diff --git a/538_race_income_gap.ipynb b/538_race_income_gap.ipynb index 2a5ca9c..9441090 100644 --- a/538_race_income_gap.ipynb +++ b/538_race_income_gap.ipynb @@ -20,10 +20,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "# basic setup. You'd do this every time you set out to use pandas with Census Reporter's SQL\n", @@ -42,10 +40,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "# load in white and black median income for Census places (sumlevel = 160)\n", @@ -63,17 +59,15 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "# put the parts together and compute the gap\n", "df = white.join(black)\n", "df = df.dropna()\n", "df['gap'] = df.white - df.black\n", - "df.sort('gap',ascending=True,inplace=True)\n" + "df.sort_values(by='gap',ascending=True,inplace=True)\n" ] }, { @@ -85,10 +79,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "df.rename(columns={'white': 'white_income', 'black': 'black_income'}, inplace=True)\n", @@ -101,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -115,15 +107,13 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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16000US0672520Soledad city, California60441147632-87191262513391331260441.0147632.0-87191.026251.03391.03312.00.126167
16000US3676089Uniondale CDP, New York3699198148-611572590524161163536991.098148.0-61157.025905.02416.011635.00.449141
16000US3676705Valley Stream village, New York78736122880-441443761813727676378736.0122880.0-44144.037618.013727.06763.00.179781
16000US3624273Elmont CDP, New York6391396223-323103976162601926563913.096223.0-32310.039761.06260.019265.00.484520
16000US0611530Carson city, California5672881520-247929227765271738556728.081520.0-24792.092277.06527.017385.00.188400
16000US4816468Converse city, Texas5434674891-20545197056999354554346.074891.0-20545.019705.06999.03545.00.179904
16000US3627485Freeport village, New York7081685997-1518143095100981371770816.085997.0-15181.043095.010098.013717.00.318297
16000US0684144West Carson CDP, California5382668750-14924213104097243853826.068750.0-14924.021310.04097.02438.00.114406
16000US3613552Central Islip CDP, New York6128775091-13804364577244907661287.075091.0-13804.036457.07244.09076.00.248951
16000US0615044Compton city, California2788540189-123049749512943056827885.040189.0-12304.097495.01294.030568.00.313534
\n", " \n", " \n", @@ -353,34 +341,34 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -388,26 +376,26 @@ "" ], "text/plain": [ - " name white_income black_income gap \\\n", - "geoid \n", - "16000US0624680 Fontana city, California 76469 79154 -2685 \n", - "16000US0660466 Rialto city, California 46141 49056 -2915 \n", - "16000US4856348 Pearland city, Texas 86944 96335 -9391 \n", + " name white_income black_income gap \\\n", + "geoid \n", + "16000US0624680 Fontana city, California 76469.0 79154.0 -2685.0 \n", + "16000US0660466 Rialto city, California 46141.0 49056.0 -2915.0 \n", + "16000US4856348 Pearland city, Texas 86944.0 96335.0 -9391.0 \n", "\n", " total_pop white_pop black_pop black_pop_pct \n", "geoid \n", - "16000US0624680 201293 29249 20638 0.102527 \n", - "16000US0660466 101434 11722 14618 0.144113 \n", - "16000US4856348 98123 45309 17236 0.175657 " + "16000US0624680 201293.0 29249.0 20638.0 0.102527 \n", + "16000US0660466 101434.0 11722.0 14618.0 0.144113 \n", + "16000US4856348 98123.0 45309.0 17236.0 0.175657 " ] }, - "execution_count": 21, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df2.sort('total_pop',ascending=False).head(3)" + "df2.sort_values(by='total_pop',ascending=False).head(3)" ] }, { @@ -419,15 +407,13 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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16000US0624680Fontana city, California7646979154-2685201293292492063876469.079154.0-2685.0201293.029249.020638.00.102527
16000US0660466Rialto city, California4614149056-2915101434117221461846141.049056.0-2915.0101434.011722.014618.00.144113
16000US4856348Pearland city, Texas8694496335-939198123453091723686944.096335.0-9391.098123.045309.017236.00.175657
