Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
Kaggle 5 Day Data Challenge - Day 2 - Cereal
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kaggle 5 Day Data Challenge - Day 2 - Cereal\n",
"## Plotting a numeric value using a histogram\n",
"\n",
"Welcome to day 2 of Kaggle's 5 Day Data Challenge! \n",
"\n",
"Today we're going to look at this cereal database from Kaggle: ('https://www.kaggle.com/crawford/80-cereals/data') and choose a numeric value from it to plot on a histogram.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 1 - Import the libraries"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 2 - Read your data into a dataframe"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cereal = pd.read_csv('cereal.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 3 - Pick a column with a numeric value for our histogram"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" name mfr type calories protein fat sodium fiber \\\n",
"0 100% Bran N C 70 4 1 130 10.0 \n",
"1 100% Natural Bran Q C 120 3 5 15 2.0 \n",
"2 All-Bran K C 70 4 1 260 9.0 \n",
"3 All-Bran with Extra Fiber K C 50 4 0 140 14.0 \n",
"4 Almond Delight R C 110 2 2 200 1.0 \n",
"\n",
" carbo sugars potass vitamins shelf weight cups rating \n",
"0 5.0 6 280 25 3 1.0 0.33 68.402973 \n",
"1 8.0 8 135 0 3 1.0 1.00 33.983679 \n",
"2 7.0 5 320 25 3 1.0 0.33 59.425505 \n",
"3 8.0 0 330 25 3 1.0 0.50 93.704912 \n",
"4 14.0 8 -1 25 3 1.0 0.75 34.384843 \n"
]
}
],
"source": [
"print(cereal[0:5])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"name 0\n",
"mfr 0\n",
"type 0\n",
"calories 0\n",
"protein 0\n",
"fat 0\n",
"sodium 0\n",
"fiber 0\n",
"carbo 0\n",
"sugars 0\n",
"potass 0\n",
"vitamins 0\n",
"shelf 0\n",
"weight 0\n",
"cups 0\n",
"rating 0\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cereal.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'm going to go with the 'rating' column, I wonder if there's some big outliers!"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"Rating = cereal['rating']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 4 - Plot a histogram of the column"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just for fun, we'll change the colour palette to make for a prettier histogram\n",
"\n",
"Check out this matplotlib documentation to see the full range of colour palettes you can choose:\n",
"\n",
"('https://matplotlib.org/users/colormaps.html')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sns.set_palette(\"spring\")"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Distribution of ratings for cereal')"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xfd5706a550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(Rating).set_title('Distribution of ratings for cereal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### All done! \n",
"We've successfully read in the .csv, summarized it, chose a numeric value and plotted it on a (pink) histogram.\n",
"Check back here tomorrow for Day 3 of the Kaggle 5 Day Data Challenge!"
]
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.