diff --git a/four/four.go b/four/four.go
new file mode 100644
index 0000000..a25f658
--- /dev/null
+++ b/four/four.go
@@ -0,0 +1,28 @@
+package main
+
+import (
+	"fmt"
+	"os"
+	"regexp"
+	"strconv"
+)
+
+func check(e error) {
+	if e != nil {
+		panic(e)
+	}
+}
+
+
+func main() {
+	f, err := os.Open("./four.txt")
+	defer f.Close()
+	check(err)
+
+	n_safe := 0
+	n_dampener_safe := 0
+
+	scanner := bufio.NewScanner(f)
+	for scanner.Scan() {
+    }
+}
diff --git a/four/four.py b/four/four.py
new file mode 100755
index 0000000..5a6e57a
--- /dev/null
+++ b/four/four.py
@@ -0,0 +1,110 @@
+import pprint
+import pandas as pd
+import numpy as np
+
+mas_patterns = [
+    pd.DataFrame(
+        [
+            ["M", ".", "M"],
+            [".", "A", "."],
+            ["S", ".", "S"],
+        ]
+    ),
+    pd.DataFrame(
+        [
+            ["M", ".", "S"],
+            [".", "A", "."],
+            ["M", ".", "S"],
+        ]
+    ),
+    pd.DataFrame(
+        [
+            ["S", ".", "M"],
+            [".", "A", "."],
+            ["S", ".", "M"],
+        ]
+    ),
+    pd.DataFrame(
+        [
+            ["S", ".", "S"],
+            [".", "A", "."],
+            ["M", ".", "M"],
+        ]
+    ),
+]
+
+
+def get_diagonals(input_df: pd.DataFrame):
+    n_cols = len(input_df.columns)
+    n_rows = len(input_df)
+
+    diagonals = []
+
+    for i in range(-1 * n_rows + 1, n_cols):
+        diagonals.append("".join(np.diag(input_df, i)))
+
+    return diagonals
+
+
+def get_sub_arrays(input_df: pd.DataFrame):
+    n_cols = len(input_df.columns)
+    n_rows = len(input_df)
+
+    sub_arrays = []
+
+    for i in range(n_rows - 2):
+        for j in range(n_cols - 2):
+            sub = input_df.iloc[i : i + 3, j : j + 3].copy()
+            sub.iloc[0, 1] = "."
+            sub.iloc[1, 0] = "."
+            sub.iloc[1, 2] = "."
+            sub.iloc[2, 1] = "."
+            sub_arrays.append(sub)
+
+    return sub_arrays
+
+
+with open("./four.txt") as f:
+    wordsearch_input = f.readlines()
+
+lines = [list(s.strip()) for s in wordsearch_input if s]
+lines = pd.DataFrame(lines)
+
+possible_strings = []
+
+# Rows
+possible_strings.extend("".join(line.to_list()) for _, line in lines.iterrows())
+possible_strings.extend("".join(line.to_list()) for _, line in lines.items())
+
+# Diagonals tl - br
+possible_strings.extend(get_diagonals(lines))
+
+# Diagonals bl - tr
+possible_strings.extend(get_diagonals(pd.DataFrame(np.rot90(lines))))
+
+# Find xmas
+occurences = 0
+for string in possible_strings:
+    occurences += string.upper().count("XMAS")
+    occurences += string.upper().count("SAMX")
+
+print(occurences)
+
+sub_arrays = get_sub_arrays(lines)
+
+
+better_occurences = 0
+for i, sub_array in enumerate(sub_arrays):
+    for j, pattern in enumerate(mas_patterns):
+        try:
+            equality = (sub_array.values == pattern.values).all()
+            if equality:
+                better_occurences += 1
+                break
+        except Exception:
+            print(i, j)
+            print(sub_array)
+            print(pattern)
+            raise
+
+print(better_occurences)