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Data Analysis with Python 101/2
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Pandas-Analysis.ipynb
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"source": [
"# Data Analysis with Python\n",
"## Pandas, Jupyter, and Friends\n",
"\n",
"Andreas Herten, 4 May 2017"
]
},
{
"cell_type": "markdown",
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},
"source": [
"<span style=\"font-size: 180%\"><em>»The data analyst's three foundations in Python«</em></span>\n",
"\n",
"Matplotlib • Pandas • Jupyter Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"## Matplotlib"
]
},
{
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"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* Standard for plotting with Python\n",
"* Recently v. 2.0.0 released\n",
"* → https://matplotlib.org/index.html"
]
},
{
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"source": [
"### Using the *global API*\n",
"\n",
"* Using the MATLAB-like interface\n",
"* Everything works through `plt.…`"
]
},
{
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"source": [
"import matplotlib.pyplot as plt\n",
"x = range(10)\n",
"y = [i**2 for i in range(10)]"
]
},
{
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"execution_count": 2,
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"plt.rcParams[\"figure.figsize\"] = (4, 3)"
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"import numpy as np\n",
"x = np.arange(0, 100, 0.2)\n",
"y = np.sin(np.sqrt(x))\n",
"plt.plot(x, y, color=\"green\")\n",
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"source": [
"### Object API\n",
"\n",
"* Instead of operation on *global objects* with `plt`, rather use `Figure` and `Axis` (axes ≈ plots)\n",
"* Cleaner approach (*IMHO*)\n",
"* Used under the hood of *global API* by leveraging `plt.gca().…` (*get current axis*)"
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"source": [
"x = np.linspace(0, 2*np.pi, 400)\n",
"y = np.sin(x**2)"
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"execution_count": 6,
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"outputs": [],
"source": [
"%matplotlib inline"
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"### Multiple Plots"
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"source": [
"## Pandas\n",
"Introduction"
]
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"metadata": {
"slideshow": {
"slide_type": "subslide"
}
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"source": [
"### Introduction\n",
"\n",
">pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.\n",
"\n",
"* Most important feature: `DataFrame`s and operations with them\n",
"* → http://pandas.pydata.org/"
]
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"execution_count": 9,
"metadata": {
"collapsed": true,
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"slide_type": "fragment"
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"outputs": [],
"source": [
"import pandas as pd"
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Creating a DataFrame\n",
"\n",
"Using a dictionary as an input"
]
},
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"cell_type": "code",
"execution_count": 10,
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"4 1.2 2017-05-03 -0.718282 entries Same"
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"metadata": {},
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"source": [
"frame = pd.DataFrame({\n",
" \"A\": 1.2,\n",
" \"B\": pd.Timestamp('20170503'),\n",
" \"C\": [(-1)**i * np.sqrt(i) + np.e * (-1)**(i-1) for i in range(5)],\n",
" \"D\": pd.Categorical([\"This\", \"column\", \"has\", \"entries\", \"entries\"]),\n",
" \"E\": \"Same\"\n",
"})\n",
"frame"
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"source": [
"Also available: `.read_csv` and `.read_excel`"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Popular Functions on Frames"
]
},
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"execution_count": 11,
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"source": [
"### Popular Functions on Frames II"
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" <td>2017-05-03</td>\n",
" <td>0.986231</td>\n",
" <td>entries</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>1.718282</td>\n",
" <td>column</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"0 1.2 2017-05-03 -2.718282 This Same\n",
"2 1.2 2017-05-03 -1.304068 has Same\n",
"4 1.2 2017-05-03 -0.718282 entries Same\n",
"3 1.2 2017-05-03 0.986231 entries Same\n",
"1 1.2 2017-05-03 1.718282 column Same"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame.sort_values(\"C\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Popular Functions on Frames III"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>-2.72</td>\n",
" <td>This</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>1.72</td>\n",
" <td>column</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>-1.30</td>\n",
" <td>has</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>0.99</td>\n",
" <td>entries</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>-0.72</td>\n",
" <td>entries</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"0 1.2 2017-05-03 -2.72 This Same\n",
"1 1.2 2017-05-03 1.72 column Same\n",
"2 1.2 2017-05-03 -1.30 has Same\n",
"3 1.2 2017-05-03 0.99 entries Same\n",
"4 1.2 2017-05-03 -0.72 entries Same"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"round(frame,2)\n",
"frame.round(2)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"scrolled": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"A 6.000000\n",
"C -2.036119\n",
"dtype: float64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame.sum()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"A 6.00\n",
"C -2.03\n",
"dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame.round(2).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Popular Functions on Frames IV"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\\begin{tabular}{lrlrll}\n",
"\\toprule\n",
"{} & A & B & C & D & E \\\\\n",
"\\midrule\n",
"0 & 1.2 & 2017-05-03 & -2.72 & This & Same \\\\\n",
"1 & 1.2 & 2017-05-03 & 1.72 & column & Same \\\\\n",
"2 & 1.2 & 2017-05-03 & -1.30 & has & Same \\\\\n",
"3 & 1.2 & 2017-05-03 & 0.99 & entries & Same \\\\\n",
"4 & 1.2 & 2017-05-03 & -0.72 & entries & Same \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n",
"\n"
]
}
],
"source": [
"print(frame.round(2).to_latex())"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Index, Columns"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" <th>NewIdx</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>-2.718282</td>\n",
" <td>This</td>\n",
" <td>Same</td>\n",
" <td>2017-05-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>1.718282</td>\n",
" <td>column</td>\n",
" <td>Same</td>\n",
" <td>2017-05-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>-1.304068</td>\n",
" <td>has</td>\n",
" <td>Same</td>\n",
" <td>2017-05-06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E NewIdx\n",
"0 1.2 2017-05-03 -2.718282 This Same 2017-05-04\n",
"1 1.2 2017-05-03 1.718282 column Same 2017-05-05\n",
"2 1.2 2017-05-03 -1.304068 has Same 2017-05-06"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame[\"NewIdx\"] = pd.date_range('20170504', periods=5)\n",
"frame.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Index, Columns II"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NewIdx</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-05-04</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>-2.718282</td>\n",
" <td>This</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-05</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>1.718282</td>\n",
" <td>column</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-06</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>-1.304068</td>\n",
" <td>has</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"NewIdx \n",
"2017-05-04 1.2 2017-05-03 -2.718282 This Same\n",
"2017-05-05 1.2 2017-05-03 1.718282 column Same\n",
"2017-05-06 1.2 2017-05-03 -1.304068 has Same"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame = frame.set_index(\"NewIdx\") # Also: inplace=True\n",
"frame.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2017-05-04', '2017-05-05', '2017-05-06', '2017-05-07',\n",
" '2017-05-08'],\n",
" dtype='datetime64[ns]', name='NewIdx', freq=None)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame.index"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['A', 'B', 'C', 'D', 'E'], dtype='object')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Slicing"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"Select only column \"A\""
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
"NewIdx\n",
"2017-05-04 1.2\n",
"2017-05-05 1.2\n",
"2017-05-06 1.2\n",
"2017-05-07 1.2\n",
"2017-05-08 1.2\n",
"Name: A, dtype: float64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame[\"A\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Select columns \"A\" and \"C\""
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"scrolled": true,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>C</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NewIdx</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-05-04</th>\n",
" <td>1.2</td>\n",
" <td>-2.718282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-06</th>\n",
" <td>1.2</td>\n",
" <td>-1.304068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-08</th>\n",
" <td>1.2</td>\n",
" <td>-0.718282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-07</th>\n",
" <td>1.2</td>\n",
" <td>0.986231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-05</th>\n",
" <td>1.2</td>\n",
" <td>1.718282</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A C\n",
"NewIdx \n",
"2017-05-04 1.2 -2.718282\n",
"2017-05-06 1.2 -1.304068\n",
"2017-05-08 1.2 -0.718282\n",
"2017-05-07 1.2 0.986231\n",
"2017-05-05 1.2 1.718282"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame[[\"A\", \"C\"]].sort_values(\"C\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Slicing II"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NewIdx</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-05-05</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>1.718282</td>\n",
" <td>column</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-06</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>-1.304068</td>\n",
" <td>has</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"NewIdx \n",
"2017-05-05 1.2 2017-05-03 1.718282 column Same\n",
"2017-05-06 1.2 2017-05-03 -1.304068 has Same"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame[1:3]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"A 1.2\n",
"B 2017-05-03 00:00:00\n",
"C -1.30407\n",
"D has\n",
"E Same\n",
"Name: 2017-05-06 00:00:00, dtype: object"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame.loc[\"2017-05-06\"]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"A 1.2\n",
"B 2017-05-03 00:00:00\n",
"C -1.30407\n",
"D has\n",
"E Same\n",
"Name: 2017-05-06 00:00:00, dtype: object"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame.iloc[2]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Slicing III"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NewIdx</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-05-05</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>1.718282</td>\n",
" <td>column</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-07</th>\n",
" <td>1.2</td>\n",
" <td>2017-05-03</td>\n",
" <td>0.986231</td>\n",
" <td>entries</td>\n",
" <td>Same</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"NewIdx \n",
"2017-05-05 1.2 2017-05-03 1.718282 column Same\n",
"2017-05-07 1.2 2017-05-03 0.986231 entries Same"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame[frame[\"C\"] > 0]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NewIdx</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [A, B, C, D, E]\n",
"Index: []"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame[(frame[\"C\"] > 0) & (frame[\"D\"] == \"has\")]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Plotting"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>C</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NewIdx</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-05-04</th>\n",
" <td>1.2</td>\n",
" <td>-2.718282</td>\n",
" </tr>\n",
" <tr>\n",
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"* Further `kind`s: `barh`, `box`, `hist`, `kde` (a better histogram!), `scatter`; more: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html\n",
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"## Advanced Plotting"
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"### Combine Pandas & Matplotlib\n",
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"frame[[\"A\", \"C\"]].plot(kind=\"bar\", ax=ax)\n",
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{
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"source": [
"### Combination II"
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"### Seaborn\n",
"\n",
"> Seaborn is a library for making attractive and informative statistical graphics in Python\n",
"\n",
"* Provides plotting interfaces\n",
"* And sets nice defaults\n",
"* Also: Colormaps\n",
"* → http://seaborn.pydata.org/"
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"source": [
"import seaborn as sns\n",
"sns.set(rc={\"figure.figsize\": (5, 3)})\n",
"frame[\"C\"].plot(marker=\"s\", linestyle=\"--\")"
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{
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"source": [
"### Seaborn Color Palette"
]
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"execution_count": 71,
"metadata": {
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"frame[\"G\"] = [(-1)**i * np.sqrt(i) + np.pi * (-1)**(i-1) for i in range(len(frame.index))]\n",
"frame[\"H\"] = [(-1)**i * np.sqrt(i) + np.pi * (-1.1)**(i-1) for i in range(len(frame.index))]"
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"with sns.color_palette(\"hls\", 2):\n",
" fig, ax = plt.subplots()\n",
" sns.regplot(x=\"C\", y=\"G\", data=frame, ax=ax)\n",
" sns.regplot(x=\"C\", y=\"H\", data=frame, ax=ax)"
]
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"source": [
"### Seaborn Color Palette II"
]
},
{
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"source": [
"## Complex Data"
]
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"slideshow": {
"slide_type": "subslide"
}
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"source": [
"### Some real data…\n",
"Some PAPI counters for different number of particles (=program run lengths), compiled with different compilers"
]
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"execution_count": 79,
"metadata": {
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"<div>\n",
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" <td>100000</td>\n",
" <td>PAPI_TOT_CYC</td>\n",
" <td>21246423</td>\n",
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" modules compiler n_particles \\\n",
"0 gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda gfortran 100000 \n",
"1 gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda gfortran 100000 \n",
"\n",
" hwc HWC \n",
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"1 PAPI_TOT_CYC 21246423 "
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"execution_count": 79,
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"output_type": "execute_result"
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"source": [
"dfCounters = pd.read_csv(\"juron-jube-add_one_to_list.csv\")\n",
"dfCounters.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 80,
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" <th>Modules</th>\n",
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" <th>Counter Name</th>\n",
" <th>Counter Value</th>\n",
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" <td>gfortran</td>\n",
" <td>100000</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda</td>\n",
" <td>gfortran</td>\n",
" <td>100000</td>\n",
" <td>PAPI_TOT_CYC</td>\n",
" <td>21246423</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Modules Compiler \\\n",
"0 gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda gfortran \n",
"1 gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda gfortran \n",
"\n",
" Number of Particles Counter Name Counter Value \n",
"0 100000 PAPI_TOT_INS 32809671 \n",
"1 100000 PAPI_TOT_CYC 21246423 "
]
},
"execution_count": 80,
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"dfCounters = dfCounters.rename(columns={\n",
" \"modules\": \"Modules\",\n",
" \"compiler\": \"Compiler\",\n",
" \"n_particles\": \"Number of Particles\",\n",
" \"hwc\": \"Counter Name\",\n",
" \"HWC\": \"Counter Value\"\n",
"})\n",
"dfCounters.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Massaging\n",
"I want some relative values…"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
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{
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" <thead>\n",
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" <th>Modules</th>\n",
" <th>Compiler</th>\n",
" <th>Number of Particles</th>\n",
" <th>Counter Name</th>\n",
" <th>Counter Value</th>\n",
" <th>Counter Value (rel.)</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda</td>\n",
" <td>gfortran</td>\n",
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" <td>gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda</td>\n",
" <td>gfortran</td>\n",
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" <td>21246423</td>\n",
" <td>212.46423</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"text/plain": [
" Modules Compiler \\\n",
"0 gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda gfortran \n",
"1 gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda gfortran \n",
"\n",
" Number of Particles Counter Name Counter Value Counter Value (rel.) \n",
"0 100000 PAPI_TOT_INS 32809671 328.09671 \n",
"1 100000 PAPI_TOT_CYC 21246423 212.46423 "
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfCounters[\"Counter Value (rel.)\"] = dfCounters[\"Counter Value\"] / dfCounters[\"Number of Particles\"]\n",
"dfCounters.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Some Values\n",
"\n",
"Plot relative values of `PAPI_TOT_CYC` for `gfortoran`"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
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"dfCounters[\n",
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"][\"Counter Value (rel.)\"]\\\n",
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"source": [
"### More Values\n",
"\n",
"Plot same relativ values, but also those of counter `PAPI_TOT_INS`"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
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"source": [
"dfCounters[\n",
" (dfCounters[\"Compiler\"] == \"gfortran\")\n",
" & \n",
" ((dfCounters[\"Counter Name\"] == \"PAPI_TOT_CYC\") | (dfCounters[\"Counter Name\"] == \"PAPI_TOT_INS\"))\n",
"][\"Counter Value (rel.)\"]\\\n",
".plot(marker=\"P\")"
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"source": [
"### More Values\n",
"\n",
"Plot same relativ values, but also those of counter `PAPI_TOT_INS`\n",
"\n",
"Nope! Because"
]
},
{
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"execution_count": 84,
"metadata": {
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" Modules Compiler \\\n",
"0 gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda gfortran \n",
"1 gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda gfortran \n",
"5 gcc/5.4.0 openmpi/2.0.2-gcc_5.4.0 PAPI/5.5.0-cuda gfortran \n",
"\n",
" Number of Particles Counter Name Counter Value Counter Value (rel.) \n",
"0 100000 PAPI_TOT_INS 32809671 328.096710 \n",
"1 100000 PAPI_TOT_CYC 21246423 212.464230 \n",
"5 1000000 PAPI_TOT_INS 328081236 328.081236 "
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfCounters[\n",
" (dfCounters[\"Compiler\"] == \"gfortran\")\n",
" & \n",
" ((dfCounters[\"Counter Name\"] == \"PAPI_TOT_CYC\") | (dfCounters[\"Counter Name\"] == \"PAPI_TOT_INS\"))\n",
"].head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Workaround\n",
"Create a canvas with matplotlib and explicitly draw to it\n",
"\n",
"Care about legend etc."
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x118817c50>"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
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