{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AgeBoth_sexesMaleFemale
085+23.9738.3516.87
175-8410.4015.227.36
265-746.8210.933.57
355-645.709.502.35
445-545.259.141.73
535-445.429.421.50
625-344.898.431.36
715-244.006.381.34
\n", "
" ], "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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_age" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YearBoth_sexesMaleFemale
020193.97 [2.31 – 6.31]6.61 [3.83 – 10.48]1.48 [0.88 – 2.37]
120183.92 [2.31 – 6.16]6.52 [3.81 – 10.21]1.49 [0.9 – 2.36]
220173.91 [2.31 – 6.07]6.48 [3.8 – 10]1.53 [0.92 – 2.42]
320163.92 [2.33 – 6.01]6.51 [3.83 – 9.91]1.54 [0.94 – 2.4]
420153.99 [2.37 – 6.07]6.64 [3.91 – 10.03]1.56 [0.95 – 2.43]
520144.1 [2.43 – 6.18]6.83 [4 – 10.23]1.59 [0.98 – 2.46]
620134.19 [2.47 – 6.26]6.99 [4.05 – 10.36]1.63 [1.01 – 2.52]
720124.31 [2.53 – 6.37]7.12 [4.1 – 10.44]1.75 [1.09 – 2.66]
820114.44 [2.59 – 6.52]7.33 [4.16 – 10.68]1.8 [1.14 – 2.72]
920104.53 [2.61 – 6.6]7.44 [4.15 – 10.76]1.87 [1.2 – 2.79]
1020094.64 [2.65 – 6.71]7.62 [4.17 – 10.97]1.92 [1.25 – 2.83]
1120084.79 [2.72 – 6.83]7.86 [4.26 – 11.16]1.99 [1.31 – 2.89]
1220075 [2.81 – 7.04]8.17 [4.33 – 11.43]2.12 [1.42 – 3.05]
1320064.85 [2.84 – 6.72]7.98 [4.46 – 10.99]2.01 [1.36 – 2.85]
1420054.69 [2.84 – 6.39]7.7 [4.48 – 10.41]1.96 [1.35 – 2.76]
1520044.5 [2.82 – 6.05]7.43 [4.5 – 9.88]1.86 [1.29 – 2.59]
1620034.04 [2.63 – 5.38]6.65 [4.24 – 8.77]1.69 [1.19 – 2.35]
1720023.88 [2.64 – 5.16]6.46 [4.35 – 8.51]1.57 [1.11 – 2.17]
1820013.72 [2.62 – 4.94]6.21 [4.35 – 8.16]1.5 [1.06 – 2.06]
1920003.42 [2.52 – 4.66]5.76 [4.29 – 7.85]1.33 [0.94 – 1.83]
\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]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "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 }