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@ikoustou
Created March 14, 2018 15:03
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seaborn categorical plots
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#load the inner dataset 'tips', which is included in seaborn\n",
"tips = sns.load_dataset('tips')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>total_bill</th>\n",
" <th>tip</th>\n",
" <th>sex</th>\n",
" <th>smoker</th>\n",
" <th>day</th>\n",
" <th>time</th>\n",
" <th>size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>239</th>\n",
" <td>29.03</td>\n",
" <td>5.92</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>240</th>\n",
" <td>27.18</td>\n",
" <td>2.00</td>\n",
" <td>Female</td>\n",
" <td>Yes</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>241</th>\n",
" <td>22.67</td>\n",
" <td>2.00</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242</th>\n",
" <td>17.82</td>\n",
" <td>1.75</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243</th>\n",
" <td>18.78</td>\n",
" <td>3.00</td>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>Thur</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill tip sex smoker day time size\n",
"239 29.03 5.92 Male No Sat Dinner 3\n",
"240 27.18 2.00 Female Yes Sat Dinner 2\n",
"241 22.67 2.00 Male Yes Sat Dinner 2\n",
"242 17.82 1.75 Male No Sat Dinner 2\n",
"243 18.78 3.00 Female No Thur Dinner 2"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips.tail()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a barplot. A barplot represents an estimate of central tendency (usually the mean)for a numeric variable with the height of each rectangle and provides some indication of the uncertainty around that estimate using error bars."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xc7379e8>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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m8/vrGZ02/ATgI0l+paWypQlxojqpgySvBC5hdI79/wXOATYDH2F0euWZwN8B1wC3ACdW\n1Q+a6wZHVdX2pl2WpgzDQJLkMJEkyTCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CSBPwfMuYvDXVc\nyKcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x621dc88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(x='sex', y='total_bill', data=tips)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a barplot with a different estimator. For example the standard deviation (np.std)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x621de80>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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1pSSHAy8FzgauA9ZV1VHbOeRZwEPAoyZhkeYr7xlIPST5aeA7VfURpoYGfx4wluSo7vPd\nkvxc9/OrmBo2/AXA+5M8vlHZ0k5xoDqphyS/DJzH1Bj7PwBOAbYA72dqeOXFwHuBK4AbgWOq6u7u\nvsHhVbW9YZelecMwkCTZTSRJMgwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkAf8Hnm9KRQuNq88A\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc737e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(x='sex', y='total_bill', data=tips, estimator=np.std)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a countplot for males and females (sex)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xc8a9208>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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DQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMB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\nJKnp/wPvVoEBSEguTwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc8f74a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='sex', data=tips) #y is already calculated = count "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create boxplots using 'day' for categorical value and 'total_bill' for numerical.\n",
"The box shows the quartiles of the dataset (Q1 to Q3) while the whiskers extend to show the rest of the distribution\n",
"The band is the mean. \n",
"The first quartile Q1 (start of the box) is the mean of the values between the minimum and mean.\n",
"the third quartile Q2 (end of the box) is the mean of the values between the mean and the maximum."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xc910f28>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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W+L9OSRLYMpAqNzQ0xME4yOm3nq67lFoN7Bxg6Jryrn5RuWwZSJIMA0mSYSBJwjCQJGEY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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd540438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(x='day', y='total_bill', data=tips)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Split the boxplots to smokers and non smokers. (Use hue='smoker')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xd77aa20>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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HlpnZmaSeYOuBOaTas3aQ+iLb3G3/x4GHzawFuFjtBu/S2738GPCImS0EXoow\nvrzTCGQREVE1kYiIKBmIiAhKBiIigpKBiIigZCAiIigZiJySYA6a26OOQyTflAxERETJQCSdYF77\n35vZBuAjwbb/EswE+hsze8rMhplZqZn9sXOCMjM7I5jlsqgmLJP+SclApA/BZGVfBS4A/gq4KPjo\n6WAm0POAXcBN7n6E1Fw2VwX7fBV4yt1PFjZqkVOnZCDStynAM+5+3N3fAp4Ntk8ws18FE5jdAHw8\n2P4vwN8Er/8GeKyg0YpkSclAJL2e5mx5HLjF3ScCiwlmsHT3XwOVwfz3Je6uxXikKCgZiPTteeCL\nZjbUzEqBa4LtpcD+oD3ghvccswL4CSoVSBHRRHUiaZjZXcAsUlMY7wV+R2qdhe8E23YApe4+O9j/\nA8AfgdHufiiKmEVOlZKBSJ4F6wt/3t1nRh2LSKa0noFIHpnZMuBzwJVRxyJyKlQyEBERNSCLiIiS\ngYiIoGQgIiIoGYiICEoGIiKCkoGIiAD/H24/BOeMF3cZAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd555cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(x='day', y='total_bill', data=tips, hue='smoker')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create violinplots. A violinplot is like a boxplot but it shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xb277cc0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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3N/Pmm+m3JKiU4r1332WICAMTePkShGOVYv3GjUkXXRgvMbgtTsdJWlatWkVd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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd88d240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.violinplot(x='day', y='total_bill', data=tips)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Split the violin plots based on smokers or non smokers (tip: hue='smoker')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xdad82e8>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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m9NLZaawwOHLkCAAX1EwsDBb64wwMDtHRYU0bzPe9MOgfGEibhoBkJrJQSqoZ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd99c198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Split the violin plots based on sex and compine them together (split=True).\n",
