Format: application/octet-stream
1 {
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": 1,
6 "id": "30bb2ec2",
7 "metadata": {},
8 "outputs": [],
9 "source": [
10 "import pandas as pd"
11 ]
12 },
13 {
14 "cell_type": "code",
15 "execution_count": 2,
16 "id": "a3f4d644",
17 "metadata": {},
18 "outputs": [],
19 "source": [
20 "df= pd.read_csv('question 1.csv')"
21 ]
22 },
23 {
24 "cell_type": "code",
25 "execution_count": 3,
26 "id": "2826add0",
27 "metadata": {},
28 "outputs": [
29 {
30 "name": "stdout",
31 "output_type": "stream",
32 "text": [
33 "<class 'pandas.core.frame.DataFrame'>\n",
34 "RangeIndex: 55 entries, 0 to 54\n",
35 "Data columns (total 12 columns):\n",
36 " # Column Non-Null Count Dtype\n",
37 "--- ------ -------------- -----\n",
38 " 0 Year 55 non-null int64\n",
39 " 1 Population 55 non-null int64\n",
40 " 2 Total 55 non-null int64\n",
41 " 3 Violent 55 non-null int64\n",
42 " 4 Property 55 non-null int64\n",
43 " 5 Murder 55 non-null int64\n",
44 " 6 Forcible_Rape 55 non-null int64\n",
45 " 7 Robbery 55 non-null int64\n",
46 " 8 Aggravated_assault 55 non-null int64\n",
47 " 9 Burglary 55 non-null int64\n",
48 " 10 Larceny_Theft 55 non-null int64\n",
49 " 11 Vehicle_Theft 55 non-null int64\n",
50 "dtypes: int64(12)\n",
51 "memory usage: 5.3 KB\n"
52 ]
53 }
54 ],
55 "source": [
56 "df.info()"
57 ]
58 },
59 {
60 "cell_type": "code",
61 "execution_count": 4,
62 "id": "844ac370",
63 "metadata": {},
64 "outputs": [
65 {
66 "data": {
67 "text/html": [
68 "<div>\n",
69 "<style scoped>\n",
70 " .dataframe tbody tr th:only-of-type {\n",
71 " vertical-align: middle;\n",
72 " }\n",
73 "\n",
74 " .dataframe tbody tr th {\n",
75 " vertical-align: top;\n",
76 " }\n",
77 "\n",
78 " .dataframe thead th {\n",
79 " text-align: right;\n",
80 " }\n",
81 "</style>\n",
82 "<table border=\"1\" class=\"dataframe\">\n",
83 " <thead>\n",
84 " <tr style=\"text-align: right;\">\n",
85 " <th></th>\n",
86 " <th>Year</th>\n",
87 " <th>Population</th>\n",
88 " <th>Total</th>\n",
89 " <th>Violent</th>\n",
90 " <th>Property</th>\n",
91 " <th>Murder</th>\n",
92 " <th>Forcible_Rape</th>\n",
93 " <th>Robbery</th>\n",
94 " <th>Aggravated_assault</th>\n",
95 " <th>Burglary</th>\n",
96 " <th>Larceny_Theft</th>\n",
97 " <th>Vehicle_Theft</th>\n",
98 " </tr>\n",
99 " <tr>\n",
100 " <th>Year</th>\n",
101 " <th></th>\n",
102 " <th></th>\n",
103 " <th></th>\n",
104 " <th></th>\n",
105 " <th></th>\n",
106 " <th></th>\n",
107 " <th></th>\n",
108 " <th></th>\n",
109 " <th></th>\n",
110 " <th></th>\n",
111 " <th></th>\n",
112 " <th></th>\n",
113 " </tr>\n",
114 " </thead>\n",
115 " <tbody>\n",
116 " <tr>\n",
117 " <th>1960</th>\n",
118 " <td>19645</td>\n",
119 " <td>1915053175</td>\n",
120 " <td>49295900</td>\n",
121 " <td>4134930</td>\n",
122 " <td>45160900</td>\n",
123 " <td>106180</td>\n",
124 " <td>236720</td>\n",
125 " <td>1633510</td>\n",
126 " <td>2158520</td>\n",
127 " <td>13321100</td>\n",
128 " <td>26547700</td>\n",
129 " <td>5292100</td>\n",
130 " </tr>\n",
131 " <tr>\n",
132 " <th>1970</th>\n",
133 " <td>19745</td>\n",
134 " <td>2121193298</td>\n",
135 " <td>100991600</td>\n",
136 " <td>9607930</td>\n",
137 " <td>91383800</td>\n",
138 " <td>192230</td>\n",
139 " <td>554570</td>\n",
140 " <td>4159020</td>\n",
141 " <td>4702120</td>\n",
142 " <td>28486000</td>\n",
143 " <td>53157800</td>\n",
144 " <td>9739900</td>\n",
145 " </tr>\n",
146 " <tr>\n",
147 " <th>1980</th>\n",
148 " <td>19845</td>\n",
149 " <td>2371370069</td>\n",
150 " <td>131123369</td>\n",
151 " <td>14074328</td>\n",
152 " <td>117048900</td>\n",
153 " <td>206439</td>\n",
154 " <td>865639</td>\n",
155 " <td>5383109</td>\n",
156 " <td>7619130</td>\n",
157 " <td>33073494</td>\n",
158 " <td>72040253</td>\n",
159 " <td>11935411</td>\n",
160 " </tr>\n",
161 " <tr>\n",
162 " <th>1990</th>\n",
163 " <td>19945</td>\n",
164 " <td>2612825258</td>\n",
165 " <td>136582146</td>\n",
166 " <td>17527048</td>\n",
167 " <td>119053499</td>\n",
168 " <td>211664</td>\n",
169 " <td>998827</td>\n",
170 " <td>5748930</td>\n",
171 " <td>10568963</td>\n",
172 " <td>26750015</td>\n",
173 " <td>77679366</td>\n",
174 " <td>14624418</td>\n",
175 " </tr>\n",
176 " <tr>\n",
177 " <th>2000</th>\n",
178 " <td>20045</td>\n",
179 " <td>2947969117</td>\n",
180 " <td>115012044</td>\n",
181 " <td>13968056</td>\n",
182 " <td>100944369</td>\n",
183 " <td>163068</td>\n",
184 " <td>922499</td>\n",
185 " <td>4230366</td>\n",
186 " <td>8652124</td>\n",
187 " <td>21565176</td>\n",
188 " <td>67970291</td>\n",
189 " <td>11412834</td>\n",
190 " </tr>\n",
191 " <tr>\n",
192 " <th>2010</th>\n",
193 " <td>10060</td>\n",
194 " <td>1570146307</td>\n",
195 " <td>50167967</td>\n",
196 " <td>6072017</td>\n",
197 " <td>44095950</td>\n",
198 " <td>72867</td>\n",
199 " <td>421059</td>\n",
200 " <td>1749809</td>\n",
201 " <td>3764142</td>\n",
202 " <td>10125170</td>\n",
203 " <td>30401698</td>\n",
204 " <td>3569080</td>\n",
205 " </tr>\n",
206 " </tbody>\n",
207 "</table>\n",
208 "</div>"
209 ],
210 "text/plain": [
211 " Year Population Total Violent Property Murder \\\n",
212 "Year \n",
213 "1960 19645 1915053175 49295900 4134930 45160900 106180 \n",
214 "1970 19745 2121193298 100991600 9607930 91383800 192230 \n",
215 "1980 19845 2371370069 131123369 14074328 117048900 206439 \n",
216 "1990 19945 2612825258 136582146 17527048 119053499 211664 \n",
217 "2000 20045 2947969117 115012044 13968056 100944369 163068 \n",
218 "2010 10060 1570146307 50167967 6072017 44095950 72867 \n",
219 "\n",
220 " Forcible_Rape Robbery Aggravated_assault Burglary Larceny_Theft \\\n",
221 "Year \n",
222 "1960 236720 1633510 2158520 13321100 26547700 \n",
223 "1970 554570 4159020 4702120 28486000 53157800 \n",
224 "1980 865639 5383109 7619130 33073494 72040253 \n",
225 "1990 998827 5748930 10568963 26750015 77679366 \n",
226 "2000 922499 4230366 8652124 21565176 67970291 \n",
227 "2010 421059 1749809 3764142 10125170 30401698 \n",
228 "\n",
229 " Vehicle_Theft \n",
230 "Year \n",
231 "1960 5292100 \n",
232 "1970 9739900 \n",
233 "1980 11935411 \n",
234 "1990 14624418 \n",
235 "2000 11412834 \n",
236 "2010 3569080 "
237 ]
238 },
239 "execution_count": 4,
240 "metadata": {},
241 "output_type": "execute_result"
242 }
243 ],
244 "source": [
245 "df=df.groupby((df.Year//10)*10).sum()\n",
246 "df"
247 ]
248 },
249 {
250 "cell_type": "code",
251 "execution_count": 5,
252 "id": "cbf11b46",
253 "metadata": {},
254 "outputs": [],
255 "source": [
256 "df.to_csv('answer1.csv')"
257 ]
258 },
259 {
260 "cell_type": "code",
261 "execution_count": null,
262 "id": "5a6f3e9a",
263 "metadata": {},
264 "outputs": [],
265 "source": []
266 }
267 ],
268 "metadata": {
269 "kernelspec": {
270 "display_name": "Python 3",
271 "language": "python",
272 "name": "python3"
273 },
274 "language_info": {
275 "codemirror_mode": {
276 "name": "ipython",
277 "version": 3
278 },
279 "file_extension": ".py",
280 "mimetype": "text/x-python",
281 "name": "python",
282 "nbconvert_exporter": "python",
283 "pygments_lexer": "ipython3",
284 "version": "3.8.8"
285 }
286 },
287 "nbformat": 4,
288 "nbformat_minor": 5
289 }
290