Format: application/octet-stream
1 {
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": 1,
6 "id": "cd75b6ac",
7 "metadata": {},
8 "outputs": [],
9 "source": [
10 "import pandas as pd\n",
11 "import matplotlib.pyplot as plt\n"
12 ]
13 },
14 {
15 "cell_type": "code",
16 "execution_count": 2,
17 "id": "aa90abb9",
18 "metadata": {},
19 "outputs": [],
20 "source": [
21 "al= pd.read_csv('alloted.csv')\n",
22 "paid= pd.read_csv('paid.csv')"
23 ]
24 },
25 {
26 "cell_type": "code",
27 "execution_count": 23,
28 "id": "cffb9f9e",
29 "metadata": {},
30 "outputs": [],
31 "source": [
32 "a=pd.DataFrame(al.groupby('Product').sum().iloc[:,-1].sort_values(ascending=False).astype(int))\n",
33 "b= pd.DataFrame(al.groupby('Product').count().iloc[:,-1].sort_values(ascending=False).astype(int))\n",
34 "a=pd.merge(a,b, left_index=True, right_index=True)\n",
35 "a.to_csv('F.csv')"
36 ]
37 },
38 {
39 "cell_type": "code",
40 "execution_count": 17,
41 "id": "ac6d9933",
42 "metadata": {},
43 "outputs": [
44 {
45 "data": {
46 "text/plain": [
47 "Product\n",
48 "CA 465497605\n",
49 "TW 222507193\n",
50 "UT 36260589\n",
51 "CD 11344560\n",
52 "CT 3125076\n",
53 "TT 290151\n",
54 "CP 26382\n",
55 "Name: TOTAL OD ( OD + CHARGES ), dtype: int32"
56 ]
57 },
58 "execution_count": 17,
59 "metadata": {},
60 "output_type": "execute_result"
61 }
62 ],
63 "source": [
64 "al.groupby('Product').sum().iloc[:,-1].sort_values(ascending=False).astype(int)"
65 ]
66 },
67 {
68 "cell_type": "code",
69 "execution_count": null,
70 "id": "0d24669f",
71 "metadata": {},
72 "outputs": [],
73 "source": []
74 },
75 {
76 "cell_type": "code",
77 "execution_count": 4,
78 "id": "d4cfb8ff",
79 "metadata": {},
80 "outputs": [
81 {
82 "data": {
83 "text/html": [
84 "<div>\n",
85 "<style scoped>\n",
86 " .dataframe tbody tr th:only-of-type {\n",
87 " vertical-align: middle;\n",
88 " }\n",
89 "\n",
90 " .dataframe tbody tr th {\n",
91 " vertical-align: top;\n",
92 " }\n",
93 "\n",
94 " .dataframe thead th {\n",
95 " text-align: right;\n",
96 " }\n",
97 "</style>\n",
98 "<table border=\"1\" class=\"dataframe\">\n",
99 " <thead>\n",
100 " <tr style=\"text-align: right;\">\n",
101 " <th></th>\n",
102 " <th>AMOUNT COLLECTED</th>\n",
103 " </tr>\n",
104 " </thead>\n",
105 " <tbody>\n",
106 " <tr>\n",
107 " <th>0</th>\n",
108 " <td>2577.0</td>\n",
109 " </tr>\n",
110 " <tr>\n",
111 " <th>1</th>\n",
112 " <td>2165.0</td>\n",
113 " </tr>\n",
114 " <tr>\n",
115 " <th>2</th>\n",
116 " <td>5000.0</td>\n",
117 " </tr>\n",
118 " <tr>\n",
119 " <th>3</th>\n",
120 " <td>1825.0</td>\n",
121 " </tr>\n",
122 " <tr>\n",
123 " <th>4</th>\n",
124 " <td>2300.0</td>\n",
125 " </tr>\n",
126 " <tr>\n",
127 " <th>...</th>\n",
128 " <td>...</td>\n",
129 " </tr>\n",
130 " <tr>\n",
131 " <th>3661</th>\n",
132 " <td>2450.0</td>\n",
133 " </tr>\n",
134 " <tr>\n",
135 " <th>3662</th>\n",
136 " <td>150000.0</td>\n",
137 " </tr>\n",
138 " <tr>\n",
139 " <th>3663</th>\n",
140 " <td>3415.0</td>\n",
141 " </tr>\n",
142 " <tr>\n",
143 " <th>3664</th>\n",
144 " <td>4500.0</td>\n",
145 " </tr>\n",
146 " <tr>\n",
147 " <th>3665</th>\n",
148 " <td>3555.0</td>\n",
149 " </tr>\n",
150 " </tbody>\n",
151 "</table>\n",
152 "<p>3666 rows × 1 columns</p>\n",
153 "</div>"
154 ],
155 "text/plain": [
156 " AMOUNT COLLECTED\n",
157 "0 2577.0\n",
158 "1 2165.0\n",
159 "2 5000.0\n",
160 "3 1825.0\n",
161 "4 2300.0\n",
162 "... ...\n",
163 "3661 2450.0\n",
164 "3662 150000.0\n",
165 "3663 3415.0\n",
166 "3664 4500.0\n",
167 "3665 3555.0\n",
168 "\n",
169 "[3666 rows x 1 columns]"
170 ]
171 },
172 "execution_count": 4,
173 "metadata": {},
174 "output_type": "execute_result"
175 }
176 ],
177 "source": [
178 "paid"
179 ]
180 },
181 {
182 "cell_type": "code",
183 "execution_count": 27,
184 "id": "9b7ac5af",
185 "metadata": {},
186 "outputs": [
187 {
188 "ename": "ValueError",
189 "evalue": "No objects to concatenate",
190 "output_type": "error",
191 "traceback": [
192 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
193 "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
194 "\u001b[1;32m<ipython-input-27-daf84f6405d6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpaid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'object'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
195 "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdescribe\u001b[1;34m(self, percentiles, include, exclude, datetime_is_numeric)\u001b[0m\n\u001b[0;32m 10408\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10409\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m> 10410\u001b[1;33m \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mldesc\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10411\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10412\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
196 "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[0;32m 283\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mIndexes\u001b[0m \u001b[0mhave\u001b[0m \u001b[0moverlapping\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 284\u001b[0m \"\"\"\n\u001b[1;32m--> 285\u001b[1;33m op = _Concatenator(\n\u001b[0m\u001b[0;32m 286\u001b[0m \u001b[0mobjs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 287\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
197 "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 341\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"No objects to concatenate\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 344\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
198 "\u001b[1;31mValueError\u001b[0m: No objects to concatenate"
199 ]
200 }
201 ],
202 "source": [
203 "paid.describe(include=['object'])\n"
204 ]
205 },
206 {
207 "cell_type": "code",
208 "execution_count": 28,
209 "id": "2b4088fe",
210 "metadata": {},
211 "outputs": [
212 {
213 "data": {
214 "text/plain": [
215 "0.19757477768795473"
216 ]
217 },
218 "execution_count": 28,
219 "metadata": {},
220 "output_type": "execute_result"
221 }
222 ],
223 "source": [
224 "len(paid)/len(al)"
225 ]
226 },
227 {
228 "cell_type": "code",
229 "execution_count": 1,
230 "id": "61f6bf30",
231 "metadata": {},
232 "outputs": [],
233 "source": [
234 "a=[1,5,4]"
235 ]
236 },
237 {
238 "cell_type": "code",
239 "execution_count": 3,
240 "id": "0dbce46f",
241 "metadata": {},
242 "outputs": [],
243 "source": [
244 "a.sort()"
245 ]
246 },
247 {
248 "cell_type": "code",
249 "execution_count": 4,
250 "id": "b90271b2",
251 "metadata": {},
252 "outputs": [
253 {
254 "data": {
255 "text/plain": [
256 "[1, 4, 5]"
257 ]
258 },
259 "execution_count": 4,
260 "metadata": {},
261 "output_type": "execute_result"
262 }
263 ],
264 "source": [
265 "a"
266 ]
267 },
268 {
269 "cell_type": "code",
270 "execution_count": null,
271 "id": "0c785427",
272 "metadata": {},
273 "outputs": [],
274 "source": []
275 }
276 ],
277 "metadata": {
278 "kernelspec": {
279 "display_name": "Python 3",
280 "language": "python",
281 "name": "python3"
282 },
283 "language_info": {
284 "codemirror_mode": {
285 "name": "ipython",
286 "version": 3
287 },
288 "file_extension": ".py",
289 "mimetype": "text/x-python",
290 "name": "python",
291 "nbconvert_exporter": "python",
292 "pygments_lexer": "ipython3",
293 "version": "3.8.8"
294 }
295 },
296 "nbformat": 4,
297 "nbformat_minor": 5
298 }
299