{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"import numpy as np \n",
"import matplotlib.pyplot as plt "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution to Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Great Migration\n",
"\n",
"DATASETS: migration.xlsx; uscities.xlsx "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2) We'll use pandas to import the datafile migration.xlsx into a dataframe called migration."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" | \n",
" lat | \n",
" lon | \n",
" Mig2 | \n",
" Mig1 | \n",
"
\n",
" \n",
" | city | \n",
" state | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | Seattle | \n",
" WA | \n",
" 47.6211 | \n",
" -122.3244 | \n",
" 0.1 | \n",
" 6.1 | \n",
"
\n",
" \n",
" | Spokane | \n",
" WA | \n",
" 47.6671 | \n",
" -117.4330 | \n",
" -0.2 | \n",
" 0.7 | \n",
"
\n",
" \n",
"
\n",
"