World Food Facts revised

Author: guipsamora

01_Getting_&_Knowing_Your_Data/World Food Facts/Exercises.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 1. Go to [Kaggle]( https://www.kaggle.com/openfoodfacts/world-food-facts)"
+    "### Step 1. Go to https://www.kaggle.com/openfoodfacts/world-food-facts/data"
    ]
   },
   {
@@ -25,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 3. Use the csv file and assign it to a dataframe called food"
+    "### Step 3. Use the tsv file and assign it to a dataframe called food"
    ]
   },
   {
@@ -167,8 +167,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python2"
   },
@@ -182,7 +183,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,

01_Getting_&_Knowing_Your_Data/World Food Facts/Exercises_with_solutions.ipynb

@@ -24,7 +24,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -35,7 +37,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 3. Use the csv file and assign it to a dataframe called food"
+    "### Step 3. Use the tsv file and assign it to a dataframe called food"
    ]
   },
   {
@@ -55,7 +57,7 @@
     }
    ],
    "source": [
-    "food = pd.read_csv('~/Desktop/world-food-facts/en.openfoodfacts.org.products.tsv', sep='\\t')"
+    "food = pd.read_csv('~/Desktop/en.openfoodfacts.org.products.tsv', sep='\\t')"
    ]
   },
   {
@@ -68,7 +70,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -312,7 +316,9 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -332,7 +338,9 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -359,7 +367,9 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -394,7 +404,9 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -430,7 +442,9 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -457,7 +471,9 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -484,7 +500,9 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -511,7 +529,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {