diff --git a/Python Data Scrub.ipynb b/Python Data Scrub.ipynb new file mode 100644 index 0000000..7a164e5 --- /dev/null +++ b/Python Data Scrub.ipynb @@ -0,0 +1,4817 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:8b3780a3ed558de8a22835c26643af87af4bab87eb089aeb2ea6b8acd8c49535" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!head cohort_3_python.csv" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Name,\"Lecture 1, Jan12\",\"Homework 1, Jan13\",\"Lecture 2, Jan 13\",\"Homework 2, Jan14\",\"Lecture 3, Jan 14\",\"Homework 3, Jan15\",\"Lecture 4, Jan 15\",\"Mystery Word, Jan 20\",\"Lecture 5, Jan 20\",\"Currency, Jan 21\",\"Lecture 6, 21\",\"Blackjack1, Jan 22\",\"Lecture 7, Jan 22\",\"Lecture 8, Jan 23\",\"Blackjack2, Jan26\",\"Lecture 9, Jan26\",\"Random Art, Jan 27\",\"Lecture10, Jan27\",Charting,\"Lecture11, Jan28\",PigSim,\"Lecture12, Jan29\",Traffic Sim I,\"Lecture13,Feb2\"\r\n", + "P01,3,4,3,4,4,5,5,5,4,4,4,5.5,4,5.5,,4,5,,,,,,,\r\n", + "P02,4,3.5,3,5,4,4.5,4.5,5,5,5,5,5,,,5,5,5,,,5,5,5,,\r\n", + "P03,,5,3,4,5,5,5,5,5,5,5,5,,5,6,,,5,5,5,,,,\r\n", + "P04,3,3,2,3,4,4,4,4,5,,4,,4,4,,1,3,1,3,5,5,5,5,\r\n", + "P05,,3,3,3,3,4,4,4,5,4,4,5,4,4,4,3,6,,,,,,,\r\n", + "P06,3,3.5,3,3,3,3,4,4,3,3,5,,5,5,5,4,5,3,4,4,4,,,\r\n", + "P07,3.5,4,3,4,5,4,4.5,4,5,5,5,5,4,5,,5,4,4.9,5,4,4,4.9,4.9,\r\n", + "P08,2,3,2,3,4,4,3,4,3,3,5,5.5,4,5,5,5,5,5,5,4,4,4,5,5\r\n", + "P09,,1,1,1,2,2,2,3,3,2,3,2,3,3,3,3,2,,,,,,,\r\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python = pd.read_csv(\"cohort_3_python.csv\", )\n", + "python.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameLecture 1, Jan12Homework 1, Jan13Lecture 2, Jan 13Homework 2, Jan14Lecture 3, Jan 14Homework 3, Jan15Lecture 4, Jan 15Mystery Word, Jan 20Lecture 5, Jan 20...Blackjack2, Jan26Lecture 9, Jan26Random Art, Jan 27Lecture10, Jan27ChartingLecture11, Jan28PigSimLecture12, Jan29Traffic Sim ILecture13,Feb2
0 P01 3 4.0 3 4 4 5.0 5.0 5 4...NaN 4 5NaNNaNNaNNaNNaNNaNNaN
1 P02 4 3.5 3 5 4 4.5 4.5 5 5... 5 5 5NaNNaN 5 5 5NaNNaN
2 P03NaN 5.0 3 4 5 5.0 5.0 5 5... 6NaNNaN 5 5 5NaNNaNNaNNaN
3 P04 3 3.0 2 3 4 4.0 4.0 4 5...NaN 1 3 1 3 5 5 5 5NaN
4 P05NaN 3.0 3 3 3 4.0 4.0 4 5... 4 3 6NaNNaNNaNNaNNaNNaNNaN
\n", + "

5 rows \u00d7 25 columns

\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 5, + "text": [ + " Name Lecture 1, Jan12 Homework 1, Jan13 Lecture 2, Jan 13 \\\n", + "0 P01 3 4.0 3 \n", + "1 P02 4 3.5 3 \n", + "2 P03 NaN 5.0 3 \n", + "3 P04 3 3.0 2 \n", + "4 P05 NaN 3.0 3 \n", + "\n", + " Homework 2, Jan14 Lecture 3, Jan 14 Homework 3, Jan15 Lecture 4, Jan 15 \\\n", + "0 4 4 5.0 5.0 \n", + "1 5 4 4.5 4.5 \n", + "2 4 5 5.0 5.0 \n", + "3 3 4 4.0 4.0 \n", + "4 3 3 4.0 4.0 \n", + "\n", + " Mystery Word, Jan 20 Lecture 5, Jan 20 ... Blackjack2, Jan26 \\\n", + "0 5 4 ... NaN \n", + "1 5 5 ... 5 \n", + "2 5 5 ... 6 \n", + "3 4 5 ... NaN \n", + "4 4 5 ... 4 \n", + "\n", + " Lecture 9, Jan26 Random Art, Jan 27 Lecture10, Jan27 Charting \\\n", + "0 4 5 NaN NaN \n", + "1 5 5 NaN NaN \n", + "2 NaN NaN 5 5 \n", + "3 1 3 1 3 \n", + "4 3 6 NaN NaN \n", + "\n", + " Lecture11, Jan28 PigSim Lecture12, Jan29 Traffic Sim I Lecture13,Feb2 \n", + "0 NaN NaN NaN NaN NaN \n", + "1 5 5 5 NaN NaN \n", + "2 5 NaN NaN NaN NaN \n", + "3 5 5 5 5 NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + "[5 rows x 25 columns]" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python = python.set_index('Name')\n", + "python" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Lecture 1, Jan12Homework 1, Jan13Lecture 2, Jan 13Homework 2, Jan14Lecture 3, Jan 14Homework 3, Jan15Lecture 4, Jan 15Mystery Word, Jan 20Lecture 5, Jan 20Currency, Jan 21...Blackjack2, Jan26Lecture 9, Jan26Random Art, Jan 27Lecture10, Jan27ChartingLecture11, Jan28PigSimLecture12, Jan29Traffic Sim ILecture13,Feb2
Name
P01 3.000000 4.000000 3.000000 4.0 4.000000 5.000000 5.000000 5.000000 4 4.000000... NaN 4.000000 5.000000 NaN NaN NaN NaN NaN NaNNaN
P02 4.000000 3.500000 3.000000 5.0 4.000000 4.500000 4.500000 5.000000 5 5.000000... 5.000000 5.000000 5.000000 NaN NaN 5.0 5.000000 5.0000 NaNNaN
P03 NaN 5.000000 3.000000 4.0 5.000000 5.000000 5.000000 5.000000 5 5.000000... 6.000000 NaN NaN 5.00 5.000000 5.0 NaN NaN NaNNaN
P04 3.000000 3.000000 2.000000 3.0 4.000000 4.000000 4.000000 4.000000 5 NaN... NaN 1.000000 3.000000 1.00 3.000000 5.0 5.000000 5.0000 5.0NaN
P05 NaN 3.000000 3.000000 3.0 3.000000 4.000000 4.000000 4.000000 5 4.000000... 4.000000 3.000000 6.000000 NaN NaN NaN NaN NaN NaNNaN
P06 3.000000 3.500000 3.000000 3.0 3.000000 3.000000 4.000000 4.000000 3 3.000000... 5.000000 4.000000 5.000000 3.00 4.000000 4.0 4.000000 NaN NaNNaN
P07 3.500000 4.000000 3.000000 4.0 5.000000 4.000000 4.500000 4.000000 5 5.000000... NaN 5.000000 4.000000 4.90 5.000000 4.0 4.000000 4.9000 4.9NaN
P08 2.000000 3.000000 2.000000 3.0 4.000000 4.000000 3.000000 4.000000 3 3.000000... 5.000000 5.000000 5.000000 5.00 5.000000 4.0 4.000000 4.0000 5.0 5
P09 NaN 1.000000 1.000000 1.0 2.000000 2.000000 2.000000 3.000000 3 2.000000... 3.000000 3.000000 2.000000 NaN NaN NaN NaN NaN NaNNaN
P10 2.000000 2.000000 2.000000 3.0 NaN 3.000000 3.000000 3.000000 3 4.000000... 5.000000 4.000000 5.000000 4.00 5.000000 4.0 5.000000 4.0000 NaNNaN
P11 2.000000 5.000000 4.000000 3.0 5.000000 4.000000 4.000000 4.000000 4 NaN... 4.000000 4.000000 4.000000 4.00 5.000000 5.0 4.000000 5.0000 5.0NaN
P12 3.500000 4.000000 4.000000 4.0 4.500000 5.000000 5.000000 5.000000 5 4.000000... 5.000000 4.000000 4.000000 4.00 6.000000 5.0 5.000000 6.0000 NaNNaN
P13 2.500000 3.000000 3.000000 3.0 3.000000 3.000000 4.000000 NaN 3 3.000000... 4.000000 NaN 5.000000 3.00 NaN NaN NaN NaN NaNNaN
P14 3.000000 3.000000 3.000000 3.0 4.000000 3.000000 4.000000 4.000000 4 4.000000... 4.000000 4.000000 3.000000 NaN NaN NaN NaN NaN NaNNaN
P15 2.000000 2.000000 2.000000 2.0 3.000000 3.000000 3.000000 3.000000 3 3.000000... 3.000000 3.000000 4.000000 3.00 3.000000 3.0 3.000000 3.0000 5.0NaN
NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
Average 2.791667 3.266667 2.733333 3.2 3.821429 3.766667 3.933333 4.071429 4 3.769231... 4.416667 3.769231 4.285714 3.69 4.555556 4.4 4.333333 4.6125 NaNNaN
Min 2.000000 1.000000 1.000000 1.0 2.000000 2.000000 2.000000 3.000000 3 2.000000... 3.000000 1.000000 2.000000 1.00 3.000000 3.0 3.000000 3.0000 NaNNaN
Max 4.000000 5.000000 4.000000 5.0 5.000000 5.000000 5.000000 5.000000 5 5.000000... 6.000000 5.000000 6.000000 5.00 6.000000 5.0 5.000000 6.0000 NaNNaN
\n", + "

19 rows \u00d7 24 columns

\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 6, + "text": [ + " Lecture 1, Jan12 Homework 1, Jan13 Lecture 2, Jan 13 \\\n", + "Name \n", + "P01 3.000000 4.000000 3.000000 \n", + "P02 4.000000 3.500000 3.000000 \n", + "P03 NaN 5.000000 3.000000 \n", + "P04 3.000000 3.000000 2.000000 \n", + "P05 NaN 3.000000 3.000000 \n", + "P06 3.000000 3.500000 3.000000 \n", + "P07 3.500000 4.000000 3.000000 \n", + "P08 2.000000 3.000000 2.000000 \n", + "P09 NaN 1.000000 1.000000 \n", + "P10 2.000000 2.000000 2.000000 \n", + "P11 2.000000 5.000000 4.000000 \n", + "P12 3.500000 4.000000 4.000000 \n", + "P13 2.500000 3.000000 3.000000 \n", + "P14 3.000000 3.000000 3.000000 \n", + "P15 2.000000 2.000000 2.000000 \n", + "NaN NaN NaN NaN \n", + "Average 2.791667 3.266667 2.733333 \n", + "Min 2.000000 1.000000 1.000000 \n", + "Max 4.000000 5.000000 4.000000 \n", + "\n", + " Homework 2, Jan14 Lecture 3, Jan 14 Homework 3, Jan15 \\\n", + "Name \n", + "P01 4.0 4.000000 5.000000 \n", + "P02 5.0 4.000000 4.500000 \n", + "P03 4.0 5.000000 5.000000 \n", + "P04 3.0 4.000000 4.000000 \n", + "P05 3.0 3.000000 4.000000 \n", + "P06 3.0 3.000000 3.000000 \n", + "P07 4.0 5.000000 4.000000 \n", + "P08 3.0 4.000000 4.000000 \n", + "P09 1.0 2.000000 2.000000 \n", + "P10 3.0 NaN 3.000000 \n", + "P11 3.0 5.000000 4.000000 \n", + "P12 4.0 4.500000 5.000000 \n", + "P13 3.0 3.000000 3.000000 \n", + "P14 3.0 4.000000 3.000000 \n", + "P15 2.0 3.000000 3.000000 \n", + "NaN NaN NaN NaN \n", + "Average 3.2 3.821429 3.766667 \n", + "Min 1.0 2.000000 2.000000 \n", + "Max 5.0 5.000000 5.000000 \n", + "\n", + " Lecture 4, Jan 15 Mystery Word, Jan 20 Lecture 5, Jan 20 \\\n", + "Name \n", + "P01 5.000000 5.000000 4 \n", + "P02 4.500000 5.000000 5 \n", + "P03 5.000000 5.000000 5 \n", + "P04 4.000000 4.000000 5 \n", + "P05 4.000000 4.000000 5 \n", + "P06 4.000000 4.000000 3 \n", + "P07 4.500000 4.000000 5 \n", + "P08 3.000000 4.000000 3 \n", + "P09 2.000000 3.000000 3 \n", + "P10 3.000000 3.000000 3 \n", + "P11 4.000000 4.000000 4 \n", + "P12 5.000000 5.000000 5 \n", + "P13 4.000000 NaN 3 \n", + "P14 4.000000 4.000000 4 \n", + "P15 3.000000 3.000000 3 \n", + "NaN NaN NaN NaN \n", + "Average 3.933333 4.071429 4 \n", + "Min 2.000000 3.000000 3 \n", + "Max 5.000000 5.000000 5 \n", + "\n", + " Currency, Jan 21 ... Blackjack2, Jan26 \\\n", + "Name ... \n", + "P01 4.000000 ... NaN \n", + "P02 5.000000 ... 5.000000 \n", + "P03 5.000000 ... 6.000000 \n", + "P04 NaN ... NaN \n", + "P05 4.000000 ... 4.000000 \n", + "P06 3.000000 ... 5.000000 \n", + "P07 5.000000 ... NaN \n", + "P08 3.000000 ... 5.000000 \n", + "P09 2.000000 ... 3.000000 \n", + "P10 4.000000 ... 5.000000 \n", + "P11 NaN ... 4.000000 \n", + "P12 4.000000 ... 5.000000 \n", + "P13 3.000000 ... 4.000000 \n", + "P14 4.000000 ... 4.000000 \n", + "P15 3.000000 ... 3.000000 \n", + "NaN NaN ... NaN \n", + "Average 3.769231 ... 4.416667 \n", + "Min 2.000000 ... 3.000000 \n", + "Max 5.000000 ... 6.000000 \n", + "\n", + " Lecture 9, Jan26 Random Art, Jan 27 Lecture10, Jan27 Charting \\\n", + "Name \n", + "P01 4.000000 5.000000 NaN NaN \n", + "P02 5.000000 5.000000 NaN NaN \n", + "P03 NaN NaN 5.00 5.000000 \n", + "P04 1.000000 3.000000 1.00 3.000000 \n", + "P05 3.000000 6.000000 NaN NaN \n", + "P06 4.000000 5.000000 3.00 4.000000 \n", + "P07 5.000000 4.000000 4.90 5.000000 \n", + "P08 5.000000 5.000000 5.00 5.000000 \n", + "P09 3.000000 2.000000 NaN NaN \n", + "P10 4.000000 5.000000 4.00 5.000000 \n", + "P11 4.000000 4.000000 4.00 5.000000 \n", + "P12 4.000000 4.000000 4.00 6.000000 \n", + "P13 NaN 5.000000 3.00 NaN \n", + "P14 4.000000 3.000000 NaN NaN \n", + "P15 3.000000 4.000000 3.00 3.000000 \n", + "NaN NaN NaN NaN NaN \n", + "Average 3.769231 4.285714 3.69 4.555556 \n", + "Min 1.000000 2.000000 1.00 3.000000 \n", + "Max 5.000000 6.000000 5.00 6.000000 \n", + "\n", + " Lecture11, Jan28 PigSim Lecture12, Jan29 Traffic Sim I \\\n", + "Name \n", + "P01 NaN NaN NaN NaN \n", + "P02 5.0 5.000000 5.0000 NaN \n", + "P03 5.0 NaN NaN NaN \n", + "P04 5.0 5.000000 5.0000 5.0 \n", + "P05 NaN NaN NaN NaN \n", + "P06 4.0 4.000000 NaN NaN \n", + "P07 4.0 4.000000 4.9000 4.9 \n", + "P08 4.0 4.000000 4.0000 5.0 \n", + "P09 NaN NaN NaN NaN \n", + "P10 4.0 5.000000 4.0000 NaN \n", + "P11 5.0 4.000000 5.0000 5.0 \n", + "P12 5.0 5.000000 6.0000 NaN \n", + "P13 NaN NaN NaN NaN \n", + "P14 NaN NaN NaN NaN \n", + "P15 3.0 3.000000 3.0000 5.0 \n", + "NaN NaN NaN NaN NaN \n", + "Average 4.4 4.333333 4.6125 NaN \n", + "Min 3.0 3.000000 3.0000 NaN \n", + "Max 5.0 5.000000 6.0000 NaN \n", + "\n", + " Lecture13,Feb2 \n", + "Name \n", + "P01 NaN \n", + "P02 NaN \n", + "P03 NaN \n", + "P04 NaN \n", + "P05 NaN \n", + "P06 NaN \n", + "P07 NaN \n", + "P08 5 \n", + "P09 NaN \n", + "P10 NaN \n", + "P11 NaN \n", + "P12 NaN \n", + "P13 NaN \n", + "P14 NaN \n", + "P15 NaN \n", + "NaN NaN \n", + "Average NaN \n", + "Min NaN \n", + "Max NaN \n", + "\n", + "[19 rows x 24 columns]" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python = python.transpose()\n", + "python" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameP01P02P03P04P05P06P07P08P09P10P11P12P13P14P15nanAverageMinMax
Lecture 1, Jan12 3.0 4.0NaN 3NaN 3.0 3.5 2.0NaN 2 2 3.5 2.5 3 2NaN 2.791667 2 4.0
Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 5 4.0 3.0 3 2NaN 3.266667 1 5.0
Lecture 2, Jan 13 3.0 3.0 3 2 3 3.0 3.0 2.0 1 2 4 4.0 3.0 3 2NaN 2.733333 1 4.0
Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 3 4.0 3.0 3 2NaN 3.200000 1 5.0
Lecture 3, Jan 14 4.0 4.0 5 4 3 3.0 5.0 4.0 2NaN 5 4.5 3.0 4 3NaN 3.821429 2 5.0
Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 4 5.0 3.0 3 3NaN 3.766667 2 5.0
Lecture 4, Jan 15 5.0 4.5 5 4 4 4.0 4.5 3.0 2 3 4 5.0 4.0 4 3NaN 3.933333 2 5.0
Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 4 5.0 NaN 4 3NaN 4.071429 3 5.0
Lecture 5, Jan 20 4.0 5.0 5 5 5 3.0 5.0 3.0 3 3 4 5.0 3.0 4 3NaN 4.000000 3 5.0
Currency, Jan 21 4.0 5.0 5NaN 4 3.0 5.0 3.0 2 4NaN 4.0 3.0 4 3NaN 3.769231 2 5.0
Lecture 6, 21 4.0 5.0 5 4 4 5.0 5.0 5.0 3 4NaN 4.0 3.0 4 3NaN 4.142857 3 5.0
Blackjack1, Jan 22 5.5 5.0 5NaN 5 NaN 5.0 5.5 2 4 4 4.0 4.0 4 3NaN 4.307692 2 5.5
Lecture 7, Jan 22 4.0 NaNNaN 4 4 5.0 4.0 4.0 3 4 4 NaN 4.0NaN 3NaN 3.909091 3 5.0
Lecture 8, Jan 23 5.5 NaN 5 4 4 5.0 5.0 5.0 3 5 4 5.5 4.0NaN 3NaN 4.461538 3 5.5
Blackjack2, Jan26 NaN 5.0 6NaN 4 5.0 NaN 5.0 3 5 4 5.0 4.0 4 3NaN 4.416667 3 6.0
Lecture 9, Jan26 4.0 5.0NaN 1 3 4.0 5.0 5.0 3 4 4 4.0 NaN 4 3NaN 3.769231 1 5.0
Random Art, Jan 27 5.0 5.0NaN 3 6 5.0 4.0 5.0 2 5 4 4.0 5.0 3 4NaN 4.285714 2 6.0
Lecture10, Jan27 NaN NaN 5 1NaN 3.0 4.9 5.0NaN 4 4 4.0 3.0NaN 3NaN 3.690000 1 5.0
Charting NaN NaN 5 3NaN 4.0 5.0 5.0NaN 5 5 6.0 NaNNaN 3NaN 4.555556 3 6.0
Lecture11, Jan28 NaN 5.0 5 5NaN 4.0 4.0 4.0NaN 4 5 5.0 NaNNaN 3NaN 4.400000 3 5.0
PigSim NaN 5.0NaN 5NaN 4.0 4.0 4.0NaN 5 4 5.0 NaNNaN 3NaN 4.333333 3 5.0
Lecture12, Jan29 NaN 5.0NaN 5NaN NaN 4.9 4.0NaN 4 5 6.0 NaNNaN 3NaN 4.612500 3 6.0
Traffic Sim I NaN NaNNaN 5NaN NaN 4.9 5.0NaNNaN 5 NaN NaNNaN 5NaN NaNNaN NaN
Lecture13,Feb2 NaN NaNNaNNaNNaN NaN NaN 5.0NaNNaNNaN NaN NaNNaNNaNNaN NaNNaN NaN
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + "Name P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 P11 \\\n", + "Lecture 1, Jan12 3.0 4.0 NaN 3 NaN 3.0 3.5 2.0 NaN 2 2 \n", + "Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 5 \n", + "Lecture 2, Jan 13 3.0 3.0 3 2 3 3.0 3.0 2.0 1 2 4 \n", + "Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 3 \n", + "Lecture 3, Jan 14 4.0 4.0 5 4 3 3.0 5.0 4.0 2 NaN 5 \n", + "Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 4 \n", + "Lecture 4, Jan 15 5.0 4.5 5 4 4 4.0 4.5 3.0 2 3 4 \n", + "Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 4 \n", + "Lecture 5, Jan 20 4.0 5.0 5 5 5 3.0 5.0 3.0 3 3 4 \n", + "Currency, Jan 21 4.0 5.0 5 NaN 4 3.0 5.0 3.0 2 4 NaN \n", + "Lecture 6, 21 4.0 5.0 5 4 4 5.0 5.0 5.0 3 4 NaN \n", + "Blackjack1, Jan 22 5.5 5.0 5 NaN 5 NaN 5.0 5.5 2 4 4 \n", + "Lecture 7, Jan 22 4.0 NaN NaN 4 4 5.0 4.0 4.0 3 4 4 \n", + "Lecture 8, Jan 23 5.5 NaN 5 4 4 5.0 5.0 5.0 3 5 4 \n", + "Blackjack2, Jan26 NaN 5.0 6 NaN 4 5.0 NaN 5.0 3 5 4 \n", + "Lecture 9, Jan26 4.0 5.0 NaN 1 3 4.0 5.0 5.0 3 4 4 \n", + "Random Art, Jan 27 5.0 5.0 NaN 3 6 5.0 4.0 5.0 2 5 4 \n", + "Lecture10, Jan27 NaN NaN 5 1 NaN 3.0 4.9 5.0 NaN 4 4 \n", + "Charting NaN NaN 5 3 NaN 4.0 5.0 5.0 NaN 5 5 \n", + "Lecture11, Jan28 NaN 5.0 5 5 NaN 4.0 4.0 4.0 NaN 4 5 \n", + "PigSim NaN 5.0 NaN 5 NaN 4.0 4.0 4.0 NaN 5 4 \n", + "Lecture12, Jan29 NaN 5.0 NaN 5 NaN NaN 4.9 4.0 NaN 4 5 \n", + "Traffic Sim I NaN NaN NaN 5 NaN NaN 4.9 5.0 NaN NaN 5 \n", + "Lecture13,Feb2 NaN NaN NaN NaN NaN NaN NaN 5.0 NaN NaN NaN \n", + "\n", + "Name P12 P13 P14 P15 NaN Average Min Max \n", + "Lecture 1, Jan12 3.5 2.5 3 2 NaN 2.791667 2 4.0 \n", + "Homework 1, Jan13 4.0 3.0 3 2 NaN 3.266667 1 5.0 \n", + "Lecture 2, Jan 13 4.0 3.0 3 2 NaN 2.733333 1 4.0 \n", + "Homework 2, Jan14 4.0 3.0 3 2 NaN 3.200000 1 5.0 \n", + "Lecture 3, Jan 14 4.5 3.0 4 3 NaN 3.821429 2 5.0 \n", + "Homework 3, Jan15 5.0 3.0 3 3 NaN 3.766667 2 5.0 \n", + "Lecture 4, Jan 15 5.0 4.0 4 3 NaN 3.933333 2 5.0 \n", + "Mystery Word, Jan 20 5.0 NaN 4 3 NaN 4.071429 3 5.0 \n", + "Lecture 5, Jan 20 5.0 3.0 4 3 NaN 4.000000 3 5.0 \n", + "Currency, Jan 21 4.0 3.0 4 3 NaN 3.769231 2 5.0 \n", + "Lecture 6, 21 4.0 3.0 4 3 NaN 4.142857 3 5.0 \n", + "Blackjack1, Jan 22 4.0 4.0 4 3 NaN 4.307692 2 5.5 \n", + "Lecture 7, Jan 22 NaN 4.0 NaN 3 NaN 3.909091 3 5.0 \n", + "Lecture 8, Jan 23 5.5 4.0 NaN 3 NaN 4.461538 3 5.5 \n", + "Blackjack2, Jan26 5.0 4.0 4 3 NaN 4.416667 3 6.0 \n", + "Lecture 9, Jan26 4.0 NaN 4 3 NaN 3.769231 1 5.0 \n", + "Random Art, Jan 27 4.0 5.0 3 4 NaN 4.285714 2 6.0 \n", + "Lecture10, Jan27 4.0 3.0 NaN 3 NaN 3.690000 1 5.0 \n", + "Charting 6.0 NaN NaN 3 NaN 4.555556 3 6.0 \n", + "Lecture11, Jan28 5.0 NaN NaN 3 NaN 4.400000 3 5.0 \n", + "PigSim 5.0 NaN NaN 3 NaN 4.333333 3 5.0 \n", + "Lecture12, Jan29 6.0 NaN NaN 3 NaN 4.612500 3 6.0 \n", + "Traffic Sim I NaN NaN NaN 5 NaN NaN NaN NaN \n", + "Lecture13,Feb2 NaN NaN NaN NaN NaN NaN NaN NaN " + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python = python.drop([\"Average\", \"Min\", \"Max\"], axis=1)\n", + "python" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameP01P02P03P04P05P06P07P08P09P10P11P12P13P14P15nan
Lecture 1, Jan12 3.0 4.0NaN 3NaN 3.0 3.5 2.0NaN 2 2 3.5 2.5 3 2NaN
Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 5 4.0 3.0 3 2NaN
Lecture 2, Jan 13 3.0 3.0 3 2 3 3.0 3.0 2.0 1 2 4 4.0 3.0 3 2NaN
Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 3 4.0 3.0 3 2NaN
Lecture 3, Jan 14 4.0 4.0 5 4 3 3.0 5.0 4.0 2NaN 5 4.5 3.0 4 3NaN
Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 4 5.0 3.0 3 3NaN
Lecture 4, Jan 15 5.0 4.5 5 4 4 4.0 4.5 3.0 2 3 4 5.0 4.0 4 3NaN
Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 4 5.0 NaN 4 3NaN
Lecture 5, Jan 20 4.0 5.0 5 5 5 3.0 5.0 3.0 3 3 4 5.0 3.0 4 3NaN
Currency, Jan 21 4.0 5.0 5NaN 4 3.0 5.0 3.0 2 4NaN 4.0 3.0 4 3NaN
Lecture 6, 21 4.0 5.0 5 4 4 5.0 5.0 5.0 3 4NaN 4.0 3.0 4 3NaN
Blackjack1, Jan 22 5.5 5.0 5NaN 5 NaN 5.0 5.5 2 4 4 4.0 4.0 4 3NaN
Lecture 7, Jan 22 4.0 NaNNaN 4 4 5.0 4.0 4.0 3 4 4 NaN 4.0NaN 3NaN
Lecture 8, Jan 23 5.5 NaN 5 4 4 5.0 5.0 5.0 3 5 4 5.5 4.0NaN 3NaN
Blackjack2, Jan26 NaN 5.0 6NaN 4 5.0 NaN 5.0 3 5 4 5.0 4.0 4 3NaN
Lecture 9, Jan26 4.0 5.0NaN 1 3 4.0 5.0 5.0 3 4 4 4.0 NaN 4 3NaN
Random Art, Jan 27 5.0 5.0NaN 3 6 5.0 4.0 5.0 2 5 4 4.0 5.0 3 4NaN
Lecture10, Jan27 NaN NaN 5 1NaN 3.0 4.9 5.0NaN 4 4 4.0 3.0NaN 3NaN
Charting NaN NaN 5 3NaN 4.0 5.0 5.0NaN 5 5 6.0 NaNNaN 3NaN
Lecture11, Jan28 NaN 5.0 5 5NaN 4.0 4.0 4.0NaN 4 5 5.0 NaNNaN 3NaN
PigSim NaN 5.0NaN 5NaN 4.0 4.0 4.0NaN 5 4 5.0 NaNNaN 3NaN
Lecture12, Jan29 NaN 5.0NaN 5NaN NaN 4.9 4.0NaN 4 5 6.0 NaNNaN 3NaN
Traffic Sim I NaN NaNNaN 5NaN NaN 4.9 5.0NaNNaN 5 NaN NaNNaN 5NaN
Lecture13,Feb2 NaN NaNNaNNaNNaN NaN NaN 5.0NaNNaNNaN NaN NaNNaNNaNNaN
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "Name P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 P11 \\\n", + "Lecture 1, Jan12 3.0 4.0 NaN 3 NaN 3.0 3.5 2.0 NaN 2 2 \n", + "Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 5 \n", + "Lecture 2, Jan 13 3.0 3.0 3 2 3 3.0 3.0 2.0 1 2 4 \n", + "Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 3 \n", + "Lecture 3, Jan 14 4.0 4.0 5 4 3 3.0 5.0 4.0 2 NaN 5 \n", + "Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 4 \n", + "Lecture 4, Jan 15 5.0 4.5 5 4 4 4.0 4.5 3.0 2 3 4 \n", + "Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 4 \n", + "Lecture 5, Jan 20 4.0 5.0 5 5 5 3.0 5.0 3.0 3 3 4 \n", + "Currency, Jan 21 4.0 5.0 5 NaN 4 3.0 5.0 3.0 2 4 NaN \n", + "Lecture 6, 21 4.0 5.0 5 4 4 5.0 5.0 5.0 3 4 NaN \n", + "Blackjack1, Jan 22 5.5 5.0 5 NaN 5 NaN 5.0 5.5 2 4 4 \n", + "Lecture 7, Jan 22 4.0 NaN NaN 4 4 5.0 4.0 4.0 3 4 4 \n", + "Lecture 8, Jan 23 5.5 NaN 5 4 4 5.0 5.0 5.0 3 5 4 \n", + "Blackjack2, Jan26 NaN 5.0 6 NaN 4 5.0 NaN 5.0 3 5 4 \n", + "Lecture 9, Jan26 4.0 5.0 NaN 1 3 4.0 5.0 5.0 3 4 4 \n", + "Random Art, Jan 27 5.0 5.0 NaN 3 6 5.0 4.0 5.0 2 5 4 \n", + "Lecture10, Jan27 NaN NaN 5 1 NaN 3.0 4.9 5.0 NaN 4 4 \n", + "Charting NaN NaN 5 3 NaN 4.0 5.0 5.0 NaN 5 5 \n", + "Lecture11, Jan28 NaN 5.0 5 5 NaN 4.0 4.0 4.0 NaN 4 5 \n", + "PigSim NaN 5.0 NaN 5 NaN 4.0 4.0 4.0 NaN 5 4 \n", + "Lecture12, Jan29 NaN 5.0 NaN 5 NaN NaN 4.9 4.0 NaN 4 5 \n", + "Traffic Sim I NaN NaN NaN 5 NaN NaN 4.9 5.0 NaN NaN 5 \n", + "Lecture13,Feb2 NaN NaN NaN NaN NaN NaN NaN 5.0 NaN NaN NaN \n", + "\n", + "Name P12 P13 P14 P15 NaN \n", + "Lecture 1, Jan12 3.5 2.5 3 2 NaN \n", + "Homework 1, Jan13 4.0 3.0 3 2 NaN \n", + "Lecture 2, Jan 13 4.0 3.0 3 2 NaN \n", + "Homework 2, Jan14 4.0 3.0 3 2 NaN \n", + "Lecture 3, Jan 14 4.5 3.0 4 3 NaN \n", + "Homework 3, Jan15 5.0 3.0 3 3 NaN \n", + "Lecture 4, Jan 15 5.0 4.0 4 3 NaN \n", + "Mystery Word, Jan 20 5.0 NaN 4 3 NaN \n", + "Lecture 5, Jan 20 5.0 3.0 4 3 NaN \n", + "Currency, Jan 21 4.0 3.0 4 3 NaN \n", + "Lecture 6, 21 4.0 3.0 4 3 NaN \n", + "Blackjack1, Jan 22 4.0 4.0 4 3 NaN \n", + "Lecture 7, Jan 22 NaN 4.0 NaN 3 NaN \n", + "Lecture 8, Jan 23 5.5 4.0 NaN 3 NaN \n", + "Blackjack2, Jan26 5.0 4.0 4 3 NaN \n", + "Lecture 9, Jan26 4.0 NaN 4 3 NaN \n", + "Random Art, Jan 27 4.0 5.0 3 4 NaN \n", + "Lecture10, Jan27 4.0 3.0 NaN 3 NaN \n", + "Charting 6.0 NaN NaN 3 NaN \n", + "Lecture11, Jan28 5.0 NaN NaN 3 NaN \n", + "PigSim 5.0 NaN NaN 3 NaN \n", + "Lecture12, Jan29 6.0 NaN NaN 3 NaN \n", + "Traffic Sim I NaN NaN NaN 5 NaN \n", + "Lecture13,Feb2 NaN NaN NaN NaN NaN " + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python = python.ix[::1, \"P01\":\"P15\"]\n", + "#sliced off the last column since it was all NaN\n", + "python" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameP01P02P03P04P05P06P07P08P09P10P11P12P13P14P15
Lecture 1, Jan12 3.0 4.0NaN 3NaN 3.0 3.5 2.0NaN 2 2 3.5 2.5 3 2
Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 5 4.0 3.0 3 2
Lecture 2, Jan 13 3.0 3.0 3 2 3 3.0 3.0 2.0 1 2 4 4.0 3.0 3 2
Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 3 4.0 3.0 3 2
Lecture 3, Jan 14 4.0 4.0 5 4 3 3.0 5.0 4.0 2NaN 5 4.5 3.0 4 3
Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 4 5.0 3.0 3 3
Lecture 4, Jan 15 5.0 4.5 5 4 4 4.0 4.5 3.0 2 3 4 5.0 4.0 4 3
Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 4 5.0 NaN 4 3
Lecture 5, Jan 20 4.0 5.0 5 5 5 3.0 5.0 3.0 3 3 4 5.0 3.0 4 3
Currency, Jan 21 4.0 5.0 5NaN 4 3.0 5.0 3.0 2 4NaN 4.0 3.0 4 3
Lecture 6, 21 4.0 5.0 5 4 4 5.0 5.0 5.0 3 4NaN 4.0 3.0 4 3
Blackjack1, Jan 22 5.5 5.0 5NaN 5 NaN 5.0 5.5 2 4 4 4.0 4.0 4 3
Lecture 7, Jan 22 4.0 NaNNaN 4 4 5.0 4.0 4.0 3 4 4 NaN 4.0NaN 3
Lecture 8, Jan 23 5.5 NaN 5 4 4 5.0 5.0 5.0 3 5 4 5.5 4.0NaN 3
Blackjack2, Jan26 NaN 5.0 6NaN 4 5.0 NaN 5.0 3 5 4 5.0 4.0 4 3
Lecture 9, Jan26 4.0 5.0NaN 1 3 4.0 5.0 5.0 3 4 4 4.0 NaN 4 3
Random Art, Jan 27 5.0 5.0NaN 3 6 5.0 4.0 5.0 2 5 4 4.0 5.0 3 4
Lecture10, Jan27 NaN NaN 5 1NaN 3.0 4.9 5.0NaN 4 4 4.0 3.0NaN 3
Charting NaN NaN 5 3NaN 4.0 5.0 5.0NaN 5 5 6.0 NaNNaN 3
Lecture11, Jan28 NaN 5.0 5 5NaN 4.0 4.0 4.0NaN 4 5 5.0 NaNNaN 3
PigSim NaN 5.0NaN 5NaN 4.0 4.0 4.0NaN 5 4 5.0 NaNNaN 3
Lecture12, Jan29 NaN 5.0NaN 5NaN NaN 4.9 4.0NaN 4 5 6.0 NaNNaN 3
Traffic Sim I NaN NaNNaN 5NaN NaN 4.9 5.0NaNNaN 5 NaN NaNNaN 5
Lecture13,Feb2 NaN NaNNaNNaNNaN NaN NaN 5.0NaNNaNNaN NaN NaNNaNNaN
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 9, + "text": [ + "Name P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 P11 \\\n", + "Lecture 1, Jan12 3.0 4.0 NaN 3 NaN 3.0 3.5 2.0 NaN 2 2 \n", + "Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 5 \n", + "Lecture 2, Jan 13 3.0 3.0 3 2 3 3.0 3.0 2.0 1 2 4 \n", + "Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 3 \n", + "Lecture 3, Jan 14 4.0 4.0 5 4 3 3.0 5.0 4.0 2 NaN 5 \n", + "Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 4 \n", + "Lecture 4, Jan 15 5.0 4.5 5 4 4 4.0 4.5 3.0 2 3 4 \n", + "Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 4 \n", + "Lecture 5, Jan 20 4.0 5.0 5 5 5 3.0 5.0 3.0 3 3 4 \n", + "Currency, Jan 21 4.0 5.0 5 NaN 4 3.0 5.0 3.0 2 4 NaN \n", + "Lecture 6, 21 4.0 5.0 5 4 4 5.0 5.0 5.0 3 4 NaN \n", + "Blackjack1, Jan 22 5.5 5.0 5 NaN 5 NaN 5.0 5.5 2 4 4 \n", + "Lecture 7, Jan 22 4.0 NaN NaN 4 4 5.0 4.0 4.0 3 4 4 \n", + "Lecture 8, Jan 23 5.5 NaN 5 4 4 5.0 5.0 5.0 3 5 4 \n", + "Blackjack2, Jan26 NaN 5.0 6 NaN 4 5.0 NaN 5.0 3 5 4 \n", + "Lecture 9, Jan26 4.0 5.0 NaN 1 3 4.0 5.0 5.0 3 4 4 \n", + "Random Art, Jan 27 5.0 5.0 NaN 3 6 5.0 4.0 5.0 2 5 4 \n", + "Lecture10, Jan27 NaN NaN 5 1 NaN 3.0 4.9 5.0 NaN 4 4 \n", + "Charting NaN NaN 5 3 NaN 4.0 5.0 5.0 NaN 5 5 \n", + "Lecture11, Jan28 NaN 5.0 5 5 NaN 4.0 4.0 4.0 NaN 4 5 \n", + "PigSim NaN 5.0 NaN 5 NaN 4.0 4.0 4.0 NaN 5 4 \n", + "Lecture12, Jan29 NaN 5.0 NaN 5 NaN NaN 4.9 4.0 NaN 4 5 \n", + "Traffic Sim I NaN NaN NaN 5 NaN NaN 4.9 5.0 NaN NaN 5 \n", + "Lecture13,Feb2 NaN NaN NaN NaN NaN NaN NaN 5.0 NaN NaN NaN \n", + "\n", + "Name P12 P13 P14 P15 \n", + "Lecture 1, Jan12 3.5 2.5 3 2 \n", + "Homework 1, Jan13 4.0 3.0 3 2 \n", + "Lecture 2, Jan 13 4.0 3.0 3 2 \n", + "Homework 2, Jan14 4.0 3.0 3 2 \n", + "Lecture 3, Jan 14 4.5 3.0 4 3 \n", + "Homework 3, Jan15 5.0 3.0 3 3 \n", + "Lecture 4, Jan 15 5.0 4.0 4 3 \n", + "Mystery Word, Jan 20 5.0 NaN 4 3 \n", + "Lecture 5, Jan 20 5.0 3.0 4 3 \n", + "Currency, Jan 21 4.0 3.0 4 3 \n", + "Lecture 6, 21 4.0 3.0 4 3 \n", + "Blackjack1, Jan 22 4.0 4.0 4 3 \n", + "Lecture 7, Jan 22 NaN 4.0 NaN 3 \n", + "Lecture 8, Jan 23 5.5 4.0 NaN 3 \n", + "Blackjack2, Jan26 5.0 4.0 4 3 \n", + "Lecture 9, Jan26 4.0 NaN 4 3 \n", + "Random Art, Jan 27 4.0 5.0 3 4 \n", + "Lecture10, Jan27 4.0 3.0 NaN 3 \n", + "Charting 6.0 NaN NaN 3 \n", + "Lecture11, Jan28 5.0 NaN NaN 3 \n", + "PigSim 5.0 NaN NaN 3 \n", + "Lecture12, Jan29 6.0 NaN NaN 3 \n", + "Traffic Sim I NaN NaN NaN 5 \n", + "Lecture13,Feb2 NaN NaN NaN NaN " + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Lecture Stuff" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# piece1 = python[0:14:2]\n", + "# piece2 = python[15::2]\n", + "# lecture = pd.concat(piece1, piece2)\n", + "\n", + "#broke out all the Lecture rows and them put them back together\n", + "pieces = [python[0:13:2], python[13::2]]\n", + "lecture = pd.concat(pieces)\n", + "lecture" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameP01P02P03P04P05P06P07P08P09P10P11P12P13P14P15
Lecture 1, Jan12 3.0 4.0NaN 3NaN 3 3.5 2NaN 2 2 3.5 2.5 3 2
Lecture 2, Jan 13 3.0 3.0 3 2 3 3 3.0 2 1 2 4 4.0 3.0 3 2
Lecture 3, Jan 14 4.0 4.0 5 4 3 3 5.0 4 2NaN 5 4.5 3.0 4 3
Lecture 4, Jan 15 5.0 4.5 5 4 4 4 4.5 3 2 3 4 5.0 4.0 4 3
Lecture 5, Jan 20 4.0 5.0 5 5 5 3 5.0 3 3 3 4 5.0 3.0 4 3
Lecture 6, 21 4.0 5.0 5 4 4 5 5.0 5 3 4NaN 4.0 3.0 4 3
Lecture 7, Jan 22 4.0 NaNNaN 4 4 5 4.0 4 3 4 4 NaN 4.0NaN 3
Lecture 8, Jan 23 5.5 NaN 5 4 4 5 5.0 5 3 5 4 5.5 4.0NaN 3
Lecture 9, Jan26 4.0 5.0NaN 1 3 4 5.0 5 3 4 4 4.0 NaN 4 3
Lecture10, Jan27 NaN NaN 5 1NaN 3 4.9 5NaN 4 4 4.0 3.0NaN 3
Lecture11, Jan28 NaN 5.0 5 5NaN 4 4.0 4NaN 4 5 5.0 NaNNaN 3
Lecture12, Jan29 NaN 5.0NaN 5NaNNaN 4.9 4NaN 4 5 6.0 NaNNaN 3
Lecture13,Feb2 NaN NaNNaNNaNNaNNaN NaN 5NaNNaNNaN NaN NaNNaNNaN
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 10, + "text": [ + "Name P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 P11 P12 \\\n", + "Lecture 1, Jan12 3.0 4.0 NaN 3 NaN 3 3.5 2 NaN 2 2 3.5 \n", + "Lecture 2, Jan 13 3.0 3.0 3 2 3 3 3.0 2 1 2 4 4.0 \n", + "Lecture 3, Jan 14 4.0 4.0 5 4 3 3 5.0 4 2 NaN 5 4.5 \n", + "Lecture 4, Jan 15 5.0 4.5 5 4 4 4 4.5 3 2 3 4 5.0 \n", + "Lecture 5, Jan 20 4.0 5.0 5 5 5 3 5.0 3 3 3 4 5.0 \n", + "Lecture 6, 21 4.0 5.0 5 4 4 5 5.0 5 3 4 NaN 4.0 \n", + "Lecture 7, Jan 22 4.0 NaN NaN 4 4 5 4.0 4 3 4 4 NaN \n", + "Lecture 8, Jan 23 5.5 NaN 5 4 4 5 5.0 5 3 5 4 5.5 \n", + "Lecture 9, Jan26 4.0 5.0 NaN 1 3 4 5.0 5 3 4 4 4.0 \n", + "Lecture10, Jan27 NaN NaN 5 1 NaN 3 4.9 5 NaN 4 4 4.0 \n", + "Lecture11, Jan28 NaN 5.0 5 5 NaN 4 4.0 4 NaN 4 5 5.0 \n", + "Lecture12, Jan29 NaN 5.0 NaN 5 NaN NaN 4.9 4 NaN 4 5 6.0 \n", + "Lecture13,Feb2 NaN NaN NaN NaN NaN NaN NaN 5 NaN NaN NaN NaN \n", + "\n", + "Name P13 P14 P15 \n", + "Lecture 1, Jan12 2.5 3 2 \n", + "Lecture 2, Jan 13 3.0 3 2 \n", + "Lecture 3, Jan 14 3.0 4 3 \n", + "Lecture 4, Jan 15 4.0 4 3 \n", + "Lecture 5, Jan 20 3.0 4 3 \n", + "Lecture 6, 21 3.0 4 3 \n", + "Lecture 7, Jan 22 4.0 NaN 3 \n", + "Lecture 8, Jan 23 4.0 NaN 3 \n", + "Lecture 9, Jan26 NaN 4 3 \n", + "Lecture10, Jan27 3.0 NaN 3 \n", + "Lecture11, Jan28 NaN NaN 3 \n", + "Lecture12, Jan29 NaN NaN 3 \n", + "Lecture13,Feb2 NaN NaN NaN " + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "lect_mean = lecture[::].mean(axis=1)\n", + "lect_mean" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 11, + "text": [ + "Lecture 1, Jan12 2.791667\n", + "Lecture 2, Jan 13 2.733333\n", + "Lecture 3, Jan 14 3.821429\n", + "Lecture 4, Jan 15 3.933333\n", + "Lecture 5, Jan 20 4.000000\n", + "Lecture 6, 21 4.142857\n", + "Lecture 7, Jan 22 3.909091\n", + "Lecture 8, Jan 23 4.461538\n", + "Lecture 9, Jan26 3.769231\n", + "Lecture10, Jan27 3.690000\n", + "Lecture11, Jan28 4.400000\n", + "Lecture12, Jan29 4.612500\n", + "Lecture13,Feb2 5.000000\n", + "dtype: float64" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "lect_mean.plot(kind='bar', fontsize=20, figsize=(15, 7))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 12, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAA2QAAAI+CAYAAADTisntAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text": [ + "" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#python\n", + "weeks = [lecture[0:4], lecture[4:8], lecture[8:12]]\n", + "lecture_weeks = pd.concat(weeks, keys=['Week1', 'Week2', 'Week3'])\n", + "lecture_weeks" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameP01P02P03P04P05P06P07P08P09P10P11P12P13P14P15
Week1Lecture 1, Jan12 3.0 4.0NaN 3NaN 3 3.5 2NaN 2 2 3.5 2.5 3 2
Lecture 2, Jan 13 3.0 3.0 3 2 3 3 3.0 2 1 2 4 4.0 3.0 3 2
Lecture 3, Jan 14 4.0 4.0 5 4 3 3 5.0 4 2NaN 5 4.5 3.0 4 3
Lecture 4, Jan 15 5.0 4.5 5 4 4 4 4.5 3 2 3 4 5.0 4.0 4 3
Week2Lecture 5, Jan 20 4.0 5.0 5 5 5 3 5.0 3 3 3 4 5.0 3.0 4 3
Lecture 6, 21 4.0 5.0 5 4 4 5 5.0 5 3 4NaN 4.0 3.0 4 3
Lecture 7, Jan 22 4.0 NaNNaN 4 4 5 4.0 4 3 4 4 NaN 4.0NaN 3
Lecture 8, Jan 23 5.5 NaN 5 4 4 5 5.0 5 3 5 4 5.5 4.0NaN 3
Week3Lecture 9, Jan26 4.0 5.0NaN 1 3 4 5.0 5 3 4 4 4.0 NaN 4 3
Lecture10, Jan27 NaN NaN 5 1NaN 3 4.9 5NaN 4 4 4.0 3.0NaN 3
Lecture11, Jan28 NaN 5.0 5 5NaN 4 4.0 4NaN 4 5 5.0 NaNNaN 3
Lecture12, Jan29 NaN 5.0NaN 5NaNNaN 4.9 4NaN 4 5 6.0 NaNNaN 3
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 13, + "text": [ + "Name P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 \\\n", + "Week1 Lecture 1, Jan12 3.0 4.0 NaN 3 NaN 3 3.5 2 NaN 2 \n", + " Lecture 2, Jan 13 3.0 3.0 3 2 3 3 3.0 2 1 2 \n", + " Lecture 3, Jan 14 4.0 4.0 5 4 3 3 5.0 4 2 NaN \n", + " Lecture 4, Jan 15 5.0 4.5 5 4 4 4 4.5 3 2 3 \n", + "Week2 Lecture 5, Jan 20 4.0 5.0 5 5 5 3 5.0 3 3 3 \n", + " Lecture 6, 21 4.0 5.0 5 4 4 5 5.0 5 3 4 \n", + " Lecture 7, Jan 22 4.0 NaN NaN 4 4 5 4.0 4 3 4 \n", + " Lecture 8, Jan 23 5.5 NaN 5 4 4 5 5.0 5 3 5 \n", + "Week3 Lecture 9, Jan26 4.0 5.0 NaN 1 3 4 5.0 5 3 4 \n", + " Lecture10, Jan27 NaN NaN 5 1 NaN 3 4.9 5 NaN 4 \n", + " Lecture11, Jan28 NaN 5.0 5 5 NaN 4 4.0 4 NaN 4 \n", + " Lecture12, Jan29 NaN 5.0 NaN 5 NaN NaN 4.9 4 NaN 4 \n", + "\n", + "Name P11 P12 P13 P14 P15 \n", + "Week1 Lecture 1, Jan12 2 3.5 2.5 3 2 \n", + " Lecture 2, Jan 13 4 4.0 3.0 3 2 \n", + " Lecture 3, Jan 14 5 4.5 3.0 4 3 \n", + " Lecture 4, Jan 15 4 5.0 4.0 4 3 \n", + "Week2 Lecture 5, Jan 20 4 5.0 3.0 4 3 \n", + " Lecture 6, 21 NaN 4.0 3.0 4 3 \n", + " Lecture 7, Jan 22 4 NaN 4.0 NaN 3 \n", + " Lecture 8, Jan 23 4 5.5 4.0 NaN 3 \n", + "Week3 Lecture 9, Jan26 4 4.0 NaN 4 3 \n", + " Lecture10, Jan27 4 4.0 3.0 NaN 3 \n", + " Lecture11, Jan28 5 5.0 NaN NaN 3 \n", + " Lecture12, Jan29 5 6.0 NaN NaN 3 " + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "lecture_weeks.ix['Week1']" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameP01P02P03P04P05P06P07P08P09P10P11P12P13P14P15
Lecture 1, Jan12 3 4.0NaN 3NaN 3 3.5 2NaN 2 2 3.5 2.5 3 2
Lecture 2, Jan 13 3 3.0 3 2 3 3 3.0 2 1 2 4 4.0 3.0 3 2
Lecture 3, Jan 14 4 4.0 5 4 3 3 5.0 4 2NaN 5 4.5 3.0 4 3
Lecture 4, Jan 15 5 4.5 5 4 4 4 4.5 3 2 3 4 5.0 4.0 4 3
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 14, + "text": [ + "Name P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 P11 P12 \\\n", + "Lecture 1, Jan12 3 4.0 NaN 3 NaN 3 3.5 2 NaN 2 2 3.5 \n", + "Lecture 2, Jan 13 3 3.0 3 2 3 3 3.0 2 1 2 4 4.0 \n", + "Lecture 3, Jan 14 4 4.0 5 4 3 3 5.0 4 2 NaN 5 4.5 \n", + "Lecture 4, Jan 15 5 4.5 5 4 4 4 4.5 3 2 3 4 5.0 \n", + "\n", + "Name P13 P14 P15 \n", + "Lecture 1, Jan12 2.5 3 2 \n", + "Lecture 2, Jan 13 3.0 3 2 \n", + "Lecture 3, Jan 14 3.0 4 3 \n", + "Lecture 4, Jan 15 4.0 4 3 " + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#week1_mean = lecture_weeks.ix['Week1'].mean()\n", + "#axis=0 in the mean function\n", + "week1_mean = (lecture_weeks.ix['Week1'].mean()).mean()\n", + "week2_mean = (lecture_weeks.ix['Week2'].mean()).mean()\n", + "week3_mean = (lecture_weeks.ix['Week3'].mean()).mean()\n", + "print(week1_mean)\n", + "print(week2_mean)\n", + "print(week3_mean)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "3.31111111111\n", + "4.18055555556\n", + "3.94111111111\n" + ] + } + ], + "prompt_number": 15 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#test_mean = lecture_weeks.ix[\"Week1\", \"Week2\", \"Week3\"].mean()\n", + "#***THIS DOESN'T WORK :( ..... KeyError: 'Key length (3) exceeds index depth (2)'\n", + "#test_mean" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 16 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "weekly_mean = [week1_mean, week2_mean, week3_mean]\n", + "labels = ['Week1', 'Week2', 'Week3']\n", + "week_mean = pd.Series(weekly_mean, index=labels)\n", + "week_mean.plot(kind='bar', fontsize=20, figsize=(10, 5))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 17, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAFyCAYAAAAkp9RWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJgYvSZKkmvxf2u2JFJpbH74AAAAASUVORK5CYII=\n", + "text": [ + "" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Homework stuff" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameP01P02P03P04P05P06P07P08P09P10P11P12P13P14P15
Lecture 1, Jan12 3.0 4.0NaN 3NaN 3.0 3.5 2.0NaN 2 2 3.5 2.5 3 2
Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 5 4.0 3.0 3 2
Lecture 2, Jan 13 3.0 3.0 3 2 3 3.0 3.0 2.0 1 2 4 4.0 3.0 3 2
Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 3 4.0 3.0 3 2
Lecture 3, Jan 14 4.0 4.0 5 4 3 3.0 5.0 4.0 2NaN 5 4.5 3.0 4 3
Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 4 5.0 3.0 3 3
Lecture 4, Jan 15 5.0 4.5 5 4 4 4.0 4.5 3.0 2 3 4 5.0 4.0 4 3
Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 4 5.0 NaN 4 3
Lecture 5, Jan 20 4.0 5.0 5 5 5 3.0 5.0 3.0 3 3 4 5.0 3.0 4 3
Currency, Jan 21 4.0 5.0 5NaN 4 3.0 5.0 3.0 2 4NaN 4.0 3.0 4 3
Lecture 6, 21 4.0 5.0 5 4 4 5.0 5.0 5.0 3 4NaN 4.0 3.0 4 3
Blackjack1, Jan 22 5.5 5.0 5NaN 5 NaN 5.0 5.5 2 4 4 4.0 4.0 4 3
Lecture 7, Jan 22 4.0 NaNNaN 4 4 5.0 4.0 4.0 3 4 4 NaN 4.0NaN 3
Lecture 8, Jan 23 5.5 NaN 5 4 4 5.0 5.0 5.0 3 5 4 5.5 4.0NaN 3
Blackjack2, Jan26 NaN 5.0 6NaN 4 5.0 NaN 5.0 3 5 4 5.0 4.0 4 3
Lecture 9, Jan26 4.0 5.0NaN 1 3 4.0 5.0 5.0 3 4 4 4.0 NaN 4 3
Random Art, Jan 27 5.0 5.0NaN 3 6 5.0 4.0 5.0 2 5 4 4.0 5.0 3 4
Lecture10, Jan27 NaN NaN 5 1NaN 3.0 4.9 5.0NaN 4 4 4.0 3.0NaN 3
Charting NaN NaN 5 3NaN 4.0 5.0 5.0NaN 5 5 6.0 NaNNaN 3
Lecture11, Jan28 NaN 5.0 5 5NaN 4.0 4.0 4.0NaN 4 5 5.0 NaNNaN 3
PigSim NaN 5.0NaN 5NaN 4.0 4.0 4.0NaN 5 4 5.0 NaNNaN 3
Lecture12, Jan29 NaN 5.0NaN 5NaN NaN 4.9 4.0NaN 4 5 6.0 NaNNaN 3
Traffic Sim I NaN NaNNaN 5NaN NaN 4.9 5.0NaNNaN 5 NaN NaNNaN 5
Lecture13,Feb2 NaN NaNNaNNaNNaN NaN NaN 5.0NaNNaNNaN NaN NaNNaNNaN
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 18, + "text": [ + "Name P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 P11 \\\n", + "Lecture 1, Jan12 3.0 4.0 NaN 3 NaN 3.0 3.5 2.0 NaN 2 2 \n", + "Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 5 \n", + "Lecture 2, Jan 13 3.0 3.0 3 2 3 3.0 3.0 2.0 1 2 4 \n", + "Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 3 \n", + "Lecture 3, Jan 14 4.0 4.0 5 4 3 3.0 5.0 4.0 2 NaN 5 \n", + "Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 4 \n", + "Lecture 4, Jan 15 5.0 4.5 5 4 4 4.0 4.5 3.0 2 3 4 \n", + "Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 4 \n", + "Lecture 5, Jan 20 4.0 5.0 5 5 5 3.0 5.0 3.0 3 3 4 \n", + "Currency, Jan 21 4.0 5.0 5 NaN 4 3.0 5.0 3.0 2 4 NaN \n", + "Lecture 6, 21 4.0 5.0 5 4 4 5.0 5.0 5.0 3 4 NaN \n", + "Blackjack1, Jan 22 5.5 5.0 5 NaN 5 NaN 5.0 5.5 2 4 4 \n", + "Lecture 7, Jan 22 4.0 NaN NaN 4 4 5.0 4.0 4.0 3 4 4 \n", + "Lecture 8, Jan 23 5.5 NaN 5 4 4 5.0 5.0 5.0 3 5 4 \n", + "Blackjack2, Jan26 NaN 5.0 6 NaN 4 5.0 NaN 5.0 3 5 4 \n", + "Lecture 9, Jan26 4.0 5.0 NaN 1 3 4.0 5.0 5.0 3 4 4 \n", + "Random Art, Jan 27 5.0 5.0 NaN 3 6 5.0 4.0 5.0 2 5 4 \n", + "Lecture10, Jan27 NaN NaN 5 1 NaN 3.0 4.9 5.0 NaN 4 4 \n", + "Charting NaN NaN 5 3 NaN 4.0 5.0 5.0 NaN 5 5 \n", + "Lecture11, Jan28 NaN 5.0 5 5 NaN 4.0 4.0 4.0 NaN 4 5 \n", + "PigSim NaN 5.0 NaN 5 NaN 4.0 4.0 4.0 NaN 5 4 \n", + "Lecture12, Jan29 NaN 5.0 NaN 5 NaN NaN 4.9 4.0 NaN 4 5 \n", + "Traffic Sim I NaN NaN NaN 5 NaN NaN 4.9 5.0 NaN NaN 5 \n", + "Lecture13,Feb2 NaN NaN NaN NaN NaN NaN NaN 5.0 NaN NaN NaN \n", + "\n", + "Name P12 P13 P14 P15 \n", + "Lecture 1, Jan12 3.5 2.5 3 2 \n", + "Homework 1, Jan13 4.0 3.0 3 2 \n", + "Lecture 2, Jan 13 4.0 3.0 3 2 \n", + "Homework 2, Jan14 4.0 3.0 3 2 \n", + "Lecture 3, Jan 14 4.5 3.0 4 3 \n", + "Homework 3, Jan15 5.0 3.0 3 3 \n", + "Lecture 4, Jan 15 5.0 4.0 4 3 \n", + "Mystery Word, Jan 20 5.0 NaN 4 3 \n", + "Lecture 5, Jan 20 5.0 3.0 4 3 \n", + "Currency, Jan 21 4.0 3.0 4 3 \n", + "Lecture 6, 21 4.0 3.0 4 3 \n", + "Blackjack1, Jan 22 4.0 4.0 4 3 \n", + "Lecture 7, Jan 22 NaN 4.0 NaN 3 \n", + "Lecture 8, Jan 23 5.5 4.0 NaN 3 \n", + "Blackjack2, Jan26 5.0 4.0 4 3 \n", + "Lecture 9, Jan26 4.0 NaN 4 3 \n", + "Random Art, Jan 27 4.0 5.0 3 4 \n", + "Lecture10, Jan27 4.0 3.0 NaN 3 \n", + "Charting 6.0 NaN NaN 3 \n", + "Lecture11, Jan28 5.0 NaN NaN 3 \n", + "PigSim 5.0 NaN NaN 3 \n", + "Lecture12, Jan29 6.0 NaN NaN 3 \n", + "Traffic Sim I NaN NaN NaN 5 \n", + "Lecture13,Feb2 NaN NaN NaN NaN " + ] + } + ], + "prompt_number": 18 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "hwsplit = [python[1:12:2], python[14::2]]\n", + "homework = pd.concat(hwsplit)\n", + "homework" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameP01P02P03P04P05P06P07P08P09P10P11P12P13P14P15
Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 5 4 3 3 2
Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 3 4 3 3 2
Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 4 5 3 3 3
Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 4 5NaN 4 3
Currency, Jan 21 4.0 5.0 5NaN 4 3.0 5.0 3.0 2 4NaN 4 3 4 3
Blackjack1, Jan 22 5.5 5.0 5NaN 5 NaN 5.0 5.5 2 4 4 4 4 4 3
Blackjack2, Jan26 NaN 5.0 6NaN 4 5.0 NaN 5.0 3 5 4 5 4 4 3
Random Art, Jan 27 5.0 5.0NaN 3 6 5.0 4.0 5.0 2 5 4 4 5 3 4
Charting NaN NaN 5 3NaN 4.0 5.0 5.0NaN 5 5 6NaNNaN 3
PigSim NaN 5.0NaN 5NaN 4.0 4.0 4.0NaN 5 4 5NaNNaN 3
Traffic Sim I NaN NaNNaN 5NaN NaN 4.9 5.0NaNNaN 5NaNNaNNaN 5
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 19, + "text": [ + "Name P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 P11 \\\n", + "Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 5 \n", + "Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 3 \n", + "Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 4 \n", + "Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 4 \n", + "Currency, Jan 21 4.0 5.0 5 NaN 4 3.0 5.0 3.0 2 4 NaN \n", + "Blackjack1, Jan 22 5.5 5.0 5 NaN 5 NaN 5.0 5.5 2 4 4 \n", + "Blackjack2, Jan26 NaN 5.0 6 NaN 4 5.0 NaN 5.0 3 5 4 \n", + "Random Art, Jan 27 5.0 5.0 NaN 3 6 5.0 4.0 5.0 2 5 4 \n", + "Charting NaN NaN 5 3 NaN 4.0 5.0 5.0 NaN 5 5 \n", + "PigSim NaN 5.0 NaN 5 NaN 4.0 4.0 4.0 NaN 5 4 \n", + "Traffic Sim I NaN NaN NaN 5 NaN NaN 4.9 5.0 NaN NaN 5 \n", + "\n", + "Name P12 P13 P14 P15 \n", + "Homework 1, Jan13 4 3 3 2 \n", + "Homework 2, Jan14 4 3 3 2 \n", + "Homework 3, Jan15 5 3 3 3 \n", + "Mystery Word, Jan 20 5 NaN 4 3 \n", + "Currency, Jan 21 4 3 4 3 \n", + "Blackjack1, Jan 22 4 4 4 3 \n", + "Blackjack2, Jan26 5 4 4 3 \n", + "Random Art, Jan 27 4 5 3 4 \n", + "Charting 6 NaN NaN 3 \n", + "PigSim 5 NaN NaN 3 \n", + "Traffic Sim I NaN NaN NaN 5 " + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "hw_mean = homework[::].mean(axis=1)\n", + "hw_mean" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 20, + "text": [ + "Homework 1, Jan13 3.266667\n", + "Homework 2, Jan14 3.200000\n", + "Homework 3, Jan15 3.766667\n", + "Mystery Word, Jan 20 4.071429\n", + "Currency, Jan 21 3.769231\n", + "Blackjack1, Jan 22 4.307692\n", + "Blackjack2, Jan26 4.416667\n", + "Random Art, Jan 27 4.285714\n", + "Charting 4.555556\n", + "PigSim 4.333333\n", + "Traffic Sim I 4.980000\n", + "dtype: float64" + ] + } + ], + "prompt_number": 20 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "hw_mean.plot(kind='bar', fontsize=20, figsize=(15, 7))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 21, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAA2QAAAJmCAYAAAAzV0N3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text": [ + "" + ] + } + ], + "prompt_number": 21 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "hw_weeks = [homework[0:3], homework[3:6], homework[6:11]]\n", + "hw_weeks = pd.concat(hw_weeks, keys=['Week1', 'Week2', 'Week3'])\n", + "hw_weeks" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameP01P02P03P04P05P06P07P08P09P10P11P12P13P14P15
Week1Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 5 4 3 3 2
Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 3 4 3 3 2
Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 4 5 3 3 3
Week2Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 4 5NaN 4 3
Currency, Jan 21 4.0 5.0 5NaN 4 3.0 5.0 3.0 2 4NaN 4 3 4 3
Blackjack1, Jan 22 5.5 5.0 5NaN 5 NaN 5.0 5.5 2 4 4 4 4 4 3
Week3Blackjack2, Jan26 NaN 5.0 6NaN 4 5.0 NaN 5.0 3 5 4 5 4 4 3
Random Art, Jan 27 5.0 5.0NaN 3 6 5.0 4.0 5.0 2 5 4 4 5 3 4
Charting NaN NaN 5 3NaN 4.0 5.0 5.0NaN 5 5 6NaNNaN 3
PigSim NaN 5.0NaN 5NaN 4.0 4.0 4.0NaN 5 4 5NaNNaN 3
Traffic Sim I NaN NaNNaN 5NaN NaN 4.9 5.0NaNNaN 5NaNNaNNaN 5
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 22, + "text": [ + "Name P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 \\\n", + "Week1 Homework 1, Jan13 4.0 3.5 5 3 3 3.5 4.0 3.0 1 2 \n", + " Homework 2, Jan14 4.0 5.0 4 3 3 3.0 4.0 3.0 1 3 \n", + " Homework 3, Jan15 5.0 4.5 5 4 4 3.0 4.0 4.0 2 3 \n", + "Week2 Mystery Word, Jan 20 5.0 5.0 5 4 4 4.0 4.0 4.0 3 3 \n", + " Currency, Jan 21 4.0 5.0 5 NaN 4 3.0 5.0 3.0 2 4 \n", + " Blackjack1, Jan 22 5.5 5.0 5 NaN 5 NaN 5.0 5.5 2 4 \n", + "Week3 Blackjack2, Jan26 NaN 5.0 6 NaN 4 5.0 NaN 5.0 3 5 \n", + " Random Art, Jan 27 5.0 5.0 NaN 3 6 5.0 4.0 5.0 2 5 \n", + " Charting NaN NaN 5 3 NaN 4.0 5.0 5.0 NaN 5 \n", + " PigSim NaN 5.0 NaN 5 NaN 4.0 4.0 4.0 NaN 5 \n", + " Traffic Sim I NaN NaN NaN 5 NaN NaN 4.9 5.0 NaN NaN \n", + "\n", + "Name P11 P12 P13 P14 P15 \n", + "Week1 Homework 1, Jan13 5 4 3 3 2 \n", + " Homework 2, Jan14 3 4 3 3 2 \n", + " Homework 3, Jan15 4 5 3 3 3 \n", + "Week2 Mystery Word, Jan 20 4 5 NaN 4 3 \n", + " Currency, Jan 21 NaN 4 3 4 3 \n", + " Blackjack1, Jan 22 4 4 4 4 3 \n", + "Week3 Blackjack2, Jan26 4 5 4 4 3 \n", + " Random Art, Jan 27 4 4 5 3 4 \n", + " Charting 5 6 NaN NaN 3 \n", + " PigSim 4 5 NaN NaN 3 \n", + " Traffic Sim I 5 NaN NaN NaN 5 " + ] + } + ], + "prompt_number": 22 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "hw_week1_mean = (hw_weeks.ix['Week1'].mean()).mean()\n", + "hw_week2_mean = (hw_weeks.ix['Week2'].mean()).mean()\n", + "hw_week3_mean = (hw_weeks.ix['Week3'].mean()).mean()\n", + "print(hw_week1_mean)\n", + "print(hw_week2_mean)\n", + "print(hw_week3_mean)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "3.41111111111\n", + "4.02222222222\n", + "4.45166666667\n" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "hwweek_mean = [hw_week1_mean, hw_week2_mean, hw_week3_mean]\n", + "labels = ['Week1', 'Week2', 'Week3']\n", + "hwweek_mean = pd.Series(hwweek_mean, index=labels)\n", + "hwweek_mean.plot(kind='bar', fontsize=20, figsize=(10, 5))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 24, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAFyCAYAAAAkp9RWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X2cXWV97/1PzBAwJEBCh56g3jxE+NFjTUErihqMit7y\noNanqtjS++5BRR4OPiu1xiSKKFIrFTiCRwStBwpUOVqhRwLiCYIWTmmtFn4gUMKtxERmDAnBBGTu\nP9YaGIe9Z9bemaw9a/bn/XrNa5l1rWvtX16uId91rWtfa9bIyAiSJEna8Z7S6wIkSZL6hcFLkiSp\nJgYvSZKkmhi8JEmSamLwkiRJqonBS5IkqSYD3XSKiLOA9wLLMvN/Vzh+DfCiNs3vyszzu6lDkiSp\nSToOXhFxKPBuoJMFwJYAtwOXtmi7udMaJEmSmqij4BURc4AL6eARZUTsC8wHrsrMVR1VJ0mSNIN0\nOsfrI8AzgdUd9FlSbn/U4WdJkiTNKJ2MXC0BPgx8EvhJB59h8JIkSaLio8aImA18CbgDOAP4TAef\nsYRiPtjSiLgQOBAYBq4APpaZD3ZUsSRJUkNVHfF6P3AIcHxmPtLhZywBZgGrgFuAC4ANwKnADREx\nv8PzSZIkNdKkI14RcSCwAjg3M3/YyckjYhbF6NatwDGZef+Y/ecB7yzP/b6OqpYkSWqgCYNXGZC+\nBKwDTuv05Jk5AhzWan9EvB84DngLBi9JktQHJhvxOoli4dOjMnNLi/ZZ3X5wZj4UEXcASyJiTmZu\na3fso4/+ZmRgYHa3HyVJklSntvlosuD1xnJ7VUS0av9uuX/fzFw7vrGcv/Us4IHMvLNF/6cCjwET\nzhsbHm6V+bQjDQ7OZ8OGTb0uQ9qhvM7VD7zO6zc42H76+mTB68vAdS32Hwk8H7gI+A9gY5v+hwLX\nAN8CXju2ISIWAfsDt5aPJCVJmpa2bdvGfffd2+syujI8PI+hoc29LqMrz3jGPsyZM6fXZUypCYNX\nZl7can9ELKQMXpO8q3ENsB44KiKWZuaasv8c4Jzy88/tpnBJkupy3333cupnvsnc3ffqdSl9Y8vG\n9Zz9gdewePEBvS5lSnX1kux2ImIFMJKZKwEyc1tEnABcDqyOiMuAIeAVwEHAJe3CnSRJ08nc3fdi\n3oKn9boMNVynrwwaNULrl2QvL38el5lXAssoHlm+Gjge+DVwcma+rcvPlyRJapyuRrwy8z3Ae1rs\nbxnkMvP7FPPCJEmS+la3I16SJEnqkMFLkiSpJgYvSZKkmhi8JEmSamLwkiRJqonBS5IkqSYGL0mS\npJoYvCRJkmpi8JIkSaqJwUuSJKkmBi9JkqSaGLwkSZJqYvCSJEmqicFLkiSpJgYvSZKkmgx00yki\nzgLeCyzLzP9d4fiFwCrgGGAQuA04MzMv6+bzJUmSmqjjEa+IOBR4NzBS8fhdgWuAE4Abgc8DewCX\nRsRJnX6+JElSU3UUvCJiDnBh2W9WxW6nAocAp2TmsZn5YeBg4CfApyNisJMaJEmSmqrTEa+PAM8E\nVnfQ50RgHfCF0R2ZuRk4HZgLHNthDZIkSY1UOXhFxBLgw8AnKUarqvRZDOwNrMnM8Y8mry+3h1et\nQZIkqckqBa+ImA18CbgDOIPqjxkXl9u7xjdk5jpgK3BgxXNJkiQ1WtURr/dTzNM6PjMf6eD8e5bb\nX7VpfxDYvYPzSZIkNdaky0lExIHACuDczPxhh+ffqdxubdO+Fdilw3NKmka2bdvGfffd2+syujI8\nPI+hoc29LqMrz3jGPsyZM6fXZUjq0ITBKyJmUTxiXAec1sX5Hy637f7rsDPwUBfnlTRN3HffvZz6\nmW8yd/e9el1K39iycT1nf+A1LF58QK9LkdShyUa8TgJeBByVmVtatE8212u43LZ7nLgbcP8k52DB\ngrkMDMye7DBNscHB+b0uQQ0wPDyPubvvxbwFT+t1KX1l4cJ5/o7WaHh4Xq9L6Esz8TqfLHi9sdxe\nFRGt2r9b7t83M9e2aL+j3O43viEiFlGMeOVkRQ4Pt8p82pEGB+ezYcOmXpehBmjqo7qmGxra7O9o\njbzOe6Op1/lEYXGy4PVl4LoW+48Eng9cBPwHsLFV58xcGxFrgaURMWvckhLLyu1Nk9QgSZI0I0wY\nvDLz4lb7y3cvPh+4qMK7Gr9KsfDqyRSvCyIi5pf7tpTtkiRJM15XL8luJyJWACOZuXLM7jOBPwbO\njoiXAHcDbwD2pXiN0ANTWYMkSdJ01fFLsksjtH5J9vLy53GZuQlYSvGOx6UUrxAaAt6amed1+fmS\nJEmN09WIV2a+B3hPi/0tg1xmrgeO7+azJEmSZopuR7wkSZLUIYOXJElSTQxekiRJNTF4SZIk1cTg\nJUmSVBODlyRJUk0MXpIkSTUxeEmSJNXE4CVJklQTg5ckSVJNDF6SJEk1MXhJkiTVxOAlSZJUE4OX\nJElSTQxekiRJNRmoemBE7Al8DDgaWATcA1wEfDYzf1Oh/xrgRW2a35WZ51etRZIkqYkqBa+ImA/c\nAATwTeAKYCnw6XL7mgqnWQLcDlzaou3mKnVIkiQ1WdURr9MoQtd/zcxzRndGxNeAt0bEUZl5VbvO\nEbEvMB+4KjNXbUe9kiRJjVV1jtc+wFrgvHH7/67cvmCS/kvK7Y8qfp4kSdKMU2nEKzPf1qbpoHL7\ni0lOYfCSJEl9r/Lk+rEiYi/gjcBK4F7gbyfpsgQYAZZGxIXAgcAwxVyxj2Xmg93UIUmS1CQdLycR\nER8H1gHnAL8CXpmZGyfptgSYBawCbgEuADYApwI3lJP3JUmSZrRu1vG6C/gU8A1gkCI4HdLu4IiY\nRTG6dSvwnzPzHZn5XuC5wPnA7wMruqhDkiSpUTp+1JiZF43+74g4mmJ5ia8Az25z/AhwWKv9EfF+\n4DjgLcD7Oq1FkiSpSbqa4zUqM78dEdcCR0TE4sy8q8P+D0XEHcCSiJiTmdtaHbdgwVwGBmZvT6nq\nwuCgT4A1ueHheb0uoS8tXDjP39EaeZ33xky8zicNXhExG3gpQGaubnHI2nK7J8VjyPH95wPPAh7I\nzDtb9H8q8BjwSLsahoe3TFamptjg4Hw2bNjU6zLUAENDm3tdQl8aGtrs72iNvM57o6nX+URhscoc\nr1nAt4CvRUSr4/+AIjjd06b/ocCNwFnjGyJiEbA/cGv5SFKSJGnGmjR4ZeajwN9TTKT/wNi2iHgX\nxST5b2fmhjanWAOsB46KiKVj+s6h+GbkAHBuV9VLkiQ1SNU5Xh8EDgfOiIhlwI+BQ4CXAXcD7xw9\nMCJWACOZuRIgM7dFxAnA5cDqiLgMGAJeQbEA6yWZefGU/G2mmW3btnHffff2uoyuDA/Pa+zQ+jOe\nsQ9z5szpdRmSJD1J1ZXrfx4Rz6NYh+sY4OXAz4C/Bj6RmcNjDl9OsVjqyjH9rywD218CrwZ2AhI4\nOTPHv4Zoxrjvvns59TPfZO7ue/W6lL6xZeN6zv7Aa1i8+IBelyJJ0pNU/lZjZv6CMSNbExzX8vFl\nZn4fOLJ6aTPD3N33Yt6Cp/W6DEmSNA10s4CqJEmSumDwkiRJqonBS5IkqSYGL0mSpJoYvCRJkmpi\n8JIkSaqJwUuSJKkmBi9JkqSaGLwkSZJqYvCSJEmqicFLkiSpJgYvSZKkmhi8JEmSamLwkiRJqonB\nS5IkqSYDVQ+MiD2BjwFHA4uAe4CLgM9m5m8q9F8IrAKOAQaB24AzM/OyzsuWJElqnkojXhExH7gB\nOBn4N+DzwEbg08A3KvTfFbgGOAG4sey/B3BpRJzUVeWSJEkNU/VR42lAAP81M/8oMz+UmS8ELgGO\niYijJul/KnAIcEpmHpuZHwYOBn4CfDoiBrusX5IkqTGqBq99gLXAeeP2/125fcEk/U8E1gFfGN2R\nmZuB04G5wLEV65AkSWqsSnO8MvNtbZoOKre/aNc3IhYDewOXZ+bIuObry+3hwNlVapEkSWqqypPr\nx4qIvYA3AiuBe4G/neDwxeX2rvENmbkuIrYCB3ZThyRJUpN0HLwi4uPAR8o//gJ4ZWZunKDLnuX2\nV23aHwR277QOSZKkpulmHa+7gE9RfJtxELghIg6Z4Pidyu3WNu1bgV26qEOSJKlROh7xysyLRv93\nRBwNfBP4CvDsNl0eLrdz2rTvDDzUaR2SJElN09Ucr1GZ+e2IuBY4IiIWZ+aT5nEBw+W23ePE3YD7\nJ/qcBQvmMjAwezsq7Y3h4Xm9LqEvLVw4j8HB+b0uo294nfeG13m9vM57YyZe55MGr4iYDbwUIDNX\ntzhkbbndkxYT6IE7yu1+Lc69iGLEKyeqYXh4y2RlTktDQ5t7XUJfGhrazIYNm3pdRt/wOu8Nr/N6\neZ33RlOv84nCYpU5XrOAbwFfi4hWx/8B8BjFK4SeJDPXUoSzpRExa1zzsnJ7U4U6JEmSGm3S4JWZ\njwJ/TzGR/gNj2yLiXcBzgW9n5oYJTvNV4OkUrxwa7Tuf4tuRW8p2SZKkGa3qHK8PUixyekZELAN+\nTPEKoJcBdwPvHD0wIlYAI5m5ckz/M4E/Bs6OiJeUfd4A7EvxGqEHtutvIUmS1ACVlpPIzJ8DzwO+\nCCyhePfiYuCvgedl5roxhy8vf8b23wQsBS4stycCQ8BbM3P8a4gkSZJmpMrfaszMXzBmZGuC41qG\nucxcDxxfvTRJkqSZpZsFVCVJktQFg5ckSVJNDF6SJEk1MXhJkiTVxOAlSZJUE4OXJElSTQxekiRJ\nNTF4SZIk1cTgJUmSVBODlyRJUk0MXpIkSTUxeEmSJNXE4CVJklQTg5ckSVJNDF6SJEk1Gah6YET8\nJ2AFcDSwFzAErAaWZ+Y9FfqvAV7UpvldmXl+1VokSZKaqFLwKkPXPwFPB74D/A/gIOBY4MiIeEFm\n/nSS0ywBbgcubdF2c+WKJUmSGqrqiNcKitD13sz83OjOiHgb8FXgr4DXtuscEfsC84GrMnNVt8VK\nkiQ1WdU5Xq8D1o8NXQCZ+TXgbuCVk/RfUm5/1Fl5kiRJM8ekwSsingKcTjHq1cpWYE5E7DTBaQxe\nkiSp7036qDEzHwP+plVbRBxEMdfrrsx8ZILTLAFGgKURcSFwIDAMXAF8LDMf7LRwSZKkpul6OYly\nJOwcYBZwwSSHLymPWwXcUh6/ATgVuCEi5ndbhyRJUlNUXk5irIiYBZwPvIziG4mfm+TYYeBW4JjM\nvH/M/vOAd1I8xnxfN7VIkiQ1RcfBKyIGgC8CfwbcBbw2Mx9td3xmjgCHtdofEe8HjgPegsFLkiTN\ncB0Fr4iYC1wOHAncARyRmeu6/fDMfCgi7gCWRMSczNzW6rgFC+YyMDC724/pmeHheb0uoS8tXDiP\nwUGfXtfF67w3vM7r5XXeGzPxOu9k5foFwNXAocA/A6/KzF9W6DcfeBbwQGbe2eKQpwKPAW0n5w8P\nb6la5rQyNLS51yX0paGhzWzYsKnXZfQNr/Pe8Dqvl9d5bzT1Op8oLFaaXB8RuwD/QBG6rgeWVQld\npUOBG4GzWpx3EbA/cGv5SFKSJGnGqvqtxk9SzNO6ETgyMzuJ/muA9cBREbF0dGdEzKH4VuQAcG4H\n55MkSWqkSR81lu9pPKn84+3AaRHR6tAzMnNrRKwARjJzJUBmbouIEyjmhq2OiMsoXrD9Coo1wC7J\nzIu3+28iSZI0zVWZ4/UCYCeKBVD/vM0xI8BnKVaxX17+eeVoY2ZeGRHLgL8EXl2eL4GTM/O8bouX\nJElqkior119JBwutZmbLYzPz+xTfhpQkSepLXa9cL0mSpM4YvCRJkmpi8JIkSaqJwUuSJKkmBi9J\nkqSaGLwkSZJqYvCSJEmqicFLkiSpJgYvSZKkmhi8JEmSamLwkiRJqonBS5IkqSYGL0mSpJoYvCRJ\nkmpi8JIkSarJQNUDI+I/ASuAo4G9gCFgNbA8M++p0H8hsAo4BhgEbgPOzMzLOi9bkiSpeSqNeJWh\n65+AdwA/AT5X/vlY4OaIeOYk/XcFrgFOAG4EPg/sAVwaESd1Xb0kSVKDVH3UuAJ4OvDezHxVZn4o\nM18LHAcsBP5qkv6nAocAp2TmsZn5YeBgihD36YgY7Kp6SZKkBqkavF4HrM/Mz43dmZlfA+4GXjlJ\n/xOBdcAXxvTdDJwOzKUYOZMkSZrRJg1eEfEUioC0os0hW4E5EbFTm/6Lgb2BNZk5Mq75+nJ7eJVi\nJUmSmmzSyfWZ+RjwN63aIuIg4CDgrsx8pM0pFpfbu1qce11EbAUOrFauJElSc3W9nEQ5EnYOMAu4\nYIJD9yy3v2rT/iCwe7d1SJIkNUVXwSsiZgHnAy8Dbqb4lmM7o48gt7Zp3wrs0k0dkiRJTVJ5Ha9R\nETEAfBH4M4rHh6/NzEcn6PJwuZ3Tpn1n4KFO65AkSWqajoJXRMwFLgeOBO4AjsjMdZN0Gy637R4n\n7gbcP9EJFiyYy8DA7E5KnRaGh+f1uoS+tHDhPAYH5/e6jL7hdd4bXuf18jrvjZl4nXeycv0C4Grg\nUOCfgVdl5i8rdL2j3O7X4pyLKEa8cqITDA9vqVrmtDI0tLnXJfSloaHNbNiwqddl9A2v897wOq+X\n13lvNPU6nygsVl25fhfgHyhC1/XAsoqhi8xcC6wFlpZzw8ZaVm5vqnIuSZKkJqs6uf6TwGEUr/s5\nslz8tBNfpVj5/uTRHRExH/gIsKVslyRJmtEmfdRYvqdx9H2KtwOnRUSrQ8/IzK0RsQIYycyVY9rO\nBP4YODsiXkKx2v0bgH0pXiP0QNd/A0mSpIaoMsfrBRRLQowAf97mmBHgsxRLQywv//x48MrMTRGx\nlGLk7NXAq4DbgNMy87Kuq5ckSWqQKivXX0kH631lZstjM3M9cHz10iRJkmaWrleulyRJUmcMXpIk\nSTUxeEmSJNXE4CVJklQTg5ckSVJNDF6SJEk1MXhJkiTVxOAlSZJUE4OXJElSTQxekiRJNTF4SZIk\n1cTgJUmSVBODlyRJUk0MXpIkSTUxeEmSJNVkoNMOEbE3cBuwPDPPrthnDfCiNs3vyszzO61DkiSp\naToKXhExD/g6MB8Y6aDrEuB24NIWbTd3UoMkSVJTVQ5eEbEPReg6pJMPiIh9KYLaVZm5qqPqJEmS\nZpBKc7wi4t3AvwHPBq7r8DOWlNsfddhPkiRpRqk6uf5U4B7gcOCrHX6GwUuSJInqjxrfAazOzJGI\nOKjDz1hCMR9saURcCBwIDANXAB/LzAc7PJ8kSVIjVRrxysxrMrOTyfRjLQFmAauAW4ALgA0Uo2g3\nRMT8Ls8rSZLUKB0vJ9GJiJhFMbp1K3BMZt4/Zv95wDuBFcD7dmQdkiRJ08EODV7lKNlhrfZHxPuB\n44C3YPCSJEl9YIcGr4lk5kMRcQewJCLmZOa2dscuWDCXgYHZNVY3NYaH5/W6hL60cOE8Bgd9gl0X\nr/Pe8Dqvl9d5b8zE63xHP2qcDzwLeCAz72xxyFOBx4BHJjrP8PCWHVDdjjc0tLnXJfSloaHNbNiw\nqddl9A2v897wOq+X13lvNPU6nygs7uh3NR4K3AicNb4hIhYB+wO3bsfEfUmSpMbY0cFrDbAeOCoi\nlo7ujIg5wDkUI27n7uAaJEmSpoUpfdQYESuAkcxcCZCZ2yLiBOByYHVEXAYMAa8ADgIuycyLp7IG\nSZKk6aqbEa8R2r8ge3n587jMvBJYRvGqoVcDxwO/Bk7OzLd18fmSJEmN1PGIVzlC1XKUKjNbBrnM\n/D5wZKefJUmSNJPs6DlekiRJKhm8JEmSamLwkiRJqonBS5IkqSYGL0mSpJoYvCRJkmpi8JIkSaqJ\nwUuSJKkmBi9JkqSaGLwkSZJqYvCSJEmqicFLkiSpJgYvSZKkmhi8JEmSamLwkiRJqslApx0iYm/g\nNmB5Zp5dsc9CYBVwDDBY9j8zMy/r9PMlSZKaqqMRr4iYB3wdmA+MVOyzK3ANcAJwI/B5YA/g0og4\nqaNqJUmSGqxy8IqIfYDvAYd2+BmnAocAp2TmsZn5YeBg4CfApyNisMPzSZIkNVKl4BUR7wb+DXg2\ncF2Hn3EisA74wuiOzNwMnA7MBY7t8HySJEmNVHXE61TgHuBw4KtVTx4Ri4G9gTWZOf7R5PXl9vCq\n55MkSWqyqsHrHcDBmfkDYFYH519cbu8a35CZ64CtwIEdnE+SJKmxKn2rMTOv6fL8e5bbX7VpfxDY\nvctzS5IkNcqOXsdrp3K7tU37VmCXHVyDJEnStLCjg9fD5XZOm/adgYd2cA2SJEnTQscLqHZouNy2\ne5y4G3D/ZCdZsGAuAwOzp6yougwPz+t1CX1p4cJ5DA7O73UZfcPrvDe8zuvldd4bM/E639HB645y\nu9/4hohYRDHilZOdZHh4yxSXVY+hoc29LqEvDQ1tZsOGTb0uo294nfeG13m9vM57o6nX+URhcYc+\naszMtcBaYGlEjP825LJye9OOrEGSJGm6qOMl2V8Fng6cPLojIuYDHwG20MG6YJIkSU02pY8aI2IF\nMJKZK8fsPhP4Y+DsiHgJcDfwBmBfitcIPTCVNUiSJE1X3Yx4jdD+BdnLy5/HZeYmYClwYbk9ERgC\n3pqZ53Xx+ZIkSY3U8YhXZl4MXNymrWWQy8z1wPGdfpYkSdJMUsccL0mSJGHwkiRJqo3BS5IkqSYG\nL0mSpJoYvCRJkmpi8JIkSaqJwUuSJKkmBi9JkqSaGLwkSZJqYvCSJEmqicFLkiSpJgYvSZKkmhi8\nJEmSamLwkiRJqonBS5IkqSYDVQ+MiAHgFODtwL7A/cCXgU9l5qMV+q8BXtSm+V2ZeX7VWiRJkpqo\ncvACzqUIXWuAK4EXA6uAPwDeVKH/EuB24NIWbTd3UIckSVIjVQpeEfFCitB1eWa+ecz+i4DjIuLo\nzPz2BP33BeYDV2Xmqu2qWJIkqaGqzvE6qdyuHLf/NGAEOH6S/kvK7Y8qfp4kSdKMUzV4HQ5syMx/\nH7szM+8H7izbJ2LwkiRJfW/SR40RsTPwNOAHbQ75D+DAiNgzMx9oc8wSipGxpRFxIXAgMAxcAXws\nMx/stHBJkqSmqTLitbDc/qpN+8Zyu/sE51gCzKKYjH8LcAGwATgVuCEi5leoQ5IkqdGqTK7fqdxu\nbdM+un+XVo0RMYtidOtW4Jjy8eTo/vOAdwIrgPdVK1mSJKmZqgSvh8vtnDbtO5fbh1o1ZuYIcFir\n/RHxfuA44C0YvCRJ0gxXJXhtpJif1e5R4u5l+8Y27W1l5kMRcQewJCLmZOa2VsctWDCXgYHZnZ6+\n54aH5/W6hL60cOE8Bgd9el0Xr/Pe8Dqvl9d5b8zE63zS4JWZ2yLiXmC/NofsR/GNx5ZzwMr5W88C\nHsjMO1sc8lTgMeCRdjUMD2+ZrMxpaWhoc69L6EtDQ5vZsGFTr8voG17nveF1Xi+v895o6nU+UVis\nupzEGmBRRBwwdmdE7A0cQPtvPAIcCtwInDW+ISIWAfsDt5aPJCVJkmasqsHrK+X2k+Wk+NHJ8WeU\n+y+YoO8aYD1wVEQsHd0ZEXOAcyhG3c7tpGhJkqQmqvTKoMy8NiL+DngzcFNEXA+8kOJ9jZdn5lWj\nx0bECmAkM1eWfbdFxAnA5cDqiLgMGAJeARwEXJKZF0/dX0mSJGl6qjriBfCnwHLgdyjW39oL+Cjw\nJ+OOW17+PC4zrwSWAdcBr6Z4xdCvgZMz823dFC5JktQ0lUa8ADLzUeAT5c9Ex7UMc5n5feDIjqqT\nJEmaQToZ8ZIkSdJ2MHhJkiTVxOAlSZJUE4OXJElSTQxekiRJNTF4SZIk1cTgJUmSVBODlyRJUk0M\nXpIkSTUxeEmSJNXE4CVJklQTg5ckSVJNDF6SJEk1MXhJkiTVxOAlSZJUk4GqB0bEAHAK8HZgX+B+\n4MvApzLz0Qr9FwKrgGOAQeA24MzMvKzzsiVJkpqnkxGvc4G/AjYAnwN+RhGkLpmsY0TsClwDnADc\nCHwe2AO4NCJO6rBmSZKkRqoUvCLihRQjXZdn5ksy8y8y83DgK8AbIuLoSU5xKnAIcEpmHpuZHwYO\nBn4CfDoiBrv/K0iSJDVD1RGv0VGpleP2nwaMAMdP0v9EYB3whdEdmbkZOB2YCxxbsQ5JkqTGqhq8\nDgc2ZOa/j92ZmfcDd5btLUXEYmBvYE1mjoxrvn7M+SVJkma0SYNXROwMPA24q80h/wEsiIg927Qv\nLrdP6p+Z64CtwIGTVipJktRwVUa8FpbbX7Vp31hud2/TPhrI2vV/cIK+kiRJM0aV4LVTud3apn10\n/y7b0b9dX0mSpBmjyjpeD5fbOW3ady63D21H/3Z9AXjuc3+/5f7/839+PO2P37JxPTdd/tGWxx/2\npo+33O/x3R+/ZeP6J+2fTtfDTDz+kUceYejBLbzwzZ9seXyTrp+mHD/y2G943dVz2WmnnR7fP12u\nh5l8vP89r/f4Jv/3fO3ae1vuB5g1MjJ+vvtvi4g5FOHppsx8cYv2fwReAeyZmU96nBgRRwDfAT6Z\nmX/Zov1hIDPz4AkLkSRJarhJHzVm5jbgXmC/NofsR/GNx3ZzuO4Yc9xviYhFFCNeOXmpkiRJzVZ1\nOYk1wKKIOGDszojYGzgA+EG7jpm5FlgLLI2IWeOal5XbmyrWIUmS1FhVg9dXyu0nR8NTuT2j3H/B\nJP2/CjwdOHl0R0TMBz4CbCnbJUmSZrRJ53iNiohLgDcD/0Sx8OkLgRdTvEbozWOOWwGMZObKMfvm\nA7dQjI59HbgbeAPFy7ZPyczztv+vIkmSNL118pLsPwWWA79D8e7FvYCPAn8y7rjl5c/jMnMTsBS4\nsNyeCAwBbzV0SZKkflF5xEuSJEnbp5MRL0mSJG0Hg5ckSVJNqqxcrxkkIl7Tbd/M/OZU1iLtSBGx\nJ8V801cB84CfABdk5hVtjv8Q8KHMXNiqXWqiiBigeOfyLzPzsV7XI4NXP/p7ipHO8WuqTWYEmD31\n5UhTLyJ+B/ghxcLNW4FHgSOAIyLiSuBPM3P8q8qeCuxRa6HSFIiIZwHPB34BXJ2Zj0XEPsC5wP9N\n8d/8ByPiK8Bpmbmld9XK4NV/ngdcAewP3ABcV7Gf38JQk6yiCF0fBT4FPEbxarPPAn8EXBsRr8zM\nB3tXorSsuissAAAKcUlEQVT9IuIcipUCRv1zRBwFrAYWA3dRvH3mPwOnAC+IiJdk5q9rL1aAwavv\nZOa/RMSLge8DzwHenpm+skkzzTHAdzPz9DH7vhMRhwKXA0cCV0fEEZn5cE8qlLZTRPwXitB1A8UN\n9UHAOyjW2lwMnJSZ/608dgD4BPBB4EPAyhanVA2cXN+HMnMd8HpgJ+CLPS5H2hF+l2LR5t9SPmJ5\nHfAd4DDg6xHhI3Q11YnAj4GXZubfZOaJFIHqIOA7o6ELIDMfBU6jWAT9rb0oVgWDV5/KzH8FzgZe\nHBGv6nU90hQbBp7ZqiEzt1G8OeMWivkvvrJMTXUQ8I+Z+Zsx+75cbm8df3BmjlA87divhtrUhsGr\nj2XmBzPzKZn5j72uRZpiq4HXRsQftWosJ9YfDfwUeEtEXA7Mr7E+aSo8ADxt3L6fAV8D1rfpsw/g\n3MYecuV6STNORBwA3AzsRjGy9dnMvLTFcfsA11CMjo0AZKaPHtUIEXExxWPD12fmP1Q4/s0Uoeyy\nzDx2R9en1hzx6nMRsaDCMU+JiPfUUY80FTLzTuCFFHO5DgEWtTnu3vK4KymWWOl0mRWplz5K8d7j\nb0bED9sdFBGHRcQtwCXAJsa9T1n1Mnjp2ohou2BkRDyHYjLmWfWVJG2/zPz3zHwVxeKRX5rguF9m\n5uuBPwS8wVBjZOZaiuv2UmCib+fuQfEt9huBpZn50xrKUxsuJ6GDgevLr9U/PicgInYFPk6x7sts\n4Ooe1Sdtl8zcFBFVFkb9F2DZDi5HmlKZ+f8Bkz02/B7wjMz8WQ0laRKOeOnPKRbWuz4iFgFExNEU\nr1d5N/Bz4A2ZeXTvSpS223UVR3Y/U19J0tRqN3UkM7eMhi6njvSewavPZeZFwJsoVrL/XkRcBnyL\nYk7MmcDvZeY3elehNCVGR3b3GrszInaNiM9SvF7oOYDf8FWTOXWkAQxeogxWR1OErTdSfBvs4Mz8\nsO/00gzhyK76gTcYDWDwEgCZeS3wcopvyDyN4t120ozgyK76hDcYDeA6Xn0mIoaZ+IXXc4E5wFbG\nfUsmM9sOYUtNEBEvp1g6YleKkd3/JzNv621V0tSJiNdRLBuxluILI28EHgH+GljlU4ze81uN/Wfj\nJO3tVjQ2oavxMvPaMnxdhSO7moEy8xvlKNeVFAsDe4MxzTjiJWnGcWRX/S4iDqW4wfg18PLMzB6X\npJIjXpJmIkd2NeNVvMFYCPxLRHiDMU0YvARARBwJ/L8U34pZkJmDEfE2YDFwlvMC1CSZuW+va5Bq\n4A1GAxm8REScD7y9/ONjPPG+uudQvELlqHJl+829qE+S9GTeYDSTwavPRcQ7KULXFcBfAH/CEy9Q\nXQXsBvwX4P3Aih6UKE0ZR3Yl9ZrBSycA/wa8OTNHIuLxhszcCLw9IpZQrIG0oicVSlPAkV31C28w\npjcXUFUAV2fmRM/8vwfsV1M90pQbN7J7IPAJngheq4AvAYdSjOxKjVXeYHybYv2u/Skm10Nxg7GC\n4r2l83pTncDgpeKr9HtNcswiwDskNdnYkd2fjm3IzI2Z+XaK9Y7e1IvipKngDUYzGLy0Bnh9RPxf\nrRoj4gDgdcD3a61KmlqO7KofeIPRAAYvrQJ2AX4YEe+luEsiIpZFxAeAmygWmjyjdyVK282RXfUD\nbzAawODV5zLznylGtGYDZwFvKZuuAz5N8QWMYzPzB72pUJoSjuyqH3iD0QB+q1Fk5tURsS/wGuC5\nwB7AZuBfgW+U326UmmwV8CqKkd3PMGZkF3ge8CEc2VXzjd5grMjMteMbx9xgXFt7ZXqc72qU1BfK\nr9hfDPxOi+YHgeMz84p6q5KmTkQ8B7gRGAY+A/whxVOMl/HEDcZuwOE+xegdg5cAiIhnAX8G/AGw\nMDOfV77hfk/gbzPzsZ4WKE2BiJiLI7uawbzBmP4MXiIiTgM+zhNz/kYyc3ZEnEnxteNvAW/MzEd6\nVaMkqRpvMKY353j1uYh4A3A6xfD0x4AjgfeWzV8Ang28GjgJ+FwvapSmiiO76gflyvSXlj+aZvxW\no94H3A0ckZnXUtwZAZCZd1OErtsp/rGSGqsc2f1XilHcV1Cs5A3wEuAi4BsRsVNvqpOmTkQ8KyLO\njIj/FRE3l/uOjojjIsJ/93vM/wO0BLgyM3/dqjEzHwWuBp5Za1XSFBozsvsDitD1WZ5Y0fsLwP/i\niZFdqbG8wZj+DF76DTDZe7v2KI+TmsqRXc143mA0g8FL/wS8NiIWtGqMiN8FXgvcUmtV0tRyZFf9\nwBuMBjB46Qzgd4E1EfF6ylWPI2LfiHgTxYJ8C4G/6l2J0nZzZFf9wBuMBjB49bnMvA54B8Uv4hUU\nL1mF4q7p7yje6fW+zLy6NxVKU8KRXfUDbzAawODVZyLiExHx0oiYM7ovM/87RfD6C+DvKV4n8T8p\nXrPye5n51z0pVpo6juyqH3iD0QAuoNpnImJ0naKHKdbuurb8uWWSN9pLjRYRxwPnULyTcbzfAB/0\nJkNNFhEvA1YD/w4sB46geIqxP8Urg06nuMk+2qcYvWPw6jMRcRzw4vLnoDFNvwK+RxnEMvO2HpQn\nTYmI+ATFtfz9zNw2Zv/TgT/hySt6/21m/rQXtUpTyRuM6c/g1cciYiHwIp4IYs/liV/W+4HreCKI\n3deTIqUuOLKrfuANRjMZvPS4iNiZYjj6ReXP84FBYAS4OzMP6GF5UmWO7KofeIPRTAYvPUlE7A+8\nkOJ9dscAAZCZfhlDjePIrmYqbzCayeAlImJvilWOjwCWAU8rm7ZRrIC8muKX96aeFChNIUd2NRN5\ng9EcBq8+FBG7UgSsV5Q/v1c2jVDMA7iWImytKd9yL804juxqJvMGY/oa6HUBqldEfI/iF3D0Tuge\n4IsUQeu7mfnLXtUm7UgVRna/RnHTITVeZm4FboiInwM/A5InbjAW97K2fmfw6j9LgccoVqlflZk/\n7nE90g5RYWT3UhzZ1QzjDcb056PGPhMRNwPPoXhj/SPADyn+8VkN/CAzH5ugu9QIbUZ2R69zR3Y1\nYzh1pHkMXn2onIT5cp74Rd2nbNrEE9+EWZ2ZP+lNhdL2Kb9m/xjwdRzZ1QzlDUYzGbxERDyTJ0LY\nS4Hdy6Z1PHG3tDozf9abCqXOOLKrfuANRjMZvPRbImI2xTdhXkbxleQXUKx8PJKZs3tZm9QJR3Y1\n03mD0UwGL/2WiNgNOAz4Q4p1YJ5HMTnT4KVGc2RXM5E3GM1j8OpzEfH7FEHr+RRrGgXF3RPA7cB3\ny5/rnS+gmcKRXc1U3mBMfy4n0Wci4miKf2SeDxwK7Dam+U7gv1OscPy9zFxXf4VSLXal+AdpFvBr\n4CGK4CU1WvkS7J8C/63FDcbRwNsovvHoDUaPGLz6z7fG/O97gMuB6ym+AfPznlQk7WAVRna/STmy\n24v6pB3EG4xpyODVf75CMaL1Xd/XpZnKkV31I28wmsE5XpJmnPJr9qPuoQhZ1+PIrmaQCjcY1+MN\nxrTjiJekmciRXfUDp440kCNekiQ1UERchDcYjWPwkiRJqslTel2AJElSvzB4SZIk1cTgJUmSVBOD\nlyRJUk3+f7SHixYDpf0NAAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 24 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Combine Homework & Lecture Scores" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#hwweek_mean.plot(kind='bar', fontsize=20, figsize=(10, 5))\n", + "#lect_mean.plot(kind='bar', fontsize=20, figsize=(15, 7))\n", + "#hwweek_mean.ply.show()\n", + "#lect_mean.ply.show()\n", + "#ply.show()\n", + "plt.plot(hw_mean)\n", + "plt.plot(lect_mean)\n", + "plt.xticks(range(14))\n", + "plt.xlabel(\"days\")\n", + "plt.ylabel(\"scores\")\n", + "plt.title(\"Homework & Lecture scores\")\n", + "\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEZCAYAAAB4hzlwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 40 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/Ruby Data Scrub.ipynb b/Ruby Data Scrub.ipynb new file mode 100644 index 0000000..5a07930 --- /dev/null +++ b/Ruby Data Scrub.ipynb @@ -0,0 +1,488 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:5a51be4a8a2ec4bdb6385b813582ac39187553c13fa35aac4a299e059b78efc0" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby = pd.ExcelFile(\"cohort_3_rails.xlsx\")\n", + "print(ruby)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby.sheet_names" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 5, + "text": [ + "['Lecture Score', 'HW Score']" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lec = ruby.parse(\"Lecture Score\")\n", + "ruby_lec.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Week 1Unnamed: 1Unnamed: 2Unnamed: 3Week 2Unnamed: 5Unnamed: 6Unnamed: 7Week 3Unnamed: 9...Unnamed: 26Unnamed: 27Week 8Unnamed: 29Unnamed: 30Unnamed: 31Week 9Unnamed: 33Unnamed: 34Unnamed: 35
NaN M T W Th M T W Th M T... W Th M T W Th M T W Th
R01 2 2 4 3 NaN 3 5 2 3 4... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
R02 3 3.5 4.5 4 NaN 4.5 4.5 3.5 6 4... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
R03 3 4.5 4 3.5 NaN 6 4.5 4 5 5... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
R04 2 4 4 4 NaN 5 5 4 5 4... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
\n", + "

5 rows \u00d7 36 columns

\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 11, + "text": [ + " Week 1 Unnamed: 1 Unnamed: 2 Unnamed: 3 Week 2 Unnamed: 5 Unnamed: 6 \\\n", + "NaN M T W Th M T W \n", + "R01 2 2 4 3 NaN 3 5 \n", + "R02 3 3.5 4.5 4 NaN 4.5 4.5 \n", + "R03 3 4.5 4 3.5 NaN 6 4.5 \n", + "R04 2 4 4 4 NaN 5 5 \n", + "\n", + " Unnamed: 7 Week 3 Unnamed: 9 ... Unnamed: 26 Unnamed: 27 Week 8 \\\n", + "NaN Th M T ... W Th M \n", + "R01 2 3 4 ... NaN NaN NaN \n", + "R02 3.5 6 4 ... NaN NaN NaN \n", + "R03 4 5 5 ... NaN NaN NaN \n", + "R04 4 5 4 ... NaN NaN NaN \n", + "\n", + " Unnamed: 29 Unnamed: 30 Unnamed: 31 Week 9 Unnamed: 33 Unnamed: 34 \\\n", + "NaN T W Th M T W \n", + "R01 NaN NaN NaN NaN NaN NaN \n", + "R02 NaN NaN NaN NaN NaN NaN \n", + "R03 NaN NaN NaN NaN NaN NaN \n", + "R04 NaN NaN NaN NaN NaN NaN \n", + "\n", + " Unnamed: 35 \n", + "NaN Th \n", + "R01 NaN \n", + "R02 NaN \n", + "R03 NaN \n", + "R04 NaN \n", + "\n", + "[5 rows x 36 columns]" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_hw = ruby.parse(\"HW Score\")\n", + "ruby_hw.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Week 1Unnamed: 1Unnamed: 2Unnamed: 3Week 2Unnamed: 5Unnamed: 6Unnamed: 7Week 3Unnamed: 9...Unnamed: 26Unnamed: 27Week 8Unnamed: 29Unnamed: 30Unnamed: 31Week 9Unnamed: 33Unnamed: 34Unnamed: 35
NaN M T W Th M T W Th M T... W Th M T W Th M T W Th
R01 4 3 3 NaN NaN 4 3 3 4 3... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
R02 3 4 4.5 NaN NaN 4 4 4 2.5 3... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
R03 4 4 5.5 NaN NaN 5 4 4.5 4 4... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
R04 3 4.5 3 NaN NaN 4 4 4 4 5... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
\n", + "

5 rows \u00d7 36 columns

\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 10, + "text": [ + " Week 1 Unnamed: 1 Unnamed: 2 Unnamed: 3 Week 2 Unnamed: 5 Unnamed: 6 \\\n", + "NaN M T W Th M T W \n", + "R01 4 3 3 NaN NaN 4 3 \n", + "R02 3 4 4.5 NaN NaN 4 4 \n", + "R03 4 4 5.5 NaN NaN 5 4 \n", + "R04 3 4.5 3 NaN NaN 4 4 \n", + "\n", + " Unnamed: 7 Week 3 Unnamed: 9 ... Unnamed: 26 Unnamed: 27 Week 8 \\\n", + "NaN Th M T ... W Th M \n", + "R01 3 4 3 ... NaN NaN NaN \n", + "R02 4 2.5 3 ... NaN NaN NaN \n", + "R03 4.5 4 4 ... NaN NaN NaN \n", + "R04 4 4 5 ... NaN NaN NaN \n", + "\n", + " Unnamed: 29 Unnamed: 30 Unnamed: 31 Week 9 Unnamed: 33 Unnamed: 34 \\\n", + "NaN T W Th M T W \n", + "R01 NaN NaN NaN NaN NaN NaN \n", + "R02 NaN NaN NaN NaN NaN NaN \n", + "R03 NaN NaN NaN NaN NaN NaN \n", + "R04 NaN NaN NaN NaN NaN NaN \n", + "\n", + " Unnamed: 35 \n", + "NaN Th \n", + "R01 NaN \n", + "R02 NaN \n", + "R03 NaN \n", + "R04 NaN \n", + "\n", + "[5 rows x 36 columns]" + ] + } + ], + "prompt_number": 10 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 1ac83fd..40465d3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,2 +1,5 @@ pandas xlrd +numpy +matplotlib.pyplot +seaborn \ No newline at end of file