::HashMap;
use std::fs::File;
use std::io::{BufReader, BufWriter};
use std::path::Path;
use serde_json::{Map, Value};
use crate::column::{Column, ColumnTrait, ColumnType};
use crate::error::{Error, Result};
use crate::optimized::split_dataframe::core::OptimizedDataFrame;
/// JSON output format
pub enum JsonOrient {
/// Records format [{col1:val1, col2:val2}, ...]
Records,
/// Columns format {col1: [val1, val2, ...], col2: [...]}
Columns,
}
impl OptimizedDataFrame {
/// Load DataFrame from a JSON file
///
/// # Arguments
/// * `path` - Path to the JSON file
///
/// # Returns
/// * `Result<Self>` - The loaded DataFrame
pub fn from_json<P: AsRef<Path>>(path: P) -> Result<Self> {
let file = File::open(path.as_ref()).map_err(|e| Error::Io(e))?;
let reader = BufReader::new(file);
// Parse JSON
let json_value: Value = serde_json::from_reader(reader).map_err(|e| Error::Json(e))?;
match json_value {
Value::Array(array) => Self::from_records_array(array),
Value::Object(map) => Self::from_column_oriented(map),
_ => Err(Error::Format(
"JSON must be an object or an array".to_string(),
)),
}
}
// Load from records-oriented JSON
fn from_records_array(array: Vec<Value>) -> Result<Self> {
let mut df = Self::new();
// Return empty DataFrame if array is empty
if array.is_empty() {
return Ok(df);
}
// Collect all keys
let mut all_keys = std::collections::HashSet::new();
for item in &array {
if let Value::Object(map) = item {
for key in map.keys() {
all_keys.insert(key.clone());
}
} else {
return Err(Error::Format(
"Each element of the array must be an object".to_string(),
));
}
}
// Sort keys to stabilize order
let keys: Vec<String> = all_keys.into_iter().collect();
// Collect column data
let mut string_values: HashMap<String, Vec<String>> = HashMap::new();
for key in &keys {
string_values.insert(key.clone(), Vec::with_capacity(array.len()));
}
for item in &array {
if let Value::Object(map) = item {
for key in &keys {
let value_str = if let Some(value) = map.get(key) {
match value {
Value::Null => String::new(),
Value::Bool(b) => b.to_string(),
Value::Number(n) => n.to_string(),
Value::String(s) => s.clone(),
_ => serde_json::to_string(value).unwrap_or_default(),
}
} else {
String::new()
};
string_values
.get_mut(key)
.expect("operation should succeed")
.push(value_str);
}
}
}
// Infer types and add columns
for key in &keys {
let values = &string_values[key];
// Check for non-empty values
let non_empty_values: Vec<&String> = values.iter().filter(|s| !s.is_empty()).collect();
if non_empty_values.is_empty() {
// If all values are empty, use string type
df.add_column(
key.clone(),
Column::String(crate::column::StringColumn::new(
values.iter().map(|s| s.clone()).collect(),
)),
)?;
continue;
}
// Try to parse as integers
let all_ints = non_empty_values.iter().all(|&s| s.parse::<i64>().is_ok());
if all_ints {
let int_values: Vec<i64> = values
.iter()
.map(|s| s.parse::<i64>().unwrap_or(0))
.collect();
df.add_column(
key.clone(),
Column::Int64(crate::column::Int64Column::new(int_values)),
)?;
continue;
}
// Try to parse as floating point numbers
let all_floats = non_empty_values.iter().all(|&s| s.parse::<f64>().is_ok());
if all_floats {
let float_values: Vec<f64> = values
.iter()
.map(|s| s.parse::<f64>().unwrap_or(0.0))
.collect();
df.add_column(
key.clone(),
Column::Float64(crate::column::Float64Column::new(float_values)),
)?;
continue;
}
// Try to parse as boolean values
let all_bools = non_empty_values.iter().all(|&s| {
let lower = s.to_lowercase();
lower == "true" || lower == "false"
});
if all_bools {
let bool_values: Vec<bool> =
values.iter().map(|s| s.to_lowercase() == "true").collect();
df.add_column(
key.clone(),
Column::Boolean(crate::column::BooleanColumn::new(bool_values)),
)?;
} else {
// Default to string type
df.add_column(
key.clone(),
Column::String(crate::column::StringColumn::new(
values.iter().map(|s| s.clone()).collect(),
)),
)?;
}
}
Ok(df)
}
// Load from column-oriented JSON
fn from_column_oriented(map: Map<String, Value>) -> Result<Self> {
let mut df = Self::new();
// Return empty DataFrame if object is empty
if map.is_empty() {
return Ok(df);
}
// Verify column lengths
let mut column_length = 0;
for (_, value) in &map {
if let Value::Array(array) = value {
if column_length == 0 {
column_length = array.len();
} else if array.len() != column_length {
return Err(Error::Format(
"All columns must have the same length".to_string(),
));
}
} else {
return Err(Error::Format("JSON values must be arrays".to_string()));
}
}
// Process column data
for (key, value) in map {
if let Value::Array(array) = value {
// Convert values to strings
let str_values: Vec<String> = array
.iter()
.map(|v| match v {
Value::Null => String::new(),
Value::Bool(b) => b.to_string(),
Value::Number(n) => n.to_string(),
Value::String(s) => s.clone(),
_ => serde_json::to_string(v).unwrap_or_default(),
})
.collect();
// Check for non-empty values
let non_empty_values: Vec<&String> =
str_values.iter().filter(|s| !s.is_empty()).collect();
if non_empty_values.is_empty() {
// If all values are empty, use string type
df.add_column(
key.clone(),
Column::String(crate::column::StringColumn::new(
str_values.iter().map(|s| s.clone()).collect(),
)),
)?;
continue;
}
// Try to parse as integers
let all_ints = non_empty_values.iter().all(|&s| s.parse::<i64>().is_ok());
if all_ints {
let int_values: Vec<i64> = str_values
.iter()
.map(|s| s.parse::<i64>().unwrap_or(0))
.collect();
df.add_column(
key.clone(),
Column::Int64(crate::column::Int64Column::new(int_values)),
)?;
continue;
}
// Try to parse as floating point numbers
let all_floats = non_empty_values.iter().all(|&s| s.parse::<f64>().is_ok());
if all_floats {
let float_values: Vec<f64> = str_values
.iter()
.map(|s| s.parse::<f64>().unwrap_or(0.0))
.collect();
df.add_column(
key.clone(),
Column::Float64(crate::column::Float64Column::new(float_values)),
)?;
continue;
}
// Try to parse as boolean values
let all_bools = non_empty_values.iter().all(|&s| {
let lower = s.to_lowercase();
lower == "true" || lower == "false"
});
if all_bools {
let bool_values: Vec<bool> = str_values
.iter()
.map(|s| s.to_lowercase() == "true")
.collect();
df.add_column(
key.clone(),
Column::Boolean(crate::column::BooleanColumn::new(bool_values)),
)?;
} else {
// Default to string type
df.add_column(
key.clone(),
Column::String(crate::column::StringColumn::new(
str_values.iter().map(|s| s.clone()).collect(),
)),
)?;
}
}
}
Ok(df)
}
/// Write DataFrame to a JSON file
///
/// # Arguments
/// * `path` - Path to the output JSON file
/// * `orient` - JSON output format (Records or Columns)
///
/// # Returns
/// * `Result<()>` - Ok if successful
pub fn to_json<P: AsRef<Path>>(&self, path: P, orient: JsonOrient) -> Result<()> {
let file = File::create(path.as_ref()).map_err(|e| Error::Io(e))?;
let writer = BufWriter::new(file);
// Convert to JSON format
let json_value = match orient {
JsonOrient::Records => self.to_records_json()?,
JsonOrient::Columns => self.to_column_json()?,
};
// Write JSON
serde_json::to_writer_pretty(writer, &json_value).map_err(|e| Error::Json(e))?;
Ok(())
}
// Convert DataFrame to records-oriented JSON
fn to_records_json(&self) -> Result<Value> {
let mut records = Vec::with_capacity(self.row_count());
// Return empty array if there are no rows
if self.row_count() == 0 {
return Ok(Value::Array(records));
}
// Process data for each row
for row_idx in 0..self.row_count() {
let mut record = Map::new();
// Get value from each column
for (col_idx, col_name) in self.column_names().iter().enumerate() {
let column = &self.columns[col_idx];
// Convert column value to JSON value
let value = match column {
Column::Int64(col) => {
if let Ok(Some(val)) = col.get(row_idx) {
Value::Number(serde_json::Number::from(val))
} else {
Value::Null
}
}
Column::Float64(col) => {
if let Ok(Some(val)) = col.get(row_idx) {
// Convert f64 to Number (use Null for NaN or Infinity which can't be processed)
if val.is_finite() {
serde_json::Number::from_f64(val).map_or(Value::Null, Value::Number)
} else {
Value::Null
}
} else {
Value::Null
}
}
Column::String(col) => {
if let Ok(Some(val)) = col.get(row_idx) {
Value::String(val.to_string())
} else {
Value::Null
}
}
Column::Boolean(col) => {
if let Ok(Some(val)) = col.get(row_idx) {
Value::Bool(val)
} else {
Value::Null
}
}
};
record.insert(col_name.clone(), value);
}
records.push(Value::Object(record));
}
Ok(Value::Array(records))
}
// Convert DataFrame to column-oriented JSON
fn to_column_json(&self) -> Result<Value> {
let mut columns = serde_json::Map::new();
// Return empty object if there are no rows
if self.row_count() == 0 {
return Ok(Value::Object(columns));
}
// Process each column
for (col_idx, col_name) in self.column_names().iter().enumerate() {
let mut values = Vec::new();
// Get all values in the column
for row_idx in 0..self.row_count() {
let value = match &self.columns[col_idx] {
Column::Int64(col) => {
if let Ok(Some(val)) = col.get(row_idx) {
Value::Number(serde_json::Number::from(val))
} else {
Value::Null
}
}
Column::Float64(col) => {
if let Ok(Some(val)) = col.get(row_idx) {
// Convert f64 to Number (use Null for NaN or Infinity which can't be processed)
if val.is_finite() {
serde_json::Number::from_f64(val).map_or(Value::Null, Value::Number)
} else {
Value::Null
}
} else {
Value::Null
}
}
Column::String(col) => {
if let Ok(Some(val)) = col.get(row_idx) {
Value::String(val.to_string())
} else {
Value::Null
}
}
Column::Boolean(col) => {
if let Ok(Some(val)) = col.get(row_idx) {
Value::Bool(val)
} else {
Value::Null
}
}
};
values.push(value);
}
columns.insert(col_name.clone(), Value::Array(values));
}
Ok(Value::Object(columns))
}
}