From 1dfbee8bb1c5a4e84e5bd22e1841f0252f62ac4c Mon Sep 17 00:00:00 2001 From: Ayxan Date: Mon, 23 May 2022 16:20:55 +0400 Subject: [PATCH] added bar graph --- app/main.py | 34 +- app/test.ipynb | 17566 ++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 17319 insertions(+), 281 deletions(-) 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": [ + "
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countrycontinentyearlifeExppopgdpPercapiso_alphaiso_num
0AfghanistanAsia195228.8018425333779.445314AFG4
1AfghanistanAsia195730.3329240934820.853030AFG4
2AfghanistanAsia196231.99710267083853.100710AFG4
3AfghanistanAsia196734.02011537966836.197138AFG4
4AfghanistanAsia197236.08813079460739.981106AFG4
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