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Updated the numpy and matplotlib lessons
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‎14_more_numpy.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Continuing Numpy\n",
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"# More Numpy\n",
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"\n",
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"Carrying on from yesterday we will continue learning how to manipulate data in `numpy` before using `matplotlib` to plot our data."
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"Carrying on from the last lesson we will continue learning how to manipulate data in `numpy` before using `matplotlib` to plot our data."
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]
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{
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"cell_type": "code",
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"source": [
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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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"dtype('int64')"
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]
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},
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"execution_count": 2,
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = np.array([1, 2, 3])\n",
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"a.dtype"
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]
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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"data": {
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"text/plain": [
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"dtype('float64')"
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]
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"execution_count": 3,
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"b = np.array([1., 2., 3.])\n",
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"b.dtype"
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{
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"cell_type": "code",
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"data": {
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"text/plain": [
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"dtype('float64')"
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"c = np.array([1, 2, 3], dtype=float)\n",
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"c.dtype"
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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"data": {
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"text/plain": [
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"dtype('float64')"
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]
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},
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"execution_count": 5,
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"d = np.ones((3, 3))\n",
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"d.dtype"
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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"data": {
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"text/plain": [
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"complex"
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]
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},
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"execution_count": 6,
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"e = np.array([1+2j, 3+4j, 5+6*1j])\n",
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"type(1j)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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"data": {
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"text/plain": [
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"dtype('bool')"
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"f = np.array([True, False, False, True])\n",
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"f.dtype"
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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"data": {
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"text/plain": [
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"dtype('<U7')"
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]
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},
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"g = np.array(['Bonjour', 'Hello', 'Hallo',])\n",
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"g.dtype # <--- strings containing max. 7 letters"
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},
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"cell_type": "code",
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"execution_count": 9,
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"source": [
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"10.9 ms ± 1.03 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
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]
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def python_double(a):\n",
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" for i, val in enumerate(a):\n",
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"55.4 µs ± 697 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
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]
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def numpy_double(a):\n",
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" a *= 2\n",
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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"data": {
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"text/plain": [
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"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
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]
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},
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = np.arange(10)\n",
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"a"
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"cell_type": "code",
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"True"
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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"data": {
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"text/plain": [
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"array([12, 4, 5, 6])"
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]
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},
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"metadata": {},
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"output_type": "execute_result"
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],
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"execution_count": null,
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"outputs": [],
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"source": [
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"b[0] = 12\n",
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"b"
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"cell_type": "code",
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"execution_count": 15,
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"outputs": [
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"data": {
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"text/plain": [
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"array([ 0, 1, 2, 12, 4, 5, 6, 7, 8, 9])"
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},
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a # (!)"
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"data": {
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"text/plain": [
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"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
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}
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],
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"execution_count": null,
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"outputs": [],
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"source": [
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"a = np.arange(10)\n",
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"c = a[::2].copy() # force a copy\n",
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"outputs": [
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"data": {
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"text/plain": [
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"False"
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.may_share_memory(a, c) # we made a copy so there is no shared memory"
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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": "3.6.3"
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"version": "3.5.2"
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}
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},
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"nbformat": 4,

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