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Convert solutions.ipynb to blank cells in 02 Fictional Army #131

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Convert solutions.ipynb to blank cells in 02 Fictional Army #131

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bca27f6
leave blank 02 ficitonal army
May 3, 2022
98bb471
Bump numpy from 1.13.1 to 1.21.0
dependabot[bot] May 10, 2022
c0b704d
Merge pull request #1 from jsulopz/dependabot/pip/numpy-1.21.0
jsulopzs May 10, 2022
4d968b7
remove importing libraries & steps
Jun 26, 2022
70826e3
solved notebook failing to load in github
resolvingpython Oct 12, 2022
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65 changes: 25 additions & 40 deletions 01_Getting_&_Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -14,35 +14,21 @@
"metadata": {},
"source": [
"This time we are going to pull data directly from the internet.\n",
"Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
"\n",
"### Step 1. Import the necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
"Special thanks to: https://github.com/justmarkham for sharing the dataset and materials."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
"### Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3. Assign it to a variable called chipo."
"### Assign it to a variable called chipo."
]
},
{
Expand All @@ -62,7 +48,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4. See the first 10 entries"
"### See the first 10 entries"
]
},
{
Expand Down Expand Up @@ -212,7 +198,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 5. What is the number of observations in the dataset?"
"### What is the number of observations in the dataset?"
]
},
{
Expand Down Expand Up @@ -273,7 +259,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 6. What is the number of columns in the dataset?"
"### What is the number of columns in the dataset?"
]
},
{
Expand Down Expand Up @@ -302,7 +288,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 7. Print the name of all the columns."
"### Print the name of all the columns."
]
},
{
Expand Down Expand Up @@ -333,7 +319,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 8. How is the dataset indexed?"
"### How is the dataset indexed?"
]
},
{
Expand Down Expand Up @@ -362,7 +348,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 9. Which was the most-ordered item? "
"### Which was the most-ordered item? "
]
},
{
Expand Down Expand Up @@ -421,7 +407,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 10. For the most-ordered item, how many items were ordered?"
"### For the most-ordered item, how many items were ordered?"
]
},
{
Expand Down Expand Up @@ -480,7 +466,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 11. What was the most ordered item in the choice_description column?"
"### What was the most ordered item in the choice_description column?"
]
},
{
Expand Down Expand Up @@ -539,7 +525,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 12. How many items were orderd in total?"
"### How many items were orderd in total?"
]
},
{
Expand Down Expand Up @@ -569,14 +555,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 13. Turn the item price into a float"
"### Turn the item price into a float"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 13.a. Check the item price type"
"####a. Check the item price type"
]
},
{
Expand Down Expand Up @@ -605,7 +591,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 13.b. Create a lambda function and change the type of item price"
"####b. Create a lambda function and change the type of item price"
]
},
{
Expand All @@ -624,7 +610,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 13.c. Check the item price type"
"####c. Check the item price type"
]
},
{
Expand Down Expand Up @@ -653,7 +639,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 14. How much was the revenue for the period in the dataset?"
"### How much was the revenue for the period in the dataset?"
]
},
{
Expand Down Expand Up @@ -681,7 +667,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 15. How many orders were made in the period?"
"### How many orders were made in the period?"
]
},
{
Expand Down Expand Up @@ -711,7 +697,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 16. What is the average revenue amount per order?"
"### What is the average revenue amount per order?"
]
},
{
Expand Down Expand Up @@ -768,7 +754,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 17. How many different items are sold?"
"### How many different items are sold?"
]
},
{
Expand All @@ -795,23 +781,22 @@
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python2"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
Expand Down
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