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dalejung/modifying series frames panels.ipynb

Created August 16, 2012 03:59
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Pandas Stuff #notebook-project #inactive
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modifying series frames panels.ipynb
{
"metadata": {
"name": "modifying series, frames, panels.ipynb",
"notebook_path": "https://gist.github.com/3366583/modifying series, frames, panels.ipynb"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import time \n",
" \n",
"class Timer: \n",
" \"\"\" \n",
" Usage: \n",
" \n",
" with Timer() as t: \n",
" ret = func(df) \n",
" print(t.interval) \n",
" \"\"\" \n",
" def __init__(self):\n",
" self.interval = None\n",
" \n",
" def __enter__(self): \n",
" self.start = time.clock() \n",
" return self \n",
" \n",
" def __exit__(self, *args): \n",
" self.end = time.clock() \n",
" self.interval = self.end - self.start \n",
" \n",
" def __str__(self):\n",
" return \"Timer.interval = {0}\".format(self.interval)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def append_many(start_size=1000, nrows=1000):\n",
" df = pd.DataFrame([range(10)]*start_size)\n",
" with Timer() as t:\n",
" for x in xrange(nrows):\n",
" df = df.append([range(10)], ignore_index=True)\n",
" \treturn t \n",
"print append_many()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Timer.interval = 1.952251\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def append_many_list(start_size=1000, nrows=1000):\n",
" df = pd.DataFrame([range(10)]*start_size)\n",
" rows = []\n",
" with Timer() as t:\n",
" for x in xrange(nrows):\n",
" rows.append(range(10))\n",
" df.append(rows, ignore_index=True)\n",
" return t \n",
"print append_many_list() "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Timer.interval = 0.005098\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import itertools\n",
"\n",
"start_range = np.arange(1, 5) * 10000 \n",
"nrows = np.arange(1, 5) * 1000\n",
"\n",
"test_cases = list(itertools.product(start_range, nrows))\n",
"\n",
"results = []\n",
"for start, nrows in test_cases:\n",
" t = append_many(start, nrows)\n",
" results.append((start, nrows, t.interval))\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"test_results = pd.DataFrame(results, columns=['start', 'nrows', 'time'])\n",
"test_results"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>start</th>\n",
" <th>nrows</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>0</strong></td>\n",
" <td> 10000</td>\n",
" <td> 1000</td>\n",
" <td> 2.148895</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>1</strong></td>\n",
" <td> 10000</td>\n",
" <td> 2000</td>\n",
" <td> 4.483919</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>2</strong></td>\n",
" <td> 10000</td>\n",
" <td> 3000</td>\n",
" <td> 6.400522</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>3</strong></td>\n",
" <td> 10000</td>\n",
" <td> 4000</td>\n",
" <td> 8.776699</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>4</strong></td>\n",
" <td> 20000</td>\n",
" <td> 1000</td>\n",
" <td> 2.338080</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>5</strong></td>\n",
" <td> 20000</td>\n",
" <td> 2000</td>\n",
" <td> 4.708480</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>6</strong></td>\n",
" <td> 20000</td>\n",
" <td> 3000</td>\n",
" <td> 7.142558</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>7</strong></td>\n",
" <td> 20000</td>\n",
" <td> 4000</td>\n",
" <td> 9.696720</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>8</strong></td>\n",
" <td> 30000</td>\n",
" <td> 1000</td>\n",
" <td> 2.639786</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>9</strong></td>\n",
" <td> 30000</td>\n",
" <td> 2000</td>\n",
" <td> 5.572692</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>10</strong></td>\n",
" <td> 30000</td>\n",
" <td> 3000</td>\n",
" <td> 8.245117</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>11</strong></td>\n",
" <td> 30000</td>\n",
" <td> 4000</td>\n",
