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careychum/Format for Flourish.ipynb

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Created May 30, 2020 08:03
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Format for Flourish.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"sales_projected1998.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year</th>\n",
" <th>Total Sales</th>\n",
" <th>Acura</th>\n",
" <th>Alfa Romeo</th>\n",
" <th>Aston Martin</th>\n",
" <th>Audi</th>\n",
" <th>Bentley</th>\n",
" <th>BMW</th>\n",
" <th>Buick</th>\n",
" <th>Cadillac</th>\n",
" <th>...</th>\n",
" <th>Scion</th>\n",
" <th>Smart</th>\n",
" <th>Subaru</th>\n",
" <th>Suzuki</th>\n",
" <th>Tesla</th>\n",
" <th>Toyota</th>\n",
" <th>Triumph</th>\n",
" <th>Volvo</th>\n",
" <th>Volkswagon</th>\n",
" <th>Yugo</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>2019</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>710505</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2132163</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2018</td>\n",
" <td>17177393</td>\n",
" <td>158934</td>\n",
" <td>23800</td>\n",
" <td>0</td>\n",
" <td>223323</td>\n",
" <td>0</td>\n",
" <td>311014</td>\n",
" <td>206863</td>\n",
" <td>154702</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>1276</td>\n",
" <td>680135</td>\n",
" <td>0</td>\n",
" <td>191627</td>\n",
" <td>2128201</td>\n",
" <td>0</td>\n",
" <td>98263</td>\n",
" <td>354064</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2017</td>\n",
" <td>17007887</td>\n",
" <td>154602</td>\n",
" <td>12031</td>\n",
" <td>0</td>\n",
" <td>226511</td>\n",
" <td>2405</td>\n",
" <td>305685</td>\n",
" <td>219231</td>\n",
" <td>156440</td>\n",
" <td>...</td>\n",
" <td>51158</td>\n",
" <td>3071</td>\n",
" <td>647956</td>\n",
" <td>0</td>\n",
" <td>50145</td>\n",
" <td>2129177</td>\n",
" <td>0</td>\n",
" <td>81507</td>\n",
" <td>339676</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2016</td>\n",
" <td>17558551</td>\n",
" <td>161360</td>\n",
" <td>528</td>\n",
" <td>0</td>\n",
" <td>210213</td>\n",
" <td>2581</td>\n",
" <td>313174</td>\n",
" <td>229631</td>\n",
" <td>170006</td>\n",
" <td>...</td>\n",
" <td>12027</td>\n",
" <td>6211</td>\n",
" <td>615132</td>\n",
" <td>0</td>\n",
" <td>47644</td>\n",
" <td>2106332</td>\n",
" <td>0</td>\n",
" <td>82593</td>\n",
" <td>322948</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2015</td>\n",
" <td>17433966</td>\n",
" <td>177165</td>\n",
" <td>663</td>\n",
" <td>1020</td>\n",
" <td>202202</td>\n",
" <td>2686</td>\n",
" <td>346023</td>\n",
" <td>223055</td>\n",
" <td>175267</td>\n",
" <td>...</td>\n",
" <td>56167</td>\n",
" <td>7484</td>\n",
" <td>582675</td>\n",
" <td>0</td>\n",
" <td>25416</td>\n",
" <td>2098545</td>\n",
" <td>0</td>\n",
" <td>70047</td>\n",
" <td>349440</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 54 columns</p>\n",
"</div>"
],
"text/plain": [
" Year Total Sales Acura Alfa Romeo Aston Martin Audi Bentley \\\n",
"0 2019 0 0 0 0 0 0 \n",
"1 2018 17177393 158934 23800 0 223323 0 \n",
"2 2017 17007887 154602 12031 0 226511 2405 \n",
"3 2016 17558551 161360 528 0 210213 2581 \n",
"4 2015 17433966 177165 663 1020 202202 2686 \n",
"\n",
" BMW Buick Cadillac ... Scion Smart Subaru Suzuki Tesla \\\n",
"0 0 0 0 ... 0 0 710505 0 0 \n",
"1 311014 206863 154702 ... 4 1276 680135 0 191627 \n",
"2 305685 219231 156440 ... 51158 3071 647956 0 50145 \n",
"3 313174 229631 170006 ... 12027 6211 615132 0 47644 \n",
"4 346023 223055 175267 ... 56167 7484 582675 0 25416 \n",
"\n",
" Toyota Triumph Volvo Volkswagon Yugo \n",
"0 2132163 0 0 0 0 \n",
"1 2128201 0 98263 354064 0 \n",
"2 2129177 0 81507 339676 0 \n",
"3 2106332 0 82593 322948 0 \n",
"4 2098545 0 70047 349440 0 \n",
"\n",
"[5 rows x 54 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Year</th>\n",
" <th>2019</th>\n",
" <th>2018</th>\n",
" <th>2017</th>\n",
" <th>2016</th>\n",
" <th>2015</th>\n",
" <th>2014</th>\n",
" <th>2013</th>\n",
" <th>2012</th>\n",
" <th>2011</th>\n",
" <th>2010</th>\n",
" <th>...</th>\n",
" <th>1915</th>\n",
" <th>1914</th>\n",
" <th>1913</th>\n",
" <th>1912</th>\n",
" <th>1911</th>\n",
" <th>1910</th>\n",
" <th>1909</th>\n",
" <th>1908</th>\n",
" <th>1907</th>\n",
" <th>1906</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>Total Sales</td>\n",
" <td>0</td>\n",
" <td>17177393</td>\n",
" <td>17007887</td>\n",
" <td>17558551</td>\n",
" <td>17433966</td>\n",
" <td>16424212</td>\n",
" <td>15544448</td>\n",
" <td>14123623</td>\n",
" <td>12708180</td>\n",
" <td>11587262</td>\n",
" <td>...</td>\n",
" <td>98252</td>\n",
" <td>83151</td>\n",
" <td>62578</td>\n",
" <td>32377</td>\n",
" <td>19022</td>\n",
" <td>19528</td>\n",
" <td>10129</td>\n",
" <td>1599</td>\n",
" <td>1685</td>\n",
" <td>1708</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Acura</td>\n",
" <td>0</td>\n",
" <td>158934</td>\n",
" <td>154602</td>\n",
" <td>161360</td>\n",
" <td>177165</td>\n",
" <td>167843</td>\n",
" <td>165436</td>\n",
" <td>156216</td>\n",
" <td>123299</td>\n",
" <td>133606</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Alfa Romeo</td>\n",
" <td>0</td>\n",
" <td>23800</td>\n",
" <td>12031</td>\n",
" <td>528</td>\n",
" <td>663</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Aston Martin</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1020</td>\n",
" <td>1160</td>\n",
" <td>828</td>\n",
" <td>816</td>\n",
" <td>804</td>\n",
" <td>840</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Audi</td>\n",
" <td>0</td>\n",
" <td>223323</td>\n",
" <td>226511</td>\n",
" <td>210213</td>\n",
" <td>202202</td>\n",
" <td>182011</td>\n",
" <td>158061</td>\n",
" <td>139310</td>\n",
" <td>117561</td>\n",
" <td>101629</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 114 columns</p>\n",
"</div>"
],
"text/plain": [
"Year 2019 2018 2017 2016 2015 2014 2013 \\\n",
"Total Sales 0 17177393 17007887 17558551 17433966 16424212 15544448 \n",
"Acura 0 158934 154602 161360 177165 167843 165436 \n",
"Alfa Romeo 0 23800 12031 528 663 91 0 \n",
"Aston Martin 0 0 0 0 1020 1160 828 \n",
"Audi 0 223323 226511 210213 202202 182011 158061 \n",
"\n",
"Year 2012 2011 2010 ... 1915 1914 1913 1912 \\\n",
"Total Sales 14123623 12708180 11587262 ... 98252 83151 62578 32377 \n",
"Acura 156216 123299 133606 ... 0 0 0 0 \n",
"Alfa Romeo 0 0 0 ... 0 0 0 0 \n",
"Aston Martin 816 804 840 ... 0 0 0 0 \n",
"Audi 139310 117561 101629 ... 0 0 0 0 \n",
"\n",
"Year 1911 1910 1909 1908 1907 1906 \n",
"Total Sales 19022 19528 10129 1599 1685 1708 \n",
"Acura 0 0 0 0 0 0 \n",
"Alfa Romeo 0 0 0 0 0 0 \n",
"Aston Martin 0 0 0 0 0 0 \n",
"Audi 0 0 0 0 0 0 \n",
"\n",
"[5 rows x 114 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"Year\"] = df[\"Year\"].astype(int)\n",
"df = df.T\n",
"df = df.replace(np.nan, 0)\n",
"new_header = df.iloc[0] #grab the first row for the header\n",
"df = df[1:] #take the data less the header row\n",
"df.columns = new_header #set the header row as the df header\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"flourish.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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