From e8b5cc4354f03a7fa1b9fce249a1d6df1195dd8f Mon Sep 17 00:00:00 2001
From: Palash Tyagi <23239946+Magnus167@users.noreply.github.com>
Date: Thu, 14 Nov 2024 17:08:57 +0000
Subject: [PATCH] adding docs

---
 src/utils/dftools.rs | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/src/utils/dftools.rs b/src/utils/dftools.rs
index 6d38743..83c68c9 100644
--- a/src/utils/dftools.rs
+++ b/src/utils/dftools.rs
@@ -1,15 +1,27 @@
 use polars::prelude::*;
 
-const QDF_IDX_COLUMNS: [&str; 3] = ["real_date", "cid", "xcat"];
+/// The standard metrics provided by JPMaQS (`value`, `grading`, `eop_lag`, `mop_lag`).
+pub const DEFAULT_JPMAQS_METRICS: [&str; 4] = ["value", "grading", "eop_lag", "mop_lag"];
+
+/// The required columns for a Quantamental DataFrame.
+pub const QDF_INDEX_COLUMNS: [&str; 3] = ["real_date", "cid", "xcat"];
+
 
 /// Check if a DataFrame is a quantamental DataFrame.
+/// A standard Quantamental DataFrame has the following columns:
+/// - `real_date`: Date column as a date type
+/// - `cid`: Column of cross-sectional identifiers
+/// - `xcat`: Column of extended categories
+///
+/// Additionally, the DataFrame should have atleast 1 more column.
+/// Typically, this is one (or more) of the default JPMaQS metics.
 pub fn is_quantamental_dataframe(df: &DataFrame) -> bool {
     let columns = df
         .get_column_names()
         .iter()
         .map(|s| s.as_str())
         .collect::<Vec<&str>>();
-    let has_idx_columns = QDF_IDX_COLUMNS.iter().all(|col| columns.contains(col));
+    let has_idx_columns = QDF_INDEX_COLUMNS.iter().all(|col| columns.contains(col));
     if !has_idx_columns {
         return false;
     }