" <td> 10.862880</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>12</strong></td>\n",
" <td> 40000</td>\n",
" <td> 1000</td>\n",
" <td> 3.382203</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>13</strong></td>\n",
" <td> 40000</td>\n",
" <td> 2000</td>\n",
" <td> 6.395405</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>14</strong></td>\n",
" <td> 40000</td>\n",
" <td> 3000</td>\n",
" <td> 9.968548</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>15</strong></td>\n",
" <td> 40000</td>\n",
" <td> 4000</td>\n",
" <td> 13.417624</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 7,
"text": [
" start nrows time\n",
"0 10000 1000 2.148895\n",
"1 10000 2000 4.483919\n",
"2 10000 3000 6.400522\n",
"3 10000 4000 8.776699\n",
"4 20000 1000 2.338080\n",
"5 20000 2000 4.708480\n",
"6 20000 3000 7.142558\n",
"7 20000 4000 9.696720\n",
"8 30000 1000 2.639786\n",
"9 30000 2000 5.572692\n",
"10 30000 3000 8.245117\n",
"11 30000 4000 10.862880\n",
"12 40000 1000 3.382203\n",
"13 40000 2000 6.395405\n",
"14 40000 3000 9.968548\n",
"15 40000 4000 13.417624"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from mpl_toolkits.mplot3d import Axes3D\n",
"from matplotlib import cm\n",
"from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca(projection='3d')\n",
"X = test_results.start\n",
"Y = test_results.nrows\n",
"X, Y = np.meshgrid(X, Y)\n",
"R = test_results.time \n",
"surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,\n",
" linewidth=0, antialiased=False)\n",
"ax.set_zlim(0, 10.01)\n",
"\n",
"ax.zaxis.set_major_locator(LinearLocator(10))\n",
"ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
"\n",
"fig.colorbar(surf, shrink=1.5, aspect=5)\n",
"\n",
"plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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p0qW45pprZPcNBAJ45513sGDBAgBAv3798Pvf/x719fVwuVx4++2320yASgmm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\nT5Skhd1kUbsc//rn9XFiL4cZhF2KV3EFHkI1BjRtkHxJwT0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}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
Display the source blob
Display the rendered blob
Raw
modifying series, frames, panels.ipynb
{
"metadata": {
"name": "modifying series, frames, panels.ipynb",
"notebook_path": "https://gist.github.com/3366583/modifying series, frames, panels.ipynb"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import time \n",
" \n",
"class Timer: \n",
" \"\"\" \n",
" Usage: \n",
" \n",
" with Timer() as t: \n",
" ret = func(df) \n",
" print(t.interval) \n",
" \"\"\" \n",
" def __init__(self):\n",
" self.interval = None\n",
" \n",
" def __enter__(self): \n",
" self.start = time.clock() \n",
" return self \n",
" \n",
" def __exit__(self, *args): \n",
" self.end = time.clock() \n",
" self.interval = self.end - self.start \n",
" \n",
" def __str__(self):\n",
" return \"Timer.interval = {0}\".format(self.interval)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def append_many(start_size=1000, nrows=1000):\n",
" df = pd.DataFrame([range(10)]*start_size)\n",
" with Timer() as t:\n",
" for x in xrange(nrows):\n",
" df = df.append([range(10)], ignore_index=True)\n",
" \treturn t \n",
"print append_many()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Timer.interval = 1.952251\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def append_many_list(start_size=1000, nrows=1000):\n",
" df = pd.DataFrame([range(10)]*start_size)\n",
" rows = []\n",
" with Timer() as t:\n",
" for x in xrange(nrows):\n",
" rows.append(range(10))\n",
" df.append(rows, ignore_index=True)\n",
" return t \n",
"print append_many_list() "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Timer.interval = 0.005098\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import itertools\n",
"\n",
"start_range = np.arange(1, 5) * 10000 \n",
"nrows = np.arange(1, 5) * 1000\n",
"\n",
"test_cases = list(itertools.product(start_range, nrows))\n",
"\n",
"results = []\n",
"for start, nrows in test_cases:\n",
" t = append_many(start, nrows)\n",
" results.append((start, nrows, t.interval))\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"test_results = pd.DataFrame(results, columns=['start', 'nrows', 'time'])\n",
"test_results"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>start</th>\n",
" <th>nrows</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>0</strong></td>\n",
" <td> 10000</td>\n",
" <td> 1000</td>\n",
" <td> 2.148895</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>1</strong></td>\n",
" <td> 10000</td>\n",
" <td> 2000</td>\n",
" <td> 4.483919</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>2</strong></td>\n",
" <td> 10000</td>\n",
" <td> 3000</td>\n",
" <td> 6.400522</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>3</strong></td>\n",
" <td> 10000</td>\n",
" <td> 4000</td>\n",
" <td> 8.776699</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>4</strong></td>\n",
" <td> 20000</td>\n",
" <td> 1000</td>\n",
" <td> 2.338080</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>5</strong></td>\n",
" <td> 20000</td>\n",
" <td> 2000</td>\n",
" <td> 4.708480</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>6</strong></td>\n",
" <td> 20000</td>\n",
" <td> 3000</td>\n",
" <td> 7.142558</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>7</strong></td>\n",
" <td> 20000</td>\n",
" <td> 4000</td>\n",
" <td> 9.696720</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>8</strong></td>\n",
" <td> 30000</td>\n",
" <td> 1000</td>\n",
" <td> 2.639786</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>9</strong></td>\n",
" <td> 30000</td>\n",
" <td> 2000</td>\n",
" <td> 5.572692</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>10</strong></td>\n",
" <td> 30000</td>\n",
" <td> 3000</td>\n",
" <td> 8.245117</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>11</strong></td>\n",
" <td> 30000</td>\n",
" <td> 4000</td>\n",
" <td> 10.862880</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>12</strong></td>\n",
" <td> 40000</td>\n",
" <td> 1000</td>\n",
" <td> 3.382203</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>13</strong></td>\n",
" <td> 40000</td>\n",
" <td> 2000</td>\n",
" <td> 6.395405</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>14</strong></td>\n",
" <td> 40000</td>\n",
" <td> 3000</td>\n",
" <td> 9.968548</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>15</strong></td>\n",
" <td> 40000</td>\n",
" <td> 4000</td>\n",
" <td> 13.417624</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 7,
"text": [
" start nrows time\n",
"0 10000 1000 2.148895\n",
"1 10000 2000 4.483919\n",
"2 10000 3000 6.400522\n",
"3 10000 4000 8.776699\n",
"4 20000 1000 2.338080\n",
"5 20000 2000 4.708480\n",
"6 20000 3000 7.142558\n",
"7 20000 4000 9.696720\n",
"8 30000 1000 2.639786\n",
"9 30000 2000 5.572692\n",
"10 30000 3000 8.245117\n",
"11 30000 4000 10.862880\n",
"12 40000 1000 3.382203\n",
"13 40000 2000 6.395405\n",
"14 40000 3000 9.968548\n",
"15 40000 4000 13.417624"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from mpl_toolkits.mplot3d import Axes3D\n",
"from matplotlib import cm\n",
"from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca(projection='3d')\n",
"X = test_results.start\n",
"Y = test_results.nrows\n",
"X, Y = np.meshgrid(X, Y)\n",
"R = test_results.time \n",
"surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,\n",
" linewidth=0, antialiased=False)\n",
"ax.set_zlim(0, 10.01)\n",
"\n",
"ax.zaxis.set_major_locator(LinearLocator(10))\n",
"ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
"\n",
"fig.colorbar(surf, shrink=1.5, aspect=5)\n",
"\n",
"plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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p0qW45pprZPcNBAJ45513sGDBAgBAv3798Pvf/x719fVwuVx4++2320yASgmm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\nT5Skhd1kUbsc//rn9XFiL4cZhF2KV3EFHkI1BjRtkHxJwT0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}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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subclassing pandas objects.ipynb
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