From ecab1a7924bb528a540c4c1614d2637ac332892c Mon Sep 17 00:00:00 2001 From: Priyatharsan Rajasekar Date: Fri, 1 Mar 2019 12:41:42 -0500 Subject: [PATCH] title as text to tile - object utilization --- .../python/maps/bubble-maps/bubble-maps.ipynb | 24 +-- .../2015-06-30-Choropleth_maps.html | 163 +++++++++-------- .../choropleth-maps/Choropleth_maps.ipynb | 168 ++++++++++-------- .../county-choropleth/county_choropleth.ipynb | 54 ++---- 4 files changed, 210 insertions(+), 199 deletions(-) diff --git a/_posts/python/maps/bubble-maps/bubble-maps.ipynb b/_posts/python/maps/bubble-maps/bubble-maps.ipynb index e9abd3bef243..3372c625568b 100644 --- a/_posts/python/maps/bubble-maps/bubble-maps.ipynb +++ b/_posts/python/maps/bubble-maps/bubble-maps.ipynb @@ -26,7 +26,7 @@ { "data": { "text/plain": [ - "'3.6.0'" + "'3.6.1'" ] }, "execution_count": 1, @@ -260,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -292,11 +292,11 @@ "output_type": "stream", "text": [ "Collecting git+https://github.com/plotly/publisher.git\n", - " Cloning https://github.com/plotly/publisher.git to c:\\users\\priyat~1\\appdata\\local\\temp\\pip-req-build-7gmj7xbp\n", + " Cloning https://github.com/plotly/publisher.git to c:\\users\\priyat~1\\appdata\\local\\temp\\pip-req-build-8qikjqgp\n", "Building wheels for collected packages: publisher\n", " Running setup.py bdist_wheel for publisher: started\n", " Running setup.py bdist_wheel for publisher: finished with status 'done'\n", - " Stored in directory: C:\\Users\\PRIYAT~1\\AppData\\Local\\Temp\\pip-ephem-wheel-cache-a2n74gbm\\wheels\\99\\3e\\a0\\fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966\n", + " Stored in directory: C:\\Users\\PRIYAT~1\\AppData\\Local\\Temp\\pip-ephem-wheel-cache-uu9w7cmr\\wheels\\99\\3e\\a0\\fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966\n", "Successfully built publisher\n", "Installing collected packages: publisher\n", " Found existing installation: publisher 0.13\n", @@ -304,20 +304,6 @@ " Successfully uninstalled publisher-0.13\n", "Successfully installed publisher-0.13\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Anaconda\\Anaconda3\\lib\\site-packages\\IPython\\nbconvert.py:13: ShimWarning:\n", - "\n", - "The `IPython.nbconvert` package has been deprecated since IPython 4.0. You should import from nbconvert instead.\n", - "\n", - "C:\\Anaconda\\Anaconda3\\lib\\site-packages\\publisher\\publisher.py:53: UserWarning:\n", - "\n", - "Did you \"Save\" this notebook before running this command? Remember to save, always save.\n", - "\n" - ] } ], "source": [ @@ -335,7 +321,7 @@ " has_thumbnail='true', thumbnail='thumbnail/bubble-map.jpg',\n", " language='python',\n", " page_type='example_index',\n", - " display_as='maps', order=3, # ipynb='~notebook_demo/1',\n", + " display_as='maps', order=3,\n", " uses_plotly_offline=False)" ] }, diff --git a/_posts/python/maps/choropleth-maps/2015-06-30-Choropleth_maps.html b/_posts/python/maps/choropleth-maps/2015-06-30-Choropleth_maps.html index 86f19be6b3a4..4d98859bab09 100644 --- a/_posts/python/maps/choropleth-maps/2015-06-30-Choropleth_maps.html +++ b/_posts/python/maps/choropleth-maps/2015-06-30-Choropleth_maps.html @@ -88,41 +88,49 @@

United States Choropleth Mapfor col in df.columns: df[col] = df[col].astype(str) -scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\ - [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']] - -df['text'] = df['state'] + '<br>' +\ - 'Beef '+df['beef']+' Dairy '+df['dairy']+'<br>'+\ - 'Fruits '+df['total fruits']+' Veggies ' + df['total veggies']+'<br>'+\ - 'Wheat '+df['wheat']+' Corn '+df['corn'] +scl = [ + [0.0, 'rgb(242,240,247)'], + [0.2, 'rgb(218,218,235)'], + [0.4, 'rgb(188,189,220)'], + [0.6, 'rgb(158,154,200)'], + [0.8, 'rgb(117,107,177)'], + [1.0, 'rgb(84,39,143)'] +] -data = [ go.Choropleth( - colorscale = scl, - autocolorscale = False, - locations = df['code'], - z = df['total exports'].astype(float), - locationmode = 'USA-states', - text = df['text'], - marker = dict( - line = dict ( - color = 'rgb(255,255,255)', - width = 2 - ) ), - colorbar = dict( - title = "Millions USD") - ) ] +df['text'] = df['state'] + '<br>' + \ + 'Beef ' + df['beef'] + ' Dairy ' + df['dairy'] + '<br>' + \ + 'Fruits ' + df['total fruits'] + ' Veggies ' + df['total veggies'] + '<br>' + \ + 'Wheat ' + df['wheat'] + ' Corn ' + df['corn'] + +data = [go.Choropleth( + colorscale = scl, + autocolorscale = False, + locations = df['code'], + z = df['total exports'].astype(float), + locationmode = 'USA-states', + text = df['text'], + marker = dict( + line = dict( + color = 'rgb(255,255,255)', + width = 2 + )), + colorbar = dict( + title = "Millions USD") +)] layout = go.Layout( - title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)', - geo = dict( - scope='usa', - projection=dict( type='albers usa' ), - showlakes = True, - lakecolor = 'rgb(255, 255, 255)'), - ) - -fig = go.Figure( data=data, layout=layout ) -py.iplot(fig, filename='d3-cloropleth-map' ) + title = dict( + text = '2011 US Agriculture Exports by State<br>(Hover for breakdown)' + ), + geo = dict( + scope = 'usa', + projection = dict(type = 'albers usa'), + showlakes = True, + lakecolor = 'rgb(255, 255, 255)'), +) + +fig = go.Figure(data = data, layout = layout) +py.iplot(fig, filename = 'd3-cloropleth-map') @@ -166,26 +174,34 @@

World Choropleth Mapdf = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv') -data = [ go.Choropleth( - locations = df['CODE'], - z = df['GDP (BILLIONS)'], - text = df['COUNTRY'], - colorscale = [[0,"rgb(5, 10, 172)"],[0.35,"rgb(40, 60, 190)"],[0.5,"rgb(70, 100, 245)"],\ - [0.6,"rgb(90, 120, 245)"],[0.7,"rgb(106, 137, 247)"],[1,"rgb(220, 220, 220)"]], - autocolorscale = False, - reversescale = True, - marker = dict( - line = dict ( - color = 'rgb(180,180,180)', - width = 0.5 - ) ), - colorbar = dict( - tickprefix = '$', - title = 'GDP<br>Billions US$'), - ) ] +data = [go.Choropleth( + locations = df['CODE'], + z = df['GDP (BILLIONS)'], + text = df['COUNTRY'], + colorscale = [ + [0, "rgb(5, 10, 172)"], + [0.35, "rgb(40, 60, 190)"], + [0.5, "rgb(70, 100, 245)"], + [0.6, "rgb(90, 120, 245)"], + [0.7, "rgb(106, 137, 247)"], + [1, "rgb(220, 220, 220)"] + ], + autocolorscale = False, + reversescale = True, + marker = dict( + line = dict( + color = 'rgb(180,180,180)', + width = 0.5 + )), + colorbar = dict( + tickprefix = '$', + title = 'GDP<br>Billions US$'), +)] layout = go.Layout( - title = '2014 Global GDP', + title = dict( + text = '2014 Global GDP' + ), geo = dict( showframe = False, showcoastlines = False, @@ -193,19 +209,19 @@

World Choropleth Maptype = 'equirectangular' ) ), - annotations=[dict( - x=0.55, - y=0.1, - xref='paper', - yref='paper', - text='Source: <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\ + annotations = [dict( + x = 0.55, + y = 0.1, + xref = 'paper', + yref = 'paper', + text = 'Source: <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\ CIA World Factbook</a>', - showarrow=False + showarrow = False )] ) -fig = go.Figure( data=data, layout=layout ) -py.iplot(fig, filename='d3-world-map' ) +fig = go.Figure(data = data, layout = layout) +py.iplot(fig, filename = 'd3-world-map') @@ -295,9 +311,10 @@

Choropleth Inset Map] layout = go.Layout( - title = 'Ebola cases reported by month in West Africa 2014<br> \ + title = dict( + text = 'Ebola cases reported by month in West Africa 2014<br> \ Source: <a href="https://data.hdx.rwlabs.org/dataset/rowca-ebola-cases">\ -HDX</a>', +HDX</a>'), geo = dict( resolution = 50, scope = 'africa', @@ -386,22 +403,26 @@

Full County Choroplethsdf_sample['County FIPS Code'] = df_sample['County FIPS Code'].apply(lambda x: str(x).zfill(3)) df_sample['FIPS'] = df_sample['State FIPS Code'] + df_sample['County FIPS Code'] -colorscale = ["#f7fbff","#ebf3fb","#deebf7","#d2e3f3","#c6dbef","#b3d2e9","#9ecae1", - "#85bcdb","#6baed6","#57a0ce","#4292c6","#3082be","#2171b5","#1361a9", - "#08519c","#0b4083","#08306b"] +colorscale = ["#f7fbff", "#ebf3fb", "#deebf7", "#d2e3f3", "#c6dbef", "#b3d2e9", "#9ecae1", + "#85bcdb", "#6baed6", "#57a0ce", "#4292c6", "#3082be", "#2171b5", "#1361a9", + "#08519c", "#0b4083", "#08306b" +] endpts = list(np.linspace(1, 12, len(colorscale) - 1)) fips = df_sample['FIPS'].tolist() values = df_sample['Unemployment Rate (%)'].tolist() fig = ff.create_choropleth( - fips=fips, values=values, scope=['usa'], - binning_endpoints=endpts, colorscale=colorscale, - show_state_data=False, - show_hover=True, centroid_marker={'opacity': 0}, - asp=2.9, title='USA by Unemployment %', - legend_title='% unemployed' + fips = fips, values = values, scope = ['usa'], + binning_endpoints = endpts, colorscale = colorscale, + show_state_data = False, + show_hover = True, centroid_marker = { + 'opacity': 0 + }, + asp = 2.9, + title = 'USA by Unemployment %', + legend_title = '% unemployed' ) -py.iplot(fig, filename='choropleth_full_usa') +py.iplot(fig, filename = 'choropleth_full_usa') diff --git a/_posts/python/maps/choropleth-maps/Choropleth_maps.ipynb b/_posts/python/maps/choropleth-maps/Choropleth_maps.ipynb index 27eee6bac867..cd6dc4e2e2ee 100644 --- a/_posts/python/maps/choropleth-maps/Choropleth_maps.ipynb +++ b/_posts/python/maps/choropleth-maps/Choropleth_maps.ipynb @@ -76,41 +76,49 @@ "for col in df.columns:\n", " df[col] = df[col].astype(str)\n", "\n", - "scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\\\n", - " [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]\n", + "scl = [\n", + " [0.0, 'rgb(242,240,247)'],\n", + " [0.2, 'rgb(218,218,235)'],\n", + " [0.4, 'rgb(188,189,220)'],\n", + " [0.6, 'rgb(158,154,200)'],\n", + " [0.8, 'rgb(117,107,177)'],\n", + " [1.0, 'rgb(84,39,143)']\n", + "]\n", "\n", - "df['text'] = df['state'] + '
' +\\\n", - " 'Beef '+df['beef']+' Dairy '+df['dairy']+'
'+\\\n", - " 'Fruits '+df['total fruits']+' Veggies ' + df['total veggies']+'
'+\\\n", - " 'Wheat '+df['wheat']+' Corn '+df['corn']\n", + "df['text'] = df['state'] + '
' + \\\n", + " 'Beef ' + df['beef'] + ' Dairy ' + df['dairy'] + '
' + \\\n", + " 'Fruits ' + df['total fruits'] + ' Veggies ' + df['total veggies'] + '
' + \\\n", + " 'Wheat ' + df['wheat'] + ' Corn ' + df['corn']\n", "\n", - "data = [ go.Choropleth(\n", - " colorscale = scl,\n", - " autocolorscale = False,\n", - " locations = df['code'],\n", - " z = df['total exports'].astype(float),\n", - " locationmode = 'USA-states',\n", - " text = df['text'],\n", - " marker = dict(\n", - " line = dict (\n", - " color = 'rgb(255,255,255)',\n", - " width = 2\n", - " ) ),\n", - " colorbar = dict(\n", - " title = \"Millions USD\")\n", - " ) ]\n", + "data = [go.Choropleth(\n", + " colorscale = scl,\n", + " autocolorscale = False,\n", + " locations = df['code'],\n", + " z = df['total exports'].astype(float),\n", + " locationmode = 'USA-states',\n", + " text = df['text'],\n", + " marker = dict(\n", + " line = dict(\n", + " color = 'rgb(255,255,255)',\n", + " width = 2\n", + " )),\n", + " colorbar = dict(\n", + " title = \"Millions USD\")\n", + ")]\n", "\n", "layout = go.Layout(\n", - " title = '2011 US Agriculture Exports by State
(Hover for breakdown)',\n", - " geo = dict(\n", - " scope='usa',\n", - " projection=dict( type='albers usa' ),\n", - " showlakes = True,\n", - " lakecolor = 'rgb(255, 255, 255)'),\n", - " )\n", - " \n", - "fig = go.Figure( data=data, layout=layout )\n", - "py.iplot(fig, filename='d3-cloropleth-map' )" + " title = dict(\n", + " text = '2011 US Agriculture Exports by State
(Hover for breakdown)'\n", + " ),\n", + " geo = dict(\n", + " scope = 'usa',\n", + " projection = dict(type = 'albers usa'),\n", + " showlakes = True,\n", + " lakecolor = 'rgb(255, 255, 255)'),\n", + ")\n", + "\n", + "fig = go.Figure(data = data, layout = layout)\n", + "py.iplot(fig, filename = 'd3-cloropleth-map')" ] }, { @@ -145,26 +153,34 @@ "\n", "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')\n", "\n", - "data = [ go.Choropleth(\n", - " locations = df['CODE'],\n", - " z = df['GDP (BILLIONS)'],\n", - " text = df['COUNTRY'],\n", - " colorscale = [[0,\"rgb(5, 10, 172)\"],[0.35,\"rgb(40, 60, 190)\"],[0.5,\"rgb(70, 100, 245)\"],\\\n", - " [0.6,\"rgb(90, 120, 245)\"],[0.7,\"rgb(106, 137, 247)\"],[1,\"rgb(220, 220, 220)\"]],\n", - " autocolorscale = False,\n", - " reversescale = True,\n", - " marker = dict(\n", - " line = dict (\n", - " color = 'rgb(180,180,180)',\n", - " width = 0.5\n", - " ) ),\n", - " colorbar = dict(\n", - " tickprefix = '$',\n", - " title = 'GDP
Billions US$'),\n", - " ) ]\n", + "data = [go.Choropleth(\n", + " locations = df['CODE'],\n", + " z = df['GDP (BILLIONS)'],\n", + " text = df['COUNTRY'],\n", + " colorscale = [\n", + " [0, \"rgb(5, 10, 172)\"],\n", + " [0.35, \"rgb(40, 60, 190)\"],\n", + " [0.5, \"rgb(70, 100, 245)\"],\n", + " [0.6, \"rgb(90, 120, 245)\"],\n", + " [0.7, \"rgb(106, 137, 247)\"],\n", + " [1, \"rgb(220, 220, 220)\"]\n", + " ],\n", + " autocolorscale = False,\n", + " reversescale = True,\n", + " marker = dict(\n", + " line = dict(\n", + " color = 'rgb(180,180,180)',\n", + " width = 0.5\n", + " )),\n", + " colorbar = dict(\n", + " tickprefix = '$',\n", + " title = 'GDP
Billions US$'),\n", + ")]\n", "\n", "layout = go.Layout(\n", - " title = '2014 Global GDP',\n", + " title = dict(\n", + " text = '2014 Global GDP'\n", + " ),\n", " geo = dict(\n", " showframe = False,\n", " showcoastlines = False,\n", @@ -172,19 +188,19 @@ " type = 'equirectangular'\n", " )\n", " ),\n", - " annotations=[dict(\n", - " x=0.55,\n", - " y=0.1,\n", - " xref='paper',\n", - " yref='paper',\n", - " text='Source: \\\n", + " annotations = [dict(\n", + " x = 0.55,\n", + " y = 0.1,\n", + " xref = 'paper',\n", + " yref = 'paper',\n", + " text = 'Source: \\\n", " CIA World Factbook',\n", - " showarrow=False\n", + " showarrow = False\n", " )]\n", ")\n", "\n", - "fig = go.Figure( data=data, layout=layout )\n", - "py.iplot(fig, filename='d3-world-map' )" + "fig = go.Figure(data = data, layout = layout)\n", + "py.iplot(fig, filename = 'd3-world-map')" ] }, { @@ -265,9 +281,10 @@ "]\n", "\n", "layout = go.Layout(\n", - " title = 'Ebola cases reported by month in West Africa 2014
\\\n", + " title = dict(\n", + " text = 'Ebola cases reported by month in West Africa 2014
\\\n", "Source: \\\n", - "HDX',\n", + "HDX'),\n", " geo = dict(\n", " resolution = 50,\n", " scope = 'africa',\n", @@ -354,22 +371,26 @@ "df_sample['County FIPS Code'] = df_sample['County FIPS Code'].apply(lambda x: str(x).zfill(3))\n", "df_sample['FIPS'] = df_sample['State FIPS Code'] + df_sample['County FIPS Code']\n", "\n", - "colorscale = [\"#f7fbff\",\"#ebf3fb\",\"#deebf7\",\"#d2e3f3\",\"#c6dbef\",\"#b3d2e9\",\"#9ecae1\",\n", - " \"#85bcdb\",\"#6baed6\",\"#57a0ce\",\"#4292c6\",\"#3082be\",\"#2171b5\",\"#1361a9\",\n", - " \"#08519c\",\"#0b4083\",\"#08306b\"]\n", + "colorscale = [\"#f7fbff\", \"#ebf3fb\", \"#deebf7\", \"#d2e3f3\", \"#c6dbef\", \"#b3d2e9\", \"#9ecae1\",\n", + " \"#85bcdb\", \"#6baed6\", \"#57a0ce\", \"#4292c6\", \"#3082be\", \"#2171b5\", \"#1361a9\",\n", + " \"#08519c\", \"#0b4083\", \"#08306b\"\n", + "]\n", "endpts = list(np.linspace(1, 12, len(colorscale) - 1))\n", "fips = df_sample['FIPS'].tolist()\n", "values = df_sample['Unemployment Rate (%)'].tolist()\n", "\n", "fig = ff.create_choropleth(\n", - " fips=fips, values=values, scope=['usa'],\n", - " binning_endpoints=endpts, colorscale=colorscale,\n", - " show_state_data=False,\n", - " show_hover=True, centroid_marker={'opacity': 0},\n", - " asp=2.9, title='USA by Unemployment %',\n", - " legend_title='% unemployed'\n", + " fips = fips, values = values, scope = ['usa'],\n", + " binning_endpoints = endpts, colorscale = colorscale,\n", + " show_state_data = False,\n", + " show_hover = True, centroid_marker = {\n", + " 'opacity': 0\n", + " },\n", + " asp = 2.9,\n", + " title = 'USA by Unemployment %',\n", + " legend_title = '% unemployed'\n", ")\n", - "py.iplot(fig, filename='choropleth_full_usa')" + "py.iplot(fig, filename = 'choropleth_full_usa')" ] }, { @@ -414,11 +435,11 @@ "output_type": "stream", "text": [ "Collecting git+https://github.com/plotly/publisher.git\n", - " Cloning https://github.com/plotly/publisher.git to c:\\users\\priyat~1\\appdata\\local\\temp\\pip-req-build-c7dlx5_3\n", + " Cloning https://github.com/plotly/publisher.git to c:\\users\\priyat~1\\appdata\\local\\temp\\pip-req-build-uhi9ioyx\n", "Building wheels for collected packages: publisher\n", " Running setup.py bdist_wheel for publisher: started\n", " Running setup.py bdist_wheel for publisher: finished with status 'done'\n", - " Stored in directory: C:\\Users\\PRIYAT~1\\AppData\\Local\\Temp\\pip-ephem-wheel-cache-ng_5reyj\\wheels\\99\\3e\\a0\\fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966\n", + " Stored in directory: C:\\Users\\PRIYAT~1\\AppData\\Local\\Temp\\pip-ephem-wheel-cache-1qip_4zy\\wheels\\99\\3e\\a0\\fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966\n", "Successfully built publisher\n", "Installing collected packages: publisher\n", " Found existing installation: publisher 0.13\n", @@ -442,8 +463,7 @@ " title = 'Python Choropleth Maps | Plotly',\n", " has_thumbnail='true', thumbnail='thumbnail/choropleth.jpg', \n", " language='python',\n", - " display_as='maps', order=1, ipynb='~notebook_demo/55',\n", - " uses_plotly_offline=False)" + " display_as='maps', order=1, ipynb='~notebook_demo/55')" ] }, { diff --git a/_posts/python/maps/county-choropleth/county_choropleth.ipynb b/_posts/python/maps/county-choropleth/county_choropleth.ipynb index 73c69c74872e..3a5f396b8ffc 100644 --- a/_posts/python/maps/county-choropleth/county_choropleth.ipynb +++ b/_posts/python/maps/county-choropleth/county_choropleth.ipynb @@ -27,7 +27,7 @@ { "data": { "text/plain": [ - "'2.7.0'" + "'3.6.1'" ] }, "execution_count": 1, @@ -85,37 +85,21 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/michael/.virtualenvs/plot2py2/local/lib/python2.7/site-packages/pandas/core/frame.py:6211: FutureWarning:\n", - "\n", - "Sorting because non-concatenation axis is not aligned. A future version\n", - "of pandas will change to not sort by default.\n", - "\n", - "To accept the future behavior, pass 'sort=False'.\n", - "\n", - "To retain the current behavior and silence the warning, pass 'sort=True'.\n", - "\n", - "\n" - ] - }, { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -152,19 +136,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -209,19 +193,19 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -263,19 +247,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -776,21 +760,21 @@ "metadata": { "anaconda-cloud": {}, "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": "2.7.15rc1" + "pygments_lexer": "ipython3", + "version": "3.7.1" } }, "nbformat": 4,