diff --git a/week1/README.md b/week1/README.md
index 02c62ad599830406adbe55703750d3660b3026ac..ebc19a57dc4aacb4a93b9c115faa95a042b4ed11 100644
--- a/week1/README.md
+++ b/week1/README.md
@@ -1 +1,7 @@
-# Week 1 Workshop - Python Basics, Virtual Environments and Numpy
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+# Week 1 Workshop - Python Basics, Virtual Environments and Numpy
+
+conda create -n cosc350
+conda activate cosc350
+conda install pip
+
+pip install -e .
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diff --git a/week1/material/buggy_copy.py b/week1/material/buggy_copy.py
new file mode 100644
index 0000000000000000000000000000000000000000..3636bbf33aa2fe321b0d20d5d044833ac6a0a532
--- /dev/null
+++ b/week1/material/buggy_copy.py
@@ -0,0 +1,13 @@
+import numpy as np
+
+def multiply_item(original_matrix, r, c, multiplier):
+ original_matrix[r, c] *= multiplier
+ return original_matrix
+
+my_matrix = np.array([[1, 2, 3], [4, 5, 6]])
+print("Your newly created matrix:", my_matrix)
+
+result_matrix = multiply_item(my_matrix, 1, 1, 2)
+
+print("Your matrix now:", my_matrix)
+print("Resulting matrix:", result_matrix)
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diff --git a/week1/material/lambda_loop.py b/week1/material/lambda_loop.py
new file mode 100644
index 0000000000000000000000000000000000000000..748a285671deebeb2b2116cbd858b5900d6d8461
--- /dev/null
+++ b/week1/material/lambda_loop.py
@@ -0,0 +1,14 @@
+first_list = [8, 10, 9]
+second_list = [5, 7, 1, 2]
+
+lambdas = []
+for first_item in first_list:
+ for second_item in second_list:
+ cur_lambda = lambda: first_item + second_item
+ lambdas.append(cur_lambda)
+
+results = []
+for f in lambdas:
+ results.append(f())
+
+print("Results: ", results)
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diff --git a/week1/material/my_matrix.py b/week1/material/my_matrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..33c1daaea78f92cd3c1a898f948a0a180878bdcb
--- /dev/null
+++ b/week1/material/my_matrix.py
@@ -0,0 +1,8 @@
+import numpy as np
+
+def main():
+ # Place your code for matrix operations here
+ pass
+
+if __name__ == '__main__':
+ main()
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diff --git a/week1/solution/README.md b/week1/solution/README.md
deleted file mode 100644
index bd1c2b7a1f1d81acdb2864d0344cc45e71676851..0000000000000000000000000000000000000000
--- a/week1/solution/README.md
+++ /dev/null
@@ -1 +0,0 @@
-# Week 1 Workshop - Proposed Solutions
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diff --git a/week1/solution/buggy_copy.py b/week1/solution/buggy_copy.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae9a31e5e6b36fc8e14596cc1e0ca9f29fcd2b65
--- /dev/null
+++ b/week1/solution/buggy_copy.py
@@ -0,0 +1,18 @@
+import numpy as np
+
+def multiply_item(original_matrix, r, c, multiplier):
+ # we need to create a copy of the original matrix
+ # so that we do not risk to modify it
+ matrix_copy = original_matrix.copy()
+ # now we can modify the item in the copy of the matrix
+ matrix_copy[r, c] *= multiplier
+ # and return it
+ return matrix_copy
+
+my_matrix = np.array([[1, 2, 3], [4, 5, 6]])
+print("Your newly created matrix:", my_matrix)
+
+result_matrix = multiply_item(my_matrix, 1, 1, 2)
+
+print("Your matrix now:", my_matrix)
+print("Resulting matrix:", result_matrix)
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diff --git a/week1/solution/lambda_loop.py b/week1/solution/lambda_loop.py
new file mode 100644
index 0000000000000000000000000000000000000000..c93c72afd1b583b9703f801382eda732f107d105
--- /dev/null
+++ b/week1/solution/lambda_loop.py
@@ -0,0 +1,17 @@
+first_list = [8, 10, 9]
+second_list = [5, 7, 1, 2]
+
+lambdas = []
+for first_item in first_list:
+ for second_item in second_list:
+ # we need to add two parameters to the lambda function
+ # and we can pass the desired items as the default
+ # values for this lambda function
+ cur_lambda = lambda a=first_item, b=second_item: a + b
+ lambdas.append(cur_lambda)
+
+results = []
+for f in lambdas:
+ results.append(f())
+
+print("Results: ", results)
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diff --git a/week1/solution/my_matrix.py b/week1/solution/my_matrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..71ba108b4088dfa3cbac55ea8904b813e92f2829
--- /dev/null
+++ b/week1/solution/my_matrix.py
@@ -0,0 +1,84 @@
+import numpy as np
+
+# creating a function to retrieve valid inputs from the user
+def get_valid_input(prompt, validation_function, error_message):
+ # setting return value to None
+ value = None
+
+ # looping until we do not have a valid value
+ while value is None:
+ # retrieving the input from the user
+ value = input(prompt)
+
+ # checking the value fits the validation criteria
+ if not validation_function(value):
+ # invalid input
+ # we notify the user with the error message
+ print(error_message)
+ # set value back to None so we loop again
+ value = None
+
+ # returning the valid value
+ return value
+
+def main():
+ my_matrix = np.zeros((3, 4), dtype=int)
+
+ print("Please, input the values for the matrix")
+ for r in range(0, my_matrix.shape[0]):
+ print("Row {0}:".format(r))
+ for c in range(0, my_matrix.shape[1]):
+ value = get_valid_input(
+ "Please, input item {0} for the current row:".format(c),
+ lambda v: v.isnumeric(),
+ "Invalid input. The values must be int."
+ )
+
+ # if we are here, it means the value is valid
+ # we add it to the matrix
+ my_matrix[r, c] = int(value)
+
+ # asking for the operation to perform
+ valid_operations = ['mean', 'sum', 'std']
+ operation = get_valid_input(
+ "Please, select the desired operation to perform (mean, sum or std):",
+ lambda v: v in valid_operations,
+ "The selected operation is invalid."
+ )
+
+ # asking for the row / column
+ valid_choices = ['row', 'colum']
+ choice = get_valid_input(
+ "Do you want to compute the operation on a row or on a column?",
+ lambda v: v in valid_choices,
+ "Invalid input value. The input must be 'row' or 'column'"
+ )
+
+ # asking for the index
+ valid_idx_row = range(0, my_matrix.shape[0])
+ valid_idx_col = range(0, my_matrix.shape[1])
+ idx = get_valid_input(
+ "Specify the index of the {0}:".format(choice),
+ lambda v: int(v) in valid_idx_row if choice == 'row' else int(v) in valid_idx_col,
+ "Invalid input value. The input must be a valid positive index."
+ )
+ idx = int(idx)
+
+ if choice == 'row':
+ items = my_matrix[idx, :]
+ else:
+ items = my_matrix[:, idx]
+
+ if operation == 'mean':
+ result = np.mean(items)
+ elif operation == 'sum':
+ result = np.sum(items)
+ else:
+ result = np.std(items)
+
+ print("Your matrix:")
+ print(my_matrix)
+ print("The operation '{0}' applied on the {1} at index {2} gives the result {3}".format(operation, choice, idx, result))
+
+if __name__ == '__main__':
+ main()
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