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‎Exercise01/Exercise01.ipynb

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{
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"[1, 2, 3]"
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]
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},
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"array([1, 2, 3])"
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]
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"array([1.2, 3.4, 5.6])"
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{
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"numpy.ndarray"
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]
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"numpy.ndarray"
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},
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"list"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"type(list_1)"
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},
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"cell_type": "code",
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"metadata": {},
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"execution_count": 8,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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" ('AMT', 136), ('AWK', 137), ('AMP', 138), ('ABC', 139),\n",
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" ('AME', 140), ('AMGN', 141), ('APH', 142), ('APC', 143),\n",
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" ('ADI', 144), ('ANDV', 145), ('ANSS', 146), ('ANTM', 147),\n",
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" ('AON', 148)], dtype=[('Symbol', '<U5'), ('Price', '<i8')])"
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" ('AON', 148)], dtype=[('Symbol', '<U5'), ('Price', '<i4')])"
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]
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},
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"execution_count": 9,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data = np.genfromtxt('stock.csv', delimiter=',', names=True,dtype=None, encoding='ascii')\n",
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"data = np.genfromtxt('../datasets/stock.csv', delimiter=',', names=True,dtype=None, encoding='ascii')\n",
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"data"
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]
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"outputs": [
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{
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"data": {
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"text/plain": [
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"numpy.ndarray"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"type(data)"
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},
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"source": []
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],
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"metadata": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.7.4"
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}
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},
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"nbformat": 4,

‎Exercise02/Exercise02.ipynb

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},
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"[1, 2, 3, 1, 2, 3]"
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]
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"array([2, 4, 6])"
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{
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" 290., 292., 294., 296.])"
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}
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],
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"source": [
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"data = np.genfromtxt('numbers.csv', delimiter=',', names=True)\n",
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"data = np.genfromtxt('../datasets/numbers.csv', delimiter=',', names=True)\n",
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"data = data.astype('float64')\n",
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"data + data"
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},
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"version": "3.7.4"
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}
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},
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"nbformat": 4,

‎Exercise04/Exercise04.ipynb

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" [148]]"
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"source": [
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"import pandas as pd\n",
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"df = pd.read_csv(\"numbers.csv\")\n",
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"df = pd.read_csv(\"../datasets/numbers.csv\")\n",
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"list_5 = df.values.tolist()\n",
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