{
"metadata": {
"name": "PandasBabyNameAnalysis"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"from pandas import DataFrame, Series\n",
"import urllib2\n",
"import zipfile\n",
"import os\n",
"\n",
"def download_name_data():\n",
" name_data_url = \"http://www.ssa.gov/oact/babynames/names.zip\"\n",
" zip_data = urllib2.urlopen(name_data_url).read()\n",
" with open(\"names.zip\", \"wb\") as zipfile:\n",
" zipfile.write(zip_data)\n",
" print \"Successfully saved names.zip\"\n",
"\n",
"def extract_zip(zip_file, output_dir):\n",
" zfile = zipfile.ZipFile(zip_file)\n",
" \n",
" try:\n",
" os.mkdir(output_dir)\n",
" except OSError, e:\n",
" pass # Directory may already exist\n",
"\n",
" for filename in zfile.namelist():\n",
" outfile = open(output_dir + '/' + filename, 'wb')\n",
" outfile.write(zfile.read(filename))\n",
" outfile.close()\n",
" print \"Extracted\", zip_file, \"to\", output_dir\n",
"\n",
"def parse_names_data(names_dir):\n",
" years = range(1880, 2012+1)\n",
" parts = []\n",
" for year in years:\n",
" path = names_dir + (\"/yob%d.txt\" % year)\n",
" frame = pd.read_csv(path, names=['name', 'sex', 'births'])\n",
" frame['year'] = year\n",
" parts.append(frame)\n",
" names = pd.concat(parts, ignore_index=True)\n",
" return names"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#download_name_data()\n",
"names_data_dir = \"names_data\"\n",
"extract_zip(\"names.zip\", names_data_dir)\n",
"names = parse_names_data(names_data_dir)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Extracted names.zip to names_data\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"names.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" sex | \n",
" births | \n",
" year | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" Mary | \n",
" F | \n",
" 7065 | \n",
" 1880 | \n",
"
\n",
" \n",
" | 1 | \n",
" Anna | \n",
" F | \n",
" 2604 | \n",
" 1880 | \n",
"
\n",
" \n",
" | 2 | \n",
" Emma | \n",
" F | \n",
" 2003 | \n",
" 1880 | \n",
"
\n",
" \n",
" | 3 | \n",
" Elizabeth | \n",
" F | \n",
" 1939 | \n",
" 1880 | \n",
"
\n",
" \n",
" | 4 | \n",
" Minnie | \n",
" F | \n",
" 1746 | \n",
" 1880 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" sex | \n",
" births | \n",
" year | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1758725 | \n",
" Zylin | \n",
" M | \n",
" 5 | \n",
" 2012 | \n",
"
\n",
" \n",
" | 1758726 | \n",
" Zymari | \n",
" M | \n",
" 5 | \n",
" 2012 | \n",
"
\n",
" \n",
" | 1758727 | \n",
" Zyrin | \n",
" M | \n",
" 5 | \n",
" 2012 | \n",
"
\n",
" \n",
" | 1758728 | \n",
" Zyrus | \n",
" M | \n",
" 5 | \n",
" 2012 | \n",
"
\n",
" \n",
" | 1758729 | \n",
" Zytaevius | \n",
" M | \n",
" 5 | \n",
" 2012 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 166 entries, 1268 to 1744599\n",
"Data columns (total 4 columns):\n",
"name 166 non-null values\n",
"sex 166 non-null values\n",
"births 166 non-null values\n",
"year 166 non-null values\n",
"dtypes: int64(2), object(2)\n",
""
],
"output_type": "pyout",
"prompt_number": 5,
"text": [
"\n",
"Int64Index: 166 entries, 1268 to 1744599\n",
"Data columns (total 4 columns):\n",
"name 166 non-null values\n",
"sex 166 non-null values\n",
"births 166 non-null values\n",
"year 166 non-null values\n",
"dtypes: int64(2), object(2)"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def total_number_of_births(name):\n",
" return names[names.name == name].births.sum()\n",
"\n",
"print \"Total number of people born with each name between 1880 and 2012:\"\n",
"name_series = Series(['Caleb','Whitney','Hastin','Rafe','Dan','Mary','Leah','Wesley','Ambria','Tradley'])\n",
"births_list = []\n",
"for name in name_series:\n",
" births_list.append(total_number_of_births(name))\n",
"\n",
"births_by_name_frame = DataFrame({'births since 1880 in the US': births_list}, index=name_series)\n",
"births_by_name_frame\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Total number of people born with each name between 1880 and 2012:\n"
]
},
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" births since 1880 in the US | \n",
"
\n",
" \n",
" \n",
" \n",
" | Caleb | \n",
" 234304 | \n",
"
\n",
" \n",
" | Whitney | \n",
" 99268 | \n",
"
\n",
" \n",
" | Hastin | \n",
" 43 | \n",
"
\n",
" \n",
" | Rafe | \n",
" 1632 | \n",
"
\n",
" \n",
" | Dan | \n",
" 105847 | \n",
"
\n",
" \n",
" | Mary | \n",
" 4124778 | \n",
"
\n",
" \n",
" | Leah | \n",
" 194257 | \n",
"
\n",
" \n",
" | Wesley | \n",
" 198311 | \n",
"
\n",
" \n",
" | Ambria | \n",
" 1855 | \n",
"
\n",
" \n",
" | Tradley | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" | sex | \n",
" F | \n",
" M | \n",
"
\n",
" \n",
" | year | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1880 | \n",
" 90993 | \n",
" 110491 | \n",
"
\n",
" \n",
" | 1881 | \n",
" 91955 | \n",
" 100746 | \n",
"
\n",
" \n",
" | 1882 | \n",
" 107850 | \n",
" 113687 | \n",
"
\n",
" \n",
" | 1883 | \n",
" 112322 | \n",
" 104630 | \n",
"
\n",
" \n",
" | 1884 | \n",
" 129022 | \n",
" 114445 | \n",
"
\n",
" \n",
"
\n",
"