Skip to content

Instantly share code, notes, and snippets.

@tabrez
Created December 13, 2017 09:48
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
Star You must be signed in to star a gist
Save tabrez/ca7b4949cfc6d029c514142dc6ca8ca5 to your computer and use it in GitHub Desktop.
Exercises2.ipynb
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# Ex3 - Getting and Knowing your Data"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 1. Import the necessary libraries"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 3. Assign it to a variable called users and use the 'user_id' as index"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 4. See the first 25 entries"
},
{
"metadata": {
"scrolled": true,
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 5. See the last 10 entries"
},
{
"metadata": {
"scrolled": true,
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 6. What is the number of observations in the dataset?"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 7. What is the number of columns in the dataset?"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 8. Print the name of all the columns."
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 9. How is the dataset indexed?"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 10. What is the data type of each column?"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 11. Print only the occupation column"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 12. How many different occupations there are in this dataset?"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 13. What is the most frequent occupation?"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 14. Summarize the DataFrame."
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "# HINT: describe() functions give the summary",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 15. Summarize all the columns"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 16. Summarize only the occupation column"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 17. What is the mean age of users?"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Step 18. What is the age with least occurrence?"
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.6.3",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"gist": {
"id": "",
"data": {
"description": "Exercises2.ipynb",
"public": true
}
}
},
"nbformat": 4,
"nbformat_minor": 1
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment