{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lecture/Practice 9 - Visualization of different data types with python\n",
"==========\n",
"Here, will learn some of the most basic `plotting` functionalities with `Python`, to give you the tools you need to assess basic distributions and relationships within you dataset. We will focus on the [Seaborn library](https://seaborn.pydata.org/index.html), which is designed to make nice looking `plots` quickly and (mostly) intuitively."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pandas\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first gather our dataset. We'll use participant related information from the [OpenNeuro dataset ds000228 \"MRI data of 3-12 year old children and adults during viewing of a short animated film\"](https://openneuro.org/datasets/ds000228/versions/1.0.0) ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"wget -O data/participants.tsv https://openneuro.org/crn/datasets/ds000228/snapshots/1.0.0/files/participants.tsv"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# simple histogram with seaborn\n",
"sns.displot(pheno['Age'],\n",
" #bins=30, # increase \"resolution\"\n",
" #color='red', # change color\n",
" #kde=False, # get rid of KDE (y axis=N)\n",
" #rug=True, # add \"rug\"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What kind of distribution do we have here? \n",
"\n",
"Let's try log normalization as a solution. Here's one way to do that:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\program files\\python36\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
" warnings.warn(msg, FutureWarning)\n",
"c:\\program files\\python36\\lib\\site-packages\\seaborn\\distributions.py:2056: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"\n",
"log_age = np.log(pheno['Age'])\n",
"sns.distplot(log_age,\n",
" bins=30, \n",
" color='black', \n",
" kde=False, \n",
" rug=True, \n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is another approach for log-transforming that is perhaps better practice, and generalizable to *nearly any* type of transformation. With [sklearn](https://scikit-learn.org/stable/index.html), you can great a custom transformation object, which can be applied to different datasets.\n",
"\n",
"_Advantages_ :\n",
"* Can be easily reversed at any time\n",
"* Perfect for basing transformation off one dataset and applying it to a different dataset\n",
"\n",
"_Distadvantages_ :\n",
"* Expects 2D data (but that's okay)\n",
"* More lines of code :("
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import FunctionTransformer\n",
"\n",
"log_transformer = FunctionTransformer(np.log, validate=True)\n",
"\n",
"age2d = pheno['Age'].values.reshape(-1,1)\n",
"log_transformer.fit(age2d)\n",
"\n",
"sk_log_Age = log_transformer.transform(age2d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Are two log transformed datasets are equal?"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all(sk_log_Age[:,0] == log_age)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we can easily reverse this normalization to return to the original values for age."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"reverted_age = log_transformer.inverse_transform(age2d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The inverse transform should be the same as our original values:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all(reverted_age == age2d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another strategy would be `categorization`. Two type of `categorization` have already been done for us in this dataset. We can visualize this with `pandas value_counts()` or with `seaborn countplot()`:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5yo 34\n",
"8-12yo 34\n",
"Adult 33\n",
"7yo 23\n",
"3yo 17\n",
"4yo 14\n",
"Name: AgeGroup, dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Value counts of AgeGroup\n",
"pheno['AgeGroup'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\program files\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
" FutureWarning\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Cnt9tfziw29YrT2PsN4C7quqeJI8DDgR2Bg7urmPtABw7o/+NwJO690cBO8yyz58ADxteyePBUNiGdVOEnAZ8sRumv30Bu3kTgwvTlwPPBG7aiiVqfH0KWJLkm8DfMTiFdCvwRuBrwOeAy2f0/3fgaUm+zmA6/Z/Nss9LgJVeaL5/nOZCktQ4UpAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQWMlySOTfCjJ95Jck+STSVZvYqrw9yRZ2b2fddrwJG9M8prNHHdJkjuS/P0m+sw5ZflG/X7ava5I8ieb6y9tCUNBY6ObHuGjDObXeUxVrQRexybmf6qqP+vmiLq/nglcy+AJ86010+wKwFDQVmUoaJz8EfDLqvrXDQ1VdSXwZeChSS5I8p0k52744E7yhSSTG+8oyeuTXJvkc8Bj53HsE4AzGDwRfuCM/RzWHfMrwHNntN9n9JHkqiQrNtrn6cBTuid4XzWPGqTNWtJ3AdII7QusnWPdfsDjgVuA/2IwffNXZuuY5EnA8d02SxhMxzDXfkmyM3Ao8BJgVwYB8bUkOzGYvuEQ4Hq2fFbQU4DXVNURW7idNCdHCtLA16tqXVX9CriSwamZuTwF+GhV3VNVP2bwPRObcgRwSVXdA3wYeE6S7YHHAd+vqutqMN/MB+73byHdT44UNE6uBub6ytGZ04nfy+b/bWzJpGEnAAclubFb3oPBqaw7NrGfNlV0Z6ctOJ60YI4UNE4+DzwoyZ9vaEjye8DTtnA/X2Lwv/2dkzwMePZcHZPswuBb7h5dVSuqagXwUgZB8R1g7ySP6bqfMGPTG4H9u33sD+w9y+6dKlpbnaGgsdGdonkO8IzultSrGUzVfMsW7udyBuf/r2RwOujLm+j+XODzVTVzJHIhcCSDUcJq4BPdheYfzOjzYWD3JFcCfwl8d5Z9fxNY333/theatVU4dbYkqXGkIElqvNAsbSVJ3sXgVtaZzqiq9/VRj7QQnj6SJDWePpIkNYaCJKkxFCRJjaEgSWr+D47+AOD5s2kWAAAAAElFTkSuQmCC\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Countplot of Child_Adult\n",
"\n",
"sns.countplot(pheno['Child_Adult'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bivariate visualization: Linear x Linear"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cool! Now let's play around a bit with `bivariate visualization`. \n",
"\n",
"For example, we could look at the association between `age` and a cognitive phenotype like `Theory of Mind` or `\"intelligence\"`. We can start with a `scatterplot`. A quick and easy `scatterplot` can be built with `regplot()`:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.regplot(x=pheno['Age'], y=pheno['ToM Booklet-Matched'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`regplot()` will automatically drop missing values (`pairwise`). There are also a number of handy and very quick arguments to change the nature of the plot:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## Try uncommenting these lines (one at a time) to see how\n",
"## the plot changes.\n",
"\n",
"sns.regplot(x=pheno['Age'], y=pheno['ToM Booklet-Matched'],\n",
" order=2, # fit a quadratic curve\n",
" #lowess=True, # fit a lowess curve\n",
" #fit_reg = False # no regression line\n",
" #marker = '' # no points\n",
" #marker = 'x', # xs instead of points\n",
" )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take a minute to try plotting another set of variables. Don't forget -- you may have to change the data type!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#sns.regplot(x=, y=)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This would be as good a time as any to remind you that `seaborn` is built on top of `matplotlib`. Any `seaborn` object could be built from scratch from a `matplotlib` object. For example, `regplot()` is built on top of `plt.scatter`:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=pheno['Age'], y=pheno['ToM Booklet-Matched'])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to get really funky/fancy, you can play around with `jointplot()` and change the `\"kind\"` argument.\n",
"\n",
"However, note that `jointplot` is a different `type` of `object` and therefore follows different rules when it comes to editing. More on this later ..."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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URr6LiAykBJUU1LA1Hnc2bDnA9kMdPPiDX/VV4911TSOvH+7gnQum501ScyfV8R/7j/OBR1/qNw9qbcur3LmkkRsvmcEPtxxUtZ+ISAYt8eWwo62Dja3tfckJEoUODz27la0HT/TNgcp3jMxqvlUbtnLDopnc88QmNu5tV7WfiEgAJagcDhzrJO7B85jiTt8cqHzHCHp9as7TflX7iYgEUoLKYer4WqqMwGq8mNE3ByrfMYJe7574c7qq/UREAilB5TB3Uh2XzJrA3dee368a765rGmmcMrZvDlS+Y2RW861c0shTG/dw/9KFXDJjgqr9REQChDZuIyzFHrdxtlV8mcdIVelVV8U43tnFxLrRfbOk8s2gEpERT+M2AqiKL49YzJg7eSxzJ4/Nv3OOY2RWCA5mu4hIJdISn4iIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRFJFdZIY7GDAnp44Ww4c48jJbjpO9zBjwjmcXz+WLYeOs6+9k+kTzukbOpj+ms372gdsj8ed1w93sLf9JKOrqujo6qFhYh1zJo5h19GTeWPSUEMRqTQVk6DicefpzfsLHgzY0xPnXzbvo/XoKR56dmvfa/7ypoV84bmt7Gw7RW11jPuXLmTpG2cyalSMnp44T7yyh3ue2NS3//1LF/YNJXzg6de4ramBVRsSx5sz6RzuXNLYb/+gmAYbu4jISFAxS3xBgwNzDQbcvK+drQdP9CWn1Gv+4nubuGHRzL7H9zyxqW9w4eZ97X3JJn17aijhDYtm9iUnoG9oYb6YBhu7iMhIUDEJKtvgwGyDAfe1Zx9WaNb/cWpw4b724PdIDSVMDSlMyXycLabBxi4iMhJUTILKNjgw22DA6RPOyTqsML0DfW11rG9w4fQJ5wTuPy3tvYO254tpsLGLiIwEFZOgggYH5hoMuGD6eH5ryljuuqax32v+8qaFPLVxT9/j+5cu7BtcuGD6eO5furDf/vcvXcii5FDCJ1/Zw8olZ4735Ct7BuwfFNNgYxcRGQkqamDhYAcDZlbxTZ9QywX149hy6Dj72zuZNqG2b+hg+ms272sfsD1Vxbev/SQ1AVV8+WLSUEOREa2wgYWxmFOk7+wZs2azZ/euorwXWf7+oSYoM2sGHgKqgC+7++cytjcAXwPOTe7zCXdfn+uYw5Gggkq1i1XGrXJxEQlQtIm6RZ6UW6jiTtQ1syrgYeBaoBV4yczWufurabvdAzzu7l80s4uB9cDcMOLJVaoNFKWMW+XiIiKFC/Ma1BXANnff7u5dwGPATRn7ODA++fsEYG9YweQq1S5WGbfKxUVEChdmgpoJ7E573Jp8Lt2ngfeaWSuJs6c7gw5kZsvNrMXMWg4dOnRWweQq1S5WGbfKxUVksNK//0odS7GFmaCC1qwyFz6XAY+6+yzgeuAbZjYgJndf4+5N7t5UX19/VsHkKtUuVhm3ysVFZLDSv/9KHUuxhZmgWoHZaY9nMXAJ74PA4wDu/hOgFpgcRjC5SrWLVcatcnERkcKF2YvvJaDRzOYBe4Dbgfdk7LMLuAZ41MwuIpGgzm4NL49YzGheMI0LVy4OLNXOta1YMYiIyBmhJSh37zGzO4BnSJSQP+Lum83sPqDF3dcBHwG+ZGZ/RmL57/0eYv1jLGbMrx/L/Pqxg9pWrBhEROSMULuZJ+9pWp/x3L1pv78KvC3MGEREpDxVTKsjEREpLxUzD2qwUh0f2jpOYxhHOk4zdfzAAYW5XqtuESIiZ08JKkCq40PmgMHMAYW5XqtuESIiQ6MlvgCpjg+ZAwYzBxTmeq26RYiIDI0SVIADWQYMQv8Bhblem/kadYsQERmcilviK+T60NSMAYPpCSd9QGGQ1GszX6NuESIig1NRZ1Cp60PXr3qeZV96ketXPc/Tm/cTj/e/9SrV8SFzwGBtdYz7blrIRVPHBx2+32vVLUJEZGgq6gwq2/WhC1cu7nfjbKrjw8xza/n5zqOsft+bOHT8NDWjqvj6C9tpmnNe1htt1S1CRGR4ZE1QZnaUgc1d+7j7xFAiClGu60OZCScWM0529fKZp14bcJyg/TNfq24RIiJDk+sMajKJjuSfItEf7xvJx38EjAk/tOE32OtDup4kIlI6Wa9BuXuvu/cA17n7Knc/6u5H3P3zwB8WL8ThM9jrQ7qeJCJSOoVcg3Izu43EaPbU72VpsNeHdD1JRKR0CklQ7wE+D3zRzOLAT0ks85WlwV4f0vUkEZHSyJug3H078PtFiEVERKRP3vugzOy3zOwZM3sl+XiRmX0y/NBERKSSFXKj7peBzwCpUrZfAu8NLSIREREKS1B17v5C6kFy4m13eCGJiIgUViTRZmbzSN60a2ZLgf2hRhUh6b37xtSMoqu3l0l1o3NW82kelIjI0BWSoO4AvgJcaGY7gX3AslCjioig2U4rlzSytmUXH2++KHDGk+ZBiYgMj7xLfO6+zd2XANOBN7r7le7+evihlV5Q775VG7Zyw6KZWWc8aR6UiMjwyHsGZWY1wFJgLjDKLHEW4O6fDTWyYZRtyS3z+YbzxrDr6Mm+x9l696XmRAX15BtMvz8REcmukCW+/wN0Aj8DesMNZ/hlW3K77qKpfP+1A/2ev3/pQj6/YSs7205RWx3jS+9rCuzF5569J5/694mIDI9CqvjmuPvN7v5Zd38g9RN6ZMMk25Lb5n3tA56/54lN3LBo5pnH3/slD9y8qF8vvpVLGnlq456sPfnUv09EZHgUcgb1UzO72N1fDT2aEGRbctvXnn35LmVn2ylmnlvL+pWLk1V8VXT3xmleOC1rZZ7694mIDI9c86B+QaK0vBp42cy2AqdJjNxwd7+8OCEOTbYlt+kTgp/3tAlYtdUxJtaNHnQvPvXvExEZulxnUO8uWhQhSi25ZV6DWjB9woDnU9egQEtzIhJBZqxdcdWQDjFj1uxhCiZ85p51aG5iB7M3A6+5+4nk43HABe7eUoT4BmhqavKWlsG9dapaL3PJLfP5VBWfluZEpMgK+qIxM79t9Qv5d0xau+Iq8n3HR0Tg37+Qa1BrgDelPe4AVmc8F/yOZs3AQ0AV8GV3/1zG9r8Hfjf5cAwwxd3PLSCmQcm25Bb0vJbmRESioZAEFXP3vgs17h43s+p8LzKzKuBh4FqgFXjJzNalF1u4+5+l7X8ncNlgghcRkZGrkDLz183sv5hZlZnFzOzDwI4CXncFsM3dt7t7F/AYcFOO/ZcB3yzguCIiUgEKSVArgGuAA8mf3wH+tIDXzQR2pz1uTT43gJnNAeYBG7JsX25mLWbWcujQoQLeWkRkZEj//it1LMVWyBLfPHfvV9FnZlcCB/O8LuiiV7ardbcD33b3wE4V7r6GxLUwmpqayuKKn4jIcEj//jOzivr+K+QM6h8Cnnu4gNe1Aun1jLOAvVn2vR0t74mISJpcN+peAbwVqDezlWmbxpO4eTefl4DG5CypPSSS0HsC3ucC4DzgJ4OIO9I0D0pEZOhyLfHVAZOT+9SnPX8cuCXfgd29x8zuAJ4hUWb+iLtvNrP7gBZ3X5fcdRnwmEekWD8zucwYV8um/cfYf6yTqeNGM7o6xtjR1VmTjuZBiYgMj6wJyt2fA54zs6+6+/azObi7rwfWZzx3b8bjT5/NsYci1/iN9OTSNGcCtzbN4d51m/qSzWduXMCzr+1j6WUNgUknW3PaC1cu1v1VIiKDUEiRxDEz+5/AAqBvZoS7XxdaVCHKdYaTmVz++Kr5/Pdvv9Iv2Xxq3WZWv+9NrPjGzwKTjuZBiYgMj0KKJP43ifuezgceAPYDL4cYU6hyTbzNTC6nTvcEJpvfnOzuSzqZUs1p02kelIjI4BWSoOrdfTXQ5e7PAn9C4ibcspTrDCczuYwZPSow2Zw7pjpr0tE8KBGR4VHIEl938s/9ZvZ7JErFy6cdboZcE28zO59/7YXt3HfjwgHXoP7pp69nTTqaByUiMjwKSVCfNbMJwEdJ3P80HvhYqFGFKNv4jVQSyUwuM8bVMr++rq+Kr7Y6xpvnTsyZdDQPSkRk6PImqLRy8I3A4nDDCV++M5yg5NI0d2KpwhURqVi5btR9MNcL3f3u4Q+nOHSGIyISfbnOoO4Efgl8i0STWF1EERGRosmVoGYCtyZ/OoC1wHfd/VgxAhMRkcqWtczc3Q+6+xfc/e3Ah4CpwGtm9kdFi05ERCpW3iIJM1tEol9eM/BD4JWwgxIREclVJPEXwI3Ar0lMw/1UcjKuiIhI6HKdQX0G2AZcAHwKuNfsTJ2Eu18ebmgiIlLJciWoxqJFUWTp3cynjKulKgb72jW7SUQkSnKN2/h15nNm1uzuT4cbUriCupnfdU0jX//JTo6e7NLsJhGRiCikWWy6z4YSRREFdTN/6NmtvOvyWf06m4uISGkNNkGV/WlFtm7mqctr2cZoiIhIceVNUGaWvgz4XwOeKyvZ5jWlBs5rdpOISDQUcgb176lf3P0nmc+Vm6B5TXdd08h3f96q2U0iIhGS6z6oKcB04Bwzu4Qzy3vjgTFFiG1YpFfspar00ruZ149NVPFd1nCuZjeJiERIrqW63wf+EzAL+Ie0548BfxFmUMMlqGIvVaWX2c187mR1NhcRiZJcZeZfBb5qZre6++NFjGnYBFXs3f34y1y4crFGbYiIRFwh16B+ZGarzewpADO72MzeH25YwyNbxZ6q9EREoq+QBPVV4F+B2cnHW4GPhBbRMMpWsacqPRGR6CukXHyKu/+zmX0MwN27zaw35LiGRapiL/MalKr0RKQsmbF2xVWF7x8bxczZDezZvSu8mEJUSILqMLOJgAOY2ZuB46FGNUxiMetXsacqPREpa+7ctvqFQb1kUAktYgpJUB8FngTmm9m/kpi0++5QoxpGsZgNqNgTEZHoy5ug3L3FzH4XuIjEvVCvai6UiIiELdeNujdm2dRgZrj7unwHN7Nm4CGgCviyu38uYJ9bgU+TWEJ8xd3fU0jgZyvoxt3Ukl+ubSIiUly5zqBuybHNgZwJysyqgIeBa4FW4CUzW+fur6bt0wh8Enibux9Ndq8ITa4bd4Gs25SkRESKL9eNuu8DMLOazCU9Mzu3gGNfAWxz9+3J1zwG3AS8mrbPfwYedvejyfc8OLjwByfXjbuAbuoVEYmQQookvmNmS929F/p69P1f4M15XjcT2J32uBV4S8Y+5yeP+f9ILAN+OmggopktB5YDNDQ0FBBysFw37rrTt236hFr++K1zaJg4hq0Hj3P4xGmmjBtNw8REebqWAUWkWNK//ypNIQlqPfC4md1Coi/fk8AnCnhd0Le2B7x/I3B18tjPm9lCd/9Nvxe5rwHWADQ1NWUeo2CpG3fTk1T6jbu11THOG1PDh94+n5PdvXzkW6/0Lffdfe35XDBtLB2n41oGFJGiSf/+M7Oz/v4rR3k7Sbj7F4EfA98FngLucPd/KeDYrZzpPgGJBLQ3YJ/vuXu3u78ObCGRsEIRNGojdeNuatstTbNoO9nFQ89u7bfc9+APfsXxU72By4CawCsiMvxyVfGtzHhqLvAycJmZXebuq/Ic+yWg0czmAXuA24HMCr0ngGXAo2Y2mcSS3/bCwx+cfDfuNi+YRm11jJ/v+k3gUmBHV0/WJUJdpxIRGV65lvjqMx4/meX5QO7eY2Z3AM+QuL70iLtvNrP7gJZkmfozwHVm9irQC3zM3dsG9TcYpFw37sZixtxJdbyy+zeBS4F1NaNyLhGKiMjwMffCljTN7BwAdz8VakR5NDU1eUtLS2jHj8edDVsOsPXAib5lPl2DEpGQFfQFYmZ+Nq2OCv2eL6HAv3/eIgkzuxj4GonpumZmrcD73f214Y0vGmIxY8kFU/mt+rFc3nAexzq7OfecaurTqvjU209EJHyFVPGtAf7c3X8AYGbvAL4E/HaYgZVSLGbMnTw265Rd9fYTEQlfIfOgxqWSE4C7/xAYF15IIiIihSWoHWb2STOblfz5BLAz7MBERKSyFZKg/hOJ+5nWA/+S/P0DYQYlIiJSyLiNNuC/mtmYxMPSVvGJiEhlKKSKbwHwKP2r+D6Q3pW8HMXjzuuHO9h5pIO6mlHUja7iVHcvk+pGqzJPRCQCCstY7b0AABNkSURBVKniW83AKr41lHEVX9DYjbuuaWRMdRWPvPA6H2++SPc2iYiUWEVW8QWN3Xjo2a20nezihkUz1V9PRCQCKrKKL9vYjbiD2Zn+eiIiUjpnU8U3izKv4kuN3UhXWx0jZuCu/noiIlFQcBVfEWIpmtRojWzXoB689VIazhvDjsMnOHDsNB1dPcyZWMe8ySqeEBEpllzjNiYBHwKOkqji+xzwduDXJLqOhzYWI2ypsRsX3LmYXUc6GJOs4uvs7uWr77+ChvPG8KOtBwc0jFVjWBGR4sl1BvVPwEYSo9tfBL4JvBdYDHwF+N3QowtRLGa8YcpY3jBlYE+97YdOsLG1nTU/3j5gOOGFKxerD5+ISBHkSlDT3b3ZzAzY5e6fTT6/ycxWFCG2kjlwrJO4o+GEIiIllKtIohcSrSOAQxnb4gN3Hzmmjq+lyggspFDxhIhIceQ6g5pvZt8lMUgq9TvJx/NCj6yE5k6q45JZE7jrmsYB16DmTqordXgiIhUhV4K6Oe33L2Rsy3w8omQOLTzZ1UODqvhERIoqa4Jy92eLGUjU5BtaKCIi4SrkRl0REZGiK6RZbNnr6upl49529h/rZPr4Wi6ZMYGamqpShyUiIjmM+ATV1dXLExv3cu/3NvUVO9x300KWLpqhJCUi5cWMtSuuGtRLZsyaHVIw4cvVSeK72bYBuPu7hj+c4bdxb3tfcoLEvUz3fm8T8yfX0TR3YomjExEZBHduW/1C4Ka1K64icVfQyJHrDOpqYAeJDhI/I1FeXnb2Z+lcfuCYupWLiERZrgQ1Ffg9YFnyZx3wTXffUozAhsv0ZOfy9CRVWx1j6njdcCsiEmVZq/jcvdvdn3L3PwLeBuwC/s3Myqqz+SUzJnDfTQv7ukKkrkEtmjGhxJGJiEguOYskzKwaeCeJM6jzgX8AnixCXMOmpqaKpYtmMH9yHQeOdTJ1fC2LVMUnIhJ5uYokvgJcDjwDPODuLw/24GbWDDwEVAFfdvfPZWx/P/A3wJ7kU19w9y8P9n3yqampUkGEiEiZyXUG9QHgGLACWGFmqfIQI9FDNuc3vplVAQ8D1wKtwEtmts7dX83Yda2733FW0YcoHnd2tHX0nXXNnaQ2RyIixZQrQVUP8dhXANtSgw3N7DHgJiAzQUVOPO48vXl/v4m7GlYoIlJcuYokelM/wEXAf07+XJB8Lp+ZwO60x63J5zLdbGYbzezbZhaJO8p2tHX0JSc4M6xwR1tHiSMTEakceXvxmdkdwONAQ/LnWwVW8gWdamTeRfYkMNfdFwE/BL6WJYblZtZiZi2HDmWOphp+B7LcO3XwuO6dEpHiSv/+K3UsxVZIs9jlwBXu/ufu/ufAW4APFfC6ViD9jGgWsDd9B3dvc/fTyYdfAt4UdCB3X+PuTe7eVF9fX8BbD83U5L1T6TSsUERKIf37r9SxFFshCcqA7rTH3RTWVeIloNHM5plZDXA7iZt9zxzYbHrawxuB1wo4bujmTqrjwVsv7XfvlIYViogUV64y81Hu3gN8A/ipmX0nuekPybIUl87de5LLg8+QKDN/xN03m9l9QIu7rwNWmtmNQA9wBHj/kP42wyQWM5oXTOPClYs5eLyTKeNUxSciUmyWrbmgmf3c3S9P/v5mYDGJM6cfu/tLxQuxv6amJm9pqbilWBEZ2Qr616+Z+QhtFhv4989VZt73gmRCKllSEhGRypMrQdWb2d3ZNrr7gyHEIyIiAuROUFXAWMp0zIaIiJS3XAlqn7vfV7RIRERE0uQqM9eZk4iIlEyuBHVN0aIQERHJkKsX35FiBiIiIpKukE4SIiIiRZdzou5IpnlPIiLRVpEJSvOeRESiryKX+DTvSUQk+ioyQWnek4hI9FVkgtK8JxGR6KvIBNVw3hgeuHmR5j2JiERYxRVJxOPO9187wIM/2MIHf3s+VTFomjORq+ZPUoGEiEiEVFyCSi+QePi5bUDiDGr9ysXMrx9b4uhERCSl4pb4VCAhIlIeKi5BqUBCRKQ8VNwSX8N5Y1jzviZadh4h7vDkK3v4ePNFKpAQEYmYikpQqQKJ9A4SD9y8iOsumqoCCRGRiDF3L3UMg9LU1OQtLS1n9drth05w/arn+12DUoGEiERAQf9CtljMyfadHRsF8R5mzJrNnt27hjO2Ygj8+1fUGVSuAgklKBGJPHduW/1Czl3WrriqSMGEb8QnqPSu5WNqRlFbHRtwBqUCCRGR6BnRCSqza/mcSedw/9KF3PPEpn5dzFUgISISPSM6QWV2Ld/ZdorPb9jK2uVXcqq7lynjNAdKRCSqRnSCCrrmtLPtFKe6e7ly/uQSRSUiIoUY0Tfq6qZcEZHyNaIT1NxJdTx466XqWi4iUoZCXeIzs2bgIaAK+LK7fy7Lfu8GvgW82d3P7ianALGY0bxgGheuXMzB451MGVdLw3lj+qr6po7XNSgRkagKLUGZWRXwMHAt0Aq8ZGbr3P3VjP3GASuBF8OIIxYz5tePZX792AFVfakzquYF05SkREQiJswlviuAbe6+3d27gMeAmwL2+0vgr4HQ24lnVvV1dse5+/GX2dHWEfZbi4jIIIWZoGYCu9Metyaf62NmlwGz3f2pXAcys+Vm1mJmLYcOHTrrgDRqQ0TKTfr3X6ljKbYwE1TQmllfEykziwF/D3wk34HcfY27N7l7U319/VkHpKo+ESk36d9/pY6l2MJMUK3A7LTHs4C9aY/HAQuBH5nZDuBKYJ2ZhfYfQVV9IiLlI8wqvpeARjObB+wBbgfek9ro7u1A392yZvYj4KPDWcWXKaiqT1V8IiLRFFqCcvceM7sDeIZEmfkj7r7ZzO4DWtx9XVjvnUt6VZ+IiERXqPdBuft6YH3Gc/dm2ffqMGMREZHyMqI7SYiISPlSghIRkUhSghIRkUhSghIRkUga0fOgzlb6mHg1lBURKQ0lqAxqKCsiEg0Vs8QXjzs7Dp/gxe1tbPiPA/z64AnicR+wnxrKiohEQ0WcQcXjzoYtB9h64AQPPbs155lRroayurlXRKR4KuIMakdbBxtb2/uSE2Q/M1JDWRGRaKiIBHXgWCdxp6BRG2ooKyISDRWxxDd9Qi0XThtHbXWsX5KqrY5RP7b/mZEayoqIRMOIP4OKx51X9x3nb575D/7sHef3OzO665pGXm8bWCyRaih75fzJzK8fq+QkIlICI/4MKr0qL+7O8rfPJ+7gDl//yU6Onuxi/crFKoAQEYmYEZ+g0qvyOrp6+cKGbQP2UYWeiEj0jPglvsyqPFXoiYiUhxGfoNKr8r7zs1buuqZRFXoiImVgxC/xZVblTRtfy3UXT+PQCVXoiYhE2YhPUBA85v0NU3TNSUQkyioiQYmIjAhmrF1xVc5dZsyaXaRgwqcEJSJSLty5bfULfQ/XrrgK94FNr0eKEV8kISIi5UlnUDlocKGISOkoQWWhwYUiIqWlJb4sNLhQRKS0lKCyyDW4UEREwqcElYUGF4qIlJYSVBYaXCgiUloqkshCgwtFREor1DMoM2s2sy1mts3MPhGw/UNm9ksze9nM/s3MLg4znsHS4EIRkdIJLUGZWRXwMPBO4GJgWUAC+md3v8TdLwX+GngwrHhERKS8hHkGdQWwzd23u3sX8BhwU/oO7n4s7WEdMHJ7doiIyKCEeQ1qJrA77XEr8JbMnczsw8DdQA2wJOhAZrYcWA7Q0NAw5MDUIUJEykX691+lCfMMKugbf8AZkrs/7O5vAD4O3BN0IHdf4+5N7t5UX18/pKBSHSKuX/U8y770Itevep6nN+8nHtfJm4hET/r3X6ljKbYwE1QrkN73fRawN8f+jwFLQ4wHUIcIEZFyEWaCegloNLN5ZlYD3A6sS9/BzBrTHv4+sDXEeAB1iBARKRehXYNy9x4zuwN4BqgCHnH3zWZ2H9Di7uuAO8zsHUA3cBT4k7DiSUl1iEhPUuoQISISPaHeqOvu64H1Gc/dm/b7XWG+f5BUh4jMLuXqECEiEi0V10lCHSJERMpDxSUoONMhYn792FKHIiIiWahZrIiIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRJISlIiIRNKI78Wn8e4iIuVpRCeo1Hj3zNEazQumKUmJiETciF7i03h3EZHyNaLPoHKNd9eoDREpN9XV1axdcVXf4xmzZpcwmvCN6DOo1Hj3dBrvLiLlatGiRbh738+e3btKHVKoRnSCSo13TyUpjXcXESkfI3qJT+PdRUTK14hOUKDx7iIi5WpEL/GJiEj5UoISEZFIUoISEZFIUoISEZFIUoISEZFIUoISEZFICjVBmVmzmW0xs21m9omA7Xeb2atmttHMnjWzOWHGIyIi5SO0BGVmVcDDwDuBi4FlZnZxxm6/AJrcfRHwbeCvw4pHRETKS5hnUFcA29x9u7t3AY8BN6Xv4O7PufvJ5MOfArNCjEdERMpImAlqJrA77XFr8rlsPgj8S9AGM1tuZi1m1nLo0KFhDFFEJNoq+fsvzAQV1PDOA3c0ey/QBPxN0HZ3X+PuTe7eVF9fP4whiohEWyV//4XZi68VSB9WMgvYm7mTmb0D+B/A77j76RDjERGRMhLmGdRLQKOZzTOzGuB2YF36DmZ2GbAauNHdD4YYi4iIlBlzD1x1G56Dm10P/C+gCnjE3f/KzO4DWtx9nZn9ELgE2Jd8yS53vzHPMQ8BO0ML+ozJwOEivM/ZinJ8UY4NFN9QRDk2iHZ8uWI77O7N+Q5gZk8Xst9IEWqCKmdm1uLuTaWOI5soxxfl2EDxDUWUY4Noxxfl2KJKnSRERCSSlKBERCSSlKCyW1PqAPKIcnxRjg0U31BEOTaIdnxRji2SdA1KREQiSWdQIiISSUpQIiISSUpQAcxsh5n90sxeNrOWEsfyiJkdNLNNac9NNLMfmNnW5J/nRSy+T5vZnuTn93LyfrhSxDbbzJ4zs9fMbLOZ3ZV8PhKfX474ovL51ZrZv5vZK8n4PpN8fp6ZvZj8/NYmb8SPSmyPmtnraZ/dpcWOLSPOKjP7hZk9lXxc8s+unChBZfe77n5pBO5beBTIvDHvE8Cz7t4IPJt8XCqPMjA+gL9Pfn6Xuvv6IseU0gN8xN0vAq4EPpwc+RKVzy9bfBCNz+80sMTd3whcCjSb2ZXAA8n4GoGjJBo9RyU2gI+lfXYvlyC2dHcBr6U9jsJnVzaUoCLO3X8MHMl4+ibga8nfvwYsLWpQabLEFwnuvs/df578/TiJL4qZROTzyxFfJHjCieTD6uSPA0tIzG+DEn1+OWKLDDObBfw+8OXkYyMCn105UYIK5sD3zexnZra81MEEmOru+yDxJQdMKXE8Qe5ITkp+pJRLkClmNhe4DHiRCH5+GfFBRD6/5BLVy8BB4AfAr4HfuHtPcpd8Y3SKFpu7pz67v0p+dn9vZqNLEVvS/wL+OxBPPp5ERD67cqEEFext7n45iWnAHzazt5c6oDLzReANJJZe9gF/V8pgzGws8B3gv7n7sVLGEiQgvsh8fu7e6+6XkphGcAVwUdBuxY0q+aYZsZnZQuCTwIXAm4GJwMdLEZuZ3QAcdPefpT8dsGukzvqiRgkqgLvvTf55EPg/JP7HjJIDZjYdIPlnpDrBu/uB5JdHHPgSJfz8zKyaxJf/P7n7d5NPR+bzC4ovSp9firv/BvgRiWtl55pZalRP4BidYkqLrTm5bOrJ0T1fpXSf3duAG81sB4lp4ktInFFF6rOLOiWoDGZWZ2bjUr8D1wGbcr+q6NYBf5L8/U+A75UwlgFSX/5Jf0iJPr/kmv9XgNfc/cG0TZH4/LLFF6HPr97Mzk3+fg7wDhLXyZ4D3p3crSSfX5bY/iPtHx5G4vpOST47d/+ku89y97kkRg1tcPc/IgKfXTlRJ4kMZjafxFkTJAY6/rO7/1UJ4/kmcDWJVv0HgE8BTwCPAw3ALuAWdy9JoUKW+K4msTzlwA5gReqaT5Fj+23geeCXnLkO8OckrvOU/PPLEd8yovH5LSJxIb+KxD9mH3f3+5L/jzxGYgntF8B7iz1sNEdsG4B6EstpLwMfSiumKAkzuxr4qLvfEIXPrpwoQYmISCRpiU9ERCJJCUpERCJJCUpERCJJCUpERCJJCUpERCJJCUoqlpn9oZm5mV1Y6lhEZCAlKKlky4B/I3EjpYhEjBKUVKRk/7u3kRh3cHvyuZiZ/UNyvtBTZrbezN6d3PYmM/vXZAPhZzK6PYhICJSgpFItBZ52918BR8zscuBdwFzgEuBPgbdCX7+8zwPvdvc3AY8AJesuIlIpRuXfRWREWkaieSckWs8sIzFT6FvJJq37zey55PYLgIXADxIt3qgi0WVcREKkBCUVx8wmkeguvdDMnETCcc70YBzwEmCzu7+1SCGKCFrik8r0buDr7j7H3ee6+2zgdeAwcHPyWtRUEk1vAbYA9WbWt+RnZgtKEbhIJVGCkkq0jIFnS98BZpCYcroJWE2i63m7u3eRSGoPmNkrJLpkX1W8cEUqk7qZi6Qxs7HufiK5DPjvJKYr7y91XCKVSNegRPp7KjkIrwb4SyUnkdLRGZSIiESSrkGJiEgkKUGJiEgkKUGJiEgkKUGJiEgkKUGJiEgk/X94C/dMv8HIvwAAAABJRU5ErkJggg==\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mean, cov = [0, 1], [(1, .5), (.5, 1)]\n",
"x, y = np.random.multivariate_normal(mean, cov, 1000).T\n",
"sns.jointplot(x=x, y=y, kind=\"scatter\")\n",
"sns.jointplot(x=x, y=y, kind=\"hex\")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"More on dealing with \"overplotting\" here: https://python-graph-gallery.com/134-how-to-avoid-overplotting-with-python/."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, note that `jointplot` is a different type of object and therefore follows different rules when it comes to editing. This is perhaps one of the biggest drawbacks of `seaborn`.\n",
"\n",
"For example, look at how the same change requires different syntax between `regplot` and `jointplot`:"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 0, 'Participant Age')"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"g = sns.jointplot(x=pheno['Age'], y=pheno['ToM Booklet-Matched'],\n",
" kind='scatter')\n",
"g.ax_joint.set_xlabel('Participant Age')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, `lmplot()` is another nice `scatterplot` option for observing `multivariate interactions`.\n",
"\n",
"However, `lmplot()` cannot simply take two `arrays` as input. Rather (much like `R`), you must pass `lmplot` some data (in the form of a `pandas DataFrame` for example) and `variable` names. Luckily for us, we already have our data in a `pandas DataFrame`, so this should be easy.\n",
"\n",
"Let's look at how the relationship between `Age` and `Theory of Mind` varies by `Gender`. We can do this using the `\"hue\"`, `\"col\"` or `\"row\"` arguments: "
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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hRBIoirpxFKGm83UAfp4PbgYdmwA3FGbUi9+nJStkJcYo2rp7rI3WNlIapSBhrsjcQyISsr5qxphBYyOd/vT45Q8EX3PF44dptNO19cHmlhSVERcR4Udtr5Fxq6mvzBN3g31zXC+DADup4WH/VELik4jFyDkOY9Jv0k4lriuEyePhAkqEwh593gDrq2aMGTSWdPrTujEY4fQUjkPrpv2ff4C6tj4Iu4IrsK4pCcCbsQZGeS1sa/MYW+GQCLk4xS0P7iy8HQ+XibE8eSdGJu/RHBlPrddMUiPkCeMS3M+TI7RHnzfA+qoZYwaNTa/1p3Yq5NN7Hsung9Y0h0HX1geJSIidnTlcEVxH+H5hEVHxSGiatlQOybYgGox4bvfOBZSqeJh03sNTxTvzU4yOQVQzNFPT3WNtl9SQkCwhzXN37HJSuYL1VTPGDBpLOv2Zez34OcilghbMuVTwfO71h+Xtu7Y+gGBLAZHgdptHvZNYEr+ODqeamJ/E8XIArHbfwiYdS0PUp7UQZXRFlH9752xOWvC3xC/9BjX1k4m4ygaZxFZ3Cg0VYeoaJvOzuk/xqHcSDVUxbrxkthURGGMGhfVeK0V39dqmYIRzGKvXFt/6FI0dGRKREOubOil4CgIhR5g92iGU3sGUaJqvtf8zoh6fyn2Se/25/M9lR3Py0ZOoiIYYWx07LLEYY45Yw6b3mq3plGLGwgErkb5u3nRuWPYyqVyBMZURtrZmQGFcdRTN7qLgwYfq/oK0eXQ6VfzeP41pdWFOmjYBgJp4eEDiMsaYgWDTa4Ns/swGbrxkNg1VMXyFY+orOLahEvE9xsaUD545haN3PQzAnflzyBHmXSc2II5DNGzdB4wxQ4uNdI4A82c27LPG4idb2LptM9EdzxHu2ArA7YX5VEaEd5w4FbBRjjFm6LGRzhEqnWwFtHsLgxeZwVqdzEXHjyIeCRN2HSqj9jeDMWZosd9ag6G/tjqFHKlUCifbTmLTSgB+np+PAJecGpRq1yZslGOMGXpspFNuJbTVWfmX9Xzpty/x+3t+gvg5UsT4nXcmZ01JMGFUNSHHRjnGmKHJkk659WyrIxJ8dSLBcYIOBbc88AKtySwL9U8ALC2cRYoYl5+yu2JNZNhUUBpjRhD7c7k//UyF3fLQWn74+Bskcx4VEZdrzpnGp8+b0f36ijWNPPWHX3FF8w+YyjbC5BE3CjUTyXXuIpxrAwV2rmX5v1/Ktys/TaWT53g2M1m3AXCHt4CKkM8p08dTselRqv+yhHTTG7yeH81P9J1sq3+bbVdgjBkSbKTTl36mwm55aC03P7KOdN4j5AQ9zW5+ZB23PLQWCBLOfff8jI81/xdHsQVF8RHUy6LNb+xOOEULCyu4ZtfX8TxlXuEJAF7xp/CiTqfC9UlsXsGYlV8ktWsbG1MRKvM7+Yz3Q6Y2P8ENy15mxZrGsv+IjDHmQFjS6Us/U2E/fPwNHIGQ4+CIU/waHIegr9rV3lISZPBxUNxi92cA3SPhaPFxofsMmVQHZxSCrgt3eOfioowfVUvt89/DCUXZkXEQEfJOnDwhFheW2nYFxpghwZJOX1o3Bh2le+rRYTqZ83D2WlpxJDgOQV+18dpICA8tdrHwccmx5w2dQe4JXg+hzJPniZElo2GWemdTG/FYfOY0Ih2bkXCcXMHHKa7pdG1ZYNsVGGOGAks6femnw3RFxMXfq3Wdr8FxgMl1CbZLAwVcpMewRnr82INktDtzFRAuC/8ZgPv9M2ingmvediynTx+F1B0F+TSRkINf7JnXtWWBbVdgjBkKLOn0pZ8O09ecMw1foeD7+OoXvwbHIeir9kv3MlLEcPARPASPkPj4TrSYaxQtTq4J8Kwcz9G6EYA7Cudy1pQK/ubkqVREQzhnB/GMjfmoKmE/TZgCt4cus+0KjDFDQq9JR0RaRKS5t0c5gxw0MxbChTdB1VjItAZfL7ypu3rt0+fN4PoFxxAPuxT8YEfO6xcc0129Nn9mA4su/wA/GPVZNjAJQQg5Lk7D8bhX/ZI3p16KLw4O4CE85pzG2AlB4njdH8+fdSaXnRyUSVfHwt3xVIyewNREjs7wGG5yr2HjqLNtuwJjzJDQ69YGIuIS/C3+JaAJ+Hnx+dVAQlX/o1xB9jQoWxuUQcfOLbTu3MaEey7HzXXw1fxiHqh8Fz/5yFlEwyEmj7KpM2NGsGFzY16vIx1V9VS1AJyvqreoaouqNqvqt4B3lS/EEcD3Sba3ktj0R9xcB3l1udubx7tOGIPjOFRbY09jzDBRys2hKiLvAX6tql3/Hv766492EFasaWTJyvVsbkkxuS7RfUNnJtVG3vOoff13ACz330o6XMv5J07GEaHKWt4YY4aJUgoJ3gt8ANglIjuB9xNMsQ1fJfRHO1Ar1jRyw7KXaezIUBsP09iR6b6hs7OtmVD7ZmI7ngeCe3MuPK6aRDwRFBDsXZdtjDFDVL9JR1XXq+rFqjpKVceo6iJVfb0cwQ2afm4KPRhLVq4n7AqJSAiR4GvYFX6wYg3pVJKK4ihnq47mcf8ELjtpPABVMRvlGGOGj36TjogcIyK/F5HVxecnisjnBz60QdTPTaEHY3NLivheu3zGwy4tzbvAz1Ox/kEAfl2Yz2mTEkwcW08k5NjOoMaYYaWUP6N/CHwB+E7x+V+A24GvDVRQg652ajClFulRMZZPQ6QSblsEja+SyWbp9Bz+6k/kZ86lHH/O5Xs0+tzb5LoEjR0ZEpEQHZk8TR1ZTi+s4pPub6i6fTsuSXwVfu3N558mNDHut+8h1rkF6g7PepIxxhwJSlnTqVDVJ7ueaFBjnR+4kI4A+7spNNMKyZ2w83UK6TakkKJKO5kqO/i8/ojnH72ru9Hn/lw3bzp5T9nZmWFrS5rTvWf5t9BPGC3thLQAwEr/RJxwlIvWf4VQqhGJH571JGOMOVKUknR2icg0ii3CROQy4M0BjWqw7e+m0MoGiNVArp2CKh4uikM1SXKEuda9t7vR5/7Mn9nAjZfMJpn1UOBjzn14uPjiUEEWgNu9c3m/LkNCEZxIRbBnzmFYTzLGmCNFKdNrnwR+BMwUkY3AdmDxgEZ1JJixcM8prW+eEFSyeTl8FYSgcU2EAhkiTJKm7kafvZk/s4HqeJgpoxJM3tVIiiij6UAEmrSGJ/3ZfD18Kxo6GrdnxdohricZY8yRot+ko6rrgAUiUkPQwaB14MM6AnWt87gRHC+Dr4KDkiNEjBxbtL670WdfutZ2duho6mmmWpIA3OXN43z3OUICrp9BqNh9UY8mo8YYM5SVUr0WEZF3A9cBnxCRL4jIFwY+tCNM1zpPpJqQCC4egk87FUTIc6v3zu5Gn325bt508gWf+90FVJImIsHo6FfefK50/8j6Yz6E00eTUWOMGcpKWdP5DfAeglGR1+MxsnSt84w5mlC8Fg0l6JBKNupYviYf5ZRzr+yzeq3L/JkN/OvCybxZewo7nHoA/uTNYrSTpHDqNYy66F9xLvrvXpuMGmPMUNZrw8/uE0ReUtW3lCmefg2Hhp87trxOoWUz4++5HEc9rs99grnnvYvT3nI8oytj1CSs15oxZg/Dpi1JKSOdp0Rk1oBHMkLk8zly6U4q1j+Iox5tmuDFxFmcNr0ecRwqrQOBMWYY6/U3nIg8T1AmHQZeEJG/AlmCjKuqemp5QhxeOttaQJX4X+8D4B7vbSw6eRQSraIyGtqzas0YY4aZvv6svrJsUYwQqkqqvZlo42qinZsBWMq5fG1mHRqKUR23UY4xZnjr9bdcV1NPETkNeFVVO4vPq4DjyhPe8NLR3oLv5UisC0Y5L/hHM/3Y40lUVhMOu0RD1mfNGDO8lbKmcyuQ6vE8CSwZmHCGt87WXUi2g9imFUCwhcHlsyrxI1VU21qOMWYEKCXpOKrqdz0p/tvKqw5QKp3Cy6Wo2LCckJ8jqVG2NMxnyphq3FCYStuozRgzApTym+4NEfk4wYhHgY8DGwYyqHLobRfPviz99W2Me/lWJmgjW7Seu6Pvwj/mPB5eE7TAqYi4XHPOtD3u11n9yJ24T32LMbmtJN1RVLntACzz5jKmcy1NP/1/jNNdrHLG8nDdVZx5/nuY76zuf9fSAdjZ1BhjBlop9+mMJdjWYD5B0nkU+KSqNg54dPtxOO7T6drFM+wK8bBLOu+R95QbL5nda+JZ+uvbOOWlr5AjRJoIcXJEKHBD4UM8ricTcgVfwVe4fsExfPq8Gax+5E7GPPZF8uqiuCToZCwtALw390X+M/x9MhohTZQ4OWKOxwPuAt4Xe5x4LB70XMung44EPW8Q7drZ1In0fo4xZjgZNmWtpUyvTVPVK4u7htar6ruB6QMd2EDqbRfPJSvX93rN+FduLSacKCCkiZIjxHXufSjgiEPIcXCE7m7T7lPfIk+IghMFEULFRg6v+ZM43tlEnjBpYog4ZCRGRkNc5S1jV4a+dy0dgJ1NjTGmHEpJOt/dz7Hv7OfYkNHbLp5bWlK9XAETtJE0kT2OpYvdpXsOFh2hu9v0qPx28kRx1UfUp1KD97/LexvvdR8hqZE9/nxJa5g4aTq9vZbM9u4yPQA7mxpjTDn0dXPo6cBZQL2IfLrHS9UM8UKCnrt4dknnPSbVJXq9Zps0MEpbiiOdQLzYXVp6ZA5f6e423RweT1WuEV/CJEgTlQJZDbNTa0k5FcT9JBndHUNc8qSJU+nutUfe3l2me9vZ1DpRG2OOcH2NdCqAMQSJqb7HIwf8bSlvLiIXiMhrIrJORP55P69PEZFHReR5EXlRRC468G/hwHXt4jk7+TT/r+3z3Nr8Yf4z+S/88zGbu89ZsaaRxbc+xTn/8QiLb32KFya9nwgF4mQBJU6WCAWWeIsQwFefgu/jK93dprOnfZIIecKaQYq7gz7gn8ZxE+u5w1lETArEyOCrT1QzRMjzcxYxOkbfXab3t7OpdaI2xgwBpRQSTFfV3hc7er/OBdYCC4EtwDPAYlV9pcc5twLPq+r3iv3d7lfVo/p638PV8HP1I3cy7ol/Ie25+KE4Y+NKRciDC29ihX/SfgsNrh61hpM2/7zk6rUdTbvYsOKn1K/5KdO9DQB83v2/fOKqS/nTrkpW/3EpF3fexSRpYqvW8yPeyeroafxwbisnbfppMF1WO6Wf6rU+zjHGDBfDppCglJLpdhH5GjAbiHUdVNXz+7nudGBdV8ISkTuAS4FXepyjBNN1ADXAthLjPmQnbfop1FbvOUWVS8GTN7Mk9y/dhQYAiUiIVK7ACv8Urvu3jwMwBZjbx/tn8h6Zjp1MOPkduKmX4I0NvOGP5YSzF+K7Mc6dPYnfvHg2/xp66x7TfNW5Al9fN5nbr72v729g751NjTFmCCilkOAXBPflzAD+A3gTeKGE6yYCm3s831I81tOXgfeJyBbgfuBT+3sjEblWRFaJyKqmpqYSProEfSzGH0yhwd5a2lpxvAx4OWo2/gGAB0ILmDsljkYqqY6HDsvnGGPMUFJK0qlX1SVATlUfBj5IMIrpz/6Gg3vP5S0GblPVScBFwM9FZJ+YVPVWVZ2jqnPq6+tL+OgS1E4NFt97Ki7GT65LkM7vuU9df4UGPWXyHrmOZgCcN/5Ihd9BQR30uItwHIdwooZoyD3kzzHGmKGmlKTTVUr1poj8DfAWYHIJ123Z67xJ7Dt99lHg1wCq+ieC6bsxJbz3oetjMb6r0CCVKwSdoXMF8p5y3bzSbk9qbk/iFILRivfyvQA8qqcyf/ZkNBSnKhFUwB3q5xhjzFBTStL5qojUAJ8B/oVguu2zJVz3DHCsiEwTkQhwFbBsr3M2Ae8AEJHjCZLOYZo/60fX9tP72RZ6/swGbrxkNg1VMdrSeRqqYn12K+gpnfPIJYOuA077FqZ0BjOR68ZdSHXUgWgVlcU1nEP5HGOMGYr6rV47pDcPSqC/CbjAj1X1KyJyI7BKVZcVK9Z+AFQSTL39k6r+oa/3PNK3q97a3Im3az2C0vnY9zh+0+1s11G8duEdTB+dID72GOqr4/2/kTHG7Db8q9dE5H/6ulBV/7G/N1fV+wkKBHoeu6HHv18Bzu4/zKEhmS2QTzbjouAXGLflQQAeiy/grNEx/HCCqnikn3cxxpjhq6+S6U8BfwHuBL2ZxcEAACAASURBVHYwjDLtQVm7HB76Et7OdXi+z0aZwN11Hwu6Qhenw5o7szjZoIt0ct0TTPZb8FWIT5zNmOXXE043Eho93e6pMcaMWH0lnYnAu4uPJPAr4B5VbS9HYEeUtcvht5/ATzWT1yD7HqVb+FjzTXztnixc/gFOPaqOQqadkAbVaP7LwfLVMzKb87b/ENcNQXxU0L7mgc8A1hHaGDPy9FpIoKqNqvptVZ0H/B0wFnhVRK4uW3RHiidvhmwHBRUg2KbAx6GCNFd7S/n+H1+nNZnHzbQBkGvbwXHJYN1pV3gcruuikQpc17WO0MaYEa3fjgQiciLB/TQXAA8Bqwc6qLIpdSO01o3gF/C6ZhhV8RFCFBjr72BzcwovmyTkZwFofHYZR4vSrFXMcV+HUAzHdXfPT1pHaGPMCNXrSEdE/lVEngG+ADwNnKGqH1TVl8oW3UDq2gitYwfE6nZPe61dvu+5tVPBCeGgdFX7OSgFXDYWxhALuziZ4GZQ9T2OejMoIHiuegFV1bVQyOI6PToPWEdoY8wI1dd9Ov9G0A/tOOBLwFMi8lzXoyzRDaQD2Qht7vUQrcLBx8FHil87iXOrt4ionw5a3gCbXn6K8RrcalR10qV0zFqMowWcvHWENsaYvqbXji1bFIOhdWMwwumpt2mvGQvh0u+y4X//L1NkG6rwuk7gG7qY16pOZ0x+JzABgNBrQQeCV9yZTJh6DFmOITtqAqHnllhHaGPMiNdr0lHV1/c+JiIXqOqDAxtSmRzoRmgzFnLDhFv32Pyt4PnEMykmVQarNY1Nuzg18zQItExbRBXguzGisy+GE985wN+QMcYc+Uppg9PTVwckisFwEBuh9eyV5vs+yVyBhN/BxSeMB2D7s/cSEY9O4kw8JRjJxCrrCLkH+mM2xpjh6UB/Gw6fG0T76L3Wm5690lpSeerjLn935lhOnFxDOucxa2cwCHxt1ALcSBzFoaqmtlzfkTHGHPFKKZkOqRb3WoZP7OfY0HUQG6HNn9nAOceOYXNLGkk24eaCe3NeXL2KKyVool19ymUASKyKeCR8eGM2xpghrJSRzp+7/lHcfmCPYyNRSyqPenmcXNCcQVWpeP13AGyMHE1s3HEAxCtrEBk+g0NjjDlUfd2n0yAiJwFxETlBRE4sPs4BRuwuYwXPpzNbwMm2IcU96VZvbubthSAfp48JCgZUXCorqgYtTmPMyCAiY0Xkf0VkvYg8KyJ/EpF3HYb3nS8i9x2OGHvqa3rtYuAjBJuvfbfH8XbgXw93IENF8pUHGfunW9Bd69ns1XKXfy4hL8M7JUuWCJWzz0cBN1pJrFjltmJNI0tWrmdzS4rJdQmumzfd9swxxhwyCaZSlgI/VdX3Fo9NBS4ZhFhKWnbpq2T6J8BPROTdqvrrwxrdEFV47Q8kln+OdAGasmEqaeFi/2FC5EHg1eqzGR2pBKCiqgYIEs4Ny14m7Aq18TCNHRluWPYyN4IlHmPMoVoA5FT1+10HVHUj8C0RcYGvA/OBKPAdVV0iIvOBLwM7CXaCfhZ4n6qqiFxAsAfaTqC7CYCIVADfAk4gyBtfVtXfisiHCAYoMaCiGE+f+i0kAFaIyBJgoqouKm68drqq3lbCtUek/Y08gH1HI87qPXqzaecuVMK0ZPOAkJcYj3uz+VR4KQB/8E9nMYATorKyuvs9w65039uTiIRI5QosWbneko4x5lDNpkdy2MtHgTZVPU1EosATItK1SeYpxWu3AU8AZ4vIKoJNNRcA6wh2FujyReARVf2IiNQCfxaRh4qvnQWcqKrNpQRcStL5CfBL4HPF538tBnNbKR9wpNnfyOOzd61GgZp4uPvYfff8jDPCtxGPxSFWh9+xg1Dz6+QrJlDwwBHIaJh6CarXNjCBZzITWQxE41WEQkGvtc0tKWrje1awxcMuW1pS5f3GjTHDnoh8BzgHyAEbgRNF5MriyzUEnWZywJ9VdUvxmheAo4BO4A1V/Wvx+C+Aa4vXng9cIiKfKT6PAV130i8vNeFAadVrDar6v4APoKp5wCv1A440PUceIsHXjkyBzmxhj2NXe0vZlaG7N1vBjaFOGDe1g5Ar+AorvLdwkfs0ACucM6mvigGQqN7dXmdyXYJ0fs8fVzrvMaluxNZiGGMOn5eBU7ueqOrfA+8A6gnuq/yUqp5cfExT1a6RTrbHe3jsHoBoL58jwBU93muKqr5afC15IAGXknSSIjKqKxgROQ3oOJAPOZJsbkkRD7t7HCv4Pp6/5896vDbS6QUjFE8VVSjEx+D4BUZFfFSVdj9GtaTIqctjzulcfMJ4HDdMRUVl9/v07GKgGnzNe9o9pWeMMYfgESAmIh/vcazrL9rfAx8XkTCAiMwors30Zg0wTUSOLj5f3OO13wOfKhYuICKnHGzApUyvfQa4F5guIn8k2FH0yr4vOXJNrkvs0T8NIOQ4+/Ra2C4N1LutKAQJSRXEJVdzFKFoDZtzcc7VFwB4Nnwql8+dzYmTa4jtdW/O/JkN3EgwwtrSkmKSVa8ZYw6T4uL/ZcA3ROSfgCaCkcfngDsJps2eKyaLJuCyPt4rIyLXAr8TkZ3A4wSFBgD/TlBg8GLxvTYAiw4mZunaH6bPk0QiwPEEv5pfUdXcwXzY4TBnzhxdtWrVQV/fc00nHnZJ5z3a0/nuNZ2uY6dkV/Hv4duIxGIUJIbkOhEvQ8tp/0B24pl858EX+PquTwPQuOB/yI6fA0D9pGOIxfv6Y8IYYw7YsLnLvNeRjoj0Vuc9RURQ1WUDFNOA2t/I418vngV7HVt08QeIOSeS+eM3CbVtpFDRQMfxV5GdeCZvdhQ4esfvIQQd0XFkxwVTqm4oagnHGGP60Nf02t/28ZoCQzLpQJB49je9tfexttS57LpsLpJtJ5Ru6j6+7JUW/o+7EoDCjItBgqWxRJU19zTGmL70dXPo+yGYWtt7Oq1Ypz2s+b7Smg6+bTfb1n08U/BJ/fUxRksHPg6pYy7ufq2ietj/WIwx5pCUUr12d/HOViDoyQYsH7iQjgztmTyer8Fajr875y5/Pc2l/sMAdIw7Ez8xBoBYPEE4EhuUWI0xZqgoJencD/xaRBwRmUKQcG4Y2LAGl+8rrak8AG6mtfu4qvL4S+s5x3kJgNxxu3cDrajaa+trY4wx++i3ZFpVv1esXrsHmA78vao+NuCRDaLWdB5fFcmnEX/3PVQvvJnjjM6HcEJKJjqazIQzAHBEiFdU9/p+1vDTGGMCfVWvfXqvQ0cBLwCniMgpqnrLQAZWVmuXd/dY82unkjvhOphyLk62hejWp6h65XZCye080PkhbnD/CEA0u4sxt1/AUs5jxej3sHjhuP0mEmv4aYwZTkREgV/0WPcPAduBp1W133t3+hrp1O/1/N5ejg9ta5fDA58BJxL0WGt/k9F//AJ69pdw07uoe+YbqBNmm1dHPp9jfCRoMaRAlBzv5n4KrXFuWFaz30RiDT+NMcNMEniLiMRVNQ0sBLaWenFf1Wt77JkjIvHi8fRBBnpkevLmIOFEEviA58YRX6l9/nvge6gThlCcO1pP5d3FUY6vdN+qJcBl3gPc5n5gv4nEGn4aYwbLUf/8uwuAzwLTgDeA/9rw9YsfPAxv/QDBlgZ3EbTLuR14WykX9ltIICKzROQZgu7S60TkaRE5/hCCPbK0boRwHADPC7ozqBsl1LGFUHI7hGJkNMRD+ZNY4DwPwN47UMfJ9JpIrOGnMWYwFBPOd4DxQHPx63eKxw/VHcBVIhIDTgSeLvXCUqrXbgW+oKqTVHUiwb4KPzioMI9EtVMhn8ZX8IstgSSfpFAxnkLFeChkuC/9Fs5znyMkPr7u240iLfFeE4k1/DTGDJLPEnST7vprOFV8/tlDfWNVfZFgnX8xQYVzyUpJOlWq2n1fjqo+BFQdyIcc0eZeD34OL9sJqkguiXhZOmYtpmPWYvDy/DJ5Ku9xHwUgWEPbTRHuDF3aayKZP7OBGy+ZTUNVjLZ0noaqGDdeMtvWc4wxA20auxNOl1Tx+OGwDLiJYGqtZKV0md4gIp8Hfl58/j6CzYGGhxkLSeW/Dk/eQrh9M/mKsXTOvI7sxDMBWHHs56h+pomjQjsAKISrCeXbESBDlJ877+LR+g9yYx9l0L213THGmAH0BsGUWs/EkygePxx+TLAz6V+KW2CXpJSk8xGCttb3E6ybrwQ+fDARHolUlV3j307+sreBXyDUvgnpsY/RHY1Tucr9NQDZMW+h8W++2/3a2PFTuLayrntrPWOMOYL8F8GaDgSJJwFEi8cPWXHn0ZsP9Lp+p9dUdZeqfgI4EzhdVf9eVXcdRIxHpI5sgbznA+Bk2/ZIOI3JAqs3NHKB82cAksfsLkEPuy6RRO83hBpjzGAqVqn9PcE9NKOKX//+UKvXVLVyP8dWlHKPDpQw0hGR2cBtBMM0EZEtwIdV9ZUDjPWIo6q0JoN2N/geTrZ9j9eXvprkUudxolLADyVITT23+7V4RRU4e+5AaowxR5JigjkcJdKHTSmFBEvYt3rt1oENqzza0nkKfnGUk2tD8LtfyxaUZa91dhcQpI46Dw3Fu1+vqKopb7DGGDMMjNjqNa9HU0/U32eU8/D6FEdnX+M4ZwsAncfsbu4ZCYUIxWxqzRhjDtSIrV5rSeXwVfnz+mbuffoVsh2NvD2ylsWhFVTnm/htxz/xQXcFALm6Y8mPmtF9baKqGpxS8rUxxpieSvnN+RFgMkH12gPAJIZ49Vre8+nIFPjz+mZufngt+WQzZzlreHfyf0m37eDJwnFsylezyP0TUCwg6G5DICQqbbM2Y4w5GKVsbbAL+EQZYimbvOejqtzxzGZqnDQVLlyYXk6BEDki/Cx5Bovcp6iQLIqQPOq87mtjkTBudJ/iDWOMMSXodaQjIqNF5Isi8gkRSYjILSLygojcLSLDoofL9rYU9aHgvql6fyc5IjRTxR+9E7jKfQQADUXRyO4lrERVzb7N14wxZoQQEa+YC7oeRx3I9X1Nr/0SqAHeQtDM7U2C9ZyHgB8dXLhHlulV4OWDraibnDFEyPHb/FnMkC2c7KwHIF85uft8R4REpVWtGWNGtLSqntzjseFALu5rem28ql4gIgJsUtWvFo+/JCLXHWy0g27tcqJPfJPJzRu5xRnFDwpzeYnZPBBeyLszd/E773Q+HboHABWXtpOu6b40HosgkYrBitwYYw7Ml2v22dqAL7cN6n07fY10PABVVaBpr9f8fU8fAro2bOvcgR+posZv4dOhezgr9BpP+TP5avgTpIhyuRvsxt057QKyk87qvtwKCIwxQ0aQcPbZ2qB4/FDEe0yt/eZAL+5rpDNdRO4h6LfW9W+Kzw9Xl9Ly6tqwLRyHXBZCcSLANfHHufTSK7l2WSMXph6lRlKoE6L9lN1d1UKOQ6zCptaMMUPG/rY26Dp+KKOdtKqefLAX95V0rujx72/v9drez4eG1o0QqwP1keLeOYRihJLbeaUpx5qdeb4cCToQpCedgx+r6740Hot2b/ZmjDFDwDSCEU5Ph3Nrg4PS13bVD5czkLKonQodOyAU3X2skKFQMZ67Xu5kmmznTOdVADqP2bN3nU2tGWOGmIHe2uCgjKzb6osbtpFPAQqFNOLnWT/9ah59I817ih0IChXjyI6b031Z2HWIJIZ85x9jzMjyXwRbGXRtaXxYtzY4WCMr6cxYCBfeBJVjkVw7Xnw0Laf9A3e2HYdogStDKwFIHn0RyO4fTcKm1owxQ01QpbbP1gaHWr22v60NDkQpvdcOmohcQLDJjwv8UFW/vp9z3g18GVBgtaq+dyBjYsZCspPmsmvrOqJbnyL68q+4b8vHeIezhjG0oeKQnH4h0a1PUfXK7YSS23FHTYW3fTZIWsYYM1QECeaI2tqg16TTo1ptv1T18r5eFxGXoFxvIbAFeEZElvXch0dEjgU+D5ytqi0iUrY9naNbn6LumW/w29xp7NRq3hsKOhDk6mYQan2Dume+gTphNFKNk9wVlFpzkyUeY4w5BH2NdOYDG4DbgWcJSqUPxOnAOlVdDyAidwCXAj03f/sY8B1VbQFQ1cYD/IyDVvXK7agT5meZc5jATt7mvhi84GW7XyMUx3EEIgnIpYKSa0s6xhhz0PpKOmOBvwEWFx/LgNtV9bUS33sisLnH8y3AGXudMwNARJ4gmIL7sqruMxQUkWuBawGmTJlS4sf3LZTczgscz4v5Sfyf0F04BB0InHwKp5BGI8F+Oa7r0pEpsL3No27nal6/YS5HuU1E66dTN2sBbHgsKMWunRoUKlhSMsaYXvVaSKCqeVW9T1WvBs4GNgGPi0ipHaf3NzLSvZ6HgGMJRlWLgR+KyD61yap6q6rOUdU59fX1JX583woV4/l551tx8HlvsbmnH66gUDmBQsV4KGRAoDOnbG5JUVlooYo0Y2ihyUuQaVyL/8f/gOb1wb0/HTuCKbi1y/v5ZGOMGbn6rF4TkbCIXAL8mKAK4rvAvSW+9xaCfXi6TAK27eec3xYT3BvAawRJaMBtOPpqHsieyDznRRqkFQDfjdIxazEdsxYjfh7Xz9DUkSWiWeqkg2aqyUgMEYcqTeEpkGkLuk5HEkG3gydvLkf4xhgzJPW1tcGPgD8Dc4H/UNW3quqXVHVzb9fs5RngWBGZJiIR4CqCKbqelgLnFj9vDMF02/oD/B4Oyl1tM8kT4n2hhwDwwpW0nPFZshPPJDvxTFpP/0ekahwxv4MmraGTBM3s3qI6QoECDni53W8ajkPrpnKEb4wxQ1JfI50PE7RLuA54VESai48WEdm7tcI+VLUAfBL4PfAq8GtVfVlEbiyOnii+tktEXgEeBT5b3DRuQOU9n6VrOhlDG+c6qwFoPe0fyE48c/dJU8/Gef9S/u/4n/NhvYHXdRIxdieYHCFC+OBGerxxGmoPz5qTMcYMR30lnTAwGhhTfNQXH13/7peq3q+qM1T1aFX9SvHYDaq6rPhvVdV/VNVZqnqCqt5xKN9MqZa/2sSulM+VoZW4eHiRKlJT5u1xTiIWNAa9bt50KqMhlniLiJAnphlUfTokgStArAZUg+o2PxcUExhjjNmvvgoJvK4HcDxBefPHgOOKx4asX/x5K6B8KLoCgNS088Ht0Y8NIZ6oAhHmz2zgpitPYvuYs7nR/zA7qaPeTRNrmIHz9s/BqOmQaYWqsUG3A6teM8aYXvXbkUBEPgl8gmD9BeBOEfmOqn53QCMbIC9uaeWFLe2cIWsY520HIHn0Xs09Iy5ObHevtfkzG5g/swF4O/CFvd7xcwMbsDHGDCOltMG5FjhdVTsBROSrwJMElWxDzl3PbgHgmsQK8CA7ehb5uqP3OCcRDUM4sZ+rjTHGHIpSko4A+R7P8xx4d4Ijxr9cPIu3jilw7sNPA5AsbmHw4uY21j/7B96efABXW3hJxvHwqMWcef57iqMcY4wxh6qv3muhYgXaz4GnROTu4kvvAn5ajuAGQiTkcKE+RsjP4YfipKYu4MXNbax+4n4+4v2KrB+imQrqtIX3NX+Lb96Thcs/YInHGGMOg76q1/4MoKr/STDFlgLSwN+p6k1liG1gqBJa/b8ApKa+Aw0n+N1ftnOJ/xBZDZEhAuKSIUZGQ1ztLWXJyrLcOmSMMcNeX9Nr3VNoqvoMwc2eQ9+253Cagp6jXVNrjZ1ZGnQXLRrHFwcQEEhrmPHayJaWVB9vaIwxplR9JZ16EfnH3l5U1f8ZgHgG3tbnUHHJ10wlN/p4ABoqozS2jqZK2ugkHJynEJc826WBSXVWVGCMMYdDX0nHBSoZwkUD+3X6x8hMXUD7G88EPdOAi08Yz7InzuPD+muifo40EeLkiInH993LuG7edFi7nJaHbiK/6w02az3La6+yIgNjjDlAorp34+fiCyLPqeqpZY6nX3PmzNFVq1Yd0nukUkl2bV23x7E12zp49fnHOKvtPsbrDrbTsLt6zVlN+rf/wI6UT5YocckR0jz/E76WRVZkYIwZeMPmj/+S1nRGgrnHjmHh3H+A8OeBoCX2aV0v3nYzuzKQlRiOCFliAFztLeU/V55jSccYY0rUV/XaO8oWxSAThEQsCuHY/k9o3UinF8aR3Xk4S9SKDIwx5gD11Xut307Sw0U84iLR6t5PqJ1KpZvH7zEVGSVrRQbGGHOA+tzEbaSoiIQgWtn7CXOvZ3QMoprB932imiGkeX7ZVWRgjDGmJCM+6bjiEIvFIBTt/aQZC4lf+g1q6idT5yRpopaf1X3KigiMMeYAldJ7bVhZ/cid8Pg3aPC28aaO4pHYBZxx8Qc5p66fC2cspK64bUED8NYBj9QYY4afETXSWf3InYxe+UUqCs20a4JRdHBV5nbuWno3K9Y0DnZ4xhgz7I2opOM+9S0yGiJDFBEhKxHyhLnKu8/6qxljTBmMqKQzKr+dtIbpUflMWqJMFCt9NsaYchhRSac5PJ645OnZhCGsBbaqlT4bY0w5jKik4535KWJSIEYWVSWiOSJS4JehS6302RhjymBEVa+dtOBvWQ3d1WtbtIGlFVdxxSXv67f0ecWaRpasXM/mlhST6xJcN2+6lUsbY8wB6rXh55HqcDT8TKdSRJJbcUcdBW643/NXrGnkhmUvE3aFeNglnffIe8qNl8y2xGOMKYdh0wtzRE2vdYmGHNxwrKSEA7Bk5XrCrpCIhBAJvoZdsYo3Y4w5QCMy6TiO9N32Zi+bW1LEw+4ex+Jh1yrejDHmAI3IpANApKrkUyfXJUjnvT2OpfOeVbwZY8wBGplJx42AW3oNxXXzppP3lFSugGrwNe+pVbwZY8wBGplJRw5sTW7+zAZuvGQ2DVUx2tJ5GqpiVkRgjDEHYUSVTB+K+TMbLMkYY8whGpkjHWOMMYPCko4xxpiysaRjjDGmbCzpGGOMKRtLOsYYY8rGko4xxpiysZLpIusibYwxA89GOuzuIt3YkaE2HqaxI8MNy15mxZrGwQ7NGGOGFUs6WBdpY4wpF0s6WBdpY4wpF1vTARbFX+Zv2n7FeG1khzOWe2JX8LicbF2kjTHmMLORztrlXJ9dQp3fTJtWUOc3c13yu5ySXWVdpI0x5jCzpPPkzcRjcUbX1hEOuaQ0iu9E+GLtcqteM8aYw8ym11o3QqyOKhGqYsUfhypktg9uXMYYMwyN7KSzdjlk2qB9G4RiUFEPsRrIp6F2ymBHZ4wxw87ITTprl8MDn4FIJeRSUMhC+1bIZyEcgbnXD3aExhgz7IzcpPPkzeBEIFYLoSh0NkIhA4UkXHILzFg42BEaY8ywM+KSTle7m//e/hoZt5r6qgJVsWqIVhfXclot4RhjzAAZUdVrPdvdNLnjcLw0W9vSdGQKwQm2lmOMMQNqRCWdnu1u7oldQVQ8YpqhqSMTrOv4OVvLMcaYATSikk7PdjfPRefw/cTHaXNHE/PaoWosXHiTTa0ZY8wAGlFrOpPrEjR2ZEhEgm/7uegcHpeTaaiKcfuHzhzk6IwxZvgb0JGOiFwgIq+JyDoR+ec+zrtSRFRE5gxkPNfNm07eU1K5AqrB11Oyq/hu4UvwzRPgtkVBKbUxxpj/397dx9Z5n2Uc/16xjxO7tRZ3syto3GZB7bKm29LNVF2LgiktSrcp3UZBzZi0P0AN0gZBbMAGUmCpKpECKxIqKAVCNomtFMqLVXVsWRdj+KPdPJrRZs2y4jVN1i5OlTg4shOfODd/PI+TY8dvif28OOf6SNFzzu88ti/9JPvO83b/MpFZ0ZHUADwG3AvcDGyWdPM0+7UCvwk8n1WWCd1rO9i+aR0drSs4OVrlnsqLPFTZTdv4cVjRBsNHk2d3XHjMzDKR5em124BXImIAQNITwH3A96bs9xDwCPCZDLOc172240JPtd1fgOFmaEq7STe1wBjJMzy+tmNmtuiyPL12HXC45v2RdOw8SbcCnRHx9GzfSNKDkvol9R87dmzxEg4dgkrz5LFKMwy9tng/w8zMzsuy6GiasTj/obQMeBT49FzfKCIej4iuiOhqb29fvIQrb0iezanlZ3XMzDKTZdE5AnTWvF8FvF7zvhW4BeiV9CpwO9CT9c0Ek9yxNXk2Z2wk6UbgZ3XMzDKVZdH5NnCjpLdLagIeAHomPoyIkxHxtohYHRGrgeeATRHRn2GmyW66J3k2p/XapP2Nn9UxM8tUZjcSRMRZSZ8CvgY0ALsiYr+k7UB/RPTM/h1yctM9LjJmZjlRRMy9V4l0dXVFf39+B0NmZiUw3TXyJamu2uCYmVmxXHTMzCw3ddV77bId3JM8MDp0KLnN+o6tvg5kZnYZ6vdIp3cH/PH18Plrkm3vjun3m1jWevioW+WYmS1QfRad3h3Q90jyXM6ySrLte2T6wjOxrHVTC0jJdllTMm5mZpekPovOc48BgoZGWJZuUTo+hVvlmJktmvosOmdOgRomj6khGZ/KrXLMzBZN/RWdg3uSO97HTxPVM4xVzzJaPUd1vEq18aqL93erHDOzRVNfRWfipoDlbyGA4ByNVKlQRcDucx+k98Dg5K9xqxwzs0VTX7dMT9wUsPJ6jp+GlTHEMs4B8JXlD/BE0y/T0TdwYb2dCW6VY2a2KOqr6AwdSm57Bo7RxvGGayCCVk7x5NUfozmCIydGCg5pZnblqq/TazU3BTQ1LuNcBMs5w9Fl1wIwWh1nVVtLkQnNzK5o9VV0am4KaL+qieVxmsao8tSKjzIydpbqeLBlw5qiU5qZXbHqq+jU3BTQyine0t7Jl9p+g73j76GjdQXbN627+HqOmZktGi9tYGZWfl7awMzM7FLV191rC9B7YJCdfQMcPjFCZ1sLWzas8ak4M7NL5COdeeg9MMi2nv0MDp9mZXOFweHTbOvZf/GDpGZmNisXnXnY2TdApUG0NDUiJdtKg9jZN1B0NDOzJcVFZx4OnxihuTK5QWhzpcEPkpqZXSIXnXnobGthtDo+acwPkpqZXToXnXnYsmENZkFr9wAACFxJREFU1fFgZOwsEeEHSc3MLpOLzjx0r+1g+6Z1dLSu4ORo1Q+SmpldJt8yPU/daztcZMzMFshF5+CeZMmDoUNJQ9A7tnoZAzOzjNT36bWJRd2GjyZLHgwfTd4f3FN0MjOzK1J9F52JRd2aWkBKtsuaknEzM1t09V10hg5BpXnyWKUZhl4rJo+Z2RWuvotOzaJu51VHYeX1xeQxM7vC1XfRqVnUjYhke24sGTczs0VX30WnZlE3Tg8l23v/1HevmZllxLdM33SPi4yZWU7q+0jHzMxy5aJjZma5cdExM7PcuOiYmVluXHTMzCw3LjpmZpYbFx0zM8tN3T2n03tgkJ19Axw+MUJnWwtbNqzxOjlmZjmpqyOd3gODbOvZz+DwaVY2VxgcPs22nv30HhgsOpqZWV2oq6Kzs2+ASoNoaWpESraVBrGzb6DoaGZmdaGuis7hEyM0VxomjTVXGjhyYqSgRGZm9aWuik5nWwuj1fFJY6PVcVa1tRSUyMysvtRV0dmyYQ3V8WBk7CwRybY6HmzZsKboaGZmdaGuik732g62b1pHR+sKTo5W6WhdwfZN63z3mplZTurulunutR0uMmZmBamrIx0zMyuWi46ZmeXGRcfMzHLjomNmZrnJtOhI2ijp+5JekfTZaT7/bUnfk/Q/kp6VdEOWeczMrFiZFR1JDcBjwL3AzcBmSTdP2e0FoCsi3g38E/BIVnnMzKx4WR7p3Aa8EhEDETEGPAHcV7tDROyNiIkeNM8BqzLMY2ZmBcuy6FwHHK55fyQdm8mvAl+d7gNJD0rql9R/7NixRYxoZmZ5yrLoaJqxmHZH6eNAF/An030eEY9HRFdEdLW3ty9iRDMzy1OWHQmOAJ0171cBr0/dSdLdwB8APxsRZzLMY2ZmBVPEtAcfC//GUiNwEPh54EfAt4GPRcT+mn1uJbmBYGNE/GCe3/cYcGjxE8/pbcCbBfzc+XK+hSt7RudbmLLng5kzvhkRG/MOk4XMig6ApA8Afw40ALsi4mFJ24H+iOiR9A3gXcAb6Ze8FhGbMgu0AJL6I6Kr6Bwzcb6FK3tG51uYsueDpZFxoTJt+BkRzwDPTBnbVvP67ix/vpmZlYs7EpiZWW5cdObv8aIDzMH5Fq7sGZ1vYcqeD5ZGxgXJ9JqOmZlZLR/pmJlZblx0zMwsNy46c5D0qqQXJe2T1F90HgBJuyQNSnqpZuwaSXsk/SDdtpUs3x9J+lE6j/vS2+mLytcpaa+klyXtl7Q1HS/FHM6Sr0xzuELStyR9N834+XT87ZKeT+fwHyQ1lSzfbkk/rJnD9UXkq8nZIOkFSU+n70sxf1ly0Zmfn4uI9SW6f343MPVBsc8Cz0bEjcCz6fui7ObifACPpvO4Pr2dvihngU9HxDuB24FPph3QyzKHM+WD8szhGeCuiHgPsB7YKOl2YEea8UbgBElPxTLlA/idmjncV1C+CVuBl2vel2X+MuOiswRFRB9wfMrwfcAX09dfBD6ca6gaM+QrjYh4IyL+O309TPJLfx0lmcNZ8pVGJE6lbyvpvwDuIukyAsXO4Uz5SkPSKuCDwN+k70VJ5i9LLjpzC+Drkr4j6cGiw8zi2oh4A5I/WkBHwXmm86l0wb5dRZ7+qyVpNXAr8DwlnMMp+aBEc5ieGtoHDAJ7gP8FhiLibLrLXJ3lc80XERNz+HA6h49KWl5UPpJuLb8LnEvfv5USzV9WXHTmdmdEvJdkMbpPStpQdKAl6q+AnyI51fEG8GfFxgFJVwNPAb8VEf9XdJ6ppslXqjmMiPGIWE/SzPc24J3T7ZZvqpofPCWfpFuAzwFrgZ8GrgF+r4hskj4EDEbEd2qHp9m1VEdni8FFZw4R8Xq6HQT+heSXq4yOSvoJgHQ7WHCeSSLiaPpH4Bzw1xQ8j5IqJH/Q/z4i/jkdLs0cTpevbHM4ISKGgF6S608r02a/MENn+bzV5NuYnrqMtKP931HcHN4JbJL0KskCl3eRHPmUbv4Wm4vOLCRdJal14jXwC8BLs39VYXqAT6SvPwH8W4FZLjLxxzz1EQqcx/Tc+d8CL0fEF2o+KsUczpSvZHPYLmll+roZuJvk2tNe4P50tyLncLp8B2r+UyGS6yWFzGFEfC4iVkXEauAB4JsR8SuUZP6y5I4Es5C0huToBpLmqF+OiIcLjASApK8A3SRt0I8Cfwj8K/AkcD3wGvBLEVHIxfwZ8nWTnBYK4FVgy8T1kwLy/Qzwn8CLXDif/vsk100Kn8NZ8m2mPHP4bpIL3Q0k/3l9MiK2p78zT5CcunoB+HgR62TNku+bQDvJqax9wK/X3HBQCEndwGci4kNlmb8sueiYmVlufHrNzMxy46JjZma5cdExM7PcuOiYmVluXHTMzCw3LjpmNSR9RFJIWlt0FrMrkYuO2WSbgf8ieWDPzBaZi45ZKu11didJO/kH0rFlkv4yXZPlaUnPSLo//ex9kv4jbQb7tSkdA8xsGi46Zhd8GPj3iDgIHJf0XuCjwGrgXcCvAe+H873R/gK4PyLeB+wCCu9WYVZ2jXPvYlY3NpM0XYSkFclmknVY/jFtsvljSXvTz98B3ALsSdp40UDS+dnMZuGiYwZIeitJp99bJAVJEQku9N676EuA/RHx/pwiml0RfHrNLHE/8KWIuCEiVkdEJ/BD4E3gF9NrO9eSNC4F+D7QLun86TZJ64oIbraUuOiYJTZz8VHNU8BPkqzg+BKwk6QT9cmIGCMpVDskfZekY/Ed+cU1W5rcZdpsDpKujohT6Sm4b5GsJvvjonOZLUW+pmM2t6fTBcGagIdccMwun490zMwsN76mY2ZmuXHRMTOz3LjomJlZblx0zMwsNy46ZmaWm/8HxApMzS+m9VwAAAAASUVORK5CYII=\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.lmplot(x='Age', y = 'ToM Booklet-Matched', \n",
" data = pheno, hue='Gender')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unfortunately, these plots can be a bit sub-optimal at times. The `regplot` is perhaps more flexible. You can read more about this type of plotting here: https://seaborn.pydata.org/tutorial/distributions.html."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bivariate visualization: Linear x Categorical"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a quick look at how to look at `bivariate relationships` when one `variable` is `categorical` and the other is `scalar`.\n",
"\n",
"For consistency can continue to look at the same relationship, but look at `\"AgeGroup\"` instead of `age`.\n",
"\n",
"There are many ways to visualize such relationships. While there are some advantages and disadvantes of each type of plot, much of the choice will come down to personal preference."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are several ways of visualizing the same relationship. Note that adults to not have cognitive tests, so we won't\n",
"include adults in any of these plots. Note also that we explicitly pass the order of x:\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['3yo', '4yo', '5yo', '7yo', '8-12yo']"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"order = sorted(pheno.AgeGroup.unique())[:-1]\n",
"order"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"order = sorted(pheno.AgeGroup.unique())[:-1]\n",
"\n",
"sns.barplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'])\n",
"plt.show()\n",
"\n",
"sns.boxplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'])\n",
"plt.show()\n",
"\n",
"sns.boxenplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" order = order)\n",
"plt.show()\n",
"\n",
"sns.violinplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'])\n",
"plt.show()\n",
"\n",
"sns.stripplot(x='AgeGroup', jitter=True,\n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'])\n",
"plt.show()\n",
"\n",
"sns.pointplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generally, `lineplots` and `barplots` are frowned upon because they do not show the actual data, and therefore can mask troublesome distributions and outliers. \n",
"\n",
"But perhaps you're really into `barplots`? No problem! One nice thing about many `seaborn plots` is that they can be overlaid very easily. Just call two plots at once before doing `plt.show()` (or in this case, before running the cell). Just overlay a `stripplot` on top!"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" order = order, palette='Blues')\n",
"\n",
"sns.stripplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" jitter=True,\n",
" order = order, color = 'black')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can find more info on these types of plots here: https://seaborn.pydata.org/tutorial/categorical.html.\n",
"\n",
"Having trouble deciding which type of plot you want to use? Checkout the raincloud plot, which combines multiple types of plots to achieve a highly empirical visualization. \n",
"\n",
"Read more about it here:\n",
"https://wellcomeopenresearch.org/articles/4-63/v1?src=rss.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import ptitprince as pt\n",
"\n",
"dx = \"AgeGroup\"; dy = \"ToM Booklet-Matched\"; ort = \"v\"; pal = \"Set2\"; sigma = .2\n",
"f, ax = plt.subplots(figsize=(7, 5))\n",
"\n",
"pt.RainCloud(x = dx, y = dy, data = pheno[pheno.AgeGroup!='Adult'], palette = pal, bw = sigma,\n",
" width_viol = .6, ax = ax, orient = ort)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bivariate visualization: Categorical x Categorical"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What if we want to observe the relationship between two `categorical variables`? Since we are usually just looking at `counts` or `percentages`, a simple `barplot` is fine in this case.\n",
"\n",
"Let's look at `AgeGroup` x `Gender`. `Pandas.crosstab` helps sort the data in an intuitive way. "
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
Gender
\n",
"
F
\n",
"
M
\n",
"
\n",
"
\n",
"
AgeGroup
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
3yo
\n",
"
10
\n",
"
7
\n",
"
\n",
"
\n",
"
4yo
\n",
"
8
\n",
"
6
\n",
"
\n",
"
\n",
"
5yo
\n",
"
16
\n",
"
18
\n",
"
\n",
"
\n",
"
7yo
\n",
"
11
\n",
"
12
\n",
"
\n",
"
\n",
"
8-12yo
\n",
"
19
\n",
"
15
\n",
"
\n",
"
\n",
"
Adult
\n",
"
20
\n",
"
13
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Gender F M\n",
"AgeGroup \n",
"3yo 10 7\n",
"4yo 8 6\n",
"5yo 16 18\n",
"7yo 11 12\n",
"8-12yo 19 15\n",
"Adult 20 13"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pandas.crosstab(index=pheno['AgeGroup'],\n",
" columns=pheno['Gender'],)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can actually plot this directly from `pandas`."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pandas.crosstab(index=pheno['AgeGroup'],\n",
" columns=pheno['Gender'],).plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above plot gives us absolute `counts`. Perhaps we'd rather visualize differences in `proportion` across `age groups`. Unfortunately we must do this manually."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"crosstab = pandas.crosstab(index=pheno['AgeGroup'],\n",
" columns=pheno['Gender'],)\n",
"\n",
"crosstab.apply(lambda r: r/r.sum(), axis=1).plot.bar()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Style points"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will be surprised to find out exactly how customizable your `python plots` are. Its not so important when you're first `exploring` your data, but `aesthetic value` can add a lot to `visualizations` you are communicating in the form of `manuscripts`, `posters` and `talks`.\n",
"\n",
"Once you know the relationships you want to `plot`, spend time adjusting the `colors`, `layout`, and fine details of your `plot` to `maximize interpretability`, `transparency`, and if you can spare it, `beauty`!\n",
"\n",
"You can easily edit `colors` using many `matplotlib` and `python arguments`, often listed as `col`, `color`, or `palette`. "
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## try uncommenting one of these lines at a time to see how the \n",
"## graph changes\n",
"\n",
"sns.boxplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" #palette = 'Greens_d'\n",
" #palette = 'spectral',\n",
" #color = 'black'\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can find more about your palette choices here: https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html.\n",
"\n",
"More about your color choices here:\n",
"https://matplotlib.org/3.1.0/gallery/color/named_colors.html."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also easily change the style of the plots by setting `\"style\"` or `\"context\"`:"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style('whitegrid')\n",
"sns.boxplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_context('notebook',font_scale=2)\n",
"sns.boxplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice these changes do not reset after the `plot` is shown. To learn more about controlling `figure aesthetics`, as well as how to produce temporary style changes, visit here: https://seaborn.pydata.org/tutorial/aesthetics.html."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, remember that these `plots` are `extremely customizable`. Literally every aspect can be changed. Once you know the relationship you want to `plot`, don't be afraid to spend a good chunk of time `tweaking` your `plot` to perfection:"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# set style\n",
"sns.set_style('white')\n",
"sns.set_context('notebook',font_scale=2)\n",
"\n",
"# set figure size\n",
"plt.subplots(figsize=(7,5))\n",
"\n",
"g = sns.boxplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" hue = 'Gender',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" palette = 'viridis')\n",
"\n",
"# Change X axis\n",
"new_xtics = ['Age 4','Age 3','Age 5', 'Age 7', 'Age 8-12']\n",
"g.set_xticklabels(new_xtics, rotation=90)\n",
"g.set_xlabel('Age')\n",
"\n",
"# Change Y axis\n",
"g.set_ylabel('Theory of Mind')\n",
"g.set_yticks([0,.2,.4,.6,.8,1,1.2])\n",
"g.set_ylim(0,1.2)\n",
"\n",
"# Title\n",
"g.set_title('Age vs Theory of Mind')\n",
"\n",
"# Add some text\n",
"g.text(2.5,0.2,'F = large #')\n",
"g.text(2.5,0.05,'p = small #')\n",
"\n",
"# Add significance bars and asterisks\n",
"plt.plot([0,0, 4, 4], \n",
" [1.1, 1.1, 1.1, 1.1], \n",
" linewidth=2, color='k')\n",
"plt.text(2,1.08,'*')\n",
"\n",
"# Move figure legend outside of plot\n",
"\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's all for now. There's so much more to visualization, but this should at least get you started."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Recommended reading:\n",
"\n",
"multidimensional plotting with seaborn: https://jovianlin.io/data-visualization-seaborn-part-3/\n",
"\n",
"Great resource for complicated plots, creative ideas, and data!: https://python-graph-gallery.com/\n",
"\n",
"A few don'ts of plotting: https://www.data-to-viz.com/caveats.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}