commit
ebf9336f72
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.ipynb_checkpoints/
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@ -0,0 +1,296 @@
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{
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||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4d098df7-6998-4e10-99fd-5e68aa01a997",
|
||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"# data = \"data/data-3d9Ex.csv\"\n",
|
||||
"import plotly.express as px\n",
|
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"import pandas as pd\n",
|
||||
"import numpy as np"
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f49def17-f64f-46bf-a056-1d8a063ce3a6",
|
||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"df = pd.read_csv(\"data/data-3d9Ex.csv\")\n",
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"df = df.fillna(0)"
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]
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},
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||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "17975a6c-686e-43c3-a644-18004f74880b",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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"df.head()"
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]
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},
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{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "d41f39d0-36fb-45d4-8d34-bd9dfd9545ad",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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"fig = px.scatter_geo(df.head(), lat=\"Lat\", lon=\"Lon\", scope=\"europe\", fitbounds='locations',\n",
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" size=\"Lakosság\")\n",
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"fig.show(width=1000, height=800)"
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]
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||||
},
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||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a2940dc7-1221-4545-bcee-9eecf7b75fdb",
|
||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"import plotly.express as px\n",
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"\n",
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"colorscales = px.colors.named_colorscales()"
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||||
]
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||||
},
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||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "dc5fe5a3-ba73-49ca-8c3d-24c5404b4ad5",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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"import plotly.graph_objects as go\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"df['text'] = df['Name'] + ': ' + df['Elhunytak száma 100000 főre'].astype('str')\n",
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"tdf = df.head(100)\n",
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"\n",
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"\n",
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"fig = go.Figure(data=go.Scattergeo(\n",
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" #locationmode = 'USA-states',\n",
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" lon = tdf['Lon'],\n",
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" lat = tdf['Lat'],\n",
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" text = tdf['text'],\n",
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" mode = 'markers',\n",
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" marker = dict(\n",
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" size = 5,\n",
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" opacity = 0.6,\n",
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" #color_continuous_scale=px.colors.sequential.Viridis,\n",
|
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" #reversescale = True,\n",
|
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" #autocolorscale = False,\n",
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" symbol = 'square',\n",
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" line = dict(\n",
|
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" width=1,\n",
|
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" color='rgba(102, 102, 102)'\n",
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" ),\n",
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" cmin = 0,\n",
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" color = np.log(df['Elhunytak száma 100000 főre']),\n",
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" cmax = np.log(df['Elhunytak száma 100000 főre'].max()),\n",
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" colorbar_title=\"Population\"\n",
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" )))\n",
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"\n",
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"fig.update_layout(\n",
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" title = 'Halálesetek száma',\n",
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" height=1000,\n",
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" width=1200,\n",
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" geo = dict(\n",
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" fitbounds='locations',\n",
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" scope='europe',\n",
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" #projection_type='albers usa',\n",
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" showland = True,\n",
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" landcolor = \"rgb(250, 250, 250)\",\n",
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" subunitcolor = \"rgb(217, 217, 217)\",\n",
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" countrycolor = \"rgb(217, 217, 217)\",\n",
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" #countrywidth = 0.5,\n",
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" #subunitwidth = 0.5\n",
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" ),\n",
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" )\n",
|
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"fig.show()\n"
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]
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},
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||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "109c8725-0e01-4ad3-9185-813d5db115b7",
|
||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
||||
"import plotly.graph_objects as go\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"df['text'] = df['Name'] + ': ' + df['Elhuyntak száma'].astype('str')\n",
|
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"tdf = df[df['Elhuyntak száma']>0]#.head(100)\n",
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"\n",
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"\n",
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"fig = go.Figure(data=go.Scattergeo(\n",
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" #locationmode = 'USA-states',\n",
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" lon = tdf['Lon'],\n",
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" lat = tdf['Lat'],\n",
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" text = tdf['text'],\n",
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" mode = 'markers',\n",
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" marker = dict(\n",
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" size = np.log(tdf['Elhuyntak száma'])*5,\n",
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" opacity = 0.6,\n",
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" #color_continuous_scale=px.colors.sequential.Viridis,\n",
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" #reversescale = True,\n",
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" #autocolorscale = False,\n",
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" symbol = 'circle',\n",
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" line = dict(\n",
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" width=1,\n",
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" color='rgba(102, 102, 102)'\n",
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" ),\n",
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" cmin = 0,\n",
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" color = np.log(tdf['Elhuyntak száma']),\n",
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" cmax = np.log(tdf['Elhuyntak száma'].max()),\n",
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" colorbar_title=\"Population\"\n",
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" )))\n",
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"\n",
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"fig.update_layout(\n",
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" title = 'Halálesetek száma',\n",
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" height=1000,\n",
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" width=1200,\n",
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" geo = dict(\n",
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" fitbounds='locations',\n",
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" scope='europe',\n",
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" #projection_type='albers usa',\n",
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" showland = True,\n",
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" landcolor = \"rgb(250, 250, 250)\",\n",
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" subunitcolor = \"rgb(217, 217, 217)\",\n",
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" countrycolor = \"rgb(217, 217, 217)\",\n",
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" #countrywidth = 0.5,\n",
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" #subunitwidth = 0.5\n",
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" ),\n",
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" )\n",
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"fig.show()\n"
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]
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},
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{
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||||
"cell_type": "markdown",
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||||
"id": "bb6b0eda-51f1-4bf9-b161-487db05e4ebc",
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"metadata": {},
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"source": [
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"## Heti adatok"
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]
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},
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{
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"cell_type": "markdown",
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"id": "770e6ba1-e48a-4fde-aa77-5b898e9853e5",
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||||
"metadata": {},
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"source": [
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"### Letoltes\n",
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"https://atlo.team/koronaterkep/#megyeibovebb"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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||||
"id": "40f6a53f-c929-4b86-b355-20997ca6adbe",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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"url = \"https://docs.google.com/spreadsheets/d/1djH-yUHLPwuEExCjiXS__6-8W2Yp_msFvShpL4bBcuM/export?format=xlsx&gid=1283792994\"\n",
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"heti_df = pd.read_excel(url) "
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]
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},
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{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "2e89ed3a-3afe-42ee-b923-cd7e659ae9a8",
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||||
"metadata": {},
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||||
"outputs": [],
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"source": [
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"heti_df.head()"
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]
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},
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{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "7c319fee-1e1b-4b8a-bea0-690b5e3d67d3",
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||||
"metadata": {},
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||||
"outputs": [],
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"source": [
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"df['text'] = df['Name'] + ': ' + df['Elhuyntak száma'].astype('str')\n",
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"tdf = df[df['Elhuyntak száma']>0]#.head(100)\n",
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"\n",
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"\n",
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"fig = go.Figure(data=go.Scattergeo(\n",
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" #locationmode = 'USA-states',\n",
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" lon = tdf['Lon'],\n",
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" lat = tdf['Lat'],\n",
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" text = tdf['text'],\n",
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" mode = 'markers',\n",
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" marker = dict(\n",
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" size = np.log(tdf['Elhuyntak száma'])*5,\n",
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" opacity = 0.6,\n",
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" #color_continuous_scale=px.colors.sequential.Viridis,\n",
|
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" #reversescale = True,\n",
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" #autocolorscale = False,\n",
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" symbol = 'circle',\n",
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" line = dict(\n",
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" width=1,\n",
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" color='rgba(102, 102, 102)'\n",
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" ),\n",
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" cmin = 0,\n",
|
||||
" color = np.log(tdf['Elhuyntak száma']),\n",
|
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" cmax = np.log(tdf['Elhuyntak száma'].max()),\n",
|
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" colorbar_title=\"Population\"\n",
|
||||
" )))\n",
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"\n",
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"fig.update_layout(\n",
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" title = 'Halálesetek száma',\n",
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" height=1000,\n",
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" width=1200,\n",
|
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" geo = dict(\n",
|
||||
" fitbounds='locations',\n",
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" scope='europe',\n",
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" #projection_type='albers usa',\n",
|
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" showland = True,\n",
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" landcolor = \"rgb(250, 250, 250)\",\n",
|
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" subunitcolor = \"rgb(217, 217, 217)\",\n",
|
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" countrycolor = \"rgb(217, 217, 217)\",\n",
|
||||
" #countrywidth = 0.5,\n",
|
||||
" #subunitwidth = 0.5\n",
|
||||
" ),\n",
|
||||
" )\n",
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||||
"fig.show()"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "1a60442e-74fa-45a3-a50c-6808c52c7ca9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
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||||
],
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||||
"metadata": {
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||||
"kernelspec": {
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||||
"display_name": "Python 3 (ipykernel)",
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"language": "python",
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||||
"name": "python3"
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||||
},
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||||
"language_info": {
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||||
"codemirror_mode": {
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||||
"name": "ipython",
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||||
"version": 3
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||||
},
|
||||
"file_extension": ".py",
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||||
"mimetype": "text/x-python",
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"name": "python",
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||||
"nbconvert_exporter": "python",
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||||
"pygments_lexer": "ipython3",
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"version": "3.9.13"
|
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,286 @@
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{
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"cells": [
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{
|
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"cell_type": "code",
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"execution_count": null,
|
||||
"id": "2b8d4789-78b3-4f31-9207-4ba6fcf90ad4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# data = \"data/data-3d9Ex.csv\"\n",
|
||||
"import plotly.express as px\n",
|
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"import pandas as pd\n",
|
||||
"import geopandas as gpd\n",
|
||||
"import numpy as np\n",
|
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"import json\n",
|
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"\n",
|
||||
"colorscales = px.colors.named_colorscales()"
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||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9ef312bf-3c18-4417-806b-3d07830d96d3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Hungary counties shapefile\n",
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||||
"url = \"https://maps.princeton.edu/download/file/stanford-dt251rh6351-shapefile.zip\""
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]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c6822dbd-d897-445f-9743-f7267d66537c",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!cd data && wget https://maps.princeton.edu/download/file/stanford-dt251rh6351-shapefile.zip && unzip stanford-dt251rh6351-shapefile.zip"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "bb150ab8-aa94-4b76-849f-554f32a30fde",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# read shp data into geopandas\n",
|
||||
"gg = gpd.read_file(\"data/dt251rh6351.shp\")\n",
|
||||
"ggjson = gg.to_json()\n",
|
||||
"gg.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "63e6b826-8563-4c3e-bc81-6141bf6d08f3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"gg.iloc[1].geometry"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a29a6736-1cf5-485e-abcc-dd5970773d3d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# We will need gejson\n",
|
||||
"dggjson = json.loads(ggjson)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0bf4ed85-0f56-4ede-b75a-cbc7a8bdbd5f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Turns out one of the county's name is misspelled so we rename it for now in the dataframe\n",
|
||||
"[f['properties']['name_1'] for f in dggjson['features']]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "bb6b0eda-51f1-4bf9-b161-487db05e4ebc",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Heti adatok"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "770e6ba1-e48a-4fde-aa77-5b898e9853e5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Letoltes\n",
|
||||
"https://atlo.team/koronaterkep/#megyeibovebb"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7e5eeb9f-847c-4e73-8b73-9fc6a97ba56b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2/gis-files/hungary-shapefile\n",
|
||||
"url = \"https://docs.google.com/spreadsheets/d/1djH-yUHLPwuEExCjiXS__6-8W2Yp_msFvShpL4bBcuM/export?format=xlsx&gid=1283792994\"\n",
|
||||
"heti_df = pd.read_excel(url) \n",
|
||||
"\n",
|
||||
"heti_df['date'] = pd.to_datetime(heti_df['Dátum']) #, format='%y-%m-%d')\n",
|
||||
"heti_df = heti_df.drop(columns=['Dátum', 'Összesen'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2e89ed3a-3afe-42ee-b923-cd7e659ae9a8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"heti_df.head(7)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4290d58d-5dfc-46ee-afd7-c46148c62e7c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Rename Győr to Gyor\n",
|
||||
"heti_df = heti_df.rename(columns={'Győr-Moson-Sopron':'Gyor-Moson-Sopron'})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "33c27409-bebd-4cd6-a1a6-14935565e94b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import datetime as dt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "153128f5-3e5b-4176-aebf-5bf40ddb703f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"start_date = heti_df.loc[0,'date']\n",
|
||||
"end_date = start_date + dt.timedelta(days=7)\n",
|
||||
"\n",
|
||||
"mask = (heti_df['date'] >= start_date) & (heti_df['date'] < end_date)\n",
|
||||
"\n",
|
||||
"hhdf = heti_df.loc[mask].drop(columns=['date']).transpose()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d13aee1a-6702-44fb-8382-985ff582a012",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"hhdf['sum'] = hhdf.sum(axis=1)\n",
|
||||
"hhdf = hhdf.reset_index().rename(columns={'index':'name_1'})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8b4fe7f4-796b-4b1c-bcdf-6b440b4ef3f8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"hhdf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d50af1bc-b8d4-4fee-9992-f9939071945b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the mean of the county's centroids\n",
|
||||
"cc = gg.centroid\n",
|
||||
"\n",
|
||||
"clon = cc.apply(lambda x: x.x).mean()\n",
|
||||
"clat = cc.apply(lambda x: x.y).mean()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7c319fee-1e1b-4b8a-bea0-690b5e3d67d3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = px.choropleth_mapbox(hhdf.drop(columns=[0,1,2,3,4,5,6]), geojson=dggjson, locations='name_1', color='sum',\n",
|
||||
" color_continuous_scale=\"Viridis\",\n",
|
||||
" #locationmode='geojson-id',\n",
|
||||
" featureidkey='properties.name_1',\n",
|
||||
" range_color=(0, 1800),\n",
|
||||
" mapbox_style=\"carto-positron\",\n",
|
||||
" zoom=5.7, center = {\"lat\": clat, \"lon\": clon},\n",
|
||||
" opacity=0.5,\n",
|
||||
" labels={'sum':'Megfertozodesek szama'}\n",
|
||||
" )\n",
|
||||
"fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
|
||||
"fig.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6a757a20-9c59-4e31-b1db-bc187f1e3aee",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"thhdf = hhdf.drop(columns=[0,1,2,3,4,5,6])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a2294fa4-d611-40e7-a768-a39943b9020b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Using the built-in plotly choropleth maps\n",
|
||||
"This is not very decorative"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "76163eb4-4587-4561-9dc2-02a321a4e27f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = px.choropleth(thhdf, geojson=dggjson, locations='name_1',#, color='sum',\n",
|
||||
" color_continuous_scale=\"Viridis\",\n",
|
||||
" range_color=(0, 1800),\n",
|
||||
" locationmode='geojson-id',\n",
|
||||
" featureidkey='properties.name_1',\n",
|
||||
" color=thhdf['sum'],\n",
|
||||
" labels={'sum' : 'Megfertozodesek szama'}\n",
|
||||
" )\n",
|
||||
"fig.update_layout(margin={\"r\":0,\"t\":0,\"l\": 0,\"b\":0}, geo_scope='europe'\n",
|
||||
" )\n",
|
||||
"fig.layout.geo.center.lat = clat\n",
|
||||
"fig.layout.geo.center.lon = clon\n",
|
||||
"fig.layout.geo.projection.scale = 9\n",
|
||||
"fig.show()\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
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|
||||
ISO-8859-1
|
||||
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|
||||
GEOGCS["WGS 84", DATUM["World Geodetic System 1984", SPHEROID["WGS 84", 6378137.0, 298.257223563, AUTHORITY["EPSG","7030"]], AUTHORITY["EPSG","6326"]], PRIMEM["Greenwich", 0.0, AUTHORITY["EPSG","8901"]], UNIT["degree", 0.017453292519943295], AXIS["Geodetic longitude", EAST], AXIS["Geodetic latitude", NORTH], AUTHORITY["EPSG","4326"]]
|
||||
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|
||||
https://geowebservices.stanford.edu/geoserver/wfs?outputformat=SHAPE-ZIP&request=GetFeature&service=wfs&srsName=EPSG%3A4326&typeName=druid%3Adt251rh6351&version=2.0.0
|
||||
Loading…
Reference in new issue