"When using hue nesting with a variable that takes two levels, setting split to True will draw half of a violin for each level.\n",
"This can make it easier to directly compare the distributions."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xdf7eda0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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zCyE2AXnAUmAvUCKlrHlHMoE6UyCEEHcIIdYLIdaHSyD29y1b8EYn12mOC7+H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\n34I9Fn98h5DtGQpGtPMwtK2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bC2zccuuttG7dOqR710e3rl0xu0obdSEq0UnVM20lSXa/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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xde0b710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.violinplot(x='day', y='total_bill', data=tips, hue='sex', split=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Create stripplots. A stripplot draws a scatterplot (overlapping points) where one variable is categorical."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xdfa0c50>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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IfAzMihx7VnOMm4AW7TpSPcCa0jVsLD/YtfF+2fsxHzdV8el9/fWkTjoFkpII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\nYqCUUgr4//8RPcI19bOgAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xe1bd240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# stripplot ->Draws a scatterplot where one variable is categorical\n",
"sns.stripplot(x='day', y='total_bill', data=tips)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add jitter to the previous stripplot so as the points will not overlap."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xe1f4780>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8NGc7x9JV1W/Fdfx0fsweM5uvR3/NjBEzuKbVNXy19yuO5x4HYNqaabz414ss\nOrYIg8XAwCYDeaDbA8wdN5cQvXr8V1Hk/fdBhYQQdtONiOBgh3n5y5eji46iaONGLLm5tnFzdjZ5\ny36rk1hrS03vXUyQUn4IlAAvAQghHgU+dFVg3mpcl1g+XHnYLimk5pSQmnOG7aeyWfvU1fio3ceK\niwgh6BnTk0m/TWJHmrVN+czdM3lvyHusSV5jN/dk3kn+O+K/bojS8wUNHkyrpUsoWLcefasE/Dp3\nJveXXxzmaSPCET4+aAIce244G/NkNf2uNMnJ2ORajKPeaBzqx6KHBnHflS1pEuZn91pqbgl7TudW\ncqai1I7dGbttiQCsvY+/3ve1wzz1nKBq+vh4Iu66k6CBA8ldsABZWGQ/Qaul0dPPIDQaQv72N3Sx\nsXav+TTzrg181XU6u1UIsQRoKYRYXOHHGiCzTiL0Qi2jAnluXEeGtrfvgKTTCJqFq41nimtphdZh\nzNltoF4xveoinHrBmJbmMNbouWcJHT8OAG1QENJU4Rax2Uzau/+pq/BqRXVXBhuB/wAHy34+/2Ma\nMNq1oXm/R4a2oWOs9YvQV6fh2bEdiAn2q+YsRbk8naM6MyB2gO3YX+fPg90f5I4Od9jGmgY15bn+\nz7kjPK8UOnYsaCskWY2GwvV/UlpWvM6QnIw5Pd3unJKD9iu2PJ2QVSyfspsoRCPgfBH5LVJKx1Tp\nQr1795Zbt26tfqKHKDGa+XLDCXYn59Is3J8mYX6EBegZ3bkxAXq1zNRO2gFr05pmfaFRR3dH47XO\nl6gO8AnAaDGyKmkVGUUZDIsbRmyQ9RZGUl4SWSVZdInqglbjeAWhVK5w0ybOvfEmpRVKWfs0a0ar\n338je948zr38isM5rVauRO/m20VCiG1Syt7VzavpDuQbgXex9j4WwMdCiCellAsvK8p67Jkf99g1\nvznv8zXHWPTQQJUQzts+GxY/gq0e4vgPoPfdzucWpMOpPyG6gypcd4FZ+2bx+a7PKTGVMLblWF4a\n+BKj4x0v3mODYokJiFGJ4BIE9u+P5oLCc8aUFEoPH0bf1Pk3fMOxo25PBjVV0wfIzwN9pJSTpJR3\nAX2BF1wXlnczmi22zmYXOpJWwG97z9ZxRB5s9RvYFcZd/brzeSfWwwddYMFk+KwfrH+vLqLzCkez\nj/Lu1ncpNBZilmaWHF/CD4d/cJj3/cHvuWreVfSf059n1z+L0Wx0Q7TeyXj6NAXr/8SnWTO7ceHn\nh0+TJgReeSV+Xe1L02sCA/Hv0aMuw7wsNU0GmgtuC2VexLkNjk4jCAuovEeqwaRKVNgYL1ihYSx2\nPm/161Ch1AJr3waDWg0DcDCXm9exAAAgAElEQVTb8d70xzs+ZurKqbadxqfyTvH65tfJN+TbEsb8\nw/PrOlSvlDV7NkdHjCT5vvvIX7UKfXw8ACIggMbPP4c2NBSh0RA/73si7rkbn2bNCOjdm+YzpqMN\n8Z79GzW9V7FMCPE7MLfs+GbgV9eE5P2EELwwvgNPLNiF0Wz/TCY21I8xXWIrObMB6nsfrHunwnEl\nPXhLcuyPTSVgLAG9d+3ydIXejXqj0+jsWlrmGfJYf3o9+zL3sXzicg5kHUBe0JpEtbisnqWwkLT3\nPwCL9QOcLCjAp2tXms+Yji4y0m6XsRCCRk89RaOnnnJXuJelpslAAtOBQVifGcwA6nfbn8t0bfem\nDEiIZN+ZPGJD/Vh1IA2dRnBDr2aE+qvqkDZXPwcxHSFpEzTvC51vcD6v513w29Plx+3HQWBk3cTo\n4RoHNubDqz/k052fcjznOCXmEttrWSVZ7M3YS6+YXvhofDBaym8NbTqziZT8FJoFN3P2tgpgLixE\nFttfrZrS09HHxTnMlVJSlJhIyd59lBw+jC4igoi77sSnsXe0aa3RaiIhxHYpZc8Lxnaf73RWF7xt\nNZHiAnt/hKMrIaYD9JkCPmrPxoXeSXyH2ftn2451Gh0rJq4gyj+KVadWMW3tNCyy/DblyBYj+c8Q\n71oPX9eS7rmHwo1/2Y5jnnyCyHvvtZsjjUaS7p1C0ZYtduO6JrG0WrYMja9vncTqTK2sJhJCPAhM\nBRKEELsrvBQMbLi8EL2PlJL3VxxmzpZkQvx1PDmynbrlU1cyj4HQwLB/Q3Ajd0fjse7rch97M/ay\nPW07AboAHu/9OFH+1rLK7SPb2yUCwFa3SHFOGo2E3Xwz2qhoLMVFBA8ZQtgNjlev+atXOyQCAFPq\nGQo3biT46qvrItzLUt1tojnAMuANoMI1OvlSyiznp9RfP24/zUd/HAUgo6CUh+fuYG3zMJqqdpau\ntWUm/PokIEHrC7fMgTbD3R2VRwrSB/HlqC9JL04nRB9CgE95fZymQU1pHdaaozlHbWO9Ynpx//L7\n2Z62na7RXXlpwEs0D2nujtA9jjkvj1O3307pEevfl1/XroRec43tdWk0UvDnn2R8+hnGM2cqfR9d\neLjLY60NVa4IklLmSilPSilvlVKeqvCjwSUCgM0n7CtwmCySrSer/6s4ll7A/MRkjpzLd1Vo9ZfZ\nCKtewbb81FwKf7zs1pA8kZSS97a+x4A5AxgwdwBf7v0Sf135hxSzxUyRsYiPh37MyBYjaR3Wmkkd\nJ7EmZQ1/nfmLUnMpiWcTeW6D2pV8Xs6PP9oSAUDJ7t2ce/ttpMnEmX+/yMEePUl5cCole/diznRe\nnSd49Gj8u3d3+pqnUTufLkK35mHM35piOxYCujYLAyCzoJQtJ7Jo1ziYhOjyjSk/bEvhiYW7bH0y\nXruuM7f3856GF25nNoDhgtLfxdnuicWDrUpaxVf7vrIdzz04l/Up6/l27LfsTN/Ja5teI704nYFN\nBvJIj0fYm7mXZceXca7onN377EjbgZQSIURd/xE8jjnXsahk7k8/49u6DTnz5jk9R9+2Lf7duuLX\nti3+Xbvi362bq8OsNS5NBkKI5sBsoDFgAWZIKT8UQkQA84B44CRwk5TS47/Cb+7dnP2peSzYmkKQ\nn44nRrajZVQgG49lcM/XiZQYLQgBz43twJQrEwB4b8Vhu4ZJ7y0/rJLBxdAHWlcY7amwJr7HXe6L\nx0PtzdjrMJZSkMLT659mT8YeW4XSDakb2Ji60WGZ6XkdIjqoRFAmdPx4Mj+3L/EtS0sp3rOn0nMi\nbruV8FtucXVoLuHqKwMT8LiUcrsQIhjYJoRYgbX89Sop5ZtCiKexPo/4p4tjuWw6rYbXruvCS9d0\nQqsRti+a95YfpsRYtg5Zwtu/HUQImNC9KUUG+2Y3RQaz+uR1sa79BJr0gLO7IWEIdPPOLzZX6tO4\nD1/s/cJhfNOZTQ5jlSUCndDx2qDXaj02b+XbqhWh115L7qJFtrHQCdcS0LcfuQsvqMSj0RD6t/FO\nHy57C5cmAynlGeBM2a/zhRAHgKbAtcCQsmmzsNY88vhkcJ7uguY02UUGu2ODWfLK0gN8vuYYE3o0\n5asNJ22v3dE/TiWCi6XzhQFT3R2FRxvYdCDNg5uTnJ98ye/Rq1EvgvWO3bwaMllh9ZUmLIzIBx5E\n37QJxXv2kPfrr2hDQoh68EGCrh6C9oK6Rd6mzkpKCCHigR7AZqBRWaI4nzBiKjnnfiHEViHE1vQL\nysPWNYPJwvM/76HLi78z4r21rD1cHs/NfZyvvsgoMBDip+OjW3twZ/8WvHdTN54Z06GuQlYamFZh\nrap8Pcw3jH6x/Sp9ffPZzTy2+rHaDstrFe/ZS97iJbZjS04OWbNmcWrSZLJnz8ack0PI6FGE/m28\n1ycCqKNkIIQIAn4AHpNS5lU3/zwp5QwpZW8pZe/o6GjXBVgDX/x5gm83JZFfYuJIWgEPfruN3GLr\nbs5+LSMZ07kxraMdSyNICdd0a8IrEzpzfc9maDTqqkBxjXs734uvpvLNTR8P/Zj/jfwfM0bM4I4O\ndzCh1QSCfeyvBPZl7iOtqE6r03ssc5bjCqGixESKNm+2HphMZHz2OaVHjtRxZK7h8tVEQggfrIng\nOynlj2XD54QQsVLKM0KIWMDj//dtuWBZaZHBzL7TuWQWGnh4bnmLwWBfHfml1ucEYQE+3NhbrdlW\n6kb3mO4svX4pf57+Eykl7yS+Q7HZWkqhT+M+dIu2rmwZ0GQAA5pYm9/krMphTcoa23uE+YYR7usd\n6+JdLaB/f3SxsZjO7yEQAl14OKUXzCs9eRLfNm3qPL7a5urVRAL4AjggpaxYc3gx1r7Kb5b9vMjJ\n6R4jLb+EnCL7cr++Og0dYkMY/cE6u/H8UhP/HN0OIQTXdm9CbKjakKbUncaBjZnYdiIAg5oOYmXS\nSiL9IhnRYoTTZ1XTek9jX+Y+0ovTEQj6Nu6LTqNWnANofH2J/+5bsmbNwpSdTdh112HOz6dw48by\nOUFBBPar/NabN3H1v/pA4E5gjxBiZ9nYs1iTwHwhxL1AEnCji+O4ZFJK7vpiCwfPlm8YC/XX8dYN\n3QjUazmXf+HnBLiyTRSdm4bVZZiK4iA2KJY7O95Z5ZwiYxGZxdarXolk+anl/Hz0Z65rc11dhOhx\nLAYD6e9/QMEff6BPSCDmySdp9Mwz9nNefYWchT+gDQsj6v+m2pWpllJSvH07aDQEeFEvA3D9aqI/\nsVY5dWaYK3/v2nLoXL5dIgAwmCR9W0aQnO289v74jzdwZZsopt/ZS3U0UzxWSn4Kk36bhAX7ekVr\nU9Y22GSQ8fEnZH1l3bxnOHUKw/HjJPy2zO6qKmziRMImTrQdG06dImPmTExp6RhOnMCYbF3RFdCv\nH3EzZyD0lfc28SSqQU01ooJ80V7w0LfYaObrjSeJjwrE38f5X+H6IxnM2ZxUFyEqCmD9lP/ixhe5\n8vsrGTBnAFNXTiW7xHEvZ2pBKj8d+Ykv9nxBqdnxyvZE7om6CNcjFaxfb3dsOHWKjI8/RprNTudb\nSko4dced5C78gcJ162yJAKBo82byV650aby1SX1srUZUkC/ju8ayaKd9G8uzucVoNYKv7+nLw3N3\nkJ5X6rCV50SG6sSl1I3Es4lMXTnVrpfB+tPrGb5gOHd0vIOu0V0Z0mwIG1I38OjqR+0a4VzIWYJo\nKHzbtKH0oH3nuIzPPsdSXEKjfzo2rSlKTMRUxbJ3w2nHPuieSiWDGnjtui6sPZROTnH5Q+QzuSUU\nG8z0axnJlmeHk1FQyuC3V1NkKP8EMbKTdzS1ULzfW1vesksE5xksBr7c+yUAI1qM4EzBmSoTAcDw\nuIZbETb60UfJX7sWmWe/Aj73p58I6NMbfXxLfBNaAmDKzq62raU3PVxWt4lqIMhXx5z7+qHXlf91\nrT+SwUd/lK8vjgryZcqVLTl/azHIV0t4gOpoptSN1ILUauesOLWCXIN98TWN0DAmfgzBPsEE+gRy\nXevreKTnI64K0+MVb9vqkAjAWs46Zer/cXzsWNI++ojkhx7iyIArODVpMj5Oup4B+HXuhH/XOuv/\nddnUlUENWaRjI/vEE+Xlqw0mC7M2nrIVpSsoNfPGrweZe7/qDqq43qiWo1h4eGG180J9Q+1KVoxr\nOY7Xr3zdlaF5ldKjR52/YCn/2s/87/TynsglJRiTHJ8NBo0YQZPXXnVJjK6ikkENtYoOIsRPR15J\n+SV2i8gAnvlxDwaThWu7N7HtSD4vNdf5aiNFqW1P932aaP9otp3bhhYtf539y2FOhF+EXXXTHjE9\neGngS3UZpscLuuoqMmf+r+pJFkvVrwtBo6eerPYWkqdRyaCG/PVaPrmtJ/9atJfk7GKGtIvm931n\nKSi1PiNYsiuVhKhAjld4aHwur4T7Zydyz6AEQvx8mJeYhJ9ey539W9AsPKCy36phyD0N5/ZCsz4Q\nEFHz80ryrO0vfb2/Fkxt8tX6MrX7VIxmI3MOznFIBt2ju7Mzfafd2LGcY/ho1K3MigJ690YbEYE5\nq/KmVXa7ki8gfHxo9Pzz6Jt7X+UBlQwuwuC20ax58mqklHy3OYlVB8qraBjMFofqpSVGC8v3p/HH\nwTQ0QoPBbP1E8cO206x6/CpC/RvoF+L2b2DJoyDN4BNgbWPZqpoesRYL/PoEbJ9lTQZ974dRqtxy\nRUl5Sdzz+z0ODWs0QkOvRr0ckkGToCZ1GZ7XCOjXl/xlv1X6uikrC11sLNJkwpyRQcWGJTHPPEP4\nzTfVRZi1Tj1AvgglRjPP/7SXLi/+zvM/OzYTufCZwnkmC7ZEANb+ySv3n3M6t94zm2DFC9ZEAGAs\ngpUvVn/ewSWw9QuwmKzdz/76BBZOsftCbOj+vfHfDolgXMtxfDnyS/ZmOv5/HZ8wvq5C8yqNnn6G\ngL59rQcaJ98iS0sxnTmDOSODmCceR9+yJdqwMCLuvYfwW26u22BrkUoGF+HVX/bz7eZTtltDFXVp\nGsqtfWt+adhgrwrMBii5oJ1gYQ3Kk5/b5zi2dwFsn107cXk5o8XItnPbHMZvaHMDb299m81nNtuN\na4SGYXHlRQAyizOxyGruhTcQPo1iaDF7Fq03/Fn18wEpsZSU0GrZr7Td9BfBw4Zz+h/TSHn4EQq3\nbKm7gGuJuk10EX7Z7fw+4Tf39GVg6yg0GkGx0cKCbSmVXiUA9I2PYEg795bkdht9AHS8Fvb9VD7W\n7VY4uweOLIfo9tB2jOMnslbDYO1bju93Yi30muTamL3A6qTVDh3MBILEc4nsz9xvN+6j8eHhHg9T\nYirhs52fsfjYYk4XnKZZUDPeHvw2XaK71GXoHil36S8Url+HT1xzjEmVNwzybW2tVmpISiJp8mSk\nwXqrOH/NGhJ+/gnfVlX3mPAkKhnUkNkiKXRyRdC5aQhXti3/xn6+LWapycKZ3GKeWrib7Uk5ttdj\nQ/34/v7+DbuvwYTPoXEXOLMLWl4FgTEwfTCc/2Ta8y645mP7c+L6wcjXYPlz9uOx3tNw3JVOFzjf\n6Xqm0PEDzMtXvIzBYuD6xdfbJZCUghRe2PACP0/42WVxeoOsWbM498abtmNdkyboIiMJ6N8fS14u\nOQt/ACDs+usJvGowpqws8leusiUCAIxG8lesUMmgPjqeXmB33x+sZaw/vMWxMqFOq0Gn1dA6Jpj3\nb+7Ow3N3sDsll4ToQD64uXvDTgQAPv5w5ePlx1+MLE8EADu+hWEvQmCk/XlXPGRdRbTyReuqos7X\nQ78H6iJij+esg1mUfxQHM+1LK4T5hrHlzBZ+OvaTw3yAY7nHMFlMDbqM9flv9ueZUlNpuXABugjr\nqreYadNASvJXr+Ho4Kuw5OWhd9LPwCc2tk7irS0N91/8IjULDyDYT0d+hX0GE7o3pVV01UscW0QG\nsvihQRSUmgjyVX/dNVfJg+Fek6H7HWAxWpOKAkDHyI5M7jSZWftmIZHotXquan6Vw0a0duHtKk0E\nAP0a92vQiQBAG2Zffl74+6Px8yt/PTQUU1YWZ//1L6TRurfIcOQImqAgLAUFZSeBrG4/godRD5Br\nyF+v5d0buxEVZG0r2LdlBE+MaoeUkjd+PUCnf/1G39dWMj/R+f1FlQiqcMXD1uWi53W/HQKjKp+v\n1alE4MTjvR/nr9v+4n8j/8fccXPRCcf/c1vOOn+wGeQTxMgWI3njyjdcHabHi37kYYR/+f+v6If+\nD02A/b4gw8mTtkRwni0RAEg489zzlJ486cpQa5WQXrI0r3fv3nLr1q3uDgOT2UJBqYmwAGuN8oXb\nknliwW7b60LAymlX2V0x/HftMWZtPIm/j5ZHhrVhQo+mdR63xzuzq/wBcrtxzpf0KdX69fivvLrp\nVfKN+TQLbkZKfortNb1Gj8FicDjnXwP+xcQ2E512QmuoTNnZFCUm4tu6ja0wXUWW0lKOXj20ys1p\nAPrWrWm1dImrwqwRIcQ2KWXv6uapj6sXSafV2BIBwOdrjtm9LiXsSMqxJYMV+8/x5rLy+7bT5u+k\nc9NQWseoHbR2Yruph8GXqchYxMubXqbQaN0Fn5KfQs+YnmSWZJJelE64X7jDg+bxCeO5sa3HNhp0\nG114OCEjR9qOSw4eJGf+fISPnvDbb0MfF0fzGTNIff45DIcOW7/whXDY92I4ehRjWho+MTF1/Ue4\naCoZXIbMglK78hPn9Ywrv+f417FMu9csEjafyFTJQKl154rO2RLBeRnFGSTlWwupFRUU2b3WLbob\nL/R/oc7i81alx49z8uZbkKXWPg+5ixaRsOxXSg8eKE8E4DQhaCMi0IV5RwtclQxqKDmriJeW7Gd/\nai79EyLp3DSUM7nFCOwfdcZHBpBQ4RZRt+ahDu/VVfVHLpeyDQ4vg8jW0PkG0JZtxktOhCO/Q2Sb\nsnH1X7U6LUJaEB8Sz8m8k7axcL9wWzK40NnCsxSZigjwaeB1sqqRu2SJLREAmHNyyPn5Z9L/857D\nlYDw9cUnNhbDiRNoQkOJffklr2l7qb7Cauj/5mxnd4p15+yPO07z4w7r5XbFVaIaAU+Nbs8vu8+Q\nmlPMyE6N+FvXJuxMzuG7zUn46jQ8MrQNXZo5JogG6cASmHcntnR6aBncNAv2/QwLJpePH1kOE79w\nU5DeochYxFuJb5FTmkO4bzjBvsGMjh9Ns6Bm7Erf5fScc0XnGLlgJLd2uJVVSasoNhVzS/tbeLDb\ng3UcvWfThjp+vZYePgwmxyZBwSNH0OSttzAmJ6Nr1AiNr29dhFgrVDKogdwioy0RXMgiIcRPx5gu\nsUwaEM9/lh9i1UFrAbvXfz2ATiPQaTX0iw/n9eu70jxCfQqz2fRf7K6r9v9srWa66XP78b0/WIvS\nBavOcZX5cPuH/HjkR9txkD6I/+v+f0gp2ZG2g8XHFiORDiUnjNLI7P3lJT0+2/kZCaEJjIofVWex\ne7rgYcNIe9N+93vp0WNow8MxZ5f3mPbv05vG//o3Qgj0lTS88WRqyUYNBPvpaBZe+VLGvBITP+04\nTVahwZYIwPrtzGiRFBvNrD+ayf2z3b8ayqNotPbHQmMdu3CduxAgLpir2Lmw9lByfjLrUtZhlmZe\nHvgyD3Z7kBYhLYj2r74MirMaRw2Zs4Y3xtRUIiZPQp+QgE+LFkQ9/BAtZs1CGxTohghrh0oGNaDR\nCN67qTtNw6wJwdmeAYPJwoerDlf5PgfO5pOSXVTlnAZl0GNQsZ5+RCtI/AL63GOfEHpNhqAGWsup\nhjpEdrA7Fgge/uNhRi4cyUfbP+KTnZ9wIvcE6cXVFwXsEtWwaxMVbtrM8QnXcahvP878+0Xnm8dK\nS0h//wMMx49jyc0l9JprEF6+HFrtM7gIFoskt9hIsJ+OFxbtZe6WygtYOaMRcPfAeJ4c1R4/H/VJ\nF4DMY9Zy1Fu/LB+Lagc3zYZjf0BUW2g9zLoP4fQ2aHEFxHSo/P0aqPSidJ5a9xRbz21FIzR2t4NC\n9CHkGRz7+lYU5BOEyWLixnY38kTvJ9AI7/7GdqksRUUcuWoIlvx821jk1AfJXbQY0+myZblOlpBG\n3H03jf75VF2GWmNqn4ELaDSC8EDryoB//60TJzOLHJaOVsUi4Ys/T2K2wIvXdHJVmN4lshUU59iP\nZRyC7yZarxzaDIe/PoPfnyl7UcCEz6D7bXUeqieLDojm8+Gf83bi2yw4vMDuNYPZcaNZRW3D27Jg\n/AIQNNgkcF7JoUN2iQCgePsOWn4/l6w5czBnZ6OPiyPt7Xfs5kgnD5O9TcP+l78IUkp+33eWD1Ye\nZvPxTKZ+t71GieDmXo67jVc01MY2lfF3stQ2Nxl+eRwOL7+gdLWENW86zld4f9v7DokAYEzLMbZC\ndnqN3q5MRafITsweMxuNRtPgEwGAb+vWiAtKT/h37YouOpqYRx8l9sUXibjjDnzbtLa9Lvz9Cb/J\n+zfuqSuDGnppyX6+3ngSgA84Uum81jGBHE2zbvyJjwzg4eFt+eNwBun55euUE6K99yGTSwx4CA4s\nhcI0x9eOrQJTqf2YUT13cWZdyjqHsTs63MFjvR7DV+vLwsML+XD7h+SUll+JhepDVRKoQBscTNN3\n3+Hsq69iOpdG8MgRRP39frs5Qq+nxZw55P70M+a8PEL/Nh59ixZuirj2qGRQA4WlJr7bfKrKOUPa\nRtErPoLGIX60jg7CJCU9moeh02qY0L0JM9efAKy3G69XtYnsRbaCR3ZA4v9g5b/tX2vcBfrca32u\ncF5hOnw5Gm7+zrHMdQOUW5rLrH2zKDGX2I1H+kUyrfc0fDQ+FBmLeHfruw47lDee2ciPR37k9g63\n12XIHi146FCChw5Fms0IrfNne9rgYCLuurOOI3MtlQxqQAjr6oyKa98rHum1Gny0Gv6z3LqaKNhX\nx/d/749Oq8Fskfy4vbwejJQwe9MpruvZrO7+AN7AN8j6jMBUAn9+YG2P2e1W6HqLdblpZGv4ZVp5\n34Okv2DdOzBG3TJ6aNVDDs3uI/wieHngy/iUrdY6XXDaIRGcN3P3TAoMBdzb5d4GX766osoSQX2l\n/uVrIECvY/LAeGasOw5Yk8PDQ1tz5Jy1ZO3oTo15dF75F2N+qYkZ647z4S09KDGaySqyf4B3Jsf+\nE5xSwZCn4YpHrI3v/ULKxxt1sm+AA5B+oG5j80DJeckOiaB9RHvmjJ2Dj9aHA5kHmLlnJnmleZWu\nKsosyeSTnZ9gsBh4uMfDdRW64mFUMqihZ8d2YGDrKA6cyWNQ6yg6N7VuUTebLdw2c5PD/PMtMgN9\ndQxrH8PKA+X3w6/p3qRugvZWeie7tGO7QVAjKKjw8L2N2iUbrA9Gp9FhspSvZokNjMVH68PM3TP5\naMdHDufohA5fna/DlcLKUytVMriANJkQuobxbdKlf0ohxJfAeCBNStm5bCwCmAfEAyeBm6SU2ZW9\nhye5qm00V1Xod7xkVyov/LyXnGKjw9zD5/Jp+9wyrm4fzYt/60SbRsHsPZ3LwNZRTBnkWB9dqYZW\nD7cvhFUvQ26KanlZJswvjAe7PcgnOz5BIgnzDWNq96kcyDzgNBEAmKQJk9FxKWRcsPeVUHCVws1b\nSHnkYSy5eSAEPk2bEvPkk4SMGknhxo1kfTcHofch8p578O9SPzbpuXTTmRBiMFAAzK6QDN4GsqSU\nbwohngbCpZT/rO69PGHTWUXp+aUMfPMPh77IgEMl05t6N+PtiapW/yXZ+wP8/jwUZUDXm2Hce6Dz\njiqQdSkpL4mk/CR6xvQkwCeAhYcX8tJfL9X4fI3QkBCawLP9nqVP4z4ujNTzSYOBwwMHOew3QAhi\nX3uNMy+8AGbrlb/w96fVsl+xFBVTtDUR/y5d8OvgWZsiPWLTmZRynRAi/oLha4EhZb+eBawBqk0G\nnuSnHSl8sf6E00QAjt17t57yigsfz5N/Fn78u7XfMcCOb6w7kgc+4t64PFBcSBxxIeWf7Hs16uWw\nE7kqFmnhaM5RHv3jUVbeuLJBl7U2pqY6JgIAKclZsMCWCABkcTFpH31E3s+LoKxsRfSTTxB17711\nFW6tcccC40ZSyjMAZT97fgugCpbsSuUf83axN7Xq7f0V9YoLd2FE9diZXeWJ4LzTnnN16Mlahrbk\njUFvEB8ST6RfJONajmNU/Cj6Ne7Hbe1vQ69xfnWVb8xnX+a+Oo7Ws/g0b46mkoY0+vh4h7G8H3+y\nJQKA9Hf/g/Gc920s9egnI0KI+4H7AeI8pCTsL7vPOIwF+ero0izUYUeyViMY2j6GZ8d61mWj12jS\nE7S+YK6w6SzuCvfF42XGJoxlbMJYp68NjRvKfcvvQ15wHeur9aV1WGun5zQUQqslbuZMkv5vKpa0\n8sJ+IWPH0ujZZzCePk3Rli2Vv4GUZH8/j5hHvesK1h3J4JwQIlZKeUYIEQs42XZqJaWcAcwA6zOD\nugqwKheWshYClj48iKbh/jz/015+3JFCgF7HEyPbcueAePcEWV8ERcONX8OKF6AgHbrfCn2muDuq\nemFn2k6HRBCgC+ClgS8R7qeuZP27dKbdOuuOblNmJtJkxqeR9SZGi9mzSHvvfTJnzKj0fEtBQZ3E\nWZvckQwWA5OAN8t+XuSGGC7Z/VclsO5IOofPFaAR8OCQVsRHWctLvDWxKy9P6IROo0FbsQWacuna\nj7X+UGpVfGi8w9hrg15jeIvhdR+Mh9NFOu5yDxk3jsyZMx2ql1pP0BE64do6iKx2uXpp6VysD4uj\nhBApwL+xJoH5Qoh7gSTAqyo8xQT78ftjg9mXmkdkkJ7YUH9+33eWtYfSyS024KPVMKhNNDf0bIoQ\nKiHUqsxj8NszkH4Q2oyEES8735OgVGt43HDGJ4znl+O/AHBt62sZGjfUzVF5D792bYl99VXSP/wQ\nU1r5zQ3h60vz/36Ofyfvq0rs6tVEt1by0jBX/r6uZjBbmJeYzJLdqfjptJzNs99R/PPOVFKyi3hs\neFs3RVgPSQlzb7WWtw43pUgAAAjVSURBVAZInGltgKPKUVwSrUbLG1e+wbRe0xBCEOUf5e6QvE7Y\nDddjOHmCzJn/s43J0lKMZ866MapLp8oVXoLpa4/zzaZT5BQZHRLBefMTL67xjVKN3JTyRHDesVXu\niaUeiQ6IVongMmjDHJ+vaMOdr0TydCoZXILNJ6rvYxAaoDZG1aqgRhBwwTetRt53Ke5JLNLCqqRV\nfL33a47nHnd3OF4pbOIN6Fu3sh0HXnEFQYMHuzGiS+fRS0s9VbdmYWw4WnlC0Gs1PDnKeotoya5U\n5m9NprDURKcmIdzWrwUdYkMqPVephE4P1/0XFj0EBWehSQ8Y8Yq7o/Jqz/35HEuPLwXgwx0f8umw\nT7miiVq6ezG0oaEk/PwzhZs2o/H3w79nT699Vqh6IF+CwlITTy3czbK9Z2gc4scjw9qg02oI9dNh\nMEv6xIcTE+LHb3vP8MC32+3O1Qr47r7+9E9QdfgvidkExdnWZadKjZzIPcHq5NU0CWrC8Ljh6DQ6\nzhaeZcTCEXbzrmhyBdNHTHdTlPWDMS2N/GXL0AQGEjJ2LJoA9y9w8IhyFPVVoK+OT2/vidFswUdb\n+Z22JU42qJklzP7rpEoGl0qrU4ngIiSeTeTvK/6OsWwn97C4YXxw9QdOy1TUtHSF4pwhOZkTE2/E\nkpsLQNY339JywXyE3jtuGatnBpehqkQA0DTM3+m4vprzFKW2zN4/25YIAFYlreJk7kmaBDVhZIuR\ntnGt0HJnx/rVuauu5cyfb0sEAKWHDlHw559ujOjiqCsDF7rvygTWHErj8Lny3YgBei33DkpwY1RK\nQ3d+5/Fbg99i2MlhJOUnMaT5ENpHtHdzZPWQxXuutlQycKHoYF9+e3Qwu1JyOHQ2H6PZwvCOjYgN\ndX7FoCi17c4Od/Ln6T9tzW+GNB9Cy1BrPw2dRldp7SLl4oXdeCPZ8+ZjybMWsfRt08arVhapB8iK\nUs8dyznGH0l/0DSoKSPiR9j6Iiu1z3juHHm//Gp9gDxuHNqgQHeHVOMHyCoZKN7j3H5rCeu4ARDV\nxt3RKIpXUKuJlPol8X/wy+NlB8K656DbLW4NSVHqE7WsRfEOq1+vcCAvOFYU5XKpZKB4PinBUGQ/\nZih0TyyKUk+pZKB4PiGg9z32Y6rJjaLUKvXMQPEOI1+F2K6QshVaXAGdr3d3RIpSr6hkoHgHjcb6\nwFg9NFYUl1C3iRRFURSVDBRFURSVDBRFURRUMlAURVFQD5AVpd5JPJvIlrNb6BjRkavjrnZ3OIqX\nUMlAUeqR7w9+z2ub/7+9+wuRqozDOP59sqIVN8I1SiHaG7UYra1dDS8k6iYqIywvBDF36d9NdCEh\nkVB20Z13VoT90yCk0oSsG4tYii5SA3P9gxGpZBq1haQliy6/LuYtd5ed2dndmTl7js8HFt45e87y\n48fMPOe8h33PK/+/7lnQw9rOtRlWZHnhaSKzAtl6aOuw19uObOPC4IUKe5td4jAwK5ArNPwjLQny\n+Xx2azKHgVmBPLFw+DId3aVuP7/AauJ7BmYFsnzucubNnMee03sotZVYPHtx1iVZTjgMzAqm1Fai\n1FbKugzLGU8TmZmZw8DMzBwGZmaGw8DMzHAYmJkZDgMzM8NhYGZmOAzMzAxQRGRdQ00k/Q6cyLqO\nGswC+rMuoiDcy/pyP+srL/28OSKuH2un3IRBXkjaFxFdWddRBO5lfbmf9VW0fnqayMzMHAZmZuYw\naITNWRdQIO5lfbmf9VWofvqegZmZ+crAzMwcBmOS1CZpf/r5VdIvaXxG0uGs6ysKSYND+rxfUvso\n+8yRtL351eWHpPWSDkk6kPp4V5V9uyXNaWZ9eTKeXhaBH24zhoj4A+gAkLQBOBcRG9OX1acT/buS\nroyIi/WosSDOR0RHpV+mfp0CVjSxplyRtARYBtwZEQOSZgFXVzmkGzgInGpCebkygV7mnq8MJmea\npDfT2cNuSS0AknoldaXxLEnH07hb0keSdgG7sys7H0b2S1K7pINZ1zWFzQb6I2IAICL6I+KUpBcl\n7ZV0UNJmla0AuoD301lvS6aVTz2Venk8BQOSuiT1pvEGSe+kz/5Pkp7NrvSJcRhMzlzgtYgoAWeA\nR2s4ZgmwJiLubWhl+dMyZIpo55Dt7lftdgM3SfpB0uuS7k7bX42IRRGxAGgBlkXEdmAfsCoiOiLi\nfFZFT1GVelnNLcB9wGLgJUlXNbTCOvM00eQci4j9afwd0F7DMZ9HxJ+NKym3Kk0TuV81iohzkjqB\npcA9wAeSngfOSloHTAdmAoeAXdlVOvVV6WU1n6UriQFJvwE3ACcbXGrdOAwmZ2DIeJDyWRfARS5d\ndV0z4pi/G11Uwbhf4xARg0Av0CupD3gauA3oioif032vke9JG8UovVxD9c/2yO+DXH2/epqoMY4D\nnWnsG57WFJLmS5o7ZFMHcDSN+yXNYPj78SzQ2qz68qRCL08w/LNdy7RwbuQquXJkI/ChpNXAl1kX\nY5eNGcAmSddRPoP9EXiK8v2sPspfZHuH7L8FeEPSeWCJ7xsMU6mXtwJvS3oB+DbD+urO/4FsZmae\nJjIzM4eBmZnhMDAzMxwGZmaGw8DMzHAYmI1LWoPmuazrMKs3h4GZmTkMzMaS1rU/KukLYH7a9mRa\nCfR7STskTZfUKunYfwuUSbo2rXKZqwXL7PLkMDCrIi1WthK4A3gEWJR+9XFaCfR24AjweEScpbyW\nzYNpn5XAjoi40NyqzcbPYWBW3VJgZ0T8ExF/AZ+k7QskfZ0WMFsFlNL2t4CeNO4B3m1qtWYT5DAw\nG9toa7ZsAZ6JiIXAy6QVLCPiG6A9rX8/LSL8MB7LBYeBWXVfAcsltUhqBR5K21uB0+l+wKoRx7wH\nbMNXBZYjXqjObAyS1gOPUV7C+CRwmPJzFtalbX1Aa0R0p/1vBI4BsyPiTBY1m42Xw8CsztLzhR+O\niNVZ12JWKz/PwKyOJG0C7gceyLoWs/HwlYGZmfkGspmZOQzMzAyHgZmZ4TAwMzMcBmZmhsPAzMyA\nfwHriULghYNJ0AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xdcc8ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#jitter=True -> This can be useful when you have many points and they overlap, \n",
"# so that it is easier to see the distribution\n",
"sns.stripplot(x='day', y='total_bill', data=tips, jitter=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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