diff --git a/app/main.py b/app/main.py
index 647bb970..52a281d9 100644
--- a/app/main.py
+++ b/app/main.py
@@ -43,20 +43,27 @@ df_line_graph = pd.DataFrame(
st.title('
Azerbaijan Suicide Data (2000-2019)
', 1)
st.dataframe(data_year_selection)
-st.title('Dashboard
', 1)
-st.subheader('Averages')
-st.markdown(f'#### Average of Both Sexes: _{round(average_both_sexes, 2)}_')
-st.markdown(f'#### Average of Male: _{round(average_male, 2)}_')
-st.markdown(f'#### Average of Female: _{round(average_female, 2)}_')
+st.title('Dashboard
', 1)
+dashboard_column1, dashboard_column2 = st.columns([1, 2])
+
+with dashboard_column1:
+ st.subheader('Averages')
+ st.markdown(f'#### Average of Both Sexes: _{round(average_both_sexes, 2)}_')
+ st.markdown(f'#### Average of Male: _{round(average_male, 2)}_')
+ st.markdown(f'#### Average of Female: _{round(average_female, 2)}_')
+
+with dashboard_column2:
+ if average_both_sexes > 0:
+ bar_graph = px.bar(x=['Both Sexes', 'Male', 'Female'], y=[average_both_sexes, average_male, average_female])
+ st.plotly_chart(bar_graph)
if len(df_line_graph) > 1:
st.title('Gender Line Graph
', 1)
- x, y = st.columns([1, 7])
- with x:
+ line_graph_column1, line_graph_column2 = st.columns([1, 7])
+ with line_graph_column1:
column_list = []
st.markdown(' ')
- # st.markdown(' ')
both_sexes_check_box = st.checkbox('Both Sexes', value=1)
male_check_box = st.checkbox('Male', value=1)
female_check_box = st.checkbox('Female', value=1)
@@ -64,8 +71,17 @@ if len(df_line_graph) > 1:
if male_check_box: column_list.append('Male')
if female_check_box: column_list.append('Female')
- with y:
+ with line_graph_column2:
if column_list:
line_graph = px.line(df_line_graph, x='Year', y=column_list)
st.plotly_chart(line_graph)
+
+hide_st_style = """
+
+"""
+st.markdown(hide_st_style, unsafe_allow_html=True)
\ No newline at end of file
diff --git a/app/test.ipynb b/app/test.ipynb
index bf82c661..2564cee2 100644
--- a/app/test.ipynb
+++ b/app/test.ipynb
@@ -2,17 +2,18 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
- "import plotly.express as px"
+ "import plotly.express as px\n",
+ "import plotly.graph_objects as go"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
@@ -22,136 +23,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "data_age"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "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]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 126,
- "metadata": {},
- "outputs": [],
- "source": [
- "df = pd.DataFrame({'Year': data_year.Year, 'Both sexes': test_both, 'Male': test_male, 'Female': test_female})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 124,
- "metadata": {},
- "outputs": [],
- "source": [
- "df = pd.DataFrame({'Year': [1], 'Both sexes': [2], 'Male': [3], 'Female': [4]})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 131,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['', 'Male', 'Female'], dtype='object')"
- ]
- },
- "execution_count": 131,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pd.Index(['Both sexes' if 0 else '', 'Male', 'Female'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 133,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "a\n"
- ]
- }
- ],
- "source": [
- "if [0]:\n",
- " print('a')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 118,
- "metadata": {},
- "outputs": [],
- "source": [
- "g = px.line(df, x='Year', y=df.columns[1:])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 119,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Figure({\n",
- " 'data': [],\n",
- " 'layout': {'legend': {'tracegroupgap': 0},\n",
- " 'margin': {'t': 60},\n",
- " 'template': '...',\n",
- " 'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'Year'}},\n",
- " 'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'value'}}}\n",
- "})\n"
- ]
- }
- ],
- "source": [
- "print(g)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 88,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "5.0"
- ]
- },
- "execution_count": 88,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "10 / (2 if 1 else 10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -175,7 +47,7 @@
" \n",
" \n",
" | \n",
- " Year | \n",
+ " Age | \n",
" Both_sexes | \n",
" Male | \n",
" Female | \n",
@@ -184,177 +56,17327 @@
"
\n",
" \n",
" 0 | \n",
- " 2019 | \n",
- " 3.97 [2.31 – 6.31] | \n",
- " 6.61 [3.83 – 10.48] | \n",
- " 1.48 [0.88 – 2.37] | \n",
+ " 85+ | \n",
+ " 23.97 | \n",
+ " 38.35 | \n",
+ " 16.87 | \n",
"
\n",
" \n",
" 1 | \n",
- " 2018 | \n",
- " 3.92 [2.31 – 6.16] | \n",
- " 6.52 [3.81 – 10.21] | \n",
- " 1.49 [0.9 – 2.36] | \n",
+ " 75-84 | \n",
+ " 10.40 | \n",
+ " 15.22 | \n",
+ " 7.36 | \n",
"
\n",
" \n",
" 2 | \n",
- " 2017 | \n",
- " 3.91 [2.31 – 6.07] | \n",
- " 6.48 [3.8 – 10] | \n",
- " 1.53 [0.92 – 2.42] | \n",
+ " 65-74 | \n",
+ " 6.82 | \n",
+ " 10.93 | \n",
+ " 3.57 | \n",
"
\n",
" \n",
" 3 | \n",
- " 2016 | \n",
- " 3.92 [2.33 – 6.01] | \n",
- " 6.51 [3.83 – 9.91] | \n",
- " 1.54 [0.94 – 2.4] | \n",
+ " 55-64 | \n",
+ " 5.70 | \n",
+ " 9.50 | \n",
+ " 2.35 | \n",
"
\n",
" \n",
" 4 | \n",
- " 2015 | \n",
- " 3.99 [2.37 – 6.07] | \n",
- " 6.64 [3.91 – 10.03] | \n",
- " 1.56 [0.95 – 2.43] | \n",
+ " 45-54 | \n",
+ " 5.25 | \n",
+ " 9.14 | \n",
+ " 1.73 | \n",
"
\n",
" \n",
" 5 | \n",
- " 2014 | \n",
- " 4.1 [2.43 – 6.18] | \n",
- " 6.83 [4 – 10.23] | \n",
- " 1.59 [0.98 – 2.46] | \n",
+ " 35-44 | \n",
+ " 5.42 | \n",
+ " 9.42 | \n",
+ " 1.50 | \n",
"
\n",
" \n",
" 6 | \n",
- " 2013 | \n",
- " 4.19 [2.47 – 6.26] | \n",
- " 6.99 [4.05 – 10.36] | \n",
- " 1.63 [1.01 – 2.52] | \n",
+ " 25-34 | \n",
+ " 4.89 | \n",
+ " 8.43 | \n",
+ " 1.36 | \n",
"
\n",
" \n",
" 7 | \n",
- " 2012 | \n",
- " 4.31 [2.53 – 6.37] | \n",
- " 7.12 [4.1 – 10.44] | \n",
- " 1.75 [1.09 – 2.66] | \n",
- "
\n",
- " \n",
- " 8 | \n",
- " 2011 | \n",
- " 4.44 [2.59 – 6.52] | \n",
- " 7.33 [4.16 – 10.68] | \n",
- " 1.8 [1.14 – 2.72] | \n",
- "
\n",
- " \n",
- " 9 | \n",
- " 2010 | \n",
- " 4.53 [2.61 – 6.6] | \n",
- " 7.44 [4.15 – 10.76] | \n",
- " 1.87 [1.2 – 2.79] | \n",
- "
\n",
- " \n",
- " 10 | \n",
- " 2009 | \n",
- " 4.64 [2.65 – 6.71] | \n",
- " 7.62 [4.17 – 10.97] | \n",
- " 1.92 [1.25 – 2.83] | \n",
- "
\n",
- " \n",
- " 11 | \n",
- " 2008 | \n",
- " 4.79 [2.72 – 6.83] | \n",
- " 7.86 [4.26 – 11.16] | \n",
- " 1.99 [1.31 – 2.89] | \n",
- "
\n",
- " \n",
- " 12 | \n",
- " 2007 | \n",
- " 5 [2.81 – 7.04] | \n",
- " 8.17 [4.33 – 11.43] | \n",
- " 2.12 [1.42 – 3.05] | \n",
- "
\n",
- " \n",
- " 13 | \n",
- " 2006 | \n",
- " 4.85 [2.84 – 6.72] | \n",
- " 7.98 [4.46 – 10.99] | \n",
- " 2.01 [1.36 – 2.85] | \n",
- "
\n",
- " \n",
- " 14 | \n",
- " 2005 | \n",
- " 4.69 [2.84 – 6.39] | \n",
- " 7.7 [4.48 – 10.41] | \n",
- " 1.96 [1.35 – 2.76] | \n",
- "
\n",
- " \n",
- " 15 | \n",
- " 2004 | \n",
- " 4.5 [2.82 – 6.05] | \n",
- " 7.43 [4.5 – 9.88] | \n",
- " 1.86 [1.29 – 2.59] | \n",
- "
\n",
- " \n",
- " 16 | \n",
- " 2003 | \n",
- " 4.04 [2.63 – 5.38] | \n",
- " 6.65 [4.24 – 8.77] | \n",
- " 1.69 [1.19 – 2.35] | \n",
- "
\n",
- " \n",
- " 17 | \n",
- " 2002 | \n",
- " 3.88 [2.64 – 5.16] | \n",
- " 6.46 [4.35 – 8.51] | \n",
- " 1.57 [1.11 – 2.17] | \n",
- "
\n",
- " \n",
- " 18 | \n",
- " 2001 | \n",
- " 3.72 [2.62 – 4.94] | \n",
- " 6.21 [4.35 – 8.16] | \n",
- " 1.5 [1.06 – 2.06] | \n",
- "
\n",
- " \n",
- " 19 | \n",
- " 2000 | \n",
- " 3.42 [2.52 – 4.66] | \n",
- " 5.76 [4.29 – 7.85] | \n",
- " 1.33 [0.94 – 1.83] | \n",
+ " 15-24 | \n",
+ " 4.00 | \n",
+ " 6.38 | \n",
+ " 1.34 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " Year Both_sexes Male Female\n",
- "0 2019 3.97 [2.31 – 6.31] 6.61 [3.83 – 10.48] 1.48 [0.88 – 2.37]\n",
- "1 2018 3.92 [2.31 – 6.16] 6.52 [3.81 – 10.21] 1.49 [0.9 – 2.36]\n",
- "2 2017 3.91 [2.31 – 6.07] 6.48 [3.8 – 10] 1.53 [0.92 – 2.42]\n",
- "3 2016 3.92 [2.33 – 6.01] 6.51 [3.83 – 9.91] 1.54 [0.94 – 2.4]\n",
- "4 2015 3.99 [2.37 – 6.07] 6.64 [3.91 – 10.03] 1.56 [0.95 – 2.43]\n",
- "5 2014 4.1 [2.43 – 6.18] 6.83 [4 – 10.23] 1.59 [0.98 – 2.46]\n",
- "6 2013 4.19 [2.47 – 6.26] 6.99 [4.05 – 10.36] 1.63 [1.01 – 2.52]\n",
- "7 2012 4.31 [2.53 – 6.37] 7.12 [4.1 – 10.44] 1.75 [1.09 – 2.66]\n",
- "8 2011 4.44 [2.59 – 6.52] 7.33 [4.16 – 10.68] 1.8 [1.14 – 2.72]\n",
- "9 2010 4.53 [2.61 – 6.6] 7.44 [4.15 – 10.76] 1.87 [1.2 – 2.79]\n",
- "10 2009 4.64 [2.65 – 6.71] 7.62 [4.17 – 10.97] 1.92 [1.25 – 2.83]\n",
- "11 2008 4.79 [2.72 – 6.83] 7.86 [4.26 – 11.16] 1.99 [1.31 – 2.89]\n",
- "12 2007 5 [2.81 – 7.04] 8.17 [4.33 – 11.43] 2.12 [1.42 – 3.05]\n",
- "13 2006 4.85 [2.84 – 6.72] 7.98 [4.46 – 10.99] 2.01 [1.36 – 2.85]\n",
- "14 2005 4.69 [2.84 – 6.39] 7.7 [4.48 – 10.41] 1.96 [1.35 – 2.76]\n",
- "15 2004 4.5 [2.82 – 6.05] 7.43 [4.5 – 9.88] 1.86 [1.29 – 2.59]\n",
- "16 2003 4.04 [2.63 – 5.38] 6.65 [4.24 – 8.77] 1.69 [1.19 – 2.35]\n",
- "17 2002 3.88 [2.64 – 5.16] 6.46 [4.35 – 8.51] 1.57 [1.11 – 2.17]\n",
- "18 2001 3.72 [2.62 – 4.94] 6.21 [4.35 – 8.16] 1.5 [1.06 – 2.06]\n",
- "19 2000 3.42 [2.52 – 4.66] 5.76 [4.29 – 7.85] 1.33 [0.94 – 1.83]"
+ " 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": 4,
+ "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": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " country | \n",
+ " continent | \n",
+ " year | \n",
+ " lifeExp | \n",
+ " pop | \n",
+ " gdpPercap | \n",
+ " iso_alpha | \n",
+ " iso_num | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " Afghanistan | \n",
+ " Asia | \n",
+ " 1952 | \n",
+ " 28.801 | \n",
+ " 8425333 | \n",
+ " 779.445314 | \n",
+ " AFG | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " Afghanistan | \n",
+ " Asia | \n",
+ " 1957 | \n",
+ " 30.332 | \n",
+ " 9240934 | \n",
+ " 820.853030 | \n",
+ " AFG | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " Afghanistan | \n",
+ " Asia | \n",
+ " 1962 | \n",
+ " 31.997 | \n",
+ " 10267083 | \n",
+ " 853.100710 | \n",
+ " AFG | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " Afghanistan | \n",
+ " Asia | \n",
+ " 1967 | \n",
+ " 34.020 | \n",
+ " 11537966 | \n",
+ " 836.197138 | \n",
+ " AFG | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " Afghanistan | \n",
+ " Asia | \n",
+ " 1972 | \n",
+ " 36.088 | \n",
+ " 13079460 | \n",
+ " 739.981106 | \n",
+ " AFG | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 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 "
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "px.scatter(df, x=\"gdpPercap\", y=\"lifeExp\", animation_frame=\"year\", animation_group=\"country\",\n",
+ " size=\"pop\", color=\"continent\", hover_name=\"country\",\n",
+ " log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "hovertemplate": "%{hovertext}
continent=Asia
year=1952
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}",
+ "hovertext": [
+ "Afghanistan",
+ "Bahrain",
+ "Bangladesh",
+ "Cambodia",
+ "China",
+ "Hong Kong, China",
+ "India",
+ "Indonesia",
+ "Iran",
+ "Iraq",
+ "Israel",
+ "Japan",
+ "Jordan",
+ "Korea, Dem. Rep.",
+ "Korea, Rep.",
+ "Kuwait",
+ "Lebanon",
+ "Malaysia",
+ "Mongolia",
+ "Myanmar",
+ "Nepal",
+ "Oman",
+ "Pakistan",
+ "Philippines",
+ "Saudi Arabia",
+ "Singapore",
+ "Sri Lanka",
+ "Syria",
+ "Taiwan",
+ "Thailand",
+ "Vietnam",
+ "West Bank and Gaza",
+ "Yemen, Rep."
+ ],
+ "ids": [
+ "Afghanistan",
+ "Bahrain",
+ "Bangladesh",
+ "Cambodia",
+ "China",
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+ "Israel",
+ "Japan",
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+ "Kuwait",
+ "Lebanon",
+ "Malaysia",
+ "Mongolia",
+ "Myanmar",
+ "Nepal",
+ "Oman",
+ "Pakistan",
+ "Philippines",
+ "Saudi Arabia",
+ "Singapore",
+ "Sri Lanka",
+ "Syria",
+ "Taiwan",
+ "Thailand",
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+ "Yemen, Rep."
+ ],
+ "legendgroup": "Asia",
+ "marker": {
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+ }
+ },
+ "title": {
+ "text": "A Figure Displaying Itself"
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+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ ],
+ "source": [
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+ " layout_title_text=\"A Figure Displaying Itself\"\n",
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+ ],
+ "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"
]