{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## IPython Notebook tutorial from NCAR SEA Conference 2015\n", "\n", "### PDF slides at https://dl.dropboxusercontent.com/u/72208800/docs/NCAR-SEA-IPython-tutorial.pdf" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "avar = 1\n", "bvar = 2\n", "cvar = avar + bvar\n", "\n", "print avar\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%debug\n", "a = 2\n", "b = 0\n", "c = a/b\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%pylab inline\n", "pylab.plot([1, 5, 9, 16, 50])\n", "pylab.ylabel('some numbers')\n", "\n", "pylab.plot()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## This is a heading line\n", "\n", "You can *italicize* or **bold** words.\n", "\n", "Create lists easily\n", "\n", "* Apple\n", "\n", "* Bananas\n", "\n", "* Oranges\n", "\n", "You can include code snippets like this ``python -i option``\n", "or by indenting 4 spaces\n", "\n", " # Example code\n", " print \"this string\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a regular paragraph.\n", "\n", "\n", " \n", " \n", " \n", "
Foo
\n", "\n", "This is another regular paragraph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Introduction**\n", "\n", "*Lorenz Equations* are\n", "\n", "\\begin{align}\n", "\\dot{x} & = \\sigma(y-x) \\\\\n", "\\dot{y} & = \\rho x - y - xz \\\\\n", "\\dot{z} & = -\\beta z + xy\n", "\\end{align}\n", "\n", "*Maxwell's Equations* are\n", "\n", "\\begin{align}\n", "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\ \\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n", "\\end{align}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "(2, 2)\n", "4\n", "int64\n" ] } ], "source": [ "import numpy as np\n", "arr = np.array([ [1, 2], [3, 4] ])\n", "\n", "print arr.ndim # No. of dimensions (attribute)\n", "print arr.shape # Shape of the array\n", "print arr.size # Size of the array\n", "print arr.dtype # Array data type" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-10. -9. -8. -7. -6. -5. -4. -3. -2. -1. 0. 1. 2. 3. 4.\n", " 5. 6. 7. 8. 9. 10.]\n", "[ 100. 81. 64. 49. 36. 25. 16. 9. 4. 1. 0. 1.\n", " 4. 9. 16. 25. 36. 49. 64. 81. 100.]\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(-10, 10, 21)\n", "\n", "print x\n", "\n", "y2 = x**2\n", "\n", "print y2\n", "\n", "y3 = x**3\n", "\n", "pylab.plot(x, y3)\n", "\n", "pylab.plot(x, y2, \"--r\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0, 360, 91) \n", "y = np.sin(x * np.pi / 180.0) \n", "\n", "pylab.plot(x, y, \".b\")\n", "pylab.title(\"Sine function\") \n", "pylab.xlabel(\"Angle (degrees)\") \n", "pylab.ylabel(\"Sine of Angle\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "air.mon.ltm.nc hgt.mon.ltm.nc\r\n", "etopo5.nc standard_atmosphere.nc\r\n" ] } ], "source": [ "%ls *.nc\n", "\n", "import netCDF4\n", "\n", "ncfile = netCDF4.Dataset('standard_atmosphere.nc') # Open file\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "[u'units']\n" ] }, { "data": { "text/plain": [ "u'K'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T = ncfile.variables[\"temperature\"] # Get Variable object for \"temperature\"\n", "\n", "print type(T)\n", "\n", "T_data = T[:] # Extract NumPy data array from Variable\n", "\n", "print type(T_data)\n", "\n", "print T.ncattrs() # List all variable attributes\n", "\n", "T.units # Units attribute" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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jIUJiF+C7pNOmX0BqEvp3O6OKmVljnRwP4ZA8bsGpDWZHROzfnydtKZgLwpAg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\nA+D/A6BAX6e4pBQsAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "alt = ncfile.variables['altitude']\n", "temp = ncfile.variables['temperature']\n", "pylab.plot(temp, alt)\n", "pylab.grid()\n", "pylab.title(\"Temperature as a function of Altitude\")\n", "pylab.ylabel(\"Altitude [\" + alt.units + \"]\")\n", "pylab.xlabel(\"Temperature [\" + temp.units + \"]\")" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "[u'long_name', u'actual_range', u'units', u'add_offset', u'scale_factor', u'missing_value', u'precision', u'least_significant_digit', u'var_desc', u'dataset', u'level_desc', u'statistic', u'parent_stat']\n" ] }, { "data": { "text/plain": [ "u'degC'" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ncfile = netCDF4.Dataset('air.mon.ltm.nc') # Open file\n", "air_temp = ncfile.variables[\"air\"] # Get Variable object for \"air\"\n", "\n", "print type(air_temp)\n", "\n", "temp_data = air_temp[:] # Extract NumPy data array from Variable\n", "\n", "print type(temp_data)\n", "\n", "print air_temp.ncattrs() # List all variable attributes\n", "\n", "air_temp.units # Units attribute" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JRJOECI88js4ZXSPtZEcpEhER0yUQbSwscrrCKX0QtQY/OUFwWhR5T5QpkqAX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\nnz0onSDaJGZhrBXs2tmJWmtPiEt7gYui8tmdtF0jqTaGPAAVuDSUEZC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VREfuNHakj6rq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\nrkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%pylab inline\n", "import netCDF4\n", "ncf = netCDF4.Dataset(\"air.mon.ltm.nc\", \"r\")\n", "lon = ncf.variables[\"lon\"][:]\n", "lat = ncf.variables[\"lat\"][:]\n", "temp_data = ncf.variables[\"air\"][0,0,:,:]\n", "temp_data.shape\n", "\n", "pylab.contourf(lon, lat, temp_data, 10, cmap=cm.rainbow)\n", "pylab.axis([0, 360, -90, 90])\n", "pylab.colorbar(orientation=\"horizontal\")\n", "\n", "cont = pylab.contour(lon, lat, temp_data, 10) # Save object\n", "pylab.clabel(cont)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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E3TxwqYV4FUuESZaK+mLxltY5TdK8oeiGQkcWaUOR1hVp3UJsg11OcLoS7O6E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\nOUFEuXCK+droijoXC07Enb1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zp+w/Mmh05G2u6SJ2JUZ8I1bgZZZbd/CWfg1p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\nZj3K/ypOKJ5353cyZoxPen3nw1w28o/E0ovpjP4hCWshlipnJgs0QPu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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%pylab inline\n", "import netCDF4\n", "\n", "ncf = netCDF4.Dataset(\"air.mon.ltm.nc\", \"r\")\n", "level_data = ncf.variables[\"level\"][:]\n", "lat_data = ncf.variables[\"lat\"][:]\n", "\n", "# Extract data for month_index 0, all levels, all lats, lon_index 0\n", "temp_data = ncf.variables[\"air\"][0,:,:,0]\n", "temp_data.shape\n", "\n", "pylab.contourf(lat_data, level_data, temp_data, 10, cmap=cm.rainbow)\n", "pylab.ylim(1000, 0)\n", "pylab.colorbar()\n", "\n", "pylab.contour(lat_data, level_data, temp_data, 10)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The sum is 3\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.html.widgets import interact, fixed\n", "\n", "def add(x, y, absolute=False):\n", " if absolute:\n", " return abs(x) + abs(y)\n", " else:\n", " return x + y\n", "\n", "def show_add(x, y, absolute=False):\n", " print \"The sum is\", add(x, y, absolute)\n", "\n", "#show_add(3,10)\n", "interact(show_add, x=3, y=(-10,10), absolute=False)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "06:15PM\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def time_stamp(hour, minute, suffix=\"PM\"):\n", " return '%02d:%02d%s' % (hour, minute, suffix)\n", " \n", "def show_time_stamp(hour, minute, suffix=\"PM\"):\n", " print time_stamp(hour,minute,suffix)\n", " \n", "interact(show_time_stamp, hour=(0,12),minute=fixed(15),suffix=(\"AM\",\"PM\"))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def sinewave(freq=1.0):\n", " x = np.linspace(0., 360., 91, endpoint=True) # Generate uniformly spaced 1-d array\n", " y = np.sin(freq*x*np.pi/180.) # Compute sin(x)\n", " pylab.plot(x, y, \"--r\", linewidth=2.5)\n", "\n", " pylab.xlim(0, 360.) # Set X-axis limits\n", " pylab.ylim(-1.5, 1.5) # Set Y-axis limits\n", "\n", "interact(sinewave, freq=(0.0, 4.0))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FSScC43jPw1wNpwAfAd/UNH3jXqrTMc76WMzT5U/sAe8E6gMNgThWLWvA8kUBDD1nK7bk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\n1wAL3f1qM7sU6OI1eLVD1MM8d58TImr+Cbzv7tfFtsmdk/VbjsaOoml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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import netCDF4\n", "ncf = netCDF4.Dataset(\"air.mon.ltm.nc\", \"r\")\n", "lon = ncf.variables[\"lon\"][:]\n", "lat = ncf.variables[\"lat\"][:]\n", "temp_data = ncf.variables[\"air\"][0,0,:,:]\n", "\n", "import mpl_toolkits.basemap as bm\n", "mapproj = bm.Basemap(projection='cyl',\n", " llcrnrlat=-60.0, llcrnrlon=0.0,\n", " urcrnrlat=60.0, urcrnrlon=360.0)\n", "mapproj.drawcoastlines()\n", "mapproj.drawparallels(np.array([-60, -40, -20, 0, 20, 40, 60]), labels=[1,0,0,0])\n", "mapproj.drawmeridians(np.array([-180, -90, 0, 90, 180]), labels=[0,0,0,1])\n", "\n", "lonall, latall = np.meshgrid(lon, lat)\n", "lonproj, latproj = mapproj(lonall, latall)\n", "pylab.contour(lonproj, latproj, temp_data)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: pylab import has clobbered these variables: ['add']\n", "`%matplotlib` prevents importing * from pylab and numpy\n" ] }, { "data": { "image/png": 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J5uy3Wzk45wDk58Etd8JtUyC6Hgxq9qht9CWwaBRM+bN63bGV4obWoor6KjsR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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import netCDF4\n", "import mpl_toolkits.basemap as bm\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%pylab inline\n", "\n", "def tempheight(month): \n", "\n", " ncf = netCDF4.Dataset(\"air.mon.ltm.nc\", \"r\")\n", " ncf2 = netCDF4.Dataset(\"hgt.mon.ltm.nc\", \"r\")\n", " lat_data = ncf.variables[\"lat\"][14:27]\n", " lon_data = ncf.variables[\"lon\"][92:121]\n", "\n", " temp_data = ncf.variables[\"air\"][month-1,0,14:27,92:121]\n", " hgt_data = ncf2.variables[\"hgt\"][month-1,5,14:27,92:121]\n", " ncf.close()\n", " ncf2.close()\n", "\n", " mapproj = bm.Basemap(projection= 'cyl',llcrnrlat=25.0, llcrnrlon=230,urcrnrlat=55.0, urcrnrlon=300)\n", " mapproj.drawcoastlines()\n", " mapproj.drawcountries()\n", " mapproj.drawstates()\n", " mapproj.drawparallels(np.array([30, 40, 50]), labels=[1,0,0,0])\n", " mapproj.drawmeridians(np.array([-120, -100, -80]), labels=[0,0,0,1])\n", " mymapf = pylab.contourf(lon_data, lat_data, temp_data, 10,cmap=pylab.cm.jet)\n", " mymap = pylab.contour(lon_data, lat_data, hgt_data, 10,colors='k') \n", " pylab.colorbar(mymapf, orientation='horizontal')\n", " pylab.title(\"Month \"+str(month))\n", " return pylab.show()\n", "\n", "tempheight(1)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5kXEjh73zzikep0K5cevoy62ziVzdfEbva6q7t5kSeRWn5h4PFEytRmuUtqyc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\nhBA9gCPAXmfaKnieZiH4MrvFZkgsAq90gJ8PZO9OLALLh8F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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import mpl_toolkits.basemap as bm\n", "\n", "import netCDF4\n", "\n", "def us_plot(month):\n", "\n", " ncf = netCDF4.Dataset(\"air.mon.ltm.nc\", \"r\")\n", " lon = ncf.variables[\"lon\"][:]\n", " lat = ncf.variables[\"lat\"][:]\n", " temp_data = ncf.variables[\"air\"][month-1,0,:,:]\n", "\n", " mapproj = bm.Basemap(projection='lcc', lat_0=30, lon_0=250,\n", " llcrnrlat=20.0, llcrnrlon=230,\n", " urcrnrlat=55.0, urcrnrlon=310)\n", " mapproj.drawcoastlines()\n", " lonall, latall = np.meshgrid(lon, lat)\n", " lonproj, latproj = mapproj(lonall, latall)\n", " pylab.contourf(lonproj, latproj, temp_data, 20)\n", " pylab.colorbar()\n", " pylab.title(\"Month \"+str(month))\n", "\n", "interact(us_plot, month=(1,12))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using ``pandas`` in the notebook\n", "\n", "``pandas`` is a powerful data analysis package for spreadsheet-like operations in Python.\n", "\n", "We will work through a simple example from *Wes McKinney's* book *Python for Data Analysis* using IPython Notebook,\n", "which provides a nicer-lokking display of ``pandas`` output.