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December 13, 2017 09:49
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Exercises3.ipynb
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{ | |
"cells": [ | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "# Exercise 1" | |
}, | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "### Step 1. Go to [Kaggle]( https://www.kaggle.com/openfoodfacts/world-food-facts/downloads/world-food-facts.zip)" | |
}, | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "### Step 2. Download the dataset to your computer and unzip it." | |
}, | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "### Step 3. Use the tsv file and assign it to a dataframe called food(values are tab separated)" | |
}, | |
{ | |
"metadata": { | |
"collapsed": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "# Hint: It's a large file, it will take some time to read data, may have to wait for a couple of minutes before you call any functions on the dataframe after read_csv()", | |
"execution_count": null, | |
"outputs": [] | |
}, | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "### Step 4. See the first 5 entries" | |
}, | |
{ | |
"metadata": { | |
"collapsed": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "", | |
"execution_count": null, | |
"outputs": [] | |
}, | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "### Step 5. What is the number of observations in the dataset?" | |
}, | |
{ | |
"metadata": { | |
"collapsed": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "", | |
"execution_count": null, | |
"outputs": [] | |
}, | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "### Step 6. What is the number of columns in the dataset?" | |
}, | |
{ | |
"metadata": { | |
"collapsed": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "", | |
"execution_count": null, | |
"outputs": [] | |
}, | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "### Step 7. Print the name of all the columns." | |
}, | |
{ | |
"metadata": { | |
"collapsed": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "", | |
"execution_count": null, | |
"outputs": [] | |
}, | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "### Step 8. What is the name of 105th column?" | |
}, | |
{ | |
"metadata": { | |
"collapsed": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "", | |
"execution_count": null, | |
"outputs": [] | |
}, | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "### Step 9. What is the type of the observations of the 105th column?" | |
}, | |
{ | |
"metadata": { | |
"collapsed": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "", | |
"execution_count": null, | |
"outputs": [] | |
}, | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "### Step 10. How is the dataset indexed?" | |
}, | |
{ | |
"metadata": { | |
"collapsed": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "", | |
"execution_count": null, | |
"outputs": [] | |
}, | |
{ | |
"metadata": {}, | |
"cell_type": "markdown", | |
"source": "### Step 11. What is the product name of the 19th observation?" | |
}, | |
{ | |
"metadata": { | |
"collapsed": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "", | |
"execution_count": null, | |
"outputs": [] | |
} | |
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"display_name": "Python 3", | |
"language": "python" | |
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"description": "Exercises3.ipynb", | |
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"nbformat_minor": 1 | |
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