\n", " \n", " \n", @@ -457,45 +443,45 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -505,32 +491,32 @@ "text/plain": [ " name white_income black_income \\\n", "geoid \n", - "16000US3624273 Elmont CDP, New York 63913 96223 \n", - "16000US3676705 Valley Stream village, New York 78736 122880 \n", - "16000US0672520 Soledad city, California 60441 147632 \n", - "16000US3676089 Uniondale CDP, New York 36991 98148 \n", + "16000US3624273 Elmont CDP, New York 63913.0 96223.0 \n", + "16000US3676705 Valley Stream village, New York 78736.0 122880.0 \n", + "16000US0672520 Soledad city, California 60441.0 147632.0 \n", + "16000US3676089 Uniondale CDP, New York 36991.0 98148.0 \n", "\n", - " gap total_pop white_pop black_pop black_pop_pct \n", - "geoid \n", - "16000US3624273 -32310 39761 6260 19265 0.484520 \n", - "16000US3676705 -44144 37618 13727 6763 0.179781 \n", - "16000US0672520 -87191 26251 3391 3312 0.126167 \n", - "16000US3676089 -61157 25905 2416 11635 0.449141 " + " gap total_pop white_pop black_pop black_pop_pct \n", + "geoid \n", + "16000US3624273 -32310.0 39761.0 6260.0 19265.0 0.484520 \n", + "16000US3676705 -44144.0 37618.0 13727.0 6763.0 0.179781 \n", + "16000US0672520 -87191.0 26251.0 3391.0 3312.0 0.126167 \n", + "16000US3676089 -61157.0 25905.0 2416.0 11635.0 0.449141 " ] }, - "execution_count": 22, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = df2[df2.gap*-1 > df2.white_income/2]\n", - "df3.sort('total_pop',ascending=False).head() " + "df3.sort_values(by='total_pop',ascending=False).head() " ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -543,23 +529,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" + "pygments_lexer": "ipython3", + "version": "3.4.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Basic Census Reporter API with Pandas.ipynb b/Basic Census Reporter API with Pandas.ipynb index 7a8c76d..8ee07c4 100644 --- a/Basic Census Reporter API with Pandas.ipynb +++ b/Basic Census Reporter API with Pandas.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -27,15 +27,13 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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16000US3624273Elmont CDP, New York6391396223-323103976162601926563913.096223.0-32310.039761.06260.019265.00.484520
16000US3676705Valley Stream village, New York78736122880-441443761813727676378736.0122880.0-44144.037618.013727.06763.00.179781
16000US0672520Soledad city, California60441147632-87191262513391331260441.0147632.0-87191.026251.03391.03312.00.126167
16000US3676089Uniondale CDP, New York3699198148-611572590524161163536991.098148.0-61157.025905.02416.011635.00.449141
\n", " \n", " \n", @@ -50,59 +48,59 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", "
04000US06California38332521190722461926027539144818.019440558.019704260.0
04000US48Texas264481931314549413302699
04000US36New York1965112795361791011494827469114.013625413.013843701.0
04000US12Florida195528609565609998725120271272.09891238.010380034.0
04000US36New York19795791.09616592.010179199.0
04000US17Illinois128821356326778655535712859995.06310435.06549560.0
\n", "
" ], "text/plain": [ - " name Total Male Female\n", - "04000US06 California 38332521 19072246 19260275\n", - "04000US48 Texas 26448193 13145494 13302699\n", - "04000US36 New York 19651127 9536179 10114948\n", - "04000US12 Florida 19552860 9565609 9987251\n", - "04000US17 Illinois 12882135 6326778 6555357" + " name Total Male Female\n", + "04000US06 California 39144818.0 19440558.0 19704260.0\n", + "04000US48 Texas 27469114.0 13625413.0 13843701.0\n", + "04000US12 Florida 20271272.0 9891238.0 10380034.0\n", + "04000US36 New York 19795791.0 9616592.0 10179199.0\n", + "04000US17 Illinois 12859995.0 6310435.0 6549560.0" ] }, - "execution_count": 12, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = get_dataframe(tables='B01001',geoids='040|01000US',column_names=True,level=1)\n", - "df.sort('Total', ascending=False).head(5)" + "df.sort_values('Total', ascending=False).head(5)" ] }, { @@ -114,15 +112,13 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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04000US47Tennessee6495978317414833218300.5113676600299.03214689.03385610.00.512948
04000US42Pennsylvania12773801624330865304930.51124112802503.06262246.06540257.00.510858
04000US39Ohio11570808565822359125850.51099211613423.05683951.05929472.00.510571
04000US29Missouri6044171296040230837690.5102056083672.02983645.03100027.00.509565
04000US26Michigan9895622485813850374840.50906204000US17Illinois12859995.06310435.06549560.00.509297
\n", "
" ], "text/plain": [ - " name Total Male Female pct_female\n", - "04000US47 Tennessee 6495978 3174148 3321830 0.511367\n", - "04000US42 Pennsylvania 12773801 6243308 6530493 0.511241\n", - "04000US39 Ohio 11570808 5658223 5912585 0.510992\n", - "04000US29 Missouri 6044171 2960402 3083769 0.510205\n", - "04000US26 Michigan 9895622 4858138 5037484 0.509062" + " name Total Male Female pct_female\n", + "04000US47 Tennessee 6600299.0 3214689.0 3385610.0 0.512948\n", + "04000US42 Pennsylvania 12802503.0 6262246.0 6540257.0 0.510858\n", + "04000US39 Ohio 11613423.0 5683951.0 5929472.0 0.510571\n", + "04000US29 Missouri 6083672.0 2983645.0 3100027.0 0.509565\n", + "04000US17 Illinois 12859995.0 6310435.0 6549560.0 0.509297" ] }, - "execution_count": 13, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -196,7 +192,7 @@ "source": [ "df = get_dataframe(tables='B01001',geoids='040|02000US2',column_names=True,level=1)\n", "df['pct_female'] = df['Female'] / df['Total']\n", - "df.sort('pct_female',ascending=False).head(5)" + "df.sort_values('pct_female',ascending=False).head(5)" ] }, { @@ -208,15 +204,13 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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05000US47095Lake County, TN7773489728760.6300017687.04937.02750.00.642253
05000US47007Bledsoe County, TN13686.07677.06009.00.560938
05000US47129Morgan County, TN219641212698380.55208521794.012150.09644.00.557493
05000US47181Wayne County, TN16996937076260.55130616897.09295.07602.00.550098
05000US47069Hardeman County, TN2689714670122270.545414
05000US47007Bledsoe County, TN12853695159020.54080826253.014297.011956.00.544585
\n", "
" ], "text/plain": [ - " name Total Male Female pct_male\n", - "05000US47095 Lake County, TN 7773 4897 2876 0.630001\n", - "05000US47129 Morgan County, TN 21964 12126 9838 0.552085\n", - "05000US47181 Wayne County, TN 16996 9370 7626 0.551306\n", - "05000US47069 Hardeman County, TN 26897 14670 12227 0.545414\n", - "05000US47007 Bledsoe County, TN 12853 6951 5902 0.540808" + " name Total Male Female pct_male\n", + "05000US47095 Lake County, TN 7687.0 4937.0 2750.0 0.642253\n", + "05000US47007 Bledsoe County, TN 13686.0 7677.0 6009.0 0.560938\n", + "05000US47129 Morgan County, TN 21794.0 12150.0 9644.0 0.557493\n", + "05000US47181 Wayne County, TN 16897.0 9295.0 7602.0 0.550098\n", + "05000US47069 Hardeman County, TN 26253.0 14297.0 11956.0 0.544585" ] }, - "execution_count": 14, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -290,29 +284,29 @@ "source": [ "df = get_dataframe(tables='B01001',geoids='050|04000US47',column_names=True,level=1)\n", "df['pct_male'] = df['Male'] / df['Total']\n", - "df.sort('pct_male',ascending=False).head(5)" + "df.sort_values('pct_male',ascending=False).head(5)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" + "pygments_lexer": "ipython3", + "version": "3.4.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Big race changes.ipynb b/Big race changes.ipynb index 0fc00dd..9e98e9f 100644 --- a/Big race changes.ipynb +++ b/Big race changes.ipynb @@ -76,9 +76,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# We leave out summary level 500 -- congressional districts -- because they changed considerably between\n", @@ -199,14 +197,12 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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This may take a moment.')\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -510,9 +504,9 @@ }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -548,9 +542,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -558,7 +550,7 @@ "\n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
" ] }, @@ -624,7 +616,7 @@ " },\n", " \n", " function(Bokeh) {\n", - " Bokeh.$(\"#23540a11-a5db-4962-9068-0f436b2be173\").text(\"BokehJS successfully loaded\");\n", + " Bokeh.$(\"#195707d2-e7a8-4a23-8c25-b28b21e886fa\").text(\"BokehJS successfully loaded\");\n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n", @@ -660,7 +652,7 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "