{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to PyCampNextLevel Outline](PyCampNextLevel.ipynb)\n",
"\n",
"# HOME FROM SCHOOL?\n",
"\n",
"Don't let that keep you behind.\n",
"\n",
"Lets practice plotting functions to keep in shape!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Explain that last command!](https://stackoverflow.com/questions/43027980/purpose-of-matplotlib-inline)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def parabola(x):\n",
" return x * x"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"domain = np.linspace(-5,5,100) # a 100 points between [5, -5]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({\"domain\":domain, \"range\":[parabola(n) for n in domain]}) # slower"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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