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AzSuicideDataVisualization/app/test.ipynb
Ayxan 1dfbee8bb1 added bar graph
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{
"cells": [
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"data_age = pd.read_excel('../data/azerbaijan_suicide_data.xlsx', sheet_name='age')\n",
"data_year = pd.read_excel('../data/azerbaijan_suicide_data.xlsx', sheet_name='year')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Both_sexes</th>\n",
" <th>Male</th>\n",
" <th>Female</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>85+</td>\n",
" <td>23.97</td>\n",
" <td>38.35</td>\n",
" <td>16.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>75-84</td>\n",
" <td>10.40</td>\n",
" <td>15.22</td>\n",
" <td>7.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>65-74</td>\n",
" <td>6.82</td>\n",
" <td>10.93</td>\n",
" <td>3.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>55-64</td>\n",
" <td>5.70</td>\n",
" <td>9.50</td>\n",
" <td>2.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>45-54</td>\n",
" <td>5.25</td>\n",
" <td>9.14</td>\n",
" <td>1.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>35-44</td>\n",
" <td>5.42</td>\n",
" <td>9.42</td>\n",
" <td>1.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>25-34</td>\n",
" <td>4.89</td>\n",
" <td>8.43</td>\n",
" <td>1.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>15-24</td>\n",
" <td>4.00</td>\n",
" <td>6.38</td>\n",
" <td>1.34</td>\n",
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"text/plain": [
" Age Both_sexes Male Female\n",
"0 85+ 23.97 38.35 16.87\n",
"1 75-84 10.40 15.22 7.36\n",
"2 65-74 6.82 10.93 3.57\n",
"3 55-64 5.70 9.50 2.35\n",
"4 45-54 5.25 9.14 1.73\n",
"5 35-44 5.42 9.42 1.50\n",
"6 25-34 4.89 8.43 1.36\n",
"7 15-24 4.00 6.38 1.34"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_age"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"test_male = [float(d[:d.find(' ')]) for d in data_year.Male]\n",
"test_both = [float(d[:d.find(' ')]) for d in data_year.Both_sexes]\n",
"test_female = [float(d[:d.find(' ')]) for d in data_year.Female]\n",
"df = pd.DataFrame({'Year': data_year.Year, 'Both sexes': test_both, 'Male': test_male, 'Female': test_female})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"both_sexes = test_both\n",
"both_sexes_len = len(both_sexes)\n",
"average_both_sexes = sum(both_sexes) / (both_sexes_len if both_sexes_len else 1)\n",
"male = test_male\n",
"male_len = len(male)\n",
"average_male = sum(male) / (male_len if male_len else 1)\n",
"female = test_female\n",
"female_len = len(female)\n",
"average_female = sum(female) / (female_len if female_len else 1)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
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" <th>lifeExp</th>\n",
" <th>pop</th>\n",
" <th>gdpPercap</th>\n",
" <th>iso_alpha</th>\n",
" <th>iso_num</th>\n",
" </tr>\n",
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" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>1952</td>\n",
" <td>28.801</td>\n",
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" <th>1</th>\n",
" <td>Afghanistan</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>1962</td>\n",
" <td>31.997</td>\n",
" <td>10267083</td>\n",
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" <td>AFG</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>1967</td>\n",
" <td>34.020</td>\n",
" <td>11537966</td>\n",
" <td>836.197138</td>\n",
" <td>AFG</td>\n",
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" <th>4</th>\n",
" <td>Afghanistan</td>\n",
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" country continent year lifeExp pop gdpPercap iso_alpha \\\n",
"0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n",
"1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n",
"2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n",
"3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n",
"4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n",
"\n",
" iso_num \n",
"0 4 \n",
"1 4 \n",
"2 4 \n",
"3 4 \n",
"4 4 "
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"source": [
"df.head()"
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"px.scatter(df, x=\"gdpPercap\", y=\"lifeExp\", animation_frame=\"year\", animation_group=\"country\",\n",
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},
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}
},
"title": {
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}
}
}
},
"metadata": {},
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}
],
"source": [
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" data=[go.Bar(y=[average_both_sexes, average_male, average_female])],\n",
" layout_title_text=\"A Figure Displaying Itself\"\n",
")"
]
},
{
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],
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}
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{
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1,
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}
],
"histogram": [
{
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"size": 10,
"solidity": 0.2
}
},
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}
],
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{
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[
0.8888888888888888,
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[
1,
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],
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}
],
"mesh3d": [
{
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"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
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}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
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}
},
"marker": {
"colorbar": {
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}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
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}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
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}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
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}
},
"type": "scattergl"
}
],
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{
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}
},
"type": "scattermapbox"
}
],
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{
"marker": {
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}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
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}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
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}
},
"type": "scatterternary"
}
],
"surface": [
{
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},
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],
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],
[
0.8888888888888888,
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],
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1,
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]
],
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}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
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}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
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0.1,
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],
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0.2,
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],
[
0.3,
"#f1b6da"
],
[
0.4,
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],
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0.5,
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],
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0.6,
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],
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0.7,
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0.8,
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],
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0.9,
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1,
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],
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],
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],
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1,
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],
"sequentialminus": [
[
0,
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],
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],
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],
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],
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0.8888888888888888,
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],
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1,
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]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"xaxis": {
"anchor": "y",
"domain": [
0,
1
],
"title": {
"text": "Year"
}
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"title": {
"text": "value"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"px.bar(df, x='Year', y=['Both sexes', 'Male', 'Female'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(g)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"10 / (2 if 1 else 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_year"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "e8c5b4b062ec146843a4e9257c4b55bed920629d075dd594dd6a143d7c4ec2fe"
},
"kernelspec": {
"display_name": "Python 3.10.3 64-bit",
"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": "3.10.3"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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