diff --git a/notebooks/Convolutional_Neural_Networks/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb b/notebooks/Convolutional_Neural_Networks/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb
index b0cc622f242b765db361893bbb9de759278edf0b..9cdf02f0917847c92cc0ffd517e2c4ad46007f48 100644
--- a/notebooks/Convolutional_Neural_Networks/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb
+++ b/notebooks/Convolutional_Neural_Networks/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb
@@ -30,9 +30,6 @@
"output_type": "stream",
"text": [
"2.7.1\n",
- "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
- "170500096/170498071 [==============================] - 9s 0us/step\n",
- "170508288/170498071 [==============================] - 9s 0us/step\n",
"Train: X=(50000, 32, 32, 3), y=(50000, 1)\n",
"Test: X=(10000, 32, 32, 3), y=(10000, 1)\n",
"(50000, 1)\n",
diff --git a/notebooks/Preliminaries_Numpy_Pandas/Checking_Correct_Installation.ipynb b/notebooks/Preliminaries_Numpy_Pandas/Checking_Correct_Installation.ipynb
index 9c6d283ea7dc2c4be703d3614526752b4f27052d..9d41c32101ce9d260d19d1823c8bb30c6398633f 100644
--- a/notebooks/Preliminaries_Numpy_Pandas/Checking_Correct_Installation.ipynb
+++ b/notebooks/Preliminaries_Numpy_Pandas/Checking_Correct_Installation.ipynb
@@ -80,7 +80,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.collections.PathCollection at 0x7fec50d77050>"
+ "<matplotlib.collections.PathCollection at 0x7fd13651a050>"
]
},
"execution_count": 5,
@@ -105,6 +105,13 @@
"%matplotlib inline\n",
"plt.scatter(range(100), np.sin(0.1 * np.array(range(100))))"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/notebooks/Preliminaries_Numpy_Pandas/Examples script/stinkbug4.JPG b/notebooks/Preliminaries_Numpy_Pandas/Examples script/stinkbug4.JPG
deleted file mode 100644
index eb88408c8e21bc0f89bb4706c7b25be747d589d0..0000000000000000000000000000000000000000
Binary files a/notebooks/Preliminaries_Numpy_Pandas/Examples script/stinkbug4.JPG and /dev/null differ
diff --git a/notebooks/Preliminaries_Numpy_Pandas/Examples script/Preliminaries_Numpy_Pandas.ipynb b/notebooks/Preliminaries_Numpy_Pandas/Jupyter Notebook - Introduction Numpy and Pandas.ipynb
similarity index 72%
rename from notebooks/Preliminaries_Numpy_Pandas/Examples script/Preliminaries_Numpy_Pandas.ipynb
rename to notebooks/Preliminaries_Numpy_Pandas/Jupyter Notebook - Introduction Numpy and Pandas.ipynb
index e4fc5e596d4e5dd193d47cf1befbdff73f29fffb..2a163231de03aff6cb3e3bf193a7d72f88f29b2f 100644
--- a/notebooks/Preliminaries_Numpy_Pandas/Examples script/Preliminaries_Numpy_Pandas.ipynb
+++ b/notebooks/Preliminaries_Numpy_Pandas/Jupyter Notebook - Introduction Numpy and Pandas.ipynb
@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 42,
"metadata": {},
"outputs": [
{
@@ -45,7 +45,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 43,
"metadata": {},
"outputs": [
{
@@ -87,7 +87,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 44,
"metadata": {},
"outputs": [
{
@@ -115,7 +115,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 45,
"metadata": {},
"outputs": [
{
@@ -171,7 +171,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
@@ -221,7 +221,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 47,
"metadata": {},
"outputs": [
{
@@ -230,7 +230,7 @@
"array(5)"
]
},
- "execution_count": 21,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
@@ -252,7 +252,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 48,
"metadata": {},
"outputs": [
{
@@ -261,7 +261,7 @@
"0"
]
},
- "execution_count": 22,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@@ -280,7 +280,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 49,
"metadata": {},
"outputs": [
{
@@ -289,7 +289,7 @@
"()"
]
},
- "execution_count": 23,
+ "execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
@@ -311,7 +311,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 50,
"metadata": {},
"outputs": [
{
@@ -320,7 +320,7 @@
"numpy.int64"
]
},
- "execution_count": 24,
+ "execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
@@ -355,7 +355,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 51,
"metadata": {},
"outputs": [
{
@@ -364,7 +364,7 @@
"array([1, 2, 3])"
]
},
- "execution_count": 25,
+ "execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
@@ -384,7 +384,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 52,
"metadata": {},
"outputs": [
{
@@ -393,7 +393,7 @@
"(3,)"
]
},
- "execution_count": 26,
+ "execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
@@ -411,7 +411,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 53,
"metadata": {},
"outputs": [
{
@@ -420,7 +420,7 @@
"1"
]
},
- "execution_count": 27,
+ "execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
@@ -442,7 +442,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 54,
"metadata": {},
"outputs": [
{
@@ -451,7 +451,7 @@
"2"
]
},
- "execution_count": 28,
+ "execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
@@ -470,7 +470,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 55,
