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mkcor/tutorial_section2.ipynb

Last active August 29, 2015 14:18
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Notebook for "From Python basics (lists and dictionaries) to data analysis"
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tutorial_section2.ipynb
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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"2. From Python basics (lists and dictionaries) to data analysis."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"This section is devoted to introducing data structures and their manipulation in Python."
]
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Python as a calculator"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"10 + 2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
"12"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"2 * 3"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"6"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"1 / 2 # Attention here: Python 2 vs 3"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"0"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Types"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"int"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(1.5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"float"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type('a')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"str"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Names"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"myname = 'Marianne'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"myage = 29"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(myname)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Marianne\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(myage)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"29\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dir() # returns a list of the names defined in the local namespace"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"['In',\n",
" 'Out',\n",
" '_',\n",
" '_1',\n",
" '_2',\n",
" '_3',\n",
" '_4',\n",
" '_5',\n",
" '_6',\n",
" '__',\n",
" '___',\n",
" '__builtin__',\n",
" '__builtins__',\n",
" '__doc__',\n",
" '__name__',\n",
" '_dh',\n",
" '_i',\n",
" '_i1',\n",
" '_i10',\n",
" '_i11',\n",
" '_i2',\n",
" '_i3',\n",
" '_i4',\n",
" '_i5',\n",
" '_i6',\n",
" '_i7',\n",
" '_i8',\n",
" '_i9',\n",
" '_ih',\n",
" '_ii',\n",
" '_iii',\n",
" '_oh',\n",
" '_sh',\n",
" 'exit',\n",
" 'get_ipython',\n",
" 'myage',\n",
" 'myname',\n",
" 'quit']"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# dir(np)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Strings"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(myname)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"str"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Length (built-in function)\n",
"len(myname)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"8"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"myname[0] # Access element"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"'M'"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"myname[1:3] # Slice"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"'ar'"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"myname + 'other_string' # Concatenation"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"'Marianneother_string'"
]
}
],
"prompt_number": 18
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Lists"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l1 = [1, 2, 3]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(l1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"list"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(l1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"3"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l2 = [5, 6]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l1 + l2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"[1, 2, 3, 5, 6]"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Access (get) elements\n",
"l1[1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"2"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l1[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"1"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(l1 + l2)[4]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"6"
]
}
],
"prompt_number": 26
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Challenge: Access the last element of a list (say, l1 + l2) in the general case."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(l1 + l2)[len(l1 + l2) - 1] # last element"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"6"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l2[-2]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"5"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Change (set) elements\n",
"l1[0] = 0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"[0, 2, 3]"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(l1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0, 2, 3]\n"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Convenient lists\n",
"range(10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(range(10))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"list"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(range(10))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
"10"
]
}
],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"range(1, 20, 2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
"[1, 3, 5, 7, 9, 11, 13, 15, 17, 19]"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"range(10, 1, -1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": [
"[10, 9, 8, 7, 6, 5, 4, 3, 2]"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"simpsons = ['Bart', 'Marge', 'Homer', 'Lisa', 'Maggie']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"range(len(simpsons))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 38,
"text": [
"[0, 1, 2, 3, 4]"
]
}
],
"prompt_number": 38
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"For-loop syntax"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in range(len(simpsons)):\n",
" row_like = 'Index: ' + str(i) + ' | Name: ' + simpsons[i]\n",
" print(row_like)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Index: 0 | Name: Bart\n",
"Index: 1 | Name: Marge\n",
"Index: 2 | Name: Homer\n",
"Index: 3 | Name: Lisa\n",
"Index: 4 | Name: Maggie\n"
]
}
],
"prompt_number": 39
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lists let you group similar items together."
]
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Dictionaries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As the name suggests, dictionaries are used to look things up. Specifically, a dictionary associates *keys* with *values*."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"meaning = {'apple': 'round edible fruit',\n",
" 'computer': 'electronic device that drives people crazy',\n",
" 'window': 'glass fitted in a wall',\n",
" 'python': 'large tropical snake',\n",
" }"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"meaning['apple'] # Look up, access, get an element"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 41,
"text": [
"'round edible fruit'"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(meaning)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 42,
"text": [
"dict"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Add an element\n",
"meaning['ruby'] = 'attractive semi-precious gem'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(meaning)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 44,
"text": [
"5"
]
}
],
"prompt_number": 44
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each *key* is unique, but values can repeat."
]
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"Challenge: Change the meaning of 'python'."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take-away: A list is a sequence: each element of a sequence is assigned a number (its position or index). The first index is 0, the second index is 1, and so forth. Dictionaries are not ordered. Lists and dictionaries are one-dimensional data structures."
]
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Methods"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A method is a function that's attached to some object. It will return different values depending on which object it's attached to."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(meaning.keys())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['python', 'window', 'computer', 'ruby', 'apple']\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(meaning.values())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['large tropical snake', 'glass fitted in a wall', 'electronic device that drives people crazy', 'attractive semi-precious gem', 'round edible fruit']\n"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# meaning. # Press tab to see all available methods"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Add elements to a list..\n",
"l1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 48,
"text": [
"[0, 2, 3]"
]
}
],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l1.append(4)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"[0, 2, 3, 4]"
]
}
],
"prompt_number": 50
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Plotly magic"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Build a trace: it's a dictionary with some required keys and optional ones\n",
"mytrace = {'x': [0, 0.5, 1, 1.5], 'y': [10, 9, 13, 8]}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot info is contained in a dictionary with keys 'data' and 'layout'.\n",
"# Value of 'data' is a list of traces.\n",
"data = [mytrace]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import plotly.plotly as py # Load plotly submodule"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"py.iplot(data, filename=\"plotly_test\", fileopt=\"overwrite\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~marianne2/1109.embed\" height=\"525\" width=\"100%\"></iframe>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 54,
"text": [
"<plotly.tools.PlotlyDisplay at 0x7faddf80edd0>"
]
}
],
"prompt_number": 54
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you need two-dimensional data structures, look into numpy arrays and pandas' data frames."
]
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Numpy arrays"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.array([[1, 2, 3], [4, 5, 6]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"array([[1, 2, 3],\n",
" [4, 5, 6]])"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
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{
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"text": [
"<matplotlib.figure.Figure at 0x7faddddc7550>"
]
}
],
"prompt_number": 80
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = pd.read_csv('https://raw.githubusercontent.com/mkcor/baseball-notebook/master/Batting.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>yearID</th>\n",
" <th>stint</th>\n",
" <th>teamID</th>\n",
" <th>lgID</th>\n",
" <th>G</th>\n",
" <th>G_batting</th>\n",
" <th>AB</th>\n",
" <th>R</th>\n",
" <th>H</th>\n",
" <th>...</th>\n",
" <th>SB</th>\n",
" <th>CS</th>\n",
" <th>BB</th>\n",
" <th>SO</th>\n",
" <th>IBB</th>\n",
" <th>HBP</th>\n",
" <th>SH</th>\n",
" <th>SF</th>\n",
" <th>GIDP</th>\n",
" <th>G_old</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> aardsda01</td>\n",
" <td> 2004</td>\n",
" <td> 1</td>\n",
" <td> SFN</td>\n",
" <td> NL</td>\n",
" <td> 11</td>\n",
" <td> 11</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> aardsda01</td>\n",
" <td> 2006</td>\n",
" <td> 1</td>\n",
" <td> CHN</td>\n",
" <td> NL</td>\n",
" <td> 45</td>\n",
" <td> 43</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> aardsda01</td>\n",
" <td> 2007</td>\n",
" <td> 1</td>\n",
" <td> CHA</td>\n",
" <td> AL</td>\n",
" <td> 25</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> aardsda01</td>\n",
" <td> 2008</td>\n",
" <td> 1</td>\n",
" <td> BOS</td>\n",
" <td> AL</td>\n",
" <td> 47</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> aardsda01</td>\n",
" <td> 2009</td>\n",
" <td> 1</td>\n",
" <td> SEA</td>\n",
" <td> AL</td>\n",
" <td> 73</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 24 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 82,
"text": [
" playerID yearID stint teamID lgID G G_batting AB R H ... SB CS \\\n",
"0 aardsda01 2004 1 SFN NL 11 11 0 0 0 ... 0 0 \n",
"1 aardsda01 2006 1 CHN NL 45 43 2 0 0 ... 0 0 \n",
"2 aardsda01 2007 1 CHA AL 25 2 0 0 0 ... 0 0 \n",
"3 aardsda01 2008 1 BOS AL 47 5 1 0 0 ... 0 0 \n",
"4 aardsda01 2009 1 SEA AL 73 3 0 0 0 ... 0 0 \n",
"\n",
" BB SO IBB HBP SH SF GIDP G_old \n",
"0 0 0 0 0 0 0 0 11 \n",
"1 0 0 0 0 1 0 0 45 \n",
"2 0 0 0 0 0 0 0 2 \n",
"3 0 1 0 0 0 0 0 5 \n",
"4 0 0 0 0 0 0 0 NaN \n",
"\n",
"[5 rows x 24 columns]"
]
}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data['yearID'].min()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 83,
"text": [
"1871"
]
}
],
"prompt_number": 83
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filtered = data[data['yearID'] > 2003]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 84
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filtered['avg'] = filtered['H'] / filtered['AB']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filtered.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>yearID</th>\n",
" <th>stint</th>\n",
" <th>teamID</th>\n",
" <th>lgID</th>\n",
" <th>G</th>\n",
" <th>G_batting</th>\n",
" <th>AB</th>\n",
" <th>R</th>\n",
" <th>H</th>\n",
" <th>...</th>\n",
" <th>CS</th>\n",
" <th>BB</th>\n",
" <th>SO</th>\n",
" <th>IBB</th>\n",
" <th>HBP</th>\n",
" <th>SH</th>\n",
" <th>SF</th>\n",
" <th>GIDP</th>\n",
" <th>G_old</th>\n",
" <th>avg</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> aardsda01</td>\n",
" <td> 2004</td>\n",
" <td> 1</td>\n",
" <td> SFN</td>\n",
" <td> NL</td>\n",
" <td> 11</td>\n",
" <td> 11</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
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" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 11</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> aardsda01</td>\n",
" <td> 2006</td>\n",
" <td> 1</td>\n",
" <td> CHN</td>\n",
" <td> NL</td>\n",
" <td> 45</td>\n",
" <td> 43</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
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" <td> 45</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> aardsda01</td>\n",
" <td> 2007</td>\n",
" <td> 1</td>\n",
" <td> CHA</td>\n",
" <td> AL</td>\n",
" <td> 25</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> aardsda01</td>\n",
" <td> 2008</td>\n",
" <td> 1</td>\n",
" <td> BOS</td>\n",
" <td> AL</td>\n",
" <td> 47</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 5</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> aardsda01</td>\n",
" <td> 2009</td>\n",
" <td> 1</td>\n",
" <td> SEA</td>\n",
" <td> AL</td>\n",
" <td> 73</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 25 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 86,
"text": [
" playerID yearID stint teamID lgID G G_batting AB R H ... CS BB \\\n",
"0 aardsda01 2004 1 SFN NL 11 11 0 0 0 ... 0 0 \n",
"1 aardsda01 2006 1 CHN NL 45 43 2 0 0 ... 0 0 \n",
"2 aardsda01 2007 1 CHA AL 25 2 0 0 0 ... 0 0 \n",
"3 aardsda01 2008 1 BOS AL 47 5 1 0 0 ... 0 0 \n",
"4 aardsda01 2009 1 SEA AL 73 3 0 0 0 ... 0 0 \n",
"\n",
" SO IBB HBP SH SF GIDP G_old avg \n",
"0 0 0 0 0 0 0 11 NaN \n",
"1 0 0 0 1 0 0 45 0 \n",
"2 0 0 0 0 0 0 2 NaN \n",
"3 1 0 0 0 0 0 5 0 \n",
"4 0 0 0 0 0 0 NaN NaN \n",
"\n",
"[5 rows x 25 columns]"
]
}
],
"prompt_number": 86
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filtered['avg'].hist()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 87,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7faddda82710>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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qMwFIUkvZA5jAHsDx7AFIubMHIEkqxQSg7FnrTYxFYiyqMwFIUkvZA5jAHsDx7AFIuZvJ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\n2T0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7faddda82e50>"
]
}
],
"prompt_number": 87
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Challenge: Create or import data as a data frame. Make a plotly plot using one or two dimensions of the data frame."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take-away: Data frames are convenient data structures for data exploration, pre-processing, and processing (leading to visualization)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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