3315 lines
211 KiB
Plaintext
3315 lines
211 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example: Names in the Wild\n",
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"\n",
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"This example is drawn from Wes McKinney's excellent book on the *Pandas* library, O'Reilly's [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do).\n",
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"\n",
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"We'll be taking a look at a freely available dataset: the database of names given to babies in the United States over the last century.\n",
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"\n",
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"First things first, we need to download the data, which can be found at http://www.ssa.gov/oact/babynames/limits.html.\n",
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"If you uncomment the following commands, it will do this automatically (note that these are linux shell commands; they will not work on Windows):"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# !curl -O http://www.ssa.gov/oact/babynames/names.zip"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# !mkdir -p data/names\n",
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"# !mv names.zip data/names/\n",
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"# !cd data/names/ && unzip names.zip"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we should have a ``data/names`` directory which contains a number of text files, one for each year of data:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"NationalReadMe.pdf yob1913.txt yob1947.txt yob1981.txt\r\n",
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"yob1880.txt yob1914.txt yob1948.txt yob1982.txt\r\n",
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"yob1881.txt yob1915.txt yob1949.txt yob1983.txt\r\n",
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"yob1882.txt yob1916.txt yob1950.txt yob1984.txt\r\n",
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"yob1883.txt yob1917.txt yob1951.txt yob1985.txt\r\n",
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"yob1884.txt yob1918.txt yob1952.txt yob1986.txt\r\n",
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"yob1885.txt yob1919.txt yob1953.txt yob1987.txt\r\n",
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"yob1886.txt yob1920.txt yob1954.txt yob1988.txt\r\n",
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"yob1887.txt yob1921.txt yob1955.txt yob1989.txt\r\n",
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"yob1888.txt yob1922.txt yob1956.txt yob1990.txt\r\n",
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"yob1889.txt yob1923.txt yob1957.txt yob1991.txt\r\n",
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"yob1890.txt yob1924.txt yob1958.txt yob1992.txt\r\n",
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"yob1891.txt yob1925.txt yob1959.txt yob1993.txt\r\n",
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"yob1892.txt yob1926.txt yob1960.txt yob1994.txt\r\n",
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"yob1893.txt yob1927.txt yob1961.txt yob1995.txt\r\n",
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"yob1894.txt yob1928.txt yob1962.txt yob1996.txt\r\n",
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"yob1895.txt yob1929.txt yob1963.txt yob1997.txt\r\n",
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"yob1896.txt yob1930.txt yob1964.txt yob1998.txt\r\n",
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"yob1897.txt yob1931.txt yob1965.txt yob1999.txt\r\n",
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"yob1898.txt yob1932.txt yob1966.txt yob2000.txt\r\n",
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"yob1899.txt yob1933.txt yob1967.txt yob2001.txt\r\n",
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"yob1900.txt yob1934.txt yob1968.txt yob2002.txt\r\n",
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"yob1901.txt yob1935.txt yob1969.txt yob2003.txt\r\n",
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"yob1902.txt yob1936.txt yob1970.txt yob2004.txt\r\n",
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"yob1903.txt yob1937.txt yob1971.txt yob2005.txt\r\n",
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"yob1904.txt yob1938.txt yob1972.txt yob2006.txt\r\n",
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"yob1905.txt yob1939.txt yob1973.txt yob2007.txt\r\n",
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"yob1906.txt yob1940.txt yob1974.txt yob2008.txt\r\n",
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"yob1907.txt yob1941.txt yob1975.txt yob2009.txt\r\n",
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"yob1908.txt yob1942.txt yob1976.txt yob2010.txt\r\n",
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"yob1909.txt yob1943.txt yob1977.txt yob2011.txt\r\n",
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"yob1910.txt yob1944.txt yob1978.txt yob2012.txt\r\n",
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"yob1911.txt yob1945.txt yob1979.txt yob2013.txt\r\n",
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"yob1912.txt yob1946.txt yob1980.txt\r\n"
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]
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}
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],
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"source": [
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"!ls data/names"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's take a quick look at one of these files:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mary,F,7065\r",
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"\r\n",
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"Anna,F,2604\r",
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"\r\n",
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"Emma,F,2003\r",
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"\r\n",
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"Elizabeth,F,1939\r",
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"\r\n",
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"Minnie,F,1746\r",
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"\r\n",
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"Margaret,F,1578\r",
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"\r\n",
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"Ida,F,1472\r",
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"\r\n",
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"Alice,F,1414\r",
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"\r\n",
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"Bertha,F,1320\r",
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"\r\n",
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"Sarah,F,1288\r",
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"\r\n"
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]
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}
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],
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"source": [
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"!head data/names/yob1880.txt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Each file is just a comma-separated list of names, genders, and counts of babies with that name in each year.\n",
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"\n",
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"We can load these files using ``pd.read_csv``, which is specifically designed for this:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 63,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Mary</th>\n",
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" <th>F</th>\n",
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" <th>7065</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td> Anna</td>\n",
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" <td> F</td>\n",
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" <td> 2604</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td> Emma</td>\n",
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" <td> F</td>\n",
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" <td> 2003</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td> Elizabeth</td>\n",
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" <td> F</td>\n",
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" <td> 1939</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td> Minnie</td>\n",
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" <td> F</td>\n",
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" <td> 1746</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td> Margaret</td>\n",
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" <td> F</td>\n",
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" <td> 1578</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Mary F 7065\n",
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"0 Anna F 2604\n",
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"1 Emma F 2003\n",
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"2 Elizabeth F 1939\n",
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"3 Minnie F 1746\n",
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"4 Margaret F 1578"
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]
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},
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"execution_count": 63,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"names1880 = pd.read_csv('data/names/yob1880.txt')\n",
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"names1880.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Oops! Something went wrong. Our algorithm tried to be smart, and use the first line as index labels.\n",
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"Let's fix this by specifying the index names manually:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>name</th>\n",
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" <th>gender</th>\n",
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" <th>births</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td> Mary</td>\n",
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" <td> F</td>\n",
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" <td> 7065</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td> Anna</td>\n",
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" <td> F</td>\n",
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" <td> 2604</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td> Emma</td>\n",
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" <td> F</td>\n",
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" <td> 2003</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td> Elizabeth</td>\n",
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" <td> F</td>\n",
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" <td> 1939</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td> Minnie</td>\n",
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" <td> F</td>\n",
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" <td> 1746</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" name gender births\n",
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"0 Mary F 7065\n",
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"1 Anna F 2604\n",
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"2 Emma F 2003\n",
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"3 Elizabeth F 1939\n",
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"4 Minnie F 1746"
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]
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},
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"execution_count": 64,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"names1880 = pd.read_csv('data/names/yob1880.txt',\n",
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" names=['name', 'gender', 'births'])\n",
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"names1880.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"That looks better. Now we can start playing with the data a bit."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### GroupBy: aggregates on values\n",
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"\n",
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"First let's think about how we might count the total number of females and males born in the US in 1880.\n",
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"\n",
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"If you're used to NumPy, you might be tempted to use masking like this:\n",
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"\n",
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"First, we can get a mask over all females & males, and then use it to select a subset of the data:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"males = names1880[names1880.gender == 'M']\n",
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"females = names1880[names1880.gender == 'F']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we can take the sum of the births for each of these:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 66,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(110491, 90993)"
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]
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},
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"execution_count": 66,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"males.births.sum(), females.births.sum()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"But there's an easier way to do this, using one of Pandas' very powerful features: ``groupby``:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 67,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<pandas.core.groupby.DataFrameGroupBy object at 0x10eefb890>"
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]
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},
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"execution_count": 67,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"grouped = names1880.groupby('gender')\n",
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"grouped"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This grouped object is now an abstract representation of the data, where the data is split on the given column.\n",
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"In order to actually do something with this data, we need to specify an **aggregation** operation to do across the data.\n",
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"In this case, what we want is the sum:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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|
"text/html": [
|
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"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
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" <th>births</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>gender</th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>F</th>\n",
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" <td> 90993</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>M</th>\n",
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" <td> 110491</td>\n",
|
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" </tr>\n",
|
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
|
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" births\n",
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"gender \n",
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"F 90993\n",
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"M 110491"
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]
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},
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"execution_count": 68,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
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|
}
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|
],
|
|
"source": [
|
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"grouped.sum()"
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|
]
|
|
},
|
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{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
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"We can do other aggregations as well:"
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]
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|
},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 69,
|
|
"metadata": {
|
|
"collapsed": false
|
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},
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"outputs": [
|
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{
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"data": {
|
|
"text/plain": [
|
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"gender\n",
|
|
"F 942\n",
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"M 1058\n",
|
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"dtype: int64"
|
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]
|
|
},
|
|
"execution_count": 69,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
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}
|
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],
|
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"source": [
|
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"grouped.size()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 70,
|
|
"metadata": {
|
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"collapsed": false
|
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},
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"outputs": [
|
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{
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|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>births</th>\n",
|
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" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>gender</th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>F</th>\n",
|
|
" <td> 96.595541</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>M</th>\n",
|
|
" <td> 104.433837</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" births\n",
|
|
"gender \n",
|
|
"F 96.595541\n",
|
|
"M 104.433837"
|
|
]
|
|
},
|
|
"execution_count": 70,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"grouped.mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Or, if we wish, we can get a description of the grouping:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 71,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th>births</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>gender</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th rowspan=\"8\" valign=\"top\">F</th>\n",
|
|
" <th>count</th>\n",
|
|
" <td> 942.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td> 96.595541</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td> 328.152904</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td> 5.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td> 7.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td> 13.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td> 43.750000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td> 7065.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th rowspan=\"8\" valign=\"top\">M</th>\n",
|
|
" <th>count</th>\n",
|
|
" <td> 1058.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td> 104.433837</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td> 561.232488</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td> 5.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td> 7.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td> 12.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td> 41.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td> 9655.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" births\n",
|
|
"gender \n",
|
|
"F count 942.000000\n",
|
|
" mean 96.595541\n",
|
|
" std 328.152904\n",
|
|
" min 5.000000\n",
|
|
" 25% 7.000000\n",
|
|
" 50% 13.000000\n",
|
|
" 75% 43.750000\n",
|
|
" max 7065.000000\n",
|
|
"M count 1058.000000\n",
|
|
" mean 104.433837\n",
|
|
" std 561.232488\n",
|
|
" min 5.000000\n",
|
|
" 25% 7.000000\n",
|
|
" 50% 12.000000\n",
|
|
" 75% 41.000000\n",
|
|
" max 9655.000000"
|
|
]
|
|
},
|
|
"execution_count": 71,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"grouped.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Concatenating multiple data sources\n",
|
|
"\n",
|
|
"But here we've just been looking at a single year. Let's try to put together all the data in all the years.\n",
|
|
"To do this, we'll have to use pandas ``concat`` function to concatenate all the data together.\n",
|
|
"First we'll create a function which loads the data as we did the above data:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 72,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def load_year(year):\n",
|
|
" data = pd.read_csv('data/names/yob{0}.txt'.format(year),\n",
|
|
" names=['name', 'gender', 'births'])\n",
|
|
" data['year'] = year\n",
|
|
" return data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now let's load all the data into a list, and call ``pd.concat`` on that list:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 73,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>name</th>\n",
|
|
" <th>gender</th>\n",
|
|
" <th>births</th>\n",
|
|
" <th>year</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td> Mary</td>\n",
|
|
" <td> F</td>\n",
|
|
" <td> 7065</td>\n",
|
|
" <td> 1880</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td> Anna</td>\n",
|
|
" <td> F</td>\n",
|
|
" <td> 2604</td>\n",
|
|
" <td> 1880</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td> Emma</td>\n",
|
|
" <td> F</td>\n",
|
|
" <td> 2003</td>\n",
|
|
" <td> 1880</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td> Elizabeth</td>\n",
|
|
" <td> F</td>\n",
|
|
" <td> 1939</td>\n",
|
|
" <td> 1880</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td> Minnie</td>\n",
|
|
" <td> F</td>\n",
|
|
" <td> 1746</td>\n",
|
|
" <td> 1880</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" name gender births year\n",
|
|
"0 Mary F 7065 1880\n",
|
|
"1 Anna F 2604 1880\n",
|
|
"2 Emma F 2003 1880\n",
|
|
"3 Elizabeth F 1939 1880\n",
|
|
"4 Minnie F 1746 1880"
|
|
]
|
|
},
|
|
"execution_count": 73,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"names = pd.concat([load_year(year) for year in range(1880, 2014)])\n",
|
|
"names.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"It looks like we've done it!\n",
|
|
"\n",
|
|
"Let's start with something easy: we'll use ``groupby`` again to see the total number of births per year:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 74,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"year\n",
|
|
"1880 201484\n",
|
|
"1881 192700\n",
|
|
"1882 221537\n",
|
|
"1883 216952\n",
|
|
"1884 243468\n",
|
|
"Name: births, dtype: int64"
|
|
]
|
|
},
|
|
"execution_count": 74,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"births = names.groupby('year').births.sum()\n",
|
|
"births.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can use the ``plot()`` method to see a quick plot of these (note that because we used the ``%matplotlib inline`` magic at the start of the notebook, the resulting plot will be shown inline within the notebook)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 75,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
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],
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"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x10c558710>"
|
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"births.plot();"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The so-called \"baby boom\" generation after the second world war is abundantly clear!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can also use other aggregates: let's see *how many* names are used each year:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 76,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
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"image/png": [
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],
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"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x10b4b0650>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"names.groupby('year').births.count().plot();"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Apparently there's been a huge increase of the diversity of names with time!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"``groupby`` can also be used to add columns to the data: think of it as a *view* of the data that you're modifying. Let's add a column giving the frequency of each name within each year & gender:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 77,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>name</th>\n",
|
|
" <th>gender</th>\n",
|
|
" <th>births</th>\n",
|
|
" <th>year</th>\n",
|
|
" <th>birth_freq</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td> Mary</td>\n",
|
|
" <td> F</td>\n",
|
|
" <td> 7065</td>\n",
|
|
" <td> 1880</td>\n",
|
|
" <td> 0.077643</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td> Anna</td>\n",
|
|
" <td> F</td>\n",
|
|
" <td> 2604</td>\n",
|
|
" <td> 1880</td>\n",
|
|
" <td> 0.028618</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td> Emma</td>\n",
|
|
" <td> F</td>\n",
|
|
" <td> 2003</td>\n",
|
|
" <td> 1880</td>\n",
|
|
" <td> 0.022013</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td> Elizabeth</td>\n",
|
|
" <td> F</td>\n",
|
|
" <td> 1939</td>\n",
|
|
" <td> 1880</td>\n",
|
|
" <td> 0.021309</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td> Minnie</td>\n",
|
|
" <td> F</td>\n",
|
|
" <td> 1746</td>\n",
|
|
" <td> 1880</td>\n",
|
|
" <td> 0.019188</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" name gender births year birth_freq\n",
|
|
"0 Mary F 7065 1880 0.077643\n",
|
|
"1 Anna F 2604 1880 0.028618\n",
|
|
"2 Emma F 2003 1880 0.022013\n",
|
|
"3 Elizabeth F 1939 1880 0.021309\n",
|
|
"4 Minnie F 1746 1880 0.019188"
|
|
]
|
|
},
|
|
"execution_count": 77,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"def add_frequency(group):\n",
|
|
" group['birth_freq'] = group.births / group.births.sum()\n",
|
|
" return group\n",
|
|
"\n",
|
|
"names = names.groupby(['year', 'gender']).apply(add_frequency)\n",
|
|
"names.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Notice that the ``apply()`` function iterates over each group, and calls a function which modifies the group.\n",
|
|
"This result is then re-constructed into a container which looks ike the original dataframe."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Pivot Tables\n",
|
|
"\n",
|
|
"Next we'll discuss Pivot Tables, which are an even more powerful way of (re)organizing your data.\n",
|
|
"\n",
|
|
"Let's say that we want to plot the men and women separately.\n",
|
|
"We could do this by using masking, as follows:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 78,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"men = names[names.gender == 'M']\n",
|
|
"women = names[names.gender == 'W']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"And then we could proceed as above, using ``groupby`` to group on the year.\n",
|
|
"But we would end up with two different views of the data. A better way to do this is to use a ``pivot_table``, which is essentially a groupby in multiple dimensions at once:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 79,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th>gender</th>\n",
|
|
" <th>F</th>\n",
|
|
" <th>M</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>year</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>1880</th>\n",
|
|
" <td> 90993</td>\n",
|
|
" <td> 110491</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1881</th>\n",
|
|
" <td> 91954</td>\n",
|
|
" <td> 100746</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1882</th>\n",
|
|
" <td> 107850</td>\n",
|
|
" <td> 113687</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1883</th>\n",
|
|
" <td> 112322</td>\n",
|
|
" <td> 104630</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1884</th>\n",
|
|
" <td> 129022</td>\n",
|
|
" <td> 114446</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
"gender F M\n",
|
|
"year \n",
|
|
"1880 90993 110491\n",
|
|
"1881 91954 100746\n",
|
|
"1882 107850 113687\n",
|
|
"1883 112322 104630\n",
|
|
"1884 129022 114446"
|
|
]
|
|
},
|
|
"execution_count": 79,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"births = names.pivot_table('births',\n",
|
|
" index='year', columns='gender',\n",
|
|
" aggfunc=sum)\n",
|
|
"births.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Note that this has grouped the index by the value of ``year``, and grouped the columns by the value of ``gender``.\n",
|
|
"Let's plot the results now:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 80,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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|
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"SUVORK5CYII=\n"
|
|
],
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x10b36a9d0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"births.plot(title='Total Births');"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Name Evolution Over Time\n",
|
|
"\n",
|
|
"Some names have shifted from being girls names to being boys names. Let's take a look at some of these:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 81,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": [
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],
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"text/plain": [
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"<matplotlib.figure.Figure at 0x101935690>"
|
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]
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|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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|
}
|
|
],
|
|
"source": [
|
|
"names_to_check = ['Allison', 'Alison']\n",
|
|
"\n",
|
|
"# filter on just the names we're interested in\n",
|
|
"births = names[names.name.isin(names_to_check)]\n",
|
|
"\n",
|
|
"# pivot table to get year vs. gender\n",
|
|
"births = births.pivot_table('births', index='year', columns='gender')\n",
|
|
"\n",
|
|
"# fill all NaNs with zeros\n",
|
|
"births = births.fillna(0)\n",
|
|
"\n",
|
|
"# normalize along columns\n",
|
|
"births = births.div(births.sum(1), axis=0)\n",
|
|
"\n",
|
|
"births.plot(title='Fraction of babies named Allison');"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can see that prior to about 1905, all babies named Allison were male. Over the 20th century, this reversed, until the end of the century nearly all Allisons were female!\n",
|
|
"\n",
|
|
"There's some noise in this data: we can smooth it out a bit by using a 5-year rolling mean:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 82,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
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|
"data": {
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"image/png": [
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],
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"text/plain": [
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"<matplotlib.figure.Figure at 0x116dd8610>"
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]
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},
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"metadata": {},
|
|
"output_type": "display_data"
|
|
}
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],
|
|
"source": [
|
|
"pd.rolling_mean(births, 5).plot(title=\"Allisons: 5-year moving average\");"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"This gives a smoother picture of the transition, and is an example of the bias/variance tradeoff that we'll often see in modeling: a smoother model has less variance (variation due to sampling or other noise) but at the expense of more bias (the model systematically mis-represents the data slightly).\n",
|
|
"\n",
|
|
"We'll discuss this type of tradeoff more in coming sessions."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Where to Find More\n",
|
|
"\n",
|
|
"We've just scratched the surface of what can be done with Pandas, but we'll get a chance to play with this more in the breakout session coming up.\n",
|
|
"\n",
|
|
"For more information on using Pandas, check out the [pandas documentation](http://pandas.pydata.org/) or the book [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) by Pandas creator Wes McKinney."
|
|
]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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"language_info": {
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"name": "ipython",
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.5"
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