diff --git a/book/book_second_edition.pdf b/book/book_second_edition.pdf
index 82b0f38..245e104 100644
Binary files a/book/book_second_edition.pdf and b/book/book_second_edition.pdf differ
diff --git a/src/searching_and_sorting/searching/quick_select.py b/src/searching_and_sorting/searching/quick_select.py
new file mode 100644
index 0000000..1abcb92
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+++ b/src/searching_and_sorting/searching/quick_select.py
@@ -0,0 +1,41 @@
+
+#!/usr/bin/python
+
+__author__ = "Mari Wahl"
+__email__ = "marina.w4hl@gmail.com"
+
+def quickSelect(seq, k):
+    # this part is the same as quick sort
+    len_seq = len(seq)
+    if len_seq < 2: return seq
+
+    ipivot = len_seq // 2
+    pivot = seq[ipivot]
+
+    smallerList = [x for i,x in enumerate(seq) if x <= pivot and  i != ipivot]
+    largerList = [x for i,x in enumerate(seq) if x > pivot and  i != ipivot]
+
+    # here starts the different part
+    m = len(smallerList)
+    if k == m:
+        return pivot
+    elif k < m:
+        return quickSelect(smallerList, k)
+    else:
+        return quickSelect(largerList, k-m)
+
+
+
+
+if __name__ == '__main__':
+    # Checking the Answer
+    seq = [10, 60, 100, 50, 60, 75, 31, 50, 30, 20, 120, 170, 200]
+
+    # we want the middle element
+    k = len(seq) // 2
+
+    # Note that this only work for odd arrays, since median in
+    # even arrays is the mean of the two middle elements
+    print(quickSelect(seq, k))
+    import numpy
+    print numpy.median(seq)
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