"metadata": {},
"outputs": [
{
@@ -479,7 +479,7 @@
"array([2, 3])"
]
},
- "execution_count": 29,
+ "execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
@@ -511,7 +511,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 56,
"metadata": {},
"outputs": [
{
@@ -522,7 +522,7 @@
" [7, 8, 9]])"
]
},
- "execution_count": 30,
+ "execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
@@ -542,7 +542,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 57,
"metadata": {},
"outputs": [
{
@@ -551,7 +551,7 @@
"2"
]
},
- "execution_count": 31,
+ "execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
@@ -569,7 +569,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 58,
"metadata": {},
"outputs": [
{
@@ -578,7 +578,7 @@
"(3, 3)"
]
},
- "execution_count": 32,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
@@ -597,7 +597,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 59,
"metadata": {},
"outputs": [
{
@@ -606,7 +606,7 @@
"6"
]
},
- "execution_count": 33,
+ "execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
@@ -627,7 +627,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 60,
"metadata": {},
"outputs": [
{
@@ -663,7 +663,7 @@
" [17]]]])"
]
},
- "execution_count": 34,
+ "execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
@@ -683,7 +683,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 61,
"metadata": {},
"outputs": [
{
@@ -692,7 +692,7 @@
"(3, 3, 2, 1)"
]
},
- "execution_count": 35,
+ "execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
@@ -703,7 +703,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 62,
"metadata": {},
"outputs": [
{
@@ -712,7 +712,7 @@
"4"
]
},
- "execution_count": 36,
+ "execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
@@ -730,7 +730,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 63,
"metadata": {},
"outputs": [
{
@@ -739,7 +739,7 @@
"16"
]
},
- "execution_count": 37,
+ "execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
@@ -763,7 +763,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 64,
"metadata": {},
"outputs": [
{
@@ -772,7 +772,7 @@
"(4,)"
]
},
- "execution_count": 38,
+ "execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
@@ -792,7 +792,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 65,
"metadata": {},
"outputs": [
{
@@ -801,7 +801,7 @@
"array([[1, 2, 3, 4]])"
]
},
- "execution_count": 39,
+ "execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
@@ -821,7 +821,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 66,
"metadata": {},
"outputs": [
{
@@ -833,7 +833,7 @@
" [4]])"
]
},
- "execution_count": 40,
+ "execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
@@ -857,7 +857,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 67,
"metadata": {},
"outputs": [
{
@@ -866,7 +866,7 @@
"array([[1, 2, 3, 4]])"
]
},
- "execution_count": 41,
+ "execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
@@ -878,7 +878,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 68,
"metadata": {},
"outputs": [
{
@@ -887,7 +887,7 @@
"(1, 4)"
]
},
- "execution_count": 42,
+ "execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
@@ -898,7 +898,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 69,
"metadata": {},
"outputs": [
{
@@ -910,7 +910,7 @@
" [4]])"
]
},
- "execution_count": 43,
+ "execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
@@ -922,7 +922,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 70,
"metadata": {},
"outputs": [
{
@@ -931,7 +931,7 @@
"(4, 1)"
]
},
- "execution_count": 44,
+ "execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
@@ -963,24 +963,27 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "1.03 µs ± 447 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
+ "0.00015544891357421875\n",
+ "[6, 7, 8, 9, 10]\n"
]
}
],
"source": [
- "%%timeit\n",
+ "import time\n",
+ "start = time.time()\n",
"values = [1,2,3,4,5]\n",
"for i in range(len(values)):\n",
" values[i] += 5\n",
- " \n",
- "values"
+ "end = time.time()\n",
+ "print(end - start) \n",
+ "print(values)"
]
},
{
@@ -1007,22 +1010,25 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "3.44 µs ± 427 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
+ "0.00020384788513183594\n",
+ "[ 6 7 8 9 10]\n"
]
}
],
"source": [
- "%%timeit\n",
+ "start = time.time()\n",
"values = [1,2,3,4,5]\n",
"values = np.array(values) + 5\n",
- "values"
+ "end = time.time()\n",
+ "print(end - start) \n",
+ "print(values)"
]
},
{
@@ -1035,23 +1041,26 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "3.78 µs ± 381 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
+ "0.00024271011352539062\n",
+ "[ 6 7 8 9 10]\n"
]
}
],
"source": [
- "%%timeit\n",
+ "start = time.time()\n",
"values = [1,2,3,4,5]\n",
"values = np.array(values)\n",
"values += 5\n",
- "values"
+ "end = time.time()\n",
+ "print(end - start) \n",
+ "print(values)"
]
},
{
@@ -1064,7 +1073,7 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 74,
"metadata": {},
"outputs": [
{
@@ -1073,7 +1082,7 @@
"array([ 5, 10, 15, 20])"
]
},
- "execution_count": 48,
+ "execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
@@ -1085,7 +1094,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 75,
"metadata": {},
"outputs": [
{
@@ -1094,7 +1103,7 @@
"array([ 5, 10, 15, 20])"
]
},
- "execution_count": 49,
+ "execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
@@ -1118,7 +1127,7 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 76,
"metadata": {},
"outputs": [
{
@@ -1129,7 +1138,7 @@
" [0, 0, 0]])"
]
},
- "execution_count": 50,
+ "execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
@@ -1160,7 +1169,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 77,
"metadata": {},
"outputs": [
{
@@ -1170,7 +1179,7 @@
" [5, 7]])"
]
},
- "execution_count": 51,
+ "execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
@@ -1182,7 +1191,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 78,
"metadata": {},
"outputs": [
{
@@ -1192,7 +1201,7 @@
" [6, 8]])"
]
},
- "execution_count": 52,
+ "execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
@@ -1204,7 +1213,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 79,
"metadata": {},
"outputs": [
{
@@ -1214,7 +1223,7 @@
" [11, 15]])"
]
},
- "execution_count": 53,
+ "execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
@@ -1232,7 +1241,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 80,
"metadata": {},
"outputs": [
{
@@ -1242,7 +1251,7 @@
" [5, 7]])"
]
},
- "execution_count": 54,
+ "execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
@@ -1254,7 +1263,7 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 81,
"metadata": {},
"outputs": [
{
@@ -1265,7 +1274,7 @@
" [1, 8, 7]])"
]
},
- "execution_count": 55,
+ "execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
@@ -1277,7 +1286,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 82,
"metadata": {},
"outputs": [
{
@@ -1286,7 +1295,7 @@
"(2, 2)"
]
},
- "execution_count": 56,
+ "execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
@@ -1297,7 +1306,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 83,
"metadata": {},
"outputs": [
{
@@ -1306,7 +1315,7 @@
"(3, 3)"
]
},
- "execution_count": 57,
+ "execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
@@ -1317,7 +1326,7 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 84,
"metadata": {},
"outputs": [
{
@@ -1327,7 +1336,7 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-58-e81e582b6fa9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m<ipython-input-84-e81e582b6fa9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,2) (3,3) "
]
}
@@ -1338,9 +1347,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 85,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1, 2, 3],\n",
+ " [4, 5, 6]])"
+ ]
+ },
+ "execution_count": 85,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"m = np.array([[1,2,3],[4,5,6]])\n",
"m"
@@ -1348,9 +1369,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 86,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.25, 0.5 , 0.75],\n",
+ " [1. , 1.25, 1.5 ]])"
+ ]
+ },
+ "execution_count": 86,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"n = m * 0.25\n",
"n"
@@ -1358,18 +1391,42 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 87,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.25, 1. , 2.25],\n",
+ " [4. , 6.25, 9. ]])"
+ ]
+ },
+ "execution_count": 87,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"m * n"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 88,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.25, 1. , 2.25],\n",
+ " [4. , 6.25, 9. ]])"
+ ]
+ },
+ "execution_count": 88,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"np.multiply(m, n)"
]
@@ -1385,9 +1442,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 89,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1, 2, 3, 4],\n",
+ " [5, 6, 7, 8]])"
+ ]
+ },
+ "execution_count": 89,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"a = np.array([[1,2,3,4],[5,6,7,8]])\n",
"a"
@@ -1395,18 +1464,43 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 90,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2, 4)"
+ ]
+ },
+ "execution_count": 90,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"a.shape"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 91,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1, 2, 3],\n",
+ " [ 4, 5, 6],\n",
+ " [ 7, 8, 9],\n",
+ " [10, 11, 12]])"
+ ]
+ },
+ "execution_count": 91,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"b = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])\n",
"b"
@@ -1414,18 +1508,41 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 92,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(4, 3)"
+ ]
+ },
+ "execution_count": 92,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"b.shape"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 93,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 70, 80, 90],\n",
+ " [158, 184, 210]])"
+ ]
+ },
+ "execution_count": 93,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"c = np.matmul(a, b)\n",
"c"
@@ -1433,9 +1550,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 94,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2, 3)"
+ ]
+ },
+ "execution_count": 94,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"c.shape"
]
@@ -1449,9 +1577,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 95,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 3)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-95-af3b88aa2232>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 3)"
+ ]
+ }
+ ],
"source": [
"np.matmul(b, a)"
]
@@ -1470,9 +1610,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 96,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1, 2],\n",
+ " [3, 4]])"
+ ]
+ },
+ "execution_count": 96,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"a = np.array([[1,2],[3,4]])\n",
"a"
@@ -1480,27 +1632,63 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 97,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 7, 10],\n",
+ " [15, 22]])"
+ ]
+ },
+ "execution_count": 97,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"np.dot(a,a)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 98,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 7, 10],\n",
+ " [15, 22]])"
+ ]
+ },
+ "execution_count": 98,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"a.dot(a)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 99,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 7, 10],\n",
+ " [15, 22]])"
+ ]
+ },
+ "execution_count": 99,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"np.matmul(a,a)"
]
@@ -1527,9 +1715,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 100,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1, 2, 3, 4],\n",
+ " [ 5, 6, 7, 8],\n",
+ " [ 9, 10, 11, 12]])"
+ ]
+ },
+ "execution_count": 100,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"m = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
"m"
@@ -1537,9 +1738,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 101,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1, 5, 9],\n",
+ " [ 2, 6, 10],\n",
+ " [ 3, 7, 11],\n",
+ " [ 4, 8, 12]])"
+ ]
+ },
+ "execution_count": 101,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"m.T"
]
@@ -1560,9 +1775,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 102,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1, 5, 9],\n",
+ " [ 2, 6, 10],\n",
+ " [ 3, 7, 11],\n",
+ " [ 4, 200, 12]])"
+ ]
+ },
+ "execution_count": 102,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"m_t = m.T\n",
"m_t[3][1] = 200\n",
@@ -1571,9 +1800,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 103,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1, 2, 3, 4],\n",
+ " [ 5, 6, 7, 200],\n",
+ " [ 9, 10, 11, 12]])"
+ ]
+ },
+ "execution_count": 103,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"m"
]
@@ -1606,9 +1848,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 104,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: 'stinkbug4.JPG'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-104-3d285766ff57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmpimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'stinkbug4.JPG'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mimread\u001b[0;34m(fname, format)\u001b[0m\n\u001b[1;32m 1484\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1485\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1486\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mimg_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1487\u001b[0m return (_pil_png_to_float_array(image)\n\u001b[1;32m 1488\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPIL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPngImagePlugin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPngImageFile\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode, formats)\u001b[0m\n\u001b[1;32m 2889\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2890\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2891\u001b[0;31m \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2892\u001b[0m \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'stinkbug4.JPG'"
+ ]
+ }
+ ],
"source": [
"%matplotlib inline \n",
"\n",
@@ -1630,9 +1886,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 105,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'skimage'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-105-16d8c7ff678e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mskimage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mskimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'skimage'"
+ ]
+ }
+ ],
"source": [
"import skimage\n",
"from skimage.transform import rescale\n",
@@ -1674,9 +1942,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 106,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[-0.27 0.45 0.64 0.31]] (1, 4)\n",
+ "[[ 0.02 0.001 -0.03 0.036]\n",
+ " [ 0.04 -0.003 0.025 0.009]\n",
+ " [ 0.012 -0.045 0.28 -0.067]] (3, 4)\n",
+ "Matrix multiplication gives:\n",
+ " [[-0.01299 0.00664 0.13494]] \n",
+ "or, equivalently:\n",
+ " [[-0.01299]\n",
+ " [ 0.00664]\n",
+ " [ 0.13494]]\n"
+ ]
+ }
+ ],
"source": [
"inputs = np.array([[-0.27, 0.45, 0.64, 0.31]])\n",
