{ "metadata": { "name": "Untitled0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": "%matplotlib inline\nimport pandas as pd\ndata = pd.read_csv(\"emdata-tsv (1).csv\")\n ", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": "data.describe()\ndata.shape\ndata.Country.describe()\ndata.Type.describe()", "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": "count 17828\nunique 15\ntop Transport Accident\nfreq 4351\nName: Type, dtype: object" } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": "data.describe()", "language": "python", "metadata": {}, "outputs": [ { "html": "
\n | Start | \nEnd | \nDuration | \nKilled | \nCost | \nAffected | \nColumn 12 | \n
---|---|---|---|---|---|---|---|
count | \n17828.000000 | \n17828.000000 | \n17828.000000 | \n13996.000000 | \n1.108500e+04 | \n3772.000000 | \n0 | \n
mean | \n1990.883049 | \n1990.918387 | \n0.035338 | \n2718.355173 | \n5.586713e+05 | \n488.759659 | \nNaN | \n
std | \n17.851370 | \n17.836943 | \n0.302913 | \n75162.832728 | \n6.951056e+06 | \n3384.235083 | \nNaN | \n
min | \n1900.000000 | \n1900.000000 | \n0.000000 | \n1.000000 | \n1.000000e+00 | \n0.003000 | \nNaN | \n
25% | \n1986.000000 | \n1986.000000 | \n0.000000 | \n12.000000 | \n6.000000e+01 | \n5.000000 | \nNaN | \n
50% | \n1996.000000 | \n1996.000000 | \n0.000000 | \n24.000000 | \n1.000000e+03 | \n35.000000 | \nNaN | \n
75% | \n2003.000000 | \n2003.000000 | \n0.000000 | \n57.000000 | \n1.975000e+04 | \n200.000000 | \nNaN | \n
max | \n2008.000000 | \n2009.000000 | \n9.000000 | \n5000000.000000 | \n3.000000e+08 | \n125000.000000 | \nNaN | \n
8 rows \u00d7 7 columns
\n