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conclusions-quiz-solutions.ipynb.txt
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Drawing Conclusions Quiz\n",
"Use the space below to explore `store_data.csv` to answer the quiz questions below."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>storeA</th>\n",
" <th>storeB</th>\n",
" <th>storeC</th>\n",
" <th>storeD</th>\n",
" <th>storeE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-05-04</td>\n",
" <td>2643</td>\n",
" <td>8257</td>\n",
" <td>3893</td>\n",
" <td>6231</td>\n",
" <td>1294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014-05-11</td>\n",
" <td>6444</td>\n",
" <td>5736</td>\n",
" <td>5634</td>\n",
" <td>7092</td>\n",
" <td>2907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-05-18</td>\n",
" <td>9646</td>\n",
" <td>2552</td>\n",
" <td>4253</td>\n",
" <td>5447</td>\n",
" <td>4736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014-05-25</td>\n",
" <td>5960</td>\n",
" <td>10740</td>\n",
" <td>8264</td>\n",
" <td>6063</td>\n",
" <td>949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-06-01</td>\n",
" <td>7412</td>\n",
" <td>7374</td>\n",
" <td>3208</td>\n",
" <td>3985</td>\n",
" <td>3023</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" week storeA storeB storeC storeD storeE\n",
"0 2014-05-04 2643 8257 3893 6231 1294\n",
"1 2014-05-11 6444 5736 5634 7092 2907\n",
"2 2014-05-18 9646 2552 4253 5447 4736\n",
"3 2014-05-25 5960 10740 8264 6063 949\n",
"4 2014-06-01 7412 7374 3208 3985 3023"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# imports and load data\n",
"import pandas as pd\n",
"% matplotlib inline\n",
"\n",
"df = pd.read_csv('store_data.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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kqWUmY0mSWrZoMk7ykSQHknytq+zoJNcl2dP8PmqwYUpaLbZ5afX1cmX8UeDV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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f730864fe80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# explore data\n",
"df.hist(figsize=(8, 8));"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>storeA</th>\n",
" <th>storeB</th>\n",
" <th>storeC</th>\n",
" <th>storeD</th>\n",
" <th>storeE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>2017-10-15</td>\n",
" <td>8556</td>\n",
" <td>11984</td>\n",
" <td>4792</td>\n",
" <td>5995</td>\n",
" <td>2508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>2017-10-22</td>\n",
" <td>3751</td>\n",
" <td>697</td>\n",
" <td>3990</td>\n",
" <td>4236</td>\n",
" <td>360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>2017-10-29</td>\n",
" <td>4997</td>\n",
" <td>9759</td>\n",
" <td>4290</td>\n",
" <td>4568</td>\n",
" <td>2393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>2017-11-05</td>\n",
" <td>12785</td>\n",
" <td>1800</td>\n",
" <td>6163</td>\n",
" <td>5157</td>\n",
" <td>578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>2017-11-12</td>\n",
" <td>137</td>\n",
" <td>12261</td>\n",
" <td>5455</td>\n",
" <td>7695</td>\n",
" <td>2599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>2017-11-19</td>\n",
" <td>9960</td>\n",
" <td>8529</td>\n",
" <td>4501</td>\n",
" <td>7631</td>\n",
" <td>505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>2017-11-26</td>\n",
" <td>6866</td>\n",
" <td>5011</td>\n",
" <td>5401</td>\n",
" <td>4736</td>\n",
" <td>3232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>2017-12-03</td>\n",
" <td>5179</td>\n",
" <td>3850</td>\n",
" <td>6121</td>\n",
" <td>6778</td>\n",
" <td>113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td>2017-12-10</td>\n",
" <td>9348</td>\n",
" <td>5624</td>\n",
" <td>5446</td>\n",
" <td>5448</td>\n",
" <td>227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td>2017-12-17</td>\n",
" <td>5310</td>\n",
" <td>8647</td>\n",
" <td>5680</td>\n",
" <td>7049</td>\n",
" <td>3578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>2017-12-24</td>\n",
" <td>8976</td>\n",
" <td>9503</td>\n",
" <td>6240</td>\n",
" <td>3882</td>\n",
" <td>2890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>2017-12-31</td>\n",
" <td>11875</td>\n",
" <td>1527</td>\n",
" <td>6711</td>\n",
" <td>5265</td>\n",
" <td>1701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>2018-01-07</td>\n",
" <td>8978</td>\n",
" <td>11312</td>\n",
" <td>4158</td>\n",
" <td>5019</td>\n",
" <td>3842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>2018-01-14</td>\n",
" <td>6963</td>\n",
" <td>4014</td>\n",
" <td>4215</td>\n",
" <td>7153</td>\n",
" <td>3097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>2018-01-21</td>\n",
" <td>5553</td>\n",
" <td>3971</td>\n",
" <td>3761</td>\n",
" <td>6255</td>\n",
" <td>3071</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td>2018-01-28</td>\n",
" <td>282</td>\n",
" <td>6351</td>\n",
" <td>7759</td>\n",
" <td>5558</td>\n",
" <td>1028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>196</th>\n",
" <td>2018-02-04</td>\n",
" <td>4853</td>\n",
" <td>6503</td>\n",
" <td>4187</td>\n",
" <td>5956</td>\n",
" <td>1458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>197</th>\n",
" <td>2018-02-11</td>\n",
" <td>9202</td>\n",
" <td>3677</td>\n",
" <td>4540</td>\n",
" <td>6186</td>\n",
" <td>243</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>2018-02-18</td>\n",
" <td>3512</td>\n",
" <td>7511</td>\n",
" <td>4151</td>\n",
" <td>5596</td>\n",
" <td>3501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>2018-02-25</td>\n",
" <td>7560</td>\n",
" <td>6904</td>\n",
" <td>3569</td>\n",
" <td>5045</td>\n",
" <td>2585</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" week storeA storeB storeC storeD storeE\n",
"180 2017-10-15 8556 11984 4792 5995 2508\n",
"181 2017-10-22 3751 697 3990 4236 360\n",
"182 2017-10-29 4997 9759 4290 4568 2393\n",
"183 2017-11-05 12785 1800 6163 5157 578\n",
"184 2017-11-12 137 12261 5455 7695 2599\n",
"185 2017-11-19 9960 8529 4501 7631 505\n",
"186 2017-11-26 6866 5011 5401 4736 3232\n",
"187 2017-12-03 5179 3850 6121 6778 113\n",
"188 2017-12-10 9348 5624 5446 5448 227\n",
"189 2017-12-17 5310 8647 5680 7049 3578\n",
"190 2017-12-24 8976 9503 6240 3882 2890\n",
"191 2017-12-31 11875 1527 6711 5265 1701\n",
"192 2018-01-07 8978 11312 4158 5019 3842\n",
"193 2018-01-14 6963 4014 4215 7153 3097\n",
"194 2018-01-21 5553 3971 3761 6255 3071\n",
"195 2018-01-28 282 6351 7759 5558 1028\n",
"196 2018-02-04 4853 6503 4187 5956 1458\n",
"197 2018-02-11 9202 3677 4540 6186 243\n",
"198 2018-02-18 3512 7511 4151 5596 3501\n",
"199 2018-02-25 7560 6904 3569 5045 2585"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail(20)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"storeA 25127\n",
"storeB 24595\n",
"storeC 16447\n",
"storeD 22783\n",
"storeE 7787\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# total sales for the last month\n",
"df.iloc[196:, 1:].sum()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"storeA 5865.480\n",
"storeB 6756.710\n",
"storeC 4942.105\n",
"storeD 5431.405\n",
"storeE 2580.025\n",
"dtype: float64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# average sales\n",
"df.mean()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>storeA</th>\n",
" <th>storeB</th>\n",
" <th>storeC</th>\n",
" <th>storeD</th>\n",
" <th>storeE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>2016-03-13</td>\n",
" <td>2054</td>\n",
" <td>1390</td>\n",
" <td>5112</td>\n",
" <td>5513</td>\n",
" <td>2536</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" week storeA storeB storeC storeD storeE\n",
"97 2016-03-13 2054 1390 5112 5513 2536"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sales on march 13, 2016\n",
"df[df['week'] == '2016-03-13']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>storeA</th>\n",
" <th>storeB</th>\n",
" <th>storeC</th>\n",
" <th>storeD</th>\n",
" <th>storeE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2014-07-06</td>\n",
" <td>8567</td>\n",
" <td>3228</td>\n",
" <td>927</td>\n",
" <td>3277</td>\n",
" <td>168</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" week storeA storeB storeC storeD storeE\n",
"9 2014-07-06 8567 3228 927 3277 168"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# worst week for store C\n",
"df[df['storeC'] == df['storeC'].min()]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"storeA 87591\n",
"storeB 79394\n",
"storeC 66538\n",
"storeD 75190\n",
"storeE 27334\n",
"dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# total sales during most recent 3 month period\n",
"last_three_months = df[df['week'] >= '2017-12-01']\n",
"last_three_months.iloc[:, 1:].sum() # exclude sum of week column"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.1"
}
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"nbformat": 4,
"nbformat_minor": 2
}