\n", "\n", "First, install ``pandas``:\n", "\n", " conda install pandas\n", "\n", "From the Social Security Administration website http://www.ssa.gov/oact/babynames/limits.html\n", "download the National Data for popular baby names in the U.S. in the file ``naames.zip``\n", "\n", "Create sub-folder named ``names`` in the folder where your notebook is running.\n", "Unzip the downloaded file in the ``names`` sub-folder (this create a large number of files)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading the baby names data\n", "\n", " import pandas as pd\n", "\n", " names1880 = pd.read_csv('names/yob1880.txt', names=['names', 'sex', 'births'])\n", "\n", " # Display the top 10 names\n", " names1880[:10]" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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namessexbirths
0 Mary F 7065
1 Anna F 2604
2 Emma F 2003
3 Elizabeth F 1939
4 Minnie F 1746
5 Margaret F 1578
6 Ida F 1472
7 Alice F 1414
8 Bertha F 1320
9 Sarah F 1288
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" ], "text/plain": [ " names sex births\n", "0 Mary F 7065\n", "1 Anna F 2604\n", "2 Emma F 2003\n", "3 Elizabeth F 1939\n", "4 Minnie F 1746\n", "5 Margaret F 1578\n", "6 Ida F 1472\n", "7 Alice F 1414\n", "8 Bertha F 1320\n", "9 Sarah F 1288" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Reading the baby names data\n", "\n", "import pandas as pd\n", "\n", "names1880 = pd.read_csv('names/yob1880.txt', names=['names', 'sex', 'births'])\n", "\n", "# Display the top 10 names\n", "names1880[:10]" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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namessexbirths
0 Sophia F 21075
1 Emma F 20788
2 Olivia F 18256
3 Isabella F 17490
4 Ava F 15129
5 Mia F 13066
6 Emily F 13044
7 Abigail F 12313
8 Madison F 10529
9 Elizabeth F 9345
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" ], "text/plain": [ " names sex births\n", "0 Sophia F 21075\n", "1 Emma F 20788\n", "2 Olivia F 18256\n", "3 Isabella F 17490\n", "4 Ava F 15129\n", "5 Mia F 13066\n", "6 Emily F 13044\n", "7 Abigail F 12313\n", "8 Madison F 10529\n", "9 Elizabeth F 9345" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names2013 = pd.read_csv('names/yob2013.txt', names=['names', 'sex', 'births'])\n", "\n", "# Display the top 10 names\n", "names2013[:10]" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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namessexbirthsyear
1792081 Zmari M 5 2013
1792082 Zolan M 5 2013
1792083 Zurich M 5 2013
1792084 Zyeer M 5 2013
1792085 Zyere M 5 2013
1792086 Zyhier M 5 2013
1792087 Zylar M 5 2013
1792088 Zymari M 5 2013
1792089 Zymeer M 5 2013
1792090 Zyree M 5 2013
\n", "
" ], "text/plain": [ " names sex births year\n", "1792081 Zmari M 5 2013\n", "1792082 Zolan M 5 2013\n", "1792083 Zurich M 5 2013\n", "1792084 Zyeer M 5 2013\n", "1792085 Zyere M 5 2013\n", "1792086 Zyhier M 5 2013\n", "1792087 Zylar M 5 2013\n", "1792088 Zymari M 5 2013\n", "1792089 Zymeer M 5 2013\n", "1792090 Zyree M 5 2013" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "years = range(1880, 2014)\n", "pieces = []\n", "columns = ['names', 'sex', 'births']\n", "\n", "for year in years:\n", " path = 'names/yob%d.txt' %year\n", " frame = pd.read_csv(path, names=columns)\n", " frame['year'] = year\n", " pieces.append(frame)\n", "names = pd.concat(pieces, ignore_index=True)\n", "names[-10:]" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Yyv4L3Ab8NT54KoYk3pOmUIgeSqs7YVTcqcCflNZ2xZKrFjcYjlPhKK1jCOvLn2Upm7+8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\nkfQuMAsYHuuaLelXhDUaANIx+O0UzvK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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "total_births = names.pivot_table('births', index='year',\n", " columns='sex', aggfunc=sum)\n", "total_births.tail()\n", "total_births.plot(title='Total births by sex and year')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }