├── .gitignore
├── requirements.txt
├── README.md
├── india_census.csv
└── ChoroplethTutorial.ipynb


/.gitignore:
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1 | .ipynb_checkpoints/
2 | 


--------------------------------------------------------------------------------
/requirements.txt:
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1 | plotly
2 | pandas
3 | numpy
4 | 


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/README.md:
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 1 | # Plotting Choropleth Maps using Python
 2 | 
 3 | 
 4 | [Video Tutorial](https://youtu.be/aJmaw3QKMvk)
 5 | 
 6 | 
 7 | A choropleth map uses colour coding to indicate quantitative values across geographical areas on a map.
 8 | 
 9 | <img src="https://datavizcatalogue.com/methods/images/anatomy/choropleth.png" width=300>
10 | 
11 | >A choropleth map is a type of thematic map in which areas are shaded or patterned in proportion to a statistical variable that represents an aggregate summary of a geographic characteristic within each area, such as population density or per-capita income.
12 | 
13 | ## Terminologies
14 | 
15 | 
16 | ### [Plotly](https://plotly.com/)
17 | Plotly is a technical computing company that develops online data analytics and visualization tools. Plotly provides online graphing, analytics, and statistics tools for individuals and collaboration, as well as scientific graphing libraries for Python, R, MATLAB, Perl, Julia, Arduino, and REST.
18 | 
19 | ### [plotly.py](https://plotly.com/python/)
20 | An interactive, open-source, and browser-based graphing library for Python.
21 | 
22 | ```bash
23 | $ pip install plotly
24 | ```
25 | 
26 | >Plotly Express is a new high-level Python visualization library: it's a wrapper for Plotly.py that exposes a simple syntax for complex charts.
27 | 
28 | ### [GeoJSON](https://geojson.org/)
29 | An open standard format designed for representing simple geographical features, along with their non-spatial attributes.
30 | 
31 | ### [Mapbox](https://mapbox.com/)
32 | An open source mapping platform for custom designed maps.
33 | 
34 | 
35 | ## References
36 | 
37 | 
38 | - https://en.wikipedia.org/wiki/List_of_states_and_union_territories_of_India_by_population
39 | 
40 | - https://un-mapped.carto.com/tables/states_india/public/map
41 | 
42 | - https://plotly.com/python/choropleth-maps/
43 | 
44 | - https://plotly.com/python/colorscales/
45 | 
46 | - https://www.mapbox.com/
47 | 
48 | - https://plotly.com/python/mapbox-county-choropleth/
49 | 
50 | - https://plotly.com/python/mapbox-layers/


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/india_census.csv:
--------------------------------------------------------------------------------
 1 | Rank,State or union territory,Population,Population (%),Decadal growth(2001–2011),Rural population,Percent rural,Urban population,Percent urban,Area[16],Density[a],Sex ratio
 2 | 1,Uttar Pradesh,199812341,,20.20%,155317278,,44495063,,"240,928 km2 (93,023 sq mi)","828/km2 (2,140/sq mi)",912
 3 | 2,Maharashtra,112374333,,20.00%,61556074,,50818259,,"307,713 km2 (118,809 sq mi)",365/km2 (950/sq mi),929
 4 | 3,Bihar,104099452,,25.40%,92341436,,11758016,,"94,163 km2 (36,357 sq mi)","1,102/km2 (2,850/sq mi)",918
 5 | 4,West Bengal,91276115,,13.80%,62183113,,29093002,,"88,752 km2 (34,267 sq mi)","1,029/km2 (2,670/sq mi)",953
 6 | 5,Madhya Pradesh,72626809,,16.30%,52557404,,20069405,,"308,245 km2 (119,014 sq mi)",236/km2 (610/sq mi),931
 7 | 6,Tamil Nadu,72147030,,15.60%,37229590,,34917440,,"130,058 km2 (50,216 sq mi)","555/km2 (1,440/sq mi)",996
 8 | 7,Rajasthan,68548437,,21.30%,51500352,,17048085,,"342,239 km2 (132,139 sq mi)",201/km2 (520/sq mi),928
 9 | 8,Karnataka,61095297,,15.60%,37469335,,23625962,,"191,791 km2 (74,051 sq mi)",319/km2 (830/sq mi),973
10 | 9,Gujarat,60439692,,19.30%,34694609,,25745083,,"196,024 km2 (75,685 sq mi)",308/km2 (800/sq mi),919
11 | 10,Andhra Pradesh,49577103,,11.00%,34966693,,14610410,,"162,968 km2 (62,922 sq mi)",303/km2 (780/sq mi),993
12 | 11,Odisha,41974219,,14.00%,34970562,,7003656,,"155,707 km2 (60,119 sq mi)",269/km2 (700/sq mi),979
13 | 12,Telangana,35003674,,13.58%,21395009,,13608665,,"112,077 km2 (43,273 sq mi)",312/km2 (810/sq mi),988
14 | 13,Kerala,33406061,,4.90%,17471135,,15934926,,"38,863 km2 (15,005 sq mi)","859/km2 (2,220/sq mi)",1084
15 | 14,Jharkhand,32988134,,22.40%,25055073,,7933061,,"79,714 km2 (30,778 sq mi)","414/km2 (1,070/sq mi)",948
16 | 15,Assam,31205576,,17.10%,26807034,,4398542,,"78,438 km2 (30,285 sq mi)","397/km2 (1,030/sq mi)",954
17 | 16,Punjab,27743338,,13.90%,17344192,,10399146,,"50,362 km2 (19,445 sq mi)","550/km2 (1,400/sq mi)",895
18 | 17,Chhattisgarh,25545198,,22.60%,19607961,,5937237,,"135,191 km2 (52,198 sq mi)",189/km2 (490/sq mi),991
19 | 18,Haryana,25351462,,19.90%,16509359,,8842103,,"44,212 km2 (17,070 sq mi)","573/km2 (1,480/sq mi)",879
20 | 19,Uttarakhand,10086292,,18.80%,7036954,,3049338,,"53,483 km2 (20,650 sq mi)",189/km2 (490/sq mi),963
21 | 20,Himachal Pradesh,6864602,,12.90%,6176050,,688552,,"55,673 km2 (21,495 sq mi)",123/km2 (320/sq mi),972
22 | 21,Tripura,3673917,,14.80%,2712464,,961453,,"10,486 km2 (4,049 sq mi)",350/km2 (910/sq mi),960
23 | 22,Meghalaya,2966889,,27.90%,2371439,,595450,,"22,429 km2 (8,660 sq mi)",132/km2 (340/sq mi),989
24 | 23,Manipur,2570390,,18.60%,1793875,,776515,,"22,327 km2 (8,621 sq mi)",122/km2 (320/sq mi),992
25 | 24,Nagaland,1978502,,−0.6%,1407536,,570966,,"16,579 km2 (6,401 sq mi)",119/km2 (310/sq mi),931
26 | 25,Goa,1458545,,8.20%,551731,,906814,,"3,702 km2 (1,429 sq mi)","394/km2 (1,020/sq mi)",973
27 | 26,Arunanchal Pradesh,1383727,,26.00%,1066358,,317369,,"83,743 km2 (32,333 sq mi)",17/km2 (44/sq mi),938
28 | 27,Mizoram,1097206,,23.50%,525435,,571771,,"21,081 km2 (8,139 sq mi)",52/km2 (130/sq mi),976
29 | 28,Sikkim,610577,,12.90%,456999,,153578,,"7,096 km2 (2,740 sq mi)",86/km2 (220/sq mi),890
30 | ,NCT of Delhi,16787941,,21.20%,419042,,16368899,,"1,484 km2 (573 sq mi)","11,297/km2 (29,260/sq mi)",868
31 | ,Jammu & Kashmir,12267032,,23.60%,9064220,,3202812,,"42,241 km2 (16,309 sq mi)[d]",297/km2 (770/sq mi),890
32 | ,Puducherry,1247953,,28.10%,395200,,852753,,479 km2 (185 sq mi),"2,598/km2 (6,730/sq mi)",1037
33 | ,Chandigarh,1055450,,17.20%,28991,,1026459,,114 km2 (44 sq mi),"9,252/km2 (23,960/sq mi)",818
34 | ,Dadara & Nagar Havelli,585764,,55.10%,243510,,342254,,603 km2 (233 sq mi),"970/km2 (2,500/sq mi)",711
35 | ,Daman & Diu,585764,,55.10%,243510,,342254,,603 km2 (233 sq mi),"970/km2 (2,500/sq mi)",711
36 | ,Andaman & Nicobar Island,380581,,6.90%,237093,,143488,,"8,249 km2 (3,185 sq mi)",46/km2 (120/sq mi),876
37 | ,Lakshadweep,64473,,6.30%,14141,,50332,,32 km2 (12 sq mi),"2,013/km2 (5,210/sq mi)",946


--------------------------------------------------------------------------------
/ChoroplethTutorial.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Plotting Choropleth Maps using Python\n",
  8 |     "---\n",
  9 |     "A choropleth map uses colour coding to indicate quantitative values across geographical areas on a map.\n",
 10 |     "\n",
 11 |     "![](https://datavizcatalogue.com/methods/images/anatomy/choropleth.png)\n",
 12 |     "\n",
 13 |     ">A choropleth map is a type of thematic map in which areas are shaded or patterned in proportion to a statistical variable that represents an aggregate summary of a geographic characteristic within each area, such as population density or per-capita income.\n",
 14 |     "\n",
 15 |     "## Terminologies\n",
 16 |     "---\n",
 17 |     "\n",
 18 |     "## [Plotly](https://plotly.com/)\n",
 19 |     "Plotly is a technical computing company that develops online data analytics and visualization tools. Plotly provides online graphing, analytics, and statistics tools for individuals and collaboration, as well as scientific graphing libraries for Python, R, MATLAB, Perl, Julia, Arduino, and REST.\n",
 20 |     "\n",
 21 |     "### [plotly.py](https://plotly.com/python/)\n",
 22 |     "An interactive, open-source, and browser-based graphing library for Python.\n",
 23 |     "\n",
 24 |     "```bash\n",
 25 |     "$ pip install plotly\n",
 26 |     "```\n",
 27 |     "\n",
 28 |     ">Plotly Express is a new high-level Python visualization library: it's a wrapper for Plotly.py that exposes a simple syntax for complex charts.\n",
 29 |     "\n",
 30 |     "### [GeoJSON](https://geojson.org/)\n",
 31 |     "An open standard format designed for representing simple geographical features, along with their non-spatial attributes.\n",
 32 |     "\n",
 33 |     "### [Mapbox](https://mapbox.com/)\n",
 34 |     "An open source mapping platform for custom designed maps."
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": 1,
 40 |    "metadata": {},
 41 |    "outputs": [],
 42 |    "source": [
 43 |     "import json\n",
 44 |     "import numpy as np\n",
 45 |     "import pandas as pd\n",
 46 |     "import plotly.express as px"
 47 |    ]
 48 |   },
 49 |   {
 50 |    "cell_type": "code",
 51 |    "execution_count": 2,
 52 |    "metadata": {},
 53 |    "outputs": [],
 54 |    "source": [
 55 |     "import plotly.io as pio\n",
 56 |     "pio.renderers.default = 'browser'"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "code",
 61 |    "execution_count": 4,
 62 |    "metadata": {},
 63 |    "outputs": [
 64 |     {
 65 |      "data": {
 66 |       "application/javascript": [
 67 |        "\n",
 68 |        "            setTimeout(function() {\n",
 69 |        "                var nbb_cell_id = 4;\n",
 70 |        "                var nbb_unformatted_code = \"india_states = json.load(open(\\\"states_india.geojson\\\", 'r'))\";\n",
 71 |        "                var nbb_formatted_code = \"india_states = json.load(open(\\\"states_india.geojson\\\", \\\"r\\\"))\";\n",
 72 |        "                var nbb_cells = Jupyter.notebook.get_cells();\n",
 73 |        "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
 74 |        "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
 75 |        "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
 76 |        "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
 77 |        "                        }\n",
 78 |        "                        break;\n",
 79 |        "                    }\n",
 80 |        "                }\n",
 81 |        "            }, 500);\n",
 82 |        "            "
 83 |       ],
 84 |       "text/plain": [
 85 |        "<IPython.core.display.Javascript object>"
 86 |       ]
 87 |      },
 88 |      "metadata": {},
 89 |      "output_type": "display_data"
 90 |     }
 91 |    ],
 92 |    "source": [
 93 |     "india_states = json.load(open(\"states_india.geojson\", \"r\"))"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": 5,
 99 |    "metadata": {},
100 |    "outputs": [
101 |     {
102 |      "data": {
103 |       "application/javascript": [
104 |        "\n",
105 |        "            setTimeout(function() {\n",
106 |        "                var nbb_cell_id = 5;\n",
107 |        "                var nbb_unformatted_code = \"state_id_map = {}\\nfor feature in india_states['features']:\\n    feature['id'] = feature['properties']['state_code']\\n    state_id_map[feature['properties']['st_nm']] = feature['id']\";\n",
108 |        "                var nbb_formatted_code = \"state_id_map = {}\\nfor feature in india_states[\\\"features\\\"]:\\n    feature[\\\"id\\\"] = feature[\\\"properties\\\"][\\\"state_code\\\"]\\n    state_id_map[feature[\\\"properties\\\"][\\\"st_nm\\\"]] = feature[\\\"id\\\"]\";\n",
109 |        "                var nbb_cells = Jupyter.notebook.get_cells();\n",
110 |        "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
111 |        "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
112 |        "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
113 |        "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
114 |        "                        }\n",
115 |        "                        break;\n",
116 |        "                    }\n",
117 |        "                }\n",
118 |        "            }, 500);\n",
119 |        "            "
120 |       ],
121 |       "text/plain": [
122 |        "<IPython.core.display.Javascript object>"
123 |       ]
124 |      },
125 |      "metadata": {},
126 |      "output_type": "display_data"
127 |     }
128 |    ],
129 |    "source": [
130 |     "state_id_map = {}\n",
131 |     "for feature in india_states[\"features\"]:\n",
132 |     "    feature[\"id\"] = feature[\"properties\"][\"state_code\"]\n",
133 |     "    state_id_map[feature[\"properties\"][\"st_nm\"]] = feature[\"id\"]"
134 |    ]
135 |   },
136 |   {
137 |    "cell_type": "code",
138 |    "execution_count": 6,
139 |    "metadata": {},
140 |    "outputs": [
141 |     {
142 |      "data": {
143 |       "application/javascript": [
144 |        "\n",
145 |        "            setTimeout(function() {\n",
146 |        "                var nbb_cell_id = 6;\n",
147 |        "                var nbb_unformatted_code = \"df = pd.read_csv(\\\"india_census.csv\\\")\\ndf['Density'] = df['Density[a]'].apply(lambda x: int(x.split(\\\"/\\\")[0].replace(\\\",\\\",\\\"\\\")))\\ndf['id'] = df['State or union territory'].apply(lambda x: state_id_map[x])\";\n",
148 |        "                var nbb_formatted_code = \"df = pd.read_csv(\\\"india_census.csv\\\")\\ndf[\\\"Density\\\"] = df[\\\"Density[a]\\\"].apply(lambda x: int(x.split(\\\"/\\\")[0].replace(\\\",\\\", \\\"\\\")))\\ndf[\\\"id\\\"] = df[\\\"State or union territory\\\"].apply(lambda x: state_id_map[x])\";\n",
149 |        "                var nbb_cells = Jupyter.notebook.get_cells();\n",
150 |        "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
151 |        "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
152 |        "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
153 |        "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
154 |        "                        }\n",
155 |        "                        break;\n",
156 |        "                    }\n",
157 |        "                }\n",
158 |        "            }, 500);\n",
159 |        "            "
160 |       ],
161 |       "text/plain": [
162 |        "<IPython.core.display.Javascript object>"
163 |       ]
164 |      },
165 |      "metadata": {},
166 |      "output_type": "display_data"
167 |     }
168 |    ],
169 |    "source": [
170 |     "df = pd.read_csv(\"india_census.csv\")\n",
171 |     "df[\"Density\"] = df[\"Density[a]\"].apply(lambda x: int(x.split(\"/\")[0].replace(\",\", \"\")))\n",
172 |     "df[\"id\"] = df[\"State or union territory\"].apply(lambda x: state_id_map[x])"
173 |    ]
174 |   },
175 |   {
176 |    "cell_type": "code",
177 |    "execution_count": 7,
178 |    "metadata": {},
179 |    "outputs": [
180 |     {
181 |      "data": {
182 |       "text/html": [
183 |        "<div>\n",
184 |        "<style scoped>\n",
185 |        "    .dataframe tbody tr th:only-of-type {\n",
186 |        "        vertical-align: middle;\n",
187 |        "    }\n",
188 |        "\n",
189 |        "    .dataframe tbody tr th {\n",
190 |        "        vertical-align: top;\n",
191 |        "    }\n",
192 |        "\n",
193 |        "    .dataframe thead th {\n",
194 |        "        text-align: right;\n",
195 |        "    }\n",
196 |        "</style>\n",
197 |        "<table border=\"1\" class=\"dataframe\">\n",
198 |        "  <thead>\n",
199 |        "    <tr style=\"text-align: right;\">\n",
200 |        "      <th></th>\n",
201 |        "      <th>Rank</th>\n",
202 |        "      <th>State or union territory</th>\n",
203 |        "      <th>Population</th>\n",
204 |        "      <th>Population (%)</th>\n",
205 |        "      <th>Decadal growth(2001–2011)</th>\n",
206 |        "      <th>Rural population</th>\n",
207 |        "      <th>Percent rural</th>\n",
208 |        "      <th>Urban population</th>\n",
209 |        "      <th>Percent urban</th>\n",
210 |        "      <th>Area[16]</th>\n",
211 |        "      <th>Density[a]</th>\n",
212 |        "      <th>Sex ratio</th>\n",
213 |        "      <th>Density</th>\n",
214 |        "      <th>id</th>\n",
215 |        "    </tr>\n",
216 |        "  </thead>\n",
217 |        "  <tbody>\n",
218 |        "    <tr>\n",
219 |        "      <th>0</th>\n",
220 |        "      <td>1.0</td>\n",
221 |        "      <td>Uttar Pradesh</td>\n",
222 |        "      <td>199812341</td>\n",
223 |        "      <td>NaN</td>\n",
224 |        "      <td>20.20%</td>\n",
225 |        "      <td>155317278</td>\n",
226 |        "      <td>NaN</td>\n",
227 |        "      <td>44495063</td>\n",
228 |        "      <td>NaN</td>\n",
229 |        "      <td>240,928 km2 (93,023 sq mi)</td>\n",
230 |        "      <td>828/km2 (2,140/sq mi)</td>\n",
231 |        "      <td>912</td>\n",
232 |        "      <td>828</td>\n",
233 |        "      <td>9</td>\n",
234 |        "    </tr>\n",
235 |        "    <tr>\n",
236 |        "      <th>1</th>\n",
237 |        "      <td>2.0</td>\n",
238 |        "      <td>Maharashtra</td>\n",
239 |        "      <td>112374333</td>\n",
240 |        "      <td>NaN</td>\n",
241 |        "      <td>20.00%</td>\n",
242 |        "      <td>61556074</td>\n",
243 |        "      <td>NaN</td>\n",
244 |        "      <td>50818259</td>\n",
245 |        "      <td>NaN</td>\n",
246 |        "      <td>307,713 km2 (118,809 sq mi)</td>\n",
247 |        "      <td>365/km2 (950/sq mi)</td>\n",
248 |        "      <td>929</td>\n",
249 |        "      <td>365</td>\n",
250 |        "      <td>27</td>\n",
251 |        "    </tr>\n",
252 |        "    <tr>\n",
253 |        "      <th>2</th>\n",
254 |        "      <td>3.0</td>\n",
255 |        "      <td>Bihar</td>\n",
256 |        "      <td>104099452</td>\n",
257 |        "      <td>NaN</td>\n",
258 |        "      <td>25.40%</td>\n",
259 |        "      <td>92341436</td>\n",
260 |        "      <td>NaN</td>\n",
261 |        "      <td>11758016</td>\n",
262 |        "      <td>NaN</td>\n",
263 |        "      <td>94,163 km2 (36,357 sq mi)</td>\n",
264 |        "      <td>1,102/km2 (2,850/sq mi)</td>\n",
265 |        "      <td>918</td>\n",
266 |        "      <td>1102</td>\n",
267 |        "      <td>10</td>\n",
268 |        "    </tr>\n",
269 |        "    <tr>\n",
270 |        "      <th>3</th>\n",
271 |        "      <td>4.0</td>\n",
272 |        "      <td>West Bengal</td>\n",
273 |        "      <td>91276115</td>\n",
274 |        "      <td>NaN</td>\n",
275 |        "      <td>13.80%</td>\n",
276 |        "      <td>62183113</td>\n",
277 |        "      <td>NaN</td>\n",
278 |        "      <td>29093002</td>\n",
279 |        "      <td>NaN</td>\n",
280 |        "      <td>88,752 km2 (34,267 sq mi)</td>\n",
281 |        "      <td>1,029/km2 (2,670/sq mi)</td>\n",
282 |        "      <td>953</td>\n",
283 |        "      <td>1029</td>\n",
284 |        "      <td>19</td>\n",
285 |        "    </tr>\n",
286 |        "    <tr>\n",
287 |        "      <th>4</th>\n",
288 |        "      <td>5.0</td>\n",
289 |        "      <td>Madhya Pradesh</td>\n",
290 |        "      <td>72626809</td>\n",
291 |        "      <td>NaN</td>\n",
292 |        "      <td>16.30%</td>\n",
293 |        "      <td>52557404</td>\n",
294 |        "      <td>NaN</td>\n",
295 |        "      <td>20069405</td>\n",
296 |        "      <td>NaN</td>\n",
297 |        "      <td>308,245 km2 (119,014 sq mi)</td>\n",
298 |        "      <td>236/km2 (610/sq mi)</td>\n",
299 |        "      <td>931</td>\n",
300 |        "      <td>236</td>\n",
301 |        "      <td>23</td>\n",
302 |        "    </tr>\n",
303 |        "  </tbody>\n",
304 |        "</table>\n",
305 |        "</div>"
306 |       ],
307 |       "text/plain": [
308 |        "   Rank State or union territory  Population  Population (%)  \\\n",
309 |        "0   1.0            Uttar Pradesh   199812341             NaN   \n",
310 |        "1   2.0              Maharashtra   112374333             NaN   \n",
311 |        "2   3.0                    Bihar   104099452             NaN   \n",
312 |        "3   4.0              West Bengal    91276115             NaN   \n",
313 |        "4   5.0           Madhya Pradesh    72626809             NaN   \n",
314 |        "\n",
315 |        "  Decadal growth(2001–2011)  Rural population  Percent rural  \\\n",
316 |        "0                    20.20%         155317278            NaN   \n",
317 |        "1                    20.00%          61556074            NaN   \n",
318 |        "2                    25.40%          92341436            NaN   \n",
319 |        "3                    13.80%          62183113            NaN   \n",
320 |        "4                    16.30%          52557404            NaN   \n",
321 |        "\n",
322 |        "   Urban population  Percent urban                     Area[16]  \\\n",
323 |        "0          44495063            NaN   240,928 km2 (93,023 sq mi)   \n",
324 |        "1          50818259            NaN  307,713 km2 (118,809 sq mi)   \n",
325 |        "2          11758016            NaN    94,163 km2 (36,357 sq mi)   \n",
326 |        "3          29093002            NaN    88,752 km2 (34,267 sq mi)   \n",
327 |        "4          20069405            NaN  308,245 km2 (119,014 sq mi)   \n",
328 |        "\n",
329 |        "                Density[a]  Sex ratio  Density  id  \n",
330 |        "0    828/km2 (2,140/sq mi)        912      828   9  \n",
331 |        "1      365/km2 (950/sq mi)        929      365  27  \n",
332 |        "2  1,102/km2 (2,850/sq mi)        918     1102  10  \n",
333 |        "3  1,029/km2 (2,670/sq mi)        953     1029  19  \n",
334 |        "4      236/km2 (610/sq mi)        931      236  23  "
335 |       ]
336 |      },
337 |      "execution_count": 7,
338 |      "metadata": {},
339 |      "output_type": "execute_result"
340 |     },
341 |     {
342 |      "data": {
343 |       "application/javascript": [
344 |        "\n",
345 |        "            setTimeout(function() {\n",
346 |        "                var nbb_cell_id = 7;\n",
347 |        "                var nbb_unformatted_code = \"df.head()\";\n",
348 |        "                var nbb_formatted_code = \"df.head()\";\n",
349 |        "                var nbb_cells = Jupyter.notebook.get_cells();\n",
350 |        "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
351 |        "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
352 |        "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
353 |        "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
354 |        "                        }\n",
355 |        "                        break;\n",
356 |        "                    }\n",
357 |        "                }\n",
358 |        "            }, 500);\n",
359 |        "            "
360 |       ],
361 |       "text/plain": [
362 |        "<IPython.core.display.Javascript object>"
363 |       ]
364 |      },
365 |      "metadata": {},
366 |      "output_type": "display_data"
367 |     }
368 |    ],
369 |    "source": [
370 |     "df.head()"
371 |    ]
372 |   },
373 |   {
374 |    "cell_type": "code",
375 |    "execution_count": 8,
376 |    "metadata": {},
377 |    "outputs": [
378 |     {
379 |      "data": {
380 |       "text/plain": [
381 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f114a7b9e90>"
382 |       ]
383 |      },
384 |      "execution_count": 8,
385 |      "metadata": {},
386 |      "output_type": "execute_result"
387 |     },
388 |     {
389 |      "data": {
390 |       "image/png": 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\n",
391 |       "text/plain": [
392 |        "<Figure size 432x288 with 1 Axes>"
393 |       ]
394 |      },
395 |      "metadata": {
396 |       "needs_background": "light"
397 |      },
398 |      "output_type": "display_data"
399 |     },
400 |     {
401 |      "data": {
402 |       "application/javascript": [
403 |        "\n",
404 |        "            setTimeout(function() {\n",
405 |        "                var nbb_cell_id = 8;\n",
406 |        "                var nbb_unformatted_code = \"df['Density'].plot()\";\n",
407 |        "                var nbb_formatted_code = \"df[\\\"Density\\\"].plot()\";\n",
408 |        "                var nbb_cells = Jupyter.notebook.get_cells();\n",
409 |        "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
410 |        "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
411 |        "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
412 |        "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
413 |        "                        }\n",
414 |        "                        break;\n",
415 |        "                    }\n",
416 |        "                }\n",
417 |        "            }, 500);\n",
418 |        "            "
419 |       ],
420 |       "text/plain": [
421 |        "<IPython.core.display.Javascript object>"
422 |       ]
423 |      },
424 |      "metadata": {},
425 |      "output_type": "display_data"
426 |     }
427 |    ],
428 |    "source": [
429 |     "df[\"Density\"].plot()"
430 |    ]
431 |   },
432 |   {
433 |    "cell_type": "code",
434 |    "execution_count": 9,
435 |    "metadata": {},
436 |    "outputs": [
437 |     {
438 |      "data": {
439 |       "application/javascript": [
440 |        "\n",
441 |        "            setTimeout(function() {\n",
442 |        "                var nbb_cell_id = 9;\n",
443 |        "                var nbb_unformatted_code = \"df['DensityScale'] = np.log10(df['Density'])\";\n",
444 |        "                var nbb_formatted_code = \"df[\\\"DensityScale\\\"] = np.log10(df[\\\"Density\\\"])\";\n",
445 |        "                var nbb_cells = Jupyter.notebook.get_cells();\n",
446 |        "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
447 |        "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
448 |        "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
449 |        "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
450 |        "                        }\n",
451 |        "                        break;\n",
452 |        "                    }\n",
453 |        "                }\n",
454 |        "            }, 500);\n",
455 |        "            "
456 |       ],
457 |       "text/plain": [
458 |        "<IPython.core.display.Javascript object>"
459 |       ]
460 |      },
461 |      "metadata": {},
462 |      "output_type": "display_data"
463 |     }
464 |    ],
465 |    "source": [
466 |     "df[\"DensityScale\"] = np.log10(df[\"Density\"])"
467 |    ]
468 |   },
469 |   {
470 |    "cell_type": "code",
471 |    "execution_count": 10,
472 |    "metadata": {},
473 |    "outputs": [
474 |     {
475 |      "data": {
476 |       "text/plain": [
477 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f1146bb1710>"
478 |       ]
479 |      },
480 |      "execution_count": 10,
481 |      "metadata": {},
482 |      "output_type": "execute_result"
483 |     },
484 |     {
485 |      "data": {
486 |       "image/png": 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\n",
487 |       "text/plain": [
488 |        "<Figure size 432x288 with 1 Axes>"
489 |       ]
490 |      },
491 |      "metadata": {
492 |       "needs_background": "light"
493 |      },
494 |      "output_type": "display_data"
495 |     },
496 |     {
497 |      "data": {
498 |       "application/javascript": [
499 |        "\n",
500 |        "            setTimeout(function() {\n",
501 |        "                var nbb_cell_id = 10;\n",
502 |        "                var nbb_unformatted_code = \"df['DensityScale'].plot()\";\n",
503 |        "                var nbb_formatted_code = \"df[\\\"DensityScale\\\"].plot()\";\n",
504 |        "                var nbb_cells = Jupyter.notebook.get_cells();\n",
505 |        "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
506 |        "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
507 |        "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
508 |        "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
509 |        "                        }\n",
510 |        "                        break;\n",
511 |        "                    }\n",
512 |        "                }\n",
513 |        "            }, 500);\n",
514 |        "            "
515 |       ],
516 |       "text/plain": [
517 |        "<IPython.core.display.Javascript object>"
518 |       ]
519 |      },
520 |      "metadata": {},
521 |      "output_type": "display_data"
522 |     }
523 |    ],
524 |    "source": [
525 |     "df[\"DensityScale\"].plot()"
526 |    ]
527 |   },
528 |   {
529 |    "cell_type": "code",
530 |    "execution_count": 22,
531 |    "metadata": {},
532 |    "outputs": [
533 |     {
534 |      "data": {
535 |       "application/javascript": [
536 |        "\n",
537 |        "            setTimeout(function() {\n",
538 |        "                var nbb_cell_id = 22;\n",
539 |        "                var nbb_unformatted_code = \"fig = px.choropleth(\\n    df,\\n    locations=\\\"id\\\",\\n    geojson=india_states,\\n    color=\\\"DensityScale\\\",\\n    hover_name=\\\"State or union territory\\\",\\n    hover_data=[\\\"Density\\\"],\\n    title=\\\"India Population Density\\\"\\n)\\nfig.update_geos(fitbounds=\\\"locations\\\", visible=False)\\nfig.show()\";\n",
540 |        "                var nbb_formatted_code = \"fig = px.choropleth(\\n    df,\\n    locations=\\\"id\\\",\\n    geojson=india_states,\\n    color=\\\"DensityScale\\\",\\n    hover_name=\\\"State or union territory\\\",\\n    hover_data=[\\\"Density\\\"],\\n    title=\\\"India Population Density\\\",\\n)\\nfig.update_geos(fitbounds=\\\"locations\\\", visible=False)\\nfig.show()\";\n",
541 |        "                var nbb_cells = Jupyter.notebook.get_cells();\n",
542 |        "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
543 |        "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
544 |        "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
545 |        "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
546 |        "                        }\n",
547 |        "                        break;\n",
548 |        "                    }\n",
549 |        "                }\n",
550 |        "            }, 500);\n",
551 |        "            "
552 |       ],
553 |       "text/plain": [
554 |        "<IPython.core.display.Javascript object>"
555 |       ]
556 |      },
557 |      "metadata": {},
558 |      "output_type": "display_data"
559 |     }
560 |    ],
561 |    "source": [
562 |     "fig = px.choropleth(\n",
563 |     "    df,\n",
564 |     "    locations=\"id\",\n",
565 |     "    geojson=india_states,\n",
566 |     "    color=\"DensityScale\",\n",
567 |     "    hover_name=\"State or union territory\",\n",
568 |     "    hover_data=[\"Density\"],\n",
569 |     "    title=\"India Population Density\",\n",
570 |     ")\n",
571 |     "fig.update_geos(fitbounds=\"locations\", visible=False)\n",
572 |     "fig.show()"
573 |    ]
574 |   },
575 |   {
576 |    "cell_type": "markdown",
577 |    "metadata": {},
578 |    "source": [
579 |     "![](https://i.imgur.com/WZWtu67.png)"
580 |    ]
581 |   },
582 |   {
583 |    "cell_type": "code",
584 |    "execution_count": null,
585 |    "metadata": {},
586 |    "outputs": [],
587 |    "source": []
588 |   },
589 |   {
590 |    "cell_type": "code",
591 |    "execution_count": 14,
592 |    "metadata": {},
593 |    "outputs": [
594 |     {
595 |      "data": {
596 |       "application/javascript": [
597 |        "\n",
598 |        "            setTimeout(function() {\n",
599 |        "                var nbb_cell_id = 14;\n",
600 |        "                var nbb_unformatted_code = \"fig = px.choropleth_mapbox(\\n    df,\\n    locations=\\\"id\\\",\\n    geojson=india_states,\\n    color=\\\"DensityScale\\\",\\n    hover_name=\\\"State or union territory\\\",\\n    hover_data=[\\\"Density\\\"],\\n    title=\\\"India Population Density\\\",\\n    mapbox_style=\\\"carto-positron\\\",\\n    center={\\\"lat\\\": 24, \\\"lon\\\": 78},\\n    zoom=3,\\n    opacity=0.5,\\n)\\nfig.show()\";\n",
601 |        "                var nbb_formatted_code = \"fig = px.choropleth_mapbox(\\n    df,\\n    locations=\\\"id\\\",\\n    geojson=india_states,\\n    color=\\\"DensityScale\\\",\\n    hover_name=\\\"State or union territory\\\",\\n    hover_data=[\\\"Density\\\"],\\n    title=\\\"India Population Density\\\",\\n    mapbox_style=\\\"carto-positron\\\",\\n    center={\\\"lat\\\": 24, \\\"lon\\\": 78},\\n    zoom=3,\\n    opacity=0.5,\\n)\\nfig.show()\";\n",
602 |        "                var nbb_cells = Jupyter.notebook.get_cells();\n",
603 |        "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
604 |        "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
605 |        "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
606 |        "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
607 |        "                        }\n",
608 |        "                        break;\n",
609 |        "                    }\n",
610 |        "                }\n",
611 |        "            }, 500);\n",
612 |        "            "
613 |       ],
614 |       "text/plain": [
615 |        "<IPython.core.display.Javascript object>"
616 |       ]
617 |      },
618 |      "metadata": {},
619 |      "output_type": "display_data"
620 |     }
621 |    ],
622 |    "source": [
623 |     "fig = px.choropleth_mapbox(\n",
624 |     "    df,\n",
625 |     "    locations=\"id\",\n",
626 |     "    geojson=india_states,\n",
627 |     "    color=\"DensityScale\",\n",
628 |     "    hover_name=\"State or union territory\",\n",
629 |     "    hover_data=[\"Density\"],\n",
630 |     "    title=\"India Population Density\",\n",
631 |     "    mapbox_style=\"carto-positron\",\n",
632 |     "    center={\"lat\": 24, \"lon\": 78},\n",
633 |     "    zoom=3,\n",
634 |     "    opacity=0.5,\n",
635 |     ")\n",
636 |     "fig.show()"
637 |    ]
638 |   },
639 |   {
640 |    "cell_type": "markdown",
641 |    "metadata": {},
642 |    "source": [
643 |     "![](https://i.imgur.com/az9x32O.png)"
644 |    ]
645 |   },
646 |   {
647 |    "cell_type": "code",
648 |    "execution_count": null,
649 |    "metadata": {},
650 |    "outputs": [],
651 |    "source": []
652 |   },
653 |   {
654 |    "cell_type": "code",
655 |    "execution_count": 15,
656 |    "metadata": {},
657 |    "outputs": [
658 |     {
659 |      "data": {
660 |       "application/javascript": [
661 |        "\n",
662 |        "            setTimeout(function() {\n",
663 |        "                var nbb_cell_id = 15;\n",
664 |        "                var nbb_unformatted_code = \"df['SexRatioScale'] = df['Sex ratio'] - 1000\";\n",
665 |        "                var nbb_formatted_code = \"df[\\\"SexRatioScale\\\"] = df[\\\"Sex ratio\\\"] - 1000\";\n",
666 |        "                var nbb_cells = Jupyter.notebook.get_cells();\n",
667 |        "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
668 |        "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
669 |        "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
670 |        "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
671 |        "                        }\n",
672 |        "                        break;\n",
673 |        "                    }\n",
674 |        "                }\n",
675 |        "            }, 500);\n",
676 |        "            "
677 |       ],
678 |       "text/plain": [
679 |        "<IPython.core.display.Javascript object>"
680 |       ]
681 |      },
682 |      "metadata": {},
683 |      "output_type": "display_data"
684 |     }
685 |    ],
686 |    "source": [
687 |     "df[\"SexRatioScale\"] = df[\"Sex ratio\"] - 1000"
688 |    ]
689 |   },
690 |   {
691 |    "cell_type": "code",
692 |    "execution_count": 18,
693 |    "metadata": {},
694 |    "outputs": [
695 |     {
696 |      "data": {
697 |       "application/javascript": [
698 |        "\n",
699 |        "            setTimeout(function() {\n",
700 |        "                var nbb_cell_id = 18;\n",
701 |        "                var nbb_unformatted_code = \"fig = px.choropleth(\\n    df,\\n    locations=\\\"id\\\",\\n    geojson=india_states,\\n    color=\\\"SexRatioScale\\\",\\n    hover_name=\\\"State or union territory\\\",\\n    hover_data=[\\\"Sex ratio\\\"],\\n    title='India Sex Ratio',\\n    color_continuous_scale=px.colors.diverging.BrBG,\\n    color_continuous_midpoint=0,\\n)\\nfig.update_geos(fitbounds=\\\"locations\\\", visible=False)\\nfig.show()\";\n",
702 |        "                var nbb_formatted_code = \"fig = px.choropleth(\\n    df,\\n    locations=\\\"id\\\",\\n    geojson=india_states,\\n    color=\\\"SexRatioScale\\\",\\n    hover_name=\\\"State or union territory\\\",\\n    hover_data=[\\\"Sex ratio\\\"],\\n    title=\\\"India Sex Ratio\\\",\\n    color_continuous_scale=px.colors.diverging.BrBG,\\n    color_continuous_midpoint=0,\\n)\\nfig.update_geos(fitbounds=\\\"locations\\\", visible=False)\\nfig.show()\";\n",
703 |        "                var nbb_cells = Jupyter.notebook.get_cells();\n",
704 |        "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
705 |        "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
706 |        "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
707 |        "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
708 |        "                        }\n",
709 |        "                        break;\n",
710 |        "                    }\n",
711 |        "                }\n",
712 |        "            }, 500);\n",
713 |        "            "
714 |       ],
715 |       "text/plain": [
716 |        "<IPython.core.display.Javascript object>"
717 |       ]
718 |      },
719 |      "metadata": {},
720 |      "output_type": "display_data"
721 |     }
722 |    ],
723 |    "source": [
724 |     "fig = px.choropleth(\n",
725 |     "    df,\n",
726 |     "    locations=\"id\",\n",
727 |     "    geojson=india_states,\n",
728 |     "    color=\"SexRatioScale\",\n",
729 |     "    hover_name=\"State or union territory\",\n",
730 |     "    hover_data=[\"Sex ratio\"],\n",
731 |     "    title=\"India Sex Ratio\",\n",
732 |     "    color_continuous_scale=px.colors.diverging.BrBG,\n",
733 |     "    color_continuous_midpoint=0,\n",
734 |     ")\n",
735 |     "fig.update_geos(fitbounds=\"locations\", visible=False)\n",
736 |     "fig.show()"
737 |    ]
738 |   },
739 |   {
740 |    "cell_type": "markdown",
741 |    "metadata": {},
742 |    "source": [
743 |     "![](https://i.imgur.com/0ZIBaR6.png)"
744 |    ]
745 |   },
746 |   {
747 |    "cell_type": "code",
748 |    "execution_count": null,
749 |    "metadata": {},
750 |    "outputs": [],
751 |    "source": []
752 |   },
753 |   {
754 |    "cell_type": "markdown",
755 |    "metadata": {},
756 |    "source": [
757 |     "## References\n",
758 |     "\n",
759 |     "- https://en.wikipedia.org/wiki/List_of_states_and_union_territories_of_India_by_population\n",
760 |     "\n",
761 |     "- https://un-mapped.carto.com/tables/states_india/public/map\n",
762 |     "\n",
763 |     "- https://plotly.com/python/choropleth-maps/\n",
764 |     "\n",
765 |     "- https://plotly.com/python/colorscales/\n",
766 |     "\n",
767 |     "- https://www.mapbox.com/\n",
768 |     "\n",
769 |     "- https://plotly.com/python/mapbox-county-choropleth/\n",
770 |     "\n",
771 |     "- https://plotly.com/python/mapbox-layers/"
772 |    ]
773 |   }
774 |  ],
775 |  "metadata": {
776 |   "kernelspec": {
777 |    "display_name": "pyenv37",
778 |    "language": "python",
779 |    "name": "pyenv37"
780 |   },
781 |   "language_info": {
782 |    "codemirror_mode": {
783 |     "name": "ipython",
784 |     "version": 3
785 |    },
786 |    "file_extension": ".py",
787 |    "mimetype": "text/x-python",
788 |    "name": "python",
789 |    "nbconvert_exporter": "python",
790 |    "pygments_lexer": "ipython3",
791 |    "version": "3.7.7"
792 |   }
793 |  },
794 |  "nbformat": 4,
795 |  "nbformat_minor": 2
796 | }
797 | 


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