{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Summary statistics\n",
"\n",
"Overview of three examples of how summary statistics can hide differences that are exposed through visualisation\n",
"\n",
"The three sources are:\n",
"\n",
"> https://www.sjsu.edu/faculty/gerstman/StatPrimer/anscombe1973.pdf\n",
"\n",
"> http://www.thefunctionalart.com/2016/08/download-datasaurus-never-trust-summary.html\n",
"\n",
"> https://www.autodesk.com/research/publications/same-stats-different-graphs\n",
"\n",
"See also (not considered here): https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"from IPython.display import display_html\n",
"from itertools import chain,cycle\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Anscombe (1973)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# define file locations\n",
"\n",
"path = '/Users/aidanair/Documents/DATA/ALL_DATASETS/anscombe_csvs/'\n",
"\n",
"file1 = 'ans1.csv'\n",
"file2 = 'ans2.csv'\n",
"file3 = 'ans3.csv'\n",
"file4 = 'ans4.csv'\n",
"\n",
"# read in and assign to four different variables\n",
"\n",
"one = pd.read_csv(path + file1)\n",
"two = pd.read_csv(path + file2)\n",
"three = pd.read_csv(path + file3)\n",
"four = pd.read_csv(path + file4)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# VERY HELPFUL FUNCTION TAKEN FROM THIS STACK OVERFLOW USER TO DISPLAY DATAFRAMES SIDE BY SIDE\n",
"# https://stackoverflow.com/questions/38783027/jupyter-notebook-display-two-pandas-tables-side-by-side\n",
"\n",
"def display_side_by_side(*args, titles=cycle([''])):\n",
" html_str=''\n",
" \n",
" for df,title in zip(args, chain(titles,cycle([''])) ):\n",
" html_str+='
'\n",
" html_str+=f'{title} '\n",
" html_str+=df.to_html().replace('table','table style=\"display:inline\"')\n",
" html_str+=' '\n",
" \n",
" display_html(html_str,raw=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" \n",
" \n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# run function: show data in the four dfs\n",
"\n",
"display_side_by_side(one, two, three, four, titles=['one','two', 'three', 'four']) "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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" \n",
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"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# show summary statistics (function will run with added methods chained on)\n",
"\n",
"display_side_by_side(one.describe().round(2), \n",
" two.describe().round(2), \n",
" three.describe().round(2), \n",
" four.describe().round(2), \n",
" titles=['one','two', 'three', 'four'])\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.81642\n",
"0.81624\n",
"0.81629\n",
"0.81652\n"
]
}
],
"source": [
"# what about correlation between x and y in the four datasets? The same up to three decimal points\n",
"\n",
"print(np.corrcoef(one.x1, one.y1)[1][0].round(5))\n",
"print(np.corrcoef(two.x2, two.y2)[1][0].round(5))\n",
"print(np.corrcoef(three.x3, three.y3)[1][0].round(5))\n",
"print(np.corrcoef(four.x4, four.y4)[1][0].round(5))\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
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},
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},
"output_type": "display_data"
},
{
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"text/plain": [
""
]
},
"metadata": {
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},
"output_type": "display_data"
},
{
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\n",
"text/plain": [
""
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},
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},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot the four datasets\n",
"\n",
"one.plot(kind = 'scatter', x = 'x1', y = 'y1', title = 'ONE');\n",
"two.plot(kind = 'scatter', x = 'x2', y = 'y2', title = 'TWO');\n",
"three.plot(kind = 'scatter', x = 'x3', y = 'y3', title = 'THREE');\n",
"four.plot(kind = 'scatter', x = 'x4', y = 'y4', title = 'FOUR');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lines\n",
"\n",
"> The relationships between x values and y values are similar when seen in lines, but of course are not the same, as the data is not the same (just the measures of central tendancy)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"slope: 0.50009 y-int: 3.00009\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# define the two sets of data\n",
"\n",
"x = np.array(one.x1)\n",
"y = np.array(one.y1)\n",
"plt.plot(x, y, 'o')\n",
"\n",
"# establish the slope (m) and the intercept (c)\n",
"\n",
"m, c = np.polyfit (x, y, 1)\n",
"\n",
"# plot the linear regression\n",
"\n",
"plt.plot (x, m * x + c)\n",
"\n",
"# print the value of the slope and where it's situated (in terms of the y-axis)\n",
"\n",
"print('slope:', m.round(5), 'y-int:', c.round(5))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"slope: 0.5 y-int: 3.00091\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"x = np.array(two.x2)\n",
"y = np.array(two.y2)\n",
"plt.plot(x, y, 'o')\n",
"\n",
"m, c = np.polyfit (x, y, 1)\n",
"\n",
"plt.plot (x, m * x + c)\n",
"\n",
"print('slope:', m.round(5), 'y-int:', c.round(5))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"slope: 0.49973 y-int: 3.00245\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"x = np.array(three.x3)\n",
"y = np.array(three.y3)\n",
"plt.plot(x, y, 'o')\n",
"\n",
"m, c = np.polyfit (x, y, 1)\n",
"\n",
"plt.plot (x, m * x + c)\n",
"\n",
"print('slope:', m.round(5), 'y-int:', c.round(5))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"slope: 0.49991 y-int: 3.00173\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"x = np.array(four.x4)\n",
"y = np.array(four.y4)\n",
"plt.plot(x, y, 'o')\n",
"\n",
"m, c = np.polyfit (x, y, 1)\n",
"\n",
"plt.plot (x, m * x + c)\n",
"\n",
"print('slope:', m.round(5), 'y-int:', c.round(5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Datasaurus (2016)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"path = '/Users/aidanair/Documents/DATA/ALL_DATASETS/'\n",
"file = 'datasaurus_data.csv'\n",
"\n",
"cols = ['town_A', 'town_B']\n",
"din = pd.read_csv(path + file, names = cols)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" town_A \n",
" town_B \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 55.3846 \n",
" 97.1795 \n",
" \n",
" \n",
" 1 \n",
" 51.5385 \n",
" 96.0256 \n",
" \n",
" \n",
" 2 \n",
" 46.1538 \n",
" 94.4872 \n",
" \n",
" \n",
" 3 \n",
" 42.8205 \n",
" 91.4103 \n",
" \n",
" \n",
" 4 \n",
" 40.7692 \n",
" 88.3333 \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 137 \n",
" 39.4872 \n",
" 25.3846 \n",
" \n",
" \n",
" 138 \n",
" 91.2821 \n",
" 41.5385 \n",
" \n",
" \n",
" 139 \n",
" 50.0000 \n",
" 95.7692 \n",
" \n",
" \n",
" 140 \n",
" 47.9487 \n",
" 95.0000 \n",
" \n",
" \n",
" 141 \n",
" 44.1026 \n",
" 92.6923 \n",
" \n",
" \n",
"
\n",
"
142 rows × 2 columns
\n",
"
"
],
"text/plain": [
" town_A town_B\n",
"0 55.3846 97.1795\n",
"1 51.5385 96.0256\n",
"2 46.1538 94.4872\n",
"3 42.8205 91.4103\n",
"4 40.7692 88.3333\n",
".. ... ...\n",
"137 39.4872 25.3846\n",
"138 91.2821 41.5385\n",
"139 50.0000 95.7692\n",
"140 47.9487 95.0000\n",
"141 44.1026 92.6923\n",
"\n",
"[142 rows x 2 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# say the data refers to two towns...\n",
"\n",
"din"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# scatter plot to show any trends in the relationship betwen the data in town A, and the data in town B\n",
"\n",
"din.plot.scatter(x = 'town_A', y = 'town_B');\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Datasaurus dozen (2017)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"path = '/Users/aidanair/Documents/DATA/ALL_DATASETS/'\n",
"file1 = 'DatasaurusDozen.tsv'\n",
"\n",
"d = pd.read_csv(path + file1, sep = \"\\t\")\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1846, 3)\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" dataset \n",
" x \n",
" y \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" dino \n",
" 55.3846 \n",
" 97.1795 \n",
" \n",
" \n",
" 1 \n",
" dino \n",
" 51.5385 \n",
" 96.0256 \n",
" \n",
" \n",
" 2 \n",
" dino \n",
" 46.1538 \n",
" 94.4872 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" dataset x y\n",
"0 dino 55.3846 97.1795\n",
"1 dino 51.5385 96.0256\n",
"2 dino 46.1538 94.4872"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(d.shape)\n",
"d[:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> \"These 13 datasets (the Datasaurus, plus 12 others) each have the same summary statistics (**x/y mean**, **x/y standard deviation**, and **Pearson's correlation**) to two decimal places, while being drastically different in appearance.\"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"away \n",
" \n",
" \n",
" \n",
" dataset \n",
" x \n",
" y \n",
" \n",
" \n",
" \n",
" \n",
" 142 \n",
" away \n",
" 32.331110 \n",
" 61.411101 \n",
" \n",
" \n",
" 143 \n",
" away \n",
" 53.421463 \n",
" 26.186880 \n",
" \n",
" \n",
" 144 \n",
" away \n",
" 63.920202 \n",
" 30.832194 \n",
" \n",
" \n",
" 145 \n",
" away \n",
" 70.289506 \n",
" 82.533649 \n",
" \n",
" \n",
" 146 \n",
" away \n",
" 34.118830 \n",
" 45.734551 \n",
" \n",
" \n",
"
bullseye \n",
" \n",
" \n",
" \n",
" dataset \n",
" x \n",
" y \n",
" \n",
" \n",
" \n",
" \n",
" 1278 \n",
" bullseye \n",
" 51.203891 \n",
" 83.339777 \n",
" \n",
" \n",
" 1279 \n",
" bullseye \n",
" 58.974470 \n",
" 85.499818 \n",
" \n",
" \n",
" 1280 \n",
" bullseye \n",
" 51.872073 \n",
" 85.829738 \n",
" \n",
" \n",
" 1281 \n",
" bullseye \n",
" 48.179931 \n",
" 85.045117 \n",
" \n",
" \n",
" 1282 \n",
" bullseye \n",
" 41.683200 \n",
" 84.017941 \n",
" \n",
" \n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Consider two of the dozen\n",
"\n",
"display_side_by_side(d[d.dataset == 'away'].head(), \n",
" d[d.dataset == 'bullseye'].head(), \n",
" titles=['away','bullseye'])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(Pearson) Correlation between x and y values for \"Away\" and \"Bullseye\" datasets\n",
"Away: -0.064\n",
"Bullseye: -0.069\n"
]
},
{
"data": {
"text/html": [
"away \n",
" \n",
" \n",
" \n",
" x \n",
" y \n",
" \n",
" \n",
" \n",
" \n",
" count \n",
" 142.00 \n",
" 142.00 \n",
" \n",
" \n",
" mean \n",
" 54.27 \n",
" 47.83 \n",
" \n",
" \n",
" std \n",
" 16.77 \n",
" 26.94 \n",
" \n",
" \n",
" min \n",
" 15.56 \n",
" 0.02 \n",
" \n",
" \n",
" 25% \n",
" 39.72 \n",
" 24.63 \n",
" \n",
" \n",
" 50% \n",
" 53.34 \n",
" 47.54 \n",
" \n",
" \n",
" 75% \n",
" 69.15 \n",
" 71.80 \n",
" \n",
" \n",
" max \n",
" 91.64 \n",
" 97.48 \n",
" \n",
" \n",
"
bullseye \n",
" \n",
" \n",
" \n",
" x \n",
" y \n",
" \n",
" \n",
" \n",
" \n",
" count \n",
" 142.00 \n",
" 142.00 \n",
" \n",
" \n",
" mean \n",
" 54.27 \n",
" 47.83 \n",
" \n",
" \n",
" std \n",
" 16.77 \n",
" 26.94 \n",
" \n",
" \n",
" min \n",
" 19.29 \n",
" 9.69 \n",
" \n",
" \n",
" 25% \n",
" 41.63 \n",
" 26.24 \n",
" \n",
" \n",
" 50% \n",
" 53.84 \n",
" 47.38 \n",
" \n",
" \n",
" 75% \n",
" 64.80 \n",
" 72.53 \n",
" \n",
" \n",
" max \n",
" 91.74 \n",
" 85.88 \n",
" \n",
" \n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Show the correlation measure, and the mean and std for x values and y values in each of the two datasets\n",
"\n",
"print('(Pearson) Correlation between x and y values for \"Away\" and \"Bullseye\" datasets')\n",
"print('Away:', np.corrcoef(d[d.dataset == 'away'].x, d[d.dataset == 'away'].y)[1][0].round(3))\n",
"print('Bullseye:', np.corrcoef(d[d.dataset == 'bullseye'].x, d[d.dataset == 'bullseye'].y)[1][0].round(3))\n",
"\n",
"display_side_by_side(d[d.dataset == 'away'].describe().round(2), \n",
" d[d.dataset == 'bullseye'].describe().round(2), \n",
" titles=['away','bullseye'])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot both datasets, with their (almost) identical mean, std and correlation\n",
"\n",
"df = d[d.dataset == 'away']\n",
"df.plot(kind = 'scatter', x = 'x', y = 'y');\n",
"\n",
"df = d[d.dataset == 'bullseye']\n",
"df.plot(kind = 'scatter', x = 'x', y = 'y');\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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qho/ahFL1Okv56z2WNSq6kKhPp+23rqAg0gYarcLbble+zdRpi0I1pyCj6pSx1CRrpAyEyqE0ptPK3qunIDXpijI56i0D0WlXvuPVaduwKihIVZ02ltoO6plbmj1jMkOZ0oWO7XzlOxE6qe6SgoJUyE9A7js0pCvKNrRm8BWyRUmH3V209ZXvROmUZA4FBSlRWlcnU/LlAbqiTLp872+4qEhWqquLJfNmRtgqiRNNNEtBZV0dJ5t1elNGf19KNezbQFiZjN6UaiXJEQoKUhD2hZFxSHUZwyNZrn3vfE0yJ1ynZdJI4yIJCmY23cyWm9nTZvaUmb3DzI42s++Z2S9y/50RRds6WbU6SoeGswxlnBsf2aq0xYTTDmYymqjmFG4BvuPuF5lZL3AU8BfAY+5+k5l9BvgMsDSi9nWk4tS7LozXhkszVDTJ3B46KZNGGtfyoGBm04AzgT8CcPchYMjMLgTemTvtLuD7KCi0XP4LY8vzr/Kxu9eTLirWr2GG9jGRmTQql9Feohg+eiPwMvB1M/upmd1uZv3Ase7+Qu6cF4Fjwx5sZleY2XozW//yyy+3qMmdZWBKH2e++fXcfJGGGaS2FRt3smTZKi69fV1oZVathk+elldJNbPFwI+AJe6+zsxuAV4FrnL36UXn7XH3mvMKqpLafK2+CtRVZ3KMVplVq+HjK25VUncAO9x9Xe72coL5g5fM7Dh3f8HMjgN2RdA2KdPKBTsrNu7kmuWbSXUZmaxz80X6EomzWuUyAK2GT6iWDx+5+4vAdjN7S+7QOcBWYCVwee7Y5cCKVrdNWiNsSGH3gTSffmAT6ZEsrw1lSI9k+dQDmzTsEGO10lvD0pu1d3QyRJV9dBVwTy7z6BngIwQB6n4z+yiwDbg4orZJE1UbUtjy/L6SVbYAwxlny/P7OPPNx0TUWqlltEJxWg+RTJEEBXffCISNZ53T4qZIC9UqsAdW5VHVjkscVEtv7bTKou1EtY+kZWqNQS84/nV0d0FRBizdXbDg+NeV/I3iiej839SkdLSqzTtpPUQyKShIy9Qagx6Y0sffXbyIq5dvImVdZDzLzRctLPkiKR56OjQ8gpkxqTulzJYYazRRQdln0VNQkJYZbUih1pVl2NATOMOZEUCZLe1AKazxoKAgLTXakELYleXuA2lWP72LlFWfX1AJjuQI6w2EBf2rl29SoI9ARwYFdVGj1ciQQv7qsbvLODiUqXqeMluSoVpvIGy+KT3i3LvuOa4658QIW9x5Oi4oqIuaHMVXj8X6e1OkRzIVcwoK8PFWK/ssbItQgFtXD3LJaXP12rZQRwUF7TmcLGFXj/19KW44fwFnnXRM4Rz1+JKhVvbZwjnTufKsE/nb7/13yWPyGwBVe33V6594HRUUar0p9YaKn7BspUzWOeukY0ry4SUZRtvg55LT5nLr6sG6K/Oq198cHbXz2lh2nVKVx+hoQ5j2MtrrOTClr+7KvOVbxx4eznLNg5v1OZ0AHdVTaHSVpa5EoleerQSwafveCR0u0BBE64yWfVbvgjf1+puno4IC1P+m0/xDfOSzlcYTpKt98Svwt95o2Wf1ZKdpr+nm6ajho7yBKX0snDO95htPVR7jZTzDBdU2gtEQRHJpaLF5Oq6nUK9qVyL9vakJH76Q0Y11uKBWj09DEMmm2krNMWpPwcyuMrOaO6C1o7ArkYsXz+a8W9dU3XpQmmeswwVhPb6UGauf3kV/b0pDEAmVTwABRu31S2Pq6SkcC/zEzJ4A7gT+3Vu9h2dEiq9E+ntTnHfrGs0xRGSspZjDgsnBoQzXP7SFkaxz8eLZ3L9+h8o7J4jmgZqrrj2azcyA3yXYDGcxcD9wh7v/srnNq62VezRv2r6XS29fx/70SOHY1L5u/uWPT2PhnOktaYOMLVNo5cadXPPgZlJWWSpjUk8XD195BgeHMirHnQCj7Qst9Rn3Hs3u7mb2IvAiMALMAJab2ffc/ZqJa2p8KdshHsayZ3S+x7f66V1ct3JLSWDo6eri4FCGhXOm17wCVdpqPGgeqPnqmVP4hJltAL4IrAXe6u5/ApwKvL/J7YuNRrIdtOAtfgam9JEeyVb0FPKBvVYmUrXsJWk9LUBtvnp6CkcDv+/u24oPunvWzM5rTrPiqZ5sB413xtPuA2lufGRrxfFrz5vPwJQ+Nm3fG3oFuuX5V0ddr6JeROtoAWrzjRoU3P26Gvc9NbHNib9awxda8BZfocX1elOcfPw0oPoVKHjVYAHwX798hTvXPktvSl86raIFqM2ldQoTSOOd8RVaXM+9MOxQ7Qp0wfHTKh53aHiEj3z9x2SKcjTyRdz0pdMa9cwt6fM4NgoKE0iT0fFVz7BDtSvQ4scNZTJksl4SEIrpSyc+9HkcGwWFCTTWXHppjXqGHcKuQIsft+/QEP/7n59gJBu+C5y+dOJDn8exUVCYYBO59F4TmBNvLCmtxY/bfSBNxrOh5/R1q/5O3KgURuMUFJpgrF88xZQ1EU9Bzf+F/Pn9G8nvBdPdBZ84583aNjKmJuLz2EkUFGJIWRPxlr/6DDKQnAXHT6u6XkVXqJI0Cgoxkv8S2XdoWFkTMTcwpY8z3/z6qvff86Nt3PDQFnpSXWTc1dOTxFBQiIni4aKhTJaMsiYS654fbeMvv/UkAEOZYEJaPT1JCgWFGAgbLupJGX3d0JtKKWsiQXYfSHPDw5Urp1NmbHn+VaZN7ikMJ5UPL2m4qX0l6bVVUIiBsEU2k7pTfPVDv860yb2JeCNJYMeeQ/SmjKGR0uOHhzN87O71hZXPF586m/s3HCnZHVbCW8NN7SFpSSMKCjFQbZFNtQlMia/ZMyYzkq1c2WYWrHrOr3y++0fPARQuBO7+r9LbGm5qD0lMGolsj2YzS5nZT83s4dztN5rZOjMbNLNvmFlvVG1rtbAKrNeeN58dew6psmPCFL+W/X0peru7+JPffhOTexq7/grbD1zVPpMniXu9R9lT+ATwFPC63O1lwN+7+31m9o/AR4GvRdW4ViteZPPkzn3c+PDWurubSRqv7ATlC6YAvv7DZxv6G+WJBUkbgpBAEkttRNJTMLPZwHuB23O3DTgbWJ475S7gfVG0LUoDU/qYPWMyNz6yNbSufxjV+o+ngSl9hb2Dw3qCl71jbs3bxYkFtfZ6kHhrZB+WuIiqp/APwDXA1NztAWCvu+en53YAoZdBZnYFcAXA3Llzm9vKCDRS2TGJ45WdKqzcwifOeXPN23lbnt9Hl1nJ39O6leRIWqmNlgeF3MY8u9x9g5m9s9HHu/ttwG0Q7NE8sa2LXiPdTZUGTpbycguj3YagJ3jN8k2kR0rf6nEfgpBSSSq1EcXw0RLgAjN7FriPYNjoFmC6meWD1GygI8dBGuluJnG8UuqX7wmWBwQV3pNmanlPwd0/C3wWINdT+LS7f8jMHgAuIggUlwMrWt22uKi3u6nSwO0trCd4VE+Kf/zwqTVLbEj8xTk5JE7rFJYC95nZXwM/Be6IuD2Rqre7mbTxSqlfWE8wi7Pg+NdVeYQkQdwzySJbpwDg7t939/Nyvz/j7m9393nu/gF3V2pFnYozXcoptz25kpi5IrVNRCZZsz/TceopyAQbzxVJnLu3naS4J9jfm+LgUIbdB9J6TRJqvMkhrehlKCi0qUbSVcsDQNy7t51mYEofawZf0WvSBsaTHDL40n6ufmATQxlvagp6pMNH0jz1Lq8vX/x2z4+2aaFUzGjxWvsY65Dgio07ec9X1jCUKc1Ea0bJDPUU2lQ9VyRhvYkbHtpCb3d4MNGQRTS0HqW9NJockv+cDo1U7g3ejBR09RTaVK0rkvxE1ZbnX63sTaS6Kq5Gar3xNJHdfFqP0n5qJYeUC+v1Q7A3+LXvnT/hFwbqKbSxsCuS0XZ4y7hz3fnzKwryAWzavrfkykZzD60xlvUogy/tZ+P2vSyaM515x06tep7EX9hFAUBfd4obH9nK1EndE/q5M/fkVopYvHixr1+/PupmJMbuA2mWLFvF4eGiYYiU0WWlO7xdsGhWyeRz2CTnknkzK/7WpJ4u1i49W0MaTVJvRtjnvvWzwn4NAJe9Yy5/deFbW9FEaZKVG3dyzYObSZlxcChTct9YPndmtsHdF4fdp55CB6m2w9vf/P5bOTycKbmqzC+eq5bFdNuHT634Wzg8tOl5zl94vAJDE9SzoHHwpf0lAQGCDXwuO/0E9RgSLN/rX/30Lq5/aAsH0kcCw0TPL2lOoYOEdUMPj2T41AMbueGhrZx365qK0tvVspjAQv5Wlusf2sppX/gPlfCOyMbtexs6LskxMKWPs046pmJnv4meX1JQ6CDlk8993V24O+kRr5rqWH2r0NfxxfefQl+3lf8zjGTh6uWbNPkcgUVzpjd0XJKlFavcNXzUYYonn/cdGubj9zzB/vSRXebLu6K1JjkvWDSL6Uf1cMXdGzhcli6XMqVMRmHesVO57B1zC3s+QzCnMJahI61qj6dm1ztTUOhAxfMF9aQ61noTLjh+GmGpCiNKmYzMX134Vi47/YRxZR8psyzemrk/g4aPOlgjXdFqedUDU/q4+aJT6EmVDiNl3Vk7+EpT2y/VzTt2KhctnjPmHkL5CupPL9/M4Ev7m9BSiRulpMqEDBMMvrSfd3/5cYaLsuWUoppMm7bv5dLb15UMKwL0powvfWChegxtoFZKqnoK0tDqymqe33eInlSq5FhYXRatgI6/aoulhjKumksdQEFBxm3Fxp187O71vFa2qKZ8fqK8+J7SVuMpP6xYXgMLmlOATeJFQUHGpdo+wr0p49rzjtRlaaTSp3oT0btg0SweveoMesvmilQHq/0p+0jGJWyVNIC7c+PDW5naF9RlqbfSp7Je4mPesVP50gcW1lVzKf+6pcwYzmS57vwFfOj0N0TQahkvBQUZl9kzJjOUyVQcH84GV5X5TUDGWsq7GZuISP3qyYkvft3y/vJbT4LBh05TYEgaDR/JuAxM6ePKs06sen9xb2C09Nd6NwaS1hotEWHHnkOkrHJl+w0PbdVQUgKppyDjdslpc7l19SDpUTYBGe2qU/sGJNPsGZMZzlS+9j0p06r2BFJPQcYtv4BtUk8Xk3qCt1RfykJ7A7WuOltR10Um3sCUPq47f0HF8UzWFdATSD0FmRDFvYD+3hQHhzJjWgzX7Lou0hwfOv0NYMGQUU/KyGRdAT2htKJZRCaMiuglgzbZEZGWmOhCbQoyraegEAG90UVGpzUr0VBQaDG90UVGpzUr0VH2UQs1UupBpJNpzUp0FBRaqJE3uurITCw9n8miNSvR0fBRC9X7RtcQ08Sq9Xxqfieeam0DK82llNQWW7lxZ8UbvfgLf/eBNEuWrSqpI6PNasau1vO5ZvAVBd+YU9BujlilpJrZHOBu4FjAgdvc/RYzOxr4BnAC8CxwsbvvaXX7mm20xVn1VhOV+lR7Prc8v08TmQnQzL2IJVwUcwojwKfcfT5wOvBxM5sPfAZ4zN1PBB7L3W5LtUo9aCx17MLmDcKez6FMFrBR53c0DyGdqOVBwd1fcPcncr/vB54CZgEXAnflTrsLeF+r2xYHqv8zNtV2dcs/nz1Fm8Vkslm273mtZvDVLnHSqSKdUzCzE4DHgZOB59x9eu64AXvyt8secwVwBcDcuXNP3bZtW6ua21IaS63faPMwuw+k+c2bHivZHa6v2/jceQu48ZGtFXMKmtdpT/pMHRGrOYU8M5sCPAh80t1ftaJ67O7uZhYardz9NuA2CCaaW9HWKGgstX6jzcPs2HOI3lSK9MhI4f70iLNjz2usXXp2xRdF2N9LdRmrn97FWScdo9clgZTRV79I1imYWQ9BQLjH3b+ZO/ySmR2Xu/84YFcUbZPkGW0eptrucF/7wTN8Z8uLFfM7YX/vYDrDdSu3aCgpgbRotDEtDwq5oaE7gKfc/e+K7loJXJ77/XJgRavbJsk02jxMrd3hwnYHK/57/X2pwvGDQxl9oSSQVkc3JorhoyXAh4GfmdnG3LG/AG4C7jezjwLbgIsjaJsk1GipvpecNpcvr/oFw5nSEcdqu4Pl/97qp3dx3cotHBw60tNQinCyKKOvMVFkH61xd3P3U9x9Ue7nUXff7e7nuPuJ7v4ud/9/rW6bJNtou7pd3+DuYANT+jjrpGPIlCVj6AslWZTR1xiVuZCOMZbdwcZTbkHZLvGhHf3qpzIX0nHG8mVd72Py5z25c19ouqvEXycE81impIpEZSzpvvU8Jp/22N1lHEgHcxDlJTSAtv/CSTKlriooiIxJ+dVkcdpjmJ6uLu5Z9xz/9/uDHf2FE2fa2CegoCDSoLCryTcM9FcseCs2lMnw1dWDpEc6+wsnzlSMMqBNdkQaUG0hVH9vqiLtEaC/N8Wkni6uPOtEelPKlY8zpa4GFBREGlBtIdTBoUxF2uPn/+fJ3Pux01m79GwuOW1uaLXWfYeGtRAuJpS6GtDwkUgDal1NLpwzvWbaY3Fq66HhEUYyWf7kXzaQcdf8QkwodVU9BZGG1FNSo9oCugsWzWLt0rO59PS5jGQh4yqdEUe1XsNOoJ6CSIPGezV5x9pnK46lLLzchkirKSiIjMFYS5sHZbyNoZHS44eHMx03oSnxpOEjkRaaPWMyw5nKLKWuLgs5W6T1FBREWqhaGe9J3Smlp0osKCiItNglp82lr7v0o5fOZOnvTVV5hEjrKCiItNjAlD5uvijIYJrUE3wEzZ3zbl2jXd0kcgoKIhG4YNEsHr7yDLLZoEpxOuMcHs5y9fJNSk2VSCkoiETk4FCGvu7SIaP0iHPvuueqPmb3gTSbtu9V4JCmUUqqSERmz5jMUCZTcfzW1YNcctrcipRXlXWWVlBPQSQi1TKRelOVhfKqFeJTj0EmmoKCSITCMpHCKnPu2HOIlJWuZVCVVWkGBQWJvXYeRy/ORKpVmfPJnfs4OFQ61NSJZZ2l+TSnILHWCePoo9VS2n0gzY2PbK143LXvna9aSTLh1FOQ2OqkcfRalTnD9nDo70tx8qxprWqedBAFBYmtahvadNo4etgeDpmsa+hImkJBQWJr9ozJHB4pHUc/PNJ51US1I5i0kuYUJNbcvebtTqEdwaRVFBQktnbsOcTknm72p49sPjC5p7tjN6MZ6x4OIo3Q8JHEVq39kMeinVNbRSaKgoLE1kSOpa/YuJMly1Zx6e3rWLJslaqRlgkLmAqinUnDRxJrEzGWXpzaepig53HNg5tZMm9mxw7H7D6QLjynawZfqVgL4tD260MkXKyCgpmdC9wCpIDb3f2miJskMTDesfR8ams+IEBpamunTd4WLwgcymTJZLOMZCk8P1cv3wQY6REF0U4Um6BgZingq8DvADuAn5jZSnevXMop0oBqcxNP7tzHH9z2Xx11NRzWayqXsi4o2zI6H0QVFNpfnOYU3g4Muvsz7j4E3AdcGHGbpA2EzU1c+9753PjI1o5YLV0sbEFguYxnyWRLU39VZ6lzxKanAMwCthfd3gGcFlFbpM2Uz03UGlJq56vhsF5TT8roMuhNpQo9JgiGjIp7Ue38vMgRcQoKdTGzK4ArAObOnRtxayRJyucmJjLdNSnyvabyL/ywyXwtlutMcQoKO4E5Rbdn546VcPfbgNsAFi9e3JnLW2Xcqn05dsKXX7WMrvL/dy2W60xxCgo/AU40szcSBIMPApdE2yRpZ51cOkJf+FJNbIKCu4+Y2ZXAvxOkpN7p7lsibpa0OX05ipSKTVAAcPdHgUejboeISKeKU0qqiIhETEFBREQKFBRERKRAQUFERAosyTtZmdnLwLYm/fmZwCtN+tvjpbaNXZzbp7aNTZzbBvFs3xvc/fVhdyQ6KDSTma1398VRtyOM2jZ2cW6f2jY2cW4bxL995TR8JCIiBQoKIiJSoKBQ3W1RN6AGtW3s4tw+tW1s4tw2iH/7SmhOQURECtRTEBGRAgUFEREp6PigYGZzzGy1mW01sy1m9onc8aPN7Htm9ovcf2dE0LZJZvZjM9uUa9sNueNvNLN1ZjZoZt8ws95Wt62ojSkz+6mZPRzDtj1rZj8zs41mtj53LPLXNdeO6Wa23MyeNrOnzOwdMWrbW3LPWf7nVTP7ZIza92e5z8OTZvavuc9JLN53ZvaJXLu2mNknc8di8bzVq+ODAjACfMrd5wOnAx83s/nAZ4DH3P1E4LHc7VZLA2e7+0JgEXCumZ0OLAP+3t3nAXuAj0bQtrxPAE8V3Y5T2wDOcvdFRXnicXhdAW4BvuPuJwELCZ7DWLTN3X+ee84WAacCrwH/Fof2mdks4E+Bxe5+MkGZ/Q8Sg/edmZ0MfIxgv/mFwHlmNo8YPG8NcXf9FP0AK4DfAX4OHJc7dhzw84jbdRTwBMG+1a8A3bnj7wD+PaI2zSZ4k58NPAxYXNqW+/efBWaWHYv8dQWmAb8il+gRp7aFtPV3gbVxaR9H9nI/mqD0/8PA78XhfQd8ALij6Pa1wDVxeN4a+VFPoYiZnQD8OrAOONbdX8jd9SJwbERtSpnZRmAX8D3gl8Bedx/JnbKD4IMShX8geNPnNzseID5tA3Dgu2a2Ibe3N8TjdX0j8DLw9dzQ2+1m1h+TtpX7IPCvud8jb5+77wS+BDwHvADsAzYQj/fdk8BvmdmAmR0FvIdgi+HIn7dGKCjkmNkU4EHgk+7+avF9HoT4SHJ33T3jQTd+NkG39KQo2lHOzM4Ddrn7hqjbUsMZ7v424N0Ew4JnFt8Z4evaDbwN+Jq7/zpwkLIhhSjfc3m5cfkLgAfK74uqfbnx+AsJAuvxQD9wbqvbEcbdnyIYxvou8B1gI5ApOyfy13U0CgqAmfUQBIR73P2bucMvmdlxufuPI7hSj4y77wVWE3SNp5tZfte82QR7WrfaEuACM3sWuI9gCOmWmLQNKFxV4u67CMbE3048XtcdwA53X5e7vZwgSMShbcXeDTzh7i/lbsehfe8CfuXuL7v7MPBNgvdiLN537n6Hu5/q7mcSzG38N/F43urW8UHBzAy4A3jK3f+u6K6VwOW53y8nmGtoddteb2bTc79PJpjreIogOFwUZdvc/bPuPtvdTyAYYljl7h+KQ9sAzKzfzKbmfycYG3+SGLyu7v4isN3M3pI7dA6wNQ5tK/OHHBk6gni07zngdDM7KvfZzT93cXnfHZP771zg94F7icfzVr+oJzWi/gHOIOjObSbo7m0kGAscIJhE/QXwH8DREbTtFOCnubY9CXwud/xNwI+BQYKufV/Ez+E7gYfj1LZcOzblfrYAf5k7HvnrmmvHImB97rX9FjAjLm3Lta8f2A1MKzoWi/YBNwBP5z4T/wz0xeh9958EQWoTcE6cnrd6f1TmQkRECjp++EhERI5QUBARkQIFBRERKVBQEBGRAgUFEREpUFAQEZECBQURESlQUBCZQGb2G2a2OVfjvz9XV//kqNslUi8tXhOZYGb218AkYDJBjaO/ibhJInVTUBCZYLnqoj8BDgO/6e6ZUR4iEhsaPhKZeAPAFGAqQY9BJDHUUxCZYGa2kqCc+BsJdty6MuImidSte/RTRKReZnYZMOzu95pZCvihmZ3t7quibptIPdRTEBGRAs0piIhIgYKCiIgUKCiIiEiBgoKIiBQoKIiISIGCgoiIFCgoiIhIwf8H9iB0dmDJog8AAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot the dinosaur and the dozen others\n",
"\n",
"sets = d.dataset.unique().tolist()\n",
"\n",
"for x in sets:\n",
" df = d[d.dataset == x]\n",
" df.plot(kind = 'scatter', x = 'x', y = 'y', title = x)"
]
}
],
"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": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}