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alnoda-workspaces/workspaces/notebook-workspace/tutorials/df-load-save.ipynb
bluxmit 255b1cc25b notebook and kubespray workspaces
2022-06-20 18:24:28 +00:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "19565fdc-6c54-4b5a-af52-b59f696aae4a",
"metadata": {},
"source": [
"# Pandas dataframe\n",
"This simple example shows how to import and export dataframes from/to csv "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0f709c24-24e3-4c46-8b26-bc760eae6a6b",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ba634fd7-c90e-49dd-887e-72c12bcbe02e",
"metadata": {},
"outputs": [],
"source": [
"# Load csv file into Pandas data frame\n",
"df = pd.read_csv('cars.csv') "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3d3f5a6b-7110-4de1-a44c-00252924f2b1",
"metadata": {},
"outputs": [
{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Name</th>\n",
" <th>Miles_per_Gallon</th>\n",
" <th>Cylinders</th>\n",
" <th>Displacement</th>\n",
" <th>Horsepower</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>chevrolet chevelle malibu</td>\n",
" <td>18.0</td>\n",
" <td>8</td>\n",
" <td>307.0</td>\n",
" <td>130.0</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>buick skylark 320</td>\n",
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" <td>350.0</td>\n",
" <td>165.0</td>\n",
" <td>3693</td>\n",
" <td>11.5</td>\n",
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" </tr>\n",
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" <th>2</th>\n",
" <td>2</td>\n",
" <td>plymouth satellite</td>\n",
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" <td>8</td>\n",
" <td>318.0</td>\n",
" <td>150.0</td>\n",
" <td>3436</td>\n",
" <td>11.0</td>\n",
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" <td>USA</td>\n",
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" <th>3</th>\n",
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" <td>amc rebel sst</td>\n",
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" <td>150.0</td>\n",
" <td>3433</td>\n",
" <td>12.0</td>\n",
" <td>1970-01-01</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>ford torino</td>\n",
" <td>17.0</td>\n",
" <td>8</td>\n",
" <td>302.0</td>\n",
" <td>140.0</td>\n",
" <td>3449</td>\n",
" <td>10.5</td>\n",
" <td>1970-01-01</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>401</th>\n",
" <td>401</td>\n",
" <td>ford mustang gl</td>\n",
" <td>27.0</td>\n",
" <td>4</td>\n",
" <td>140.0</td>\n",
" <td>86.0</td>\n",
" <td>2790</td>\n",
" <td>15.6</td>\n",
" <td>1982-01-01</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402</th>\n",
" <td>402</td>\n",
" <td>vw pickup</td>\n",
" <td>44.0</td>\n",
" <td>4</td>\n",
" <td>97.0</td>\n",
" <td>52.0</td>\n",
" <td>2130</td>\n",
" <td>24.6</td>\n",
" <td>1982-01-01</td>\n",
" <td>Europe</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403</th>\n",
" <td>403</td>\n",
" <td>dodge rampage</td>\n",
" <td>32.0</td>\n",
" <td>4</td>\n",
" <td>135.0</td>\n",
" <td>84.0</td>\n",
" <td>2295</td>\n",
" <td>11.6</td>\n",
" <td>1982-01-01</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404</th>\n",
" <td>404</td>\n",
" <td>ford ranger</td>\n",
" <td>28.0</td>\n",
" <td>4</td>\n",
" <td>120.0</td>\n",
" <td>79.0</td>\n",
" <td>2625</td>\n",
" <td>18.6</td>\n",
" <td>1982-01-01</td>\n",
" <td>USA</td>\n",
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" <tr>\n",
" <th>405</th>\n",
" <td>405</td>\n",
" <td>chevy s-10</td>\n",
" <td>31.0</td>\n",
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" <td>119.0</td>\n",
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" <td>2720</td>\n",
" <td>19.4</td>\n",
" <td>1982-01-01</td>\n",
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"text/plain": [
" Unnamed: 0 Name Miles_per_Gallon Cylinders \\\n",
"0 0 chevrolet chevelle malibu 18.0 8 \n",
"1 1 buick skylark 320 15.0 8 \n",
"2 2 plymouth satellite 18.0 8 \n",
"3 3 amc rebel sst 16.0 8 \n",
"4 4 ford torino 17.0 8 \n",
".. ... ... ... ... \n",
"401 401 ford mustang gl 27.0 4 \n",
"402 402 vw pickup 44.0 4 \n",
"403 403 dodge rampage 32.0 4 \n",
"404 404 ford ranger 28.0 4 \n",
"405 405 chevy s-10 31.0 4 \n",
"\n",
" Displacement Horsepower Weight_in_lbs Acceleration Year Origin \n",
"0 307.0 130.0 3504 12.0 1970-01-01 USA \n",
"1 350.0 165.0 3693 11.5 1970-01-01 USA \n",
"2 318.0 150.0 3436 11.0 1970-01-01 USA \n",
"3 304.0 150.0 3433 12.0 1970-01-01 USA \n",
"4 302.0 140.0 3449 10.5 1970-01-01 USA \n",
".. ... ... ... ... ... ... \n",
"401 140.0 86.0 2790 15.6 1982-01-01 USA \n",
"402 97.0 52.0 2130 24.6 1982-01-01 Europe \n",
"403 135.0 84.0 2295 11.6 1982-01-01 USA \n",
"404 120.0 79.0 2625 18.6 1982-01-01 USA \n",
"405 119.0 82.0 2720 19.4 1982-01-01 USA \n",
"\n",
"[406 rows x 10 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display pandas dataframe inline\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "de5960f6-c501-4ca5-a8c3-7121eb2070aa",
"metadata": {},
"outputs": [],
"source": [
"# add column\n",
"df['Liters_per_km'] = df['Miles_per_Gallon'] * 2.35215"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a7e1a608-edbf-4e87-93fa-c0d78c9ef55e",
"metadata": {},
"outputs": [
{
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" <td>130.0</td>\n",
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" <td>buick skylark 320</td>\n",
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" <td>12.0</td>\n",
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" <td>140.0</td>\n",
" <td>86.0</td>\n",
" <td>2790</td>\n",
" <td>15.6</td>\n",
" <td>1982-01-01</td>\n",
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" <td>vw pickup</td>\n",
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" <td>4</td>\n",
" <td>97.0</td>\n",
" <td>52.0</td>\n",
" <td>2130</td>\n",
" <td>24.6</td>\n",
" <td>1982-01-01</td>\n",
" <td>Europe</td>\n",
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" <tr>\n",
" <th>403</th>\n",
" <td>403</td>\n",
" <td>dodge rampage</td>\n",
" <td>32.0</td>\n",
" <td>4</td>\n",
" <td>135.0</td>\n",
" <td>84.0</td>\n",
" <td>2295</td>\n",
" <td>11.6</td>\n",
" <td>1982-01-01</td>\n",
" <td>USA</td>\n",
" <td>75.26880</td>\n",
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" <tr>\n",
" <th>404</th>\n",
" <td>404</td>\n",
" <td>ford ranger</td>\n",
" <td>28.0</td>\n",
" <td>4</td>\n",
" <td>120.0</td>\n",
" <td>79.0</td>\n",
" <td>2625</td>\n",
" <td>18.6</td>\n",
" <td>1982-01-01</td>\n",
" <td>USA</td>\n",
" <td>65.86020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405</th>\n",
" <td>405</td>\n",
" <td>chevy s-10</td>\n",
" <td>31.0</td>\n",
" <td>4</td>\n",
" <td>119.0</td>\n",
" <td>82.0</td>\n",
" <td>2720</td>\n",
" <td>19.4</td>\n",
" <td>1982-01-01</td>\n",
" <td>USA</td>\n",
" <td>72.91665</td>\n",
" </tr>\n",
" </tbody>\n",
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"<p>406 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Name Miles_per_Gallon Cylinders \\\n",
"0 0 chevrolet chevelle malibu 18.0 8 \n",
"1 1 buick skylark 320 15.0 8 \n",
"2 2 plymouth satellite 18.0 8 \n",
"3 3 amc rebel sst 16.0 8 \n",
"4 4 ford torino 17.0 8 \n",
".. ... ... ... ... \n",
"401 401 ford mustang gl 27.0 4 \n",
"402 402 vw pickup 44.0 4 \n",
"403 403 dodge rampage 32.0 4 \n",
"404 404 ford ranger 28.0 4 \n",
"405 405 chevy s-10 31.0 4 \n",
"\n",
" Displacement Horsepower Weight_in_lbs Acceleration Year \\\n",
"0 307.0 130.0 3504 12.0 1970-01-01 \n",
"1 350.0 165.0 3693 11.5 1970-01-01 \n",
"2 318.0 150.0 3436 11.0 1970-01-01 \n",
"3 304.0 150.0 3433 12.0 1970-01-01 \n",
"4 302.0 140.0 3449 10.5 1970-01-01 \n",
".. ... ... ... ... ... \n",
"401 140.0 86.0 2790 15.6 1982-01-01 \n",
"402 97.0 52.0 2130 24.6 1982-01-01 \n",
"403 135.0 84.0 2295 11.6 1982-01-01 \n",
"404 120.0 79.0 2625 18.6 1982-01-01 \n",
"405 119.0 82.0 2720 19.4 1982-01-01 \n",
"\n",
" Origin Liters_per_km \n",
"0 USA 42.33870 \n",
"1 USA 35.28225 \n",
"2 USA 42.33870 \n",
"3 USA 37.63440 \n",
"4 USA 39.98655 \n",
".. ... ... \n",
"401 USA 63.50805 \n",
"402 Europe 103.49460 \n",
"403 USA 75.26880 \n",
"404 USA 65.86020 \n",
"405 USA 72.91665 \n",
"\n",
"[406 rows x 11 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display pandas dataframe with new column\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1f613611-ae58-46d3-a110-e0d4cf49489e",
"metadata": {},
"outputs": [],
"source": [
"# save transformed data frame to new .csv file\n",
"df.to_csv('cars-new.csv')"
]
},
{
"cell_type": "markdown",
"id": "6295ddb7-6eb0-4292-9992-589f6385d0fa",
"metadata": {},
"source": [
"If you are working in Jupyter Lab, simply open file cars-new.csv to preview the full table in a separate window"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a7fafe6-17cd-49dd-a003-ce0c91a3669d",
"metadata": {},
"outputs": [],
"source": []
}
],
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"display_name": "Python 3 (ipykernel)",
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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