From 7d7794627be1eef7edc5152299ca3fe05d0300f7 Mon Sep 17 00:00:00 2001
From: Palash Tyagi <23239946+Magnus167@users.noreply.github.com>
Date: Fri, 4 Jul 2025 20:15:47 +0100
Subject: [PATCH] Refactor DataFrame usage example in README.md for clarity and
 consistency

---
 README.md | 62 ++++++++++++++++++++++++++++++++++++++++++-------------
 1 file changed, 48 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index f8fc1c9..fab8c73 100644
--- a/README.md
+++ b/README.md
@@ -98,15 +98,17 @@ assert!(check);
 
 
 ```
+
 ---
 
 ## DataFrame Usage Example
 
 ```rust
-use rustframe::dataframe::DataFrame;
 use chrono::NaiveDate;
+use rustframe::dataframe::DataFrame;
+use rustframe::utils::{BDateFreq, BDatesList};
+use std::any::TypeId;
 use std::collections::HashMap;
-use std::any::TypeId; // Required for checking TypeId
 
 // Helper for NaiveDate
 fn d(y: i32, m: u32, d: u32) -> NaiveDate {
@@ -119,25 +121,49 @@ let mut df = DataFrame::new();
 // Add columns of different types
 df.add_column("col_int1", vec![1, 2, 3, 4, 5]);
 df.add_column("col_float1", vec![1.1, 2.2, 3.3, 4.4, 5.5]);
-df.add_column("col_string", vec!["apple".to_string(), "banana".to_string(), "cherry".to_string(), "date".to_string(), "elderberry".to_string()]);
+df.add_column(
+    "col_string",
+    vec![
+        "apple".to_string(),
+        "banana".to_string(),
+        "cherry".to_string(),
+        "date".to_string(),
+        "elderberry".to_string(),
+    ],
+);
 df.add_column("col_bool", vec![true, false, true, false, true]);
-df.add_column("col_date", vec![d(2023,1,1), d(2023,1,2), d(2023,1,3), d(2023,1,4), d(2023,1,5)]);
+// df.add_column("col_date", vec![d(2023,1,1), d(2023,1,2), d(2023,1,3), d(2023,1,4), d(2023,1,5)]);
+df.add_column(
+    "col_date",
+    BDatesList::from_n_periods("2023-01-01".to_string(), BDateFreq::Daily, 5)
+        .unwrap()
+        .list()
+        .unwrap(),
+);
 
 println!("DataFrame after initial column additions:\n{}", df);
 
 // Demonstrate frame re-use when adding columns of existing types
 let initial_frames_count = df.num_internal_frames();
-println!("\nInitial number of internal frames: {}", initial_frames_count);
+println!(
+    "\nInitial number of internal frames: {}",
+    initial_frames_count
+);
 
 df.add_column("col_int2", vec![6, 7, 8, 9, 10]);
 df.add_column("col_float2", vec![6.6, 7.7, 8.8, 9.9, 10.0]);
 
 let frames_after_reuse = df.num_internal_frames();
-println!("Number of internal frames after adding more columns of existing types: {}", frames_after_reuse);
+println!(
+    "Number of internal frames after adding more columns of existing types: {}",
+    frames_after_reuse
+);
 assert_eq!(initial_frames_count, frames_after_reuse); // Should be equal, demonstrating re-use
 
-println!("\nDataFrame after adding more columns of existing types:\n{}", df);
-
+println!(
+    "\nDataFrame after adding more columns of existing types:\n{}",
+    df
+);
 
 // Get number of rows and columns
 println!("Rows: {}", df.rows()); // Output: Rows: 5
@@ -149,17 +175,21 @@ println!("Column names: {:?}", df.get_column_names());
 
 // Get a specific column by name and type
 let int_col = df.get_column::<i32>("col_int1").unwrap();
-println!("Integer column (col_int1): {:?}", int_col); // Output: Integer column: [1, 2, 3, 4, 5]
+// Output: Integer column: [1, 2, 3, 4, 5]
+println!("Integer column (col_int1): {:?}", int_col);
 
 let int_col2 = df.get_column::<i32>("col_int2").unwrap();
-println!("Integer column (col_int2): {:?}", int_col2); // Output: Integer column: [6, 7, 8, 9, 10]
+// Output: Integer column: [6, 7, 8, 9, 10]
+println!("Integer column (col_int2): {:?}", int_col2);
 
 let float_col = df.get_column::<f64>("col_float1").unwrap();
-println!("Float column (col_float1): {:?}", float_col); // Output: Float column: [1.1, 2.2, 3.3, 4.4, 5.5]
+// Output: Float column: [1.1, 2.2, 3.3, 4.4, 5.5]
+println!("Float column (col_float1): {:?}", float_col);
 
 // Attempt to get a column with incorrect type (returns None)
 let wrong_type_col = df.get_column::<bool>("col_int1");
-println!("Wrong type column: {:?}", wrong_type_col); // Output: Wrong type column: None
+// Output: Wrong type column: None
+println!("Wrong type column: {:?}", wrong_type_col);
 
 // Get a row by index
 let row_0 = df.get_row(0).unwrap();
@@ -172,7 +202,8 @@ println!("Row 2: {:?}", row_2);
 
 // Attempt to get an out-of-bounds row (returns None)
 let row_out_of_bounds = df.get_row(10);
-println!("Row out of bounds: {:?}", row_out_of_bounds); // Output: Row out of bounds: None
+// Output: Row out of bounds: None
+println!("Row out of bounds: {:?}", row_out_of_bounds);
 
 // Drop a column
 df.drop_column("col_bool");
@@ -186,7 +217,10 @@ df.drop_column("col_float1");
 println!("\nDataFrame after dropping 'col_float1':\n{}", df);
 
 println!("Columns after second drop: {}", df.cols());
-println!("Column names after second drop: {:?}", df.get_column_names());
+println!(
+    "Column names after second drop: {:?}",
+    df.get_column_names()
+);
 
 // Attempt to drop a non-existent column (will panic)
 // df.drop_column("non_existent_col"); // Uncomment to see panic