"print(inputs, inputs.shape)\n",
@@ -1698,9 +1983,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 107,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Showing some basic math on arrays\n",
+ "Max: 4\n",
+ "Average: 2.0\n",
+ "Max index: 2\n",
+ "\n",
+ "Use numpy to create a [3,3] dimension array with random number\n",
+ "[[0.55382541 0.36160037 0.68662274]\n",
+ " [0.35028249 0.09885074 0.38463975]\n",
+ " [0.10113939 0.74698869 0.02460112]]\n"
+ ]
+ }
+ ],
"source": [
"print(\"\\nShowing some basic math on arrays\")\n",
"\n",
@@ -1737,9 +2039,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 108,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 1\n",
+ "1 5\n",
+ "2 9\n",
+ "3 15\n",
+ "4 20\n",
+ "5 25\n",
+ "6 25\n",
+ "dtype: int64\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -1759,9 +2076,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 109,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "9\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -1780,9 +2105,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 110,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "jan 1\n",
+ "feb 5\n",
+ "mar 9\n",
+ "apr 15\n",
+ "mai 20\n",
+ "jun 25\n",
+ "jul 25\n",
+ "dtype: int64\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -1804,9 +2144,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 111,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "9\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -1826,9 +2174,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 112,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Luzern Basel Zuerich\n",
+ "jan 1 3 8\n",
+ "feb 5 4 6\n",
+ "mar 9 12 10\n",
+ "apr 15 16 17\n",
+ "mai 20 18 23\n",
+ "jun 25 23 22\n",
+ "jul 25 32 24\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -1855,9 +2218,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 113,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Index(['Luzern', 'Basel', 'Zuerich'], dtype='object')\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -1883,9 +2254,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 114,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "jan 1\n",
+ "feb 5\n",
+ "mar 9\n",
+ "apr 15\n",
+ "mai 20\n",
+ "jun 25\n",
+ "jul 25\n",
+ "Name: Luzern, dtype: int64\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -1909,9 +2295,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 115,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "jan 1\n",
+ "feb 5\n",
+ "mar 9\n",
+ "apr 15\n",
+ "mai 20\n",
+ "jun 25\n",
+ "jul 25\n",
+ "Name: Luzern, dtype: int64\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -1935,9 +2336,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 116,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mai 20\n",
+ "jun 25\n",
+ "jul 25\n",
+ "Name: Luzern, dtype: int64\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -1961,9 +2373,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 117,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Basel Zuerich\n",
+ "mai 18 23\n",
+ "jul 32 24\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -1987,9 +2409,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 118,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "23\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -2024,9 +2454,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 119,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Index(['jan', 'feb', 'mar', 'apr', 'mai', 'jun', 'jul'], dtype='object')\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -2046,9 +2484,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 120,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "14.285714285714286\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -2075,9 +2521,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 121,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "jan 4.000000\n",
+ "feb 5.000000\n",
+ "mar 10.333333\n",
+ "apr 16.000000\n",
+ "mai 20.333333\n",
+ "jun 23.333333\n",
+ "jul 27.000000\n",
+ "dtype: float64\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
@@ -2101,9 +2562,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 122,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "jan 1\n",
+ "feb 4\n",
+ "mar 9\n",
+ "apr 15\n",
+ "mai 18\n",
+ "jun 22\n",
+ "jul 24\n",
+ "dtype: int64\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
diff --git a/notebooks/Preliminaries_Numpy_Pandas/Solution - Basics Numpy.ipynb b/notebooks/Preliminaries_Numpy_Pandas/Solution - Basics Numpy.ipynb
index c3aa33038665da7dda83520ad0f954e5b76780e4..eb5eeee0d80d33a74dc2e00550e8de595ba42b66 100644
--- a/notebooks/Preliminaries_Numpy_Pandas/Solution - Basics Numpy.ipynb
+++ b/notebooks/Preliminaries_Numpy_Pandas/Solution - Basics Numpy.ipynb
@@ -1481,6 +1481,13 @@
"avg_kg = t_kg.mean()\n",
"print(\"\\nAverage Kilometer per liter is: \\n{}\".format(round(avg_kml, 2)), \"\\nAverage weight in kilogram is: \\n{}\".format(round(avg_kg,2)))"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {