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3-charts
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 图表（Charts）在 Jupyter notebook 里的功能演示"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notebook 可以使用图表进行数据可视化。使用 OpenBayes 提供的依赖包或安装自定义依赖达成目标。[查看依赖](https://openbayes.com/docs/runtimes/#1-%E9%80%9A%E7%94%A8%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B1%BB%E5%BA%93)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Matplotlib 常见图表"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Matplotlib](http://matplotlib.org/) 是最常见的制图依赖包，有关其详细信息，请参见 [Matplotlib 文档](http://matplotlib.org/api/pyplot_api.html)，有关其示例，请参见 [Matplotlib 示例](http://matplotlib.org/gallery.html#statistics)。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 折线图"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:26:17.366145Z",
"iopub.status.busy": "2021-03-03T10:26:17.365558Z",
"iopub.status.idle": "2021-03-03T10:26:18.405025Z",
"shell.execute_reply": "2021-03-03T10:26:18.404021Z",
"shell.execute_reply.started": "2021-03-03T10:26:17.366084Z"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
" \n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y1 = [1, 3, 5, 3, 1, 3, 5, 3, 1]\n",
"y2 = [2, 4, 6, 4, 2, 4, 6, 4, 2]\n",
"plt.plot(x, y1, label=\"line L\")\n",
"plt.plot(x, y2, label=\"line H\")\n",
"plt.plot()\n",
"\n",
"plt.xlabel(\"x 轴\")\n",
"plt.ylabel(\"y 轴\")\n",
"plt.title(\"折线图\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 条形图"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:29:02.078693Z",
"iopub.status.busy": "2021-03-03T10:29:02.078215Z",
"iopub.status.idle": "2021-03-03T10:29:02.291783Z",
"shell.execute_reply": "2021-03-03T10:29:02.291033Z",
"shell.execute_reply.started": "2021-03-03T10:29:02.078648Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# 注意 x 值为 4 和 6 的位置，这展示了重叠的情况\n",
"\n",
"x1 = [1, 3, 4, 5, 6, 7, 9]\n",
"y1 = [4, 7, 2, 4, 7, 8, 3]\n",
"\n",
"x2 = [2, 4, 6, 8, 10]\n",
"y2 = [5, 6, 2, 6, 2]\n",
"\n",
"# 颜色参考: https://matplotlib.org/api/colors_api.html\n",
"\n",
"plt.bar(x1, y1, label=\"蓝色\", color='b')\n",
"plt.bar(x2, y2, label=\"绿色\", color='g')\n",
"plt.plot()\n",
"\n",
"plt.xlabel(\"x 轴\")\n",
"plt.ylabel(\"y 轴\")\n",
"plt.title(\"条形图\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 直方图"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:30:03.067805Z",
"iopub.status.busy": "2021-03-03T10:30:03.067261Z",
"iopub.status.idle": "2021-03-03T10:30:04.944407Z",
"shell.execute_reply": "2021-03-03T10:30:04.943245Z",
"shell.execute_reply.started": "2021-03-03T10:30:03.067748Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# 使用 numpy 在生成一组 5 附近的随机数据。\n",
"n = 5 + np.random.randn(1000)\n",
"\n",
"m = [m for m in range(len(n))]\n",
"plt.bar(m, n)\n",
"plt.title(\"原始数据\")\n",
"plt.show()\n",
"\n",
"plt.hist(n, bins=20)\n",
"plt.title(\"直方图\")\n",
"plt.show()\n",
"\n",
"plt.hist(n, cumulative=True, bins=20)\n",
"plt.title(\"累积直方图\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 散点图"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:31:16.006783Z",
"iopub.status.busy": "2021-03-03T10:31:16.006180Z",
"iopub.status.idle": "2021-03-03T10:31:16.205550Z",
"shell.execute_reply": "2021-03-03T10:31:16.204297Z",
"shell.execute_reply.started": "2021-03-03T10:31:16.006723Z"
}
},
"outputs": [
{
"data": {
"image/png": 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cY5qnKl3uqOq4LndI0knuhC53SJIGx5CWpIIZ0pJUMENakgpmSEtSwWr9646IGAd2HefdzwL21lZMfUqtC8qtzbpmx7pmZyHWdW5mDnfbUWtIn4iIaE73JyiDVGpdUG5t1jU71jU7J1tdXu6QpIIZ0pJUsJJCeuOgC5hGqXVBubVZ1+xY1+ycVHUVc01akvRoJXXSkqQpDGlJKthAQjoiTomIr86w/9SIuDki7omIT0dEFFLXyyPivvZbtm6OiAv6UFPPDwIexHxVrKuv8xURSyLiixGxJSJumGbMoNZWldr6vr46zv3OiLh1mn1nRcQdEfG9iHh/v2qqUNefR8RPO+brjD7U0/MxqnuN9T2kI2IpsA14yQzD3gjcl5kXAWf2GNvPugA+kZmr2l/b57ouqn0QcN/nq2Jd0N/5uhy4JzMvAUYi4uIuYwYxV1Vrg/6vLyLiXODNMwz5S+BrwEXAKyLiGX0oq0pdAO/umK//60NZ0PsxqnWN9T2kM3N/Zv42cN8Mwy4Dvtm+fTvwokLqAnhtRNwVEV/qUxdW5YOA+z5fVP+A4n7O1zeAD7e7+icAD3UZM4i5gmq1Qf/XF7Qex+tm2H8Z8M3MPAz8O/2bs151AVwZEXdHxEd6jKtTr8eo1jVW6jXpJwGT/yo+BDxxgLV02gGsz8zn0fq0mhfO9Ql7fRBwW9/nq2JdfZ2vzPxlZj4MbAH2ZObOLsMGsrYq1tb39RURVwD3AD+YYVjf56xiXduAa4AG8OqIWDHXdVHtMap1vkoN6b3A5PWlMyjndfoPAJPXx0aBs/tx0mh9EPBf0Pog4ENdhgxkvirU1df5iognRcRjgJXAmRHRrYMZ1FxVqW0Q6+uVwIuBLwDPiYgru4wZxJxVqetnwNb22ruP/sxXlceo1vkqNaRv4+g1zsuAbw2wlk5XAa+PiEXAs4Hvz/UJo9oHAfd9virW1e/5uhp4XftJ+zCwtMuYQa2tKrX1fX1l5hWZuQp4PbAtMz/WZdhtwEvbdb2QPsxZxbo+DKxq/z7pqcBP5rouqj1Gta6xgYd0RPxmRHxoyubPAudExL20/uW6rZC6PgasBb4L/HNmzvS/YnWZ+kHAby1kvqrU1e/5+jjwloi4E9gHbC9krqrWNoj1dYyIWBkR10zZ/FHgD4B7ga9l5k8Lqet9wPuBzcB7M/N/+1DKMY8RsH+u15ivOJSkgg28k5YkTc+QlqSCGdKSVDBDWpIKZkhLUsEMaUkq2P8DAkDaH8LhXI0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x1 = [2, 3, 4]\n",
"y1 = [5, 5, 5]\n",
"\n",
"x2 = [1, 2, 3, 4, 5]\n",
"y2 = [2, 3, 2, 3, 4]\n",
"y3 = [6, 8, 7, 8, 7]\n",
"\n",
"# 标记参考: https://matplotlib.org/api/markers_api.html\n",
"\n",
"plt.scatter(x1, y1)\n",
"plt.scatter(x2, y2, marker='v', color='r')\n",
"plt.scatter(x2, y3, marker='^', color='m')\n",
"plt.title('散点图')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 堆叠图（堆栈图）"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:31:50.636645Z",
"iopub.status.busy": "2021-03-03T10:31:50.636080Z",
"iopub.status.idle": "2021-03-03T10:31:50.841234Z",
"shell.execute_reply": "2021-03-03T10:31:50.840377Z",
"shell.execute_reply.started": "2021-03-03T10:31:50.636588Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"idxes = [ 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"arr1 = [23, 40, 28, 43, 8, 44, 43, 18, 17]\n",
"arr2 = [17, 30, 22, 14, 17, 17, 29, 22, 30]\n",
"arr3 = [15, 31, 18, 22, 18, 19, 13, 32, 39]\n",
"\n",
"# 为堆栈图添加图例很麻烦\n",
"plt.plot([], [], color='r', label = 'D 1')\n",
"plt.plot([], [], color='g', label = 'D 2')\n",
"plt.plot([], [], color='b', label = 'D 3')\n",
"\n",
"plt.stackplot(idxes, arr1, arr2, arr3, colors= ['r', 'g', 'b'])\n",
"plt.title('堆栈图')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 饼状图"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:32:18.015042Z",
"iopub.status.busy": "2021-03-03T10:32:18.014478Z",
"iopub.status.idle": "2021-03-03T10:32:18.236003Z",
"shell.execute_reply": "2021-03-03T10:32:18.234829Z",
"shell.execute_reply.started": "2021-03-03T10:32:18.014983Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"labels = 'S1', 'S2', 'S3'\n",
"sections = [56, 66, 24]\n",
"colors = ['c', 'g', 'y']\n",
"\n",
"plt.pie(sections, labels=labels, colors=colors,\n",
" startangle=90,\n",
" explode = (0, 0.1, 0),\n",
" autopct = '%1.2f%%')\n",
"\n",
"plt.axis('equal')\n",
"plt.title('饼状图')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 自动填充区域（fill_between)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:32:42.044066Z",
"iopub.status.busy": "2021-03-03T10:32:42.043484Z",
"iopub.status.idle": "2021-03-03T10:32:42.227942Z",
"shell.execute_reply": "2021-03-03T10:32:42.226903Z",
"shell.execute_reply.started": "2021-03-03T10:32:42.044004Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"ys = 200 + np.random.randn(100)\n",
"x = [x for x in range(len(ys))]\n",
"\n",
"plt.plot(x, ys, '-')\n",
"plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)\n",
"\n",
"plt.title(\"自动填充示意图\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 子图（Subplot2grid）"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:32:57.106495Z",
"iopub.status.busy": "2021-03-03T10:32:57.105928Z",
"iopub.status.idle": "2021-03-03T10:32:57.713655Z",
"shell.execute_reply": "2021-03-03T10:32:57.712894Z",
"shell.execute_reply.started": "2021-03-03T10:32:57.106435Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"def random_plots():\n",
" xs = []\n",
" ys = []\n",
" \n",
" for i in range(20):\n",
" x = i\n",
" y = np.random.randint(10)\n",
" \n",
" xs.append(x)\n",
" ys.append(y)\n",
" \n",
" return xs, ys\n",
"\n",
"fig = plt.figure()\n",
"ax1 = plt.subplot2grid((5, 2), (0, 0), rowspan=1, colspan=2)\n",
"ax2 = plt.subplot2grid((5, 2), (1, 0), rowspan=3, colspan=2)\n",
"ax3 = plt.subplot2grid((5, 2), (4, 0), rowspan=1, colspan=1)\n",
"ax4 = plt.subplot2grid((5, 2), (4, 1), rowspan=1, colspan=1)\n",
"\n",
"x, y = random_plots()\n",
"ax1.plot(x, y)\n",
"\n",
"x, y = random_plots()\n",
"ax2.plot(x, y)\n",
"\n",
"x, y = random_plots()\n",
"ax3.plot(x, y)\n",
"\n",
"x, y = random_plots()\n",
"ax4.plot(x, y)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 特殊样式"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"要进一步自定义样式，请参阅 [matplotlib 文档](https://matplotlib.org/users/style_sheets.html)。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3D 图表"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3D 散点图"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:33:51.539534Z",
"iopub.status.busy": "2021-03-03T10:33:51.538964Z",
"iopub.status.idle": "2021-03-03T10:33:51.829604Z",
"shell.execute_reply": "2021-03-03T10:33:51.828570Z",
"shell.execute_reply.started": "2021-03-03T10:33:51.539473Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from mpl_toolkits.mplot3d import axes3d\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection = '3d')\n",
"\n",
"x1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
"y1 = np.random.randint(10, size=10)\n",
"z1 = np.random.randint(10, size=10)\n",
"\n",
"x2 = [-1, -2, -3, -4, -5, -6, -7, -8, -9, -10]\n",
"y2 = np.random.randint(-10, 0, size=10)\n",
"z2 = np.random.randint(10, size=10)\n",
"\n",
"ax.scatter(x1, y1, z1, c='b', marker='o', label='蓝色')\n",
"ax.scatter(x2, y2, z2, c='g', marker='D', label='绿色')\n",
"\n",
"ax.set_xlabel('x 轴')\n",
"ax.set_ylabel('y 轴')\n",
"ax.set_zlabel('z 轴')\n",
"plt.title(\"3D 散点图\")\n",
"plt.legend()\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3D 条形图"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:34:25.829454Z",
"iopub.status.busy": "2021-03-03T10:34:25.828873Z",
"iopub.status.idle": "2021-03-03T10:34:26.047341Z",
"shell.execute_reply": "2021-03-03T10:34:26.046159Z",
"shell.execute_reply.started": "2021-03-03T10:34:25.829394Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection = '3d')\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
"y = np.random.randint(10, size=10)\n",
"z = np.zeros(10)\n",
"\n",
"dx = np.ones(10)\n",
"dy = np.ones(10)\n",
"dz = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
"\n",
"ax.bar3d(x, y, z, dx, dy, dz, color='g')\n",
"\n",
"ax.set_xlabel('x 轴')\n",
"ax.set_ylabel('y 轴')\n",
"ax.set_zlabel('z 轴')\n",
"plt.title(\"3D 条形图\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 线框图"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:34:46.948707Z",
"iopub.status.busy": "2021-03-03T10:34:46.948114Z",
"iopub.status.idle": "2021-03-03T10:34:47.245130Z",
"shell.execute_reply": "2021-03-03T10:34:47.244595Z",
"shell.execute_reply.started": "2021-03-03T10:34:46.948647Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection = '3d')\n",
"\n",
"x, y, z = axes3d.get_test_data()\n",
"\n",
"ax.plot_wireframe(x, y, z, rstride = 2, cstride = 2)\n",
"\n",
"plt.title(\"线框图\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 特殊图表"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {
"iopub.execute_input": "2021-02-06T12:41:50.482778Z",
"iopub.status.busy": "2021-02-06T12:41:50.482145Z",
"iopub.status.idle": "2021-02-06T12:41:50.488900Z",
"shell.execute_reply": "2021-02-06T12:41:50.487470Z",
"shell.execute_reply.started": "2021-02-06T12:41:50.482707Z"
}
},
"source": [
"### [Seaborn](http://seaborn.pydata.org)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Seaborn 是基于 matplotlib 的图形可视化 python 包。它提供了一种高度交互式界面，便于用户能够做出各种有吸引力的统计图表。<br>\n",
"这里分别演示 Seaborn 的 [regplot](http://seaborn.pydata.org/generated/seaborn.regplot.html#seaborn.regplot) 和 [heatmap](https://seaborn.pydata.org/generated/seaborn.heatmap.html)。如欲了解更多，可以访问 [Seaborn 文档](http://seaborn.pydata.org)。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T11:22:25.279879Z",
"iopub.status.busy": "2021-03-03T11:22:25.279283Z",
"iopub.status.idle": "2021-03-03T11:22:27.070097Z",
"shell.execute_reply": "2021-03-03T11:22:27.069109Z",
"shell.execute_reply.started": "2021-03-03T11:22:25.279817Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"\n",
"# 生成一些随机数据\n",
"num_points = 20\n",
"# x 将是 5, 6, 7... 同时加上一个随机数\n",
"x = 5 + np.arange(num_points) + np.random.randn(num_points)\n",
"# y 将是 10, 11, 12... 同时也加上一个随机数\n",
"y = 10 + np.arange(num_points) + 5 * np.random.randn(num_points)\n",
"sns.regplot(x = x, y = y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:42:27.868496Z",
"iopub.status.busy": "2021-03-03T10:42:27.867892Z",
"iopub.status.idle": "2021-03-03T10:42:28.202494Z",
"shell.execute_reply": "2021-03-03T10:42:28.201431Z",
"shell.execute_reply.started": "2021-03-03T10:42:27.868396Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# 构建一个随机的 10 x 10 热图\n",
"side_length = 10\n",
"# 从一个 10 x 10 的矩阵开始，数值随机化在 5 左右\n",
"data = 5 + np.random.randn(side_length, side_length)\n",
"# 接下来的两行使值随着接近 (9, 9) 而变大\n",
"data += np.arange(side_length)\n",
"data += np.reshape(np.arange(side_length), (side_length, 1))\n",
"# 生成热图\n",
"sns.heatmap(data)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Altair"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Altair](http://altair-viz.github.io) 是一个声明性可视化库，用于在 Python 中创建交互式可视化。<br>\n",
"例如，下面是一个交互式散点图："
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:42:37.814529Z",
"iopub.status.busy": "2021-03-03T10:42:37.813963Z",
"iopub.status.idle": "2021-03-03T10:42:38.088669Z",
"shell.execute_reply": "2021-03-03T10:42:38.087815Z",
"shell.execute_reply.started": "2021-03-03T10:42:37.814470Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-eb82948ce5d1464ab09cf1372ee943bd\"></div>\n",
"<script type=\"text/javascript\">\n",
" (function(spec, embedOpt){\n",
" let outputDiv = document.currentScript.previousElementSibling;\n",
" if (outputDiv.id !== \"altair-viz-eb82948ce5d1464ab09cf1372ee943bd\") {\n",
" outputDiv = document.getElementById(\"altair-viz-eb82948ce5d1464ab09cf1372ee943bd\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//[email protected]?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
" };\n",
"\n",
" function loadScript(lib) {\n",
" return new Promise(function(resolve, reject) {\n",
" var s = document.createElement('script');\n",
" s.src = paths[lib];\n",
" s.async = true;\n",
" s.onload = () => resolve(paths[lib]);\n",
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" });\n",
" }\n",
"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
" throw err;\n",
" }\n",
"\n",
" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else if (typeof vegaEmbed === \"function\") {\n",
" displayChart(vegaEmbed);\n",
" } else {\n",
" loadScript(\"vega\")\n",
" .then(() => loadScript(\"vega-lite\"))\n",
" .then(() => loadScript(\"vega-embed\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-f02450ab61490a1363517a0190416235\"}, \"mark\": \"point\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"Origin\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"Horsepower\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"Miles_per_Gallon\"}}, \"selection\": {\"selector001\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-f02450ab61490a1363517a0190416235\": [{\"Name\": \"chevrolet chevelle malibu\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 8, \"Displacement\": 307.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 3504, \"Acceleration\": 12.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick skylark 320\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 165.0, 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\"Displacement\": 360.0, \"Horsepower\": 175.0, \"Weight_in_lbs\": 3821, \"Acceleration\": 11.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth valiant\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3121, \"Acceleration\": 16.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet nova custom\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3278, \"Acceleration\": 18.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc hornet\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2945, \"Acceleration\": 16.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford maverick\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 3021, 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107.0, \"Weight_in_lbs\": 2472, \"Acceleration\": 14.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"fiat 124 sport coupe\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2265, \"Acceleration\": 15.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"chevrolet monte carlo s\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4082, \"Acceleration\": 13.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac grand prix\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 230.0, \"Weight_in_lbs\": 4278, \"Acceleration\": 9.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"fiat 128\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 68.0, \"Horsepower\": 49.0, \"Weight_in_lbs\": 1867, \"Acceleration\": 19.5, \"Year\": 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\"Horsepower\": null, \"Weight_in_lbs\": 2875, \"Acceleration\": 17.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc hornet\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2901, \"Acceleration\": 16.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet nova\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3336, \"Acceleration\": 17.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun b210\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1950, \"Acceleration\": 19.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 2451, \"Acceleration\": 16.5, \"Year\": 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{\"Name\": \"plymouth satellite sebring\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3613, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford gran torino\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 4141, \"Acceleration\": 14.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick century luxus (sw)\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4699, \"Acceleration\": 14.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge coronet custom (sw)\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4457, \"Acceleration\": 13.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford gran torino (sw)\", 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\"Horsepower\": 78.0, \"Weight_in_lbs\": 2300, \"Acceleration\": 14.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota corona\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 76.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 1649, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 710\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 83.0, \"Horsepower\": 61.0, \"Weight_in_lbs\": 2003, \"Acceleration\": 19.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge colt\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2125, \"Acceleration\": 14.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"fiat 128\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2108, \"Acceleration\": 15.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"fiat 124 tc\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2246, \"Acceleration\": 14.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2489, \"Acceleration\": 15.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"subaru\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 93.0, \"Weight_in_lbs\": 2391, \"Acceleration\": 15.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"fiat x1.9\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2000, \"Acceleration\": 16.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"plymouth valiant custom\", 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\"Horsepower\": 170.0, \"Weight_in_lbs\": 4668, \"Acceleration\": 11.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet bel air\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4440, \"Acceleration\": 14.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth grand fury\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4498, \"Acceleration\": 14.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford ltd\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 148.0, \"Weight_in_lbs\": 4657, \"Acceleration\": 13.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick century\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3907, \"Acceleration\": 21.0, 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\"chevrolet monza 2+2\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 8, \"Displacement\": 262.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3221, \"Acceleration\": 13.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford mustang ii\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 129.0, \"Weight_in_lbs\": 3169, \"Acceleration\": 12.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2171, \"Acceleration\": 16.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 83.0, \"Weight_in_lbs\": 2639, \"Acceleration\": 17.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc gremlin\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2914, \"Acceleration\": 16.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac astro\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 2592, \"Acceleration\": 18.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corona\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 96.0, \"Weight_in_lbs\": 2702, \"Acceleration\": 13.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"volkswagen dasher\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 2223, \"Acceleration\": 16.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun 710\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2545, \"Acceleration\": 17.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 171.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2984, \"Acceleration\": 14.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen rabbit\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1937, \"Acceleration\": 14.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"amc pacer\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3211, \"Acceleration\": 17.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"audi 100ls\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 115.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2694, \"Acceleration\": 15.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"peugeot 504\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2957, \"Acceleration\": 17.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volvo 244dl\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 98.0, \"Weight_in_lbs\": 2945, \"Acceleration\": 14.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"saab 99le\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2671, \"Acceleration\": 13.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic cvcc\", \"Miles_per_Gallon\": 33.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Weight_in_lbs\": 1795, \"Acceleration\": 17.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"fiat 131\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 86.0, \"Weight_in_lbs\": 2464, \"Acceleration\": 15.5, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"opel 1900\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 81.0, \"Weight_in_lbs\": 2220, \"Acceleration\": 16.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"capri ii\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2572, \"Acceleration\": 14.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge colt\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 79.0, \"Weight_in_lbs\": 2255, \"Acceleration\": 17.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"renault 12tl\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 101.0, \"Horsepower\": 83.0, \"Weight_in_lbs\": 2202, \"Acceleration\": 15.3, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"chevrolet chevelle malibu classic\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 4215, \"Acceleration\": 13.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge coronet brougham\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4190, \"Acceleration\": 13.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc matador\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 3962, \"Acceleration\": 13.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford gran torino\", \"Miles_per_Gallon\": 14.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 152.0, \"Weight_in_lbs\": 4215, \"Acceleration\": 12.8, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth valiant\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3233, \"Acceleration\": 15.4, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet nova\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3353, \"Acceleration\": 14.5, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford maverick\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 81.0, \"Weight_in_lbs\": 3012, \"Acceleration\": 17.6, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc hornet\", \"Miles_per_Gallon\": 22.5, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3085, \"Acceleration\": 17.6, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 2035, \"Acceleration\": 22.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet woody\", \"Miles_per_Gallon\": 24.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 2164, \"Acceleration\": 22.1, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw rabbit\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1937, \"Acceleration\": 14.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic\", \"Miles_per_Gallon\": 33.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Weight_in_lbs\": 1795, \"Acceleration\": 17.4, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge aspen se\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3651, \"Acceleration\": 17.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada ghia\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 3574, \"Acceleration\": 21.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac ventura sj\", \"Miles_per_Gallon\": 18.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3645, \"Acceleration\": 16.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc pacer d/l\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 3193, \"Acceleration\": 17.8, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen rabbit\", \"Miles_per_Gallon\": 29.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 1825, \"Acceleration\": 12.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun b-210\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1990, \"Acceleration\": 17.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2155, \"Acceleration\": 16.4, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 26.5, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 2565, \"Acceleration\": 13.6, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volvo 245\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 4, \"Displacement\": 130.0, \"Horsepower\": 102.0, \"Weight_in_lbs\": 3150, \"Acceleration\": 15.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"plymouth volare premier v8\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3940, \"Acceleration\": 13.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"peugeot 504\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 3270, \"Acceleration\": 21.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota mark ii\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 108.0, \"Weight_in_lbs\": 2930, \"Acceleration\": 15.5, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mercedes-benz 280s\", \"Miles_per_Gallon\": 16.5, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 3820, \"Acceleration\": 16.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"cadillac seville\", \"Miles_per_Gallon\": 16.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Weight_in_lbs\": 4380, \"Acceleration\": 12.1, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevy c10\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4055, \"Acceleration\": 12.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford f108\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 3870, \"Acceleration\": 15.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge d100\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3755, \"Acceleration\": 14.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"honda Accelerationord cvcc\", \"Miles_per_Gallon\": 31.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2045, \"Acceleration\": 18.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"buick opel isuzu deluxe\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 111.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 2155, \"Acceleration\": 14.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"renault 5 gtl\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Weight_in_lbs\": 1825, \"Acceleration\": 18.6, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"plymouth arrow gs\", \"Miles_per_Gallon\": 25.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 96.0, \"Weight_in_lbs\": 2300, \"Acceleration\": 15.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun f-10 hatchback\", \"Miles_per_Gallon\": 33.5, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1945, \"Acceleration\": 16.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet caprice classic\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 3880, \"Acceleration\": 12.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile cutlass supreme\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 4060, \"Acceleration\": 19.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge monaco brougham\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4140, \"Acceleration\": 13.7, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury cougar brougham\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 4295, \"Acceleration\": 14.9, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet concours\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3520, \"Acceleration\": 16.4, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick skylark\", \"Miles_per_Gallon\": 20.5, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3425, \"Acceleration\": 16.9, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth volare custom\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3630, \"Acceleration\": 17.7, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada\", \"Miles_per_Gallon\": 18.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 98.0, \"Weight_in_lbs\": 3525, \"Acceleration\": 19.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac grand prix lj\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 180.0, \"Weight_in_lbs\": 4220, \"Acceleration\": 11.1, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet monte carlo landau\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 170.0, \"Weight_in_lbs\": 4165, \"Acceleration\": 11.4, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler cordoba\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 190.0, \"Weight_in_lbs\": 4325, \"Acceleration\": 12.2, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford thunderbird\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 149.0, \"Weight_in_lbs\": 4335, \"Acceleration\": 14.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen rabbit custom\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 1940, \"Acceleration\": 14.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"pontiac sunbird coupe\", \"Miles_per_Gallon\": 24.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2740, \"Acceleration\": 16.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corolla liftback\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2265, \"Acceleration\": 18.2, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford mustang ii 2+2\", \"Miles_per_Gallon\": 25.5, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 89.0, \"Weight_in_lbs\": 2755, \"Acceleration\": 15.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 30.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 63.0, \"Weight_in_lbs\": 2051, \"Acceleration\": 17.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge colt m/m\", \"Miles_per_Gallon\": 33.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 83.0, \"Weight_in_lbs\": 2075, \"Acceleration\": 15.9, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"subaru dl\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 16.4, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"volkswagen dasher\", \"Miles_per_Gallon\": 30.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 2190, \"Acceleration\": 14.1, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun 810\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2815, \"Acceleration\": 14.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"bmw 320i\", \"Miles_per_Gallon\": 21.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2600, \"Acceleration\": 12.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"mazda rx-4\", \"Miles_per_Gallon\": 21.5, \"Cylinders\": 3, \"Displacement\": 80.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2720, \"Acceleration\": 13.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"volkswagen rabbit custom diesel\", \"Miles_per_Gallon\": 43.1, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 21.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"ford fiesta\", \"Miles_per_Gallon\": 36.1, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 66.0, \"Weight_in_lbs\": 1800, \"Acceleration\": 14.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mazda glc deluxe\", \"Miles_per_Gallon\": 32.8, \"Cylinders\": 4, \"Displacement\": 78.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 19.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun b210 gx\", \"Miles_per_Gallon\": 39.4, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2070, \"Acceleration\": 18.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda civic cvcc\", \"Miles_per_Gallon\": 36.1, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 1800, \"Acceleration\": 16.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"oldsmobile cutlass salon brougham\", \"Miles_per_Gallon\": 19.9, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3365, \"Acceleration\": 15.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge diplomat\", \"Miles_per_Gallon\": 19.4, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 3735, \"Acceleration\": 13.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury monarch ghia\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Weight_in_lbs\": 3570, \"Acceleration\": 12.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac phoenix lj\", \"Miles_per_Gallon\": 19.2, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3535, \"Acceleration\": 19.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet malibu\", \"Miles_per_Gallon\": 20.5, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 3155, \"Acceleration\": 18.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont (auto)\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2965, \"Acceleration\": 15.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont (man)\", \"Miles_per_Gallon\": 25.1, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2720, \"Acceleration\": 15.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth volare\", \"Miles_per_Gallon\": 20.5, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3430, \"Acceleration\": 17.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord\", \"Miles_per_Gallon\": 19.4, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3210, \"Acceleration\": 17.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick century special\", \"Miles_per_Gallon\": 20.6, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3380, \"Acceleration\": 15.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury zephyr\", \"Miles_per_Gallon\": 20.8, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 3070, \"Acceleration\": 16.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aspen\", \"Miles_per_Gallon\": 18.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3620, \"Acceleration\": 18.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord d/l\", \"Miles_per_Gallon\": 18.1, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 3410, \"Acceleration\": 15.1, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet monte carlo landau\", \"Miles_per_Gallon\": 19.2, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 3425, \"Acceleration\": 13.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick regal sport coupe (turbo)\", \"Miles_per_Gallon\": 17.7, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 165.0, \"Weight_in_lbs\": 3445, \"Acceleration\": 13.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford futura\", \"Miles_per_Gallon\": 18.1, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Weight_in_lbs\": 3205, \"Acceleration\": 11.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge magnum xe\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 4080, \"Acceleration\": 13.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2155, \"Acceleration\": 16.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corona\", \"Miles_per_Gallon\": 27.5, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2560, \"Acceleration\": 14.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 510\", \"Miles_per_Gallon\": 27.2, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2300, \"Acceleration\": 14.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge omni\", \"Miles_per_Gallon\": 30.9, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2230, \"Acceleration\": 14.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota celica gt liftback\", \"Miles_per_Gallon\": 21.1, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2515, \"Acceleration\": 14.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth sapporo\", \"Miles_per_Gallon\": 23.2, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 2745, \"Acceleration\": 16.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile starfire sx\", \"Miles_per_Gallon\": 23.8, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2855, \"Acceleration\": 17.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 200-sx\", \"Miles_per_Gallon\": 23.9, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2405, \"Acceleration\": 14.9, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"audi 5000\", \"Miles_per_Gallon\": 20.3, \"Cylinders\": 5, \"Displacement\": 131.0, \"Horsepower\": 103.0, \"Weight_in_lbs\": 2830, \"Acceleration\": 15.9, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volvo 264gl\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 125.0, \"Weight_in_lbs\": 3140, \"Acceleration\": 13.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"saab 99gle\", \"Miles_per_Gallon\": 21.6, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2795, \"Acceleration\": 15.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"peugeot 604sl\", \"Miles_per_Gallon\": 16.2, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 133.0, \"Weight_in_lbs\": 3410, \"Acceleration\": 15.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volkswagen scirocco\", \"Miles_per_Gallon\": 31.5, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 1990, \"Acceleration\": 14.9, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda Accelerationord lx\", \"Miles_per_Gallon\": 29.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2135, \"Acceleration\": 16.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"pontiac lemans v6\", \"Miles_per_Gallon\": 21.5, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 3245, \"Acceleration\": 15.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury zephyr 6\", \"Miles_per_Gallon\": 19.8, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2990, \"Acceleration\": 18.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont 4\", \"Miles_per_Gallon\": 22.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2890, \"Acceleration\": 17.3, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord dl 6\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3265, \"Acceleration\": 18.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aspen 6\", \"Miles_per_Gallon\": 20.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3360, \"Acceleration\": 16.6, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet caprice classic\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 3840, \"Acceleration\": 15.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford ltd landau\", \"Miles_per_Gallon\": 17.6, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 129.0, \"Weight_in_lbs\": 3725, \"Acceleration\": 13.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury grand marquis\", \"Miles_per_Gallon\": 16.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 138.0, \"Weight_in_lbs\": 3955, \"Acceleration\": 13.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge st. regis\", \"Miles_per_Gallon\": 18.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 135.0, \"Weight_in_lbs\": 3830, \"Acceleration\": 15.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick estate wagon (sw)\", \"Miles_per_Gallon\": 16.9, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 155.0, \"Weight_in_lbs\": 4360, \"Acceleration\": 14.9, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford country squire (sw)\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 142.0, \"Weight_in_lbs\": 4054, \"Acceleration\": 14.3, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet malibu classic (sw)\", \"Miles_per_Gallon\": 19.2, \"Cylinders\": 8, \"Displacement\": 267.0, \"Horsepower\": 125.0, \"Weight_in_lbs\": 3605, \"Acceleration\": 15.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler lebaron town @ country (sw)\", \"Miles_per_Gallon\": 18.5, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3940, \"Acceleration\": 13.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw rabbit custom\", \"Miles_per_Gallon\": 31.9, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 1925, \"Acceleration\": 14.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"maxda glc deluxe\", \"Miles_per_Gallon\": 34.1, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 1975, \"Acceleration\": 15.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge colt hatchback custom\", \"Miles_per_Gallon\": 35.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 1915, \"Acceleration\": 14.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc spirit dl\", \"Miles_per_Gallon\": 27.4, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 2670, \"Acceleration\": 15.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercedes benz 300d\", \"Miles_per_Gallon\": 25.4, \"Cylinders\": 5, \"Displacement\": 183.0, \"Horsepower\": 77.0, \"Weight_in_lbs\": 3530, \"Acceleration\": 20.1, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"cadillac eldorado\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 125.0, \"Weight_in_lbs\": 3900, \"Acceleration\": 17.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"peugeot 504\", \"Miles_per_Gallon\": 27.2, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 3190, \"Acceleration\": 24.8, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"oldsmobile cutlass salon brougham\", \"Miles_per_Gallon\": 23.9, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3420, \"Acceleration\": 22.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth horizon\", \"Miles_per_Gallon\": 34.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2200, \"Acceleration\": 13.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth horizon tc3\", \"Miles_per_Gallon\": 34.5, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2150, \"Acceleration\": 14.9, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 210\", \"Miles_per_Gallon\": 31.8, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2020, \"Acceleration\": 19.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"fiat strada custom\", \"Miles_per_Gallon\": 37.3, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 69.0, \"Weight_in_lbs\": 2130, \"Acceleration\": 14.7, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"buick skylark limited\", \"Miles_per_Gallon\": 28.4, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2670, \"Acceleration\": 16.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet citation\", \"Miles_per_Gallon\": 28.8, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2595, \"Acceleration\": 11.3, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile omega brougham\", \"Miles_per_Gallon\": 26.8, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2700, \"Acceleration\": 12.9, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac phoenix\", \"Miles_per_Gallon\": 33.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2556, \"Acceleration\": 13.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw rabbit\", \"Miles_per_Gallon\": 41.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 76.0, \"Weight_in_lbs\": 2144, \"Acceleration\": 14.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota corolla tercel\", \"Miles_per_Gallon\": 38.1, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 1968, \"Acceleration\": 18.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 32.1, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2120, \"Acceleration\": 15.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 310\", \"Miles_per_Gallon\": 37.2, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2019, \"Acceleration\": 16.4, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet citation\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2678, \"Acceleration\": 16.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont\", \"Miles_per_Gallon\": 26.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2870, \"Acceleration\": 18.1, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord\", \"Miles_per_Gallon\": 24.3, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3003, \"Acceleration\": 20.1, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aspen\", \"Miles_per_Gallon\": 19.1, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3381, \"Acceleration\": 18.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"audi 4000\", \"Miles_per_Gallon\": 34.3, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 2188, \"Acceleration\": 15.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota corona liftback\", \"Miles_per_Gallon\": 29.8, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2711, \"Acceleration\": 15.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda 626\", \"Miles_per_Gallon\": 31.3, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2542, \"Acceleration\": 17.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 510 hatchback\", \"Miles_per_Gallon\": 37.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2434, \"Acceleration\": 15.0, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 32.2, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2265, \"Acceleration\": 15.2, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda glc\", \"Miles_per_Gallon\": 46.6, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2110, \"Acceleration\": 17.9, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge colt\", \"Miles_per_Gallon\": 27.9, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 2800, \"Acceleration\": 14.4, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 210\", \"Miles_per_Gallon\": 40.8, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2110, \"Acceleration\": 19.2, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"vw rabbit c (diesel)\", \"Miles_per_Gallon\": 44.3, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Weight_in_lbs\": 2085, \"Acceleration\": 21.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"vw dasher (diesel)\", \"Miles_per_Gallon\": 43.4, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Weight_in_lbs\": 2335, \"Acceleration\": 23.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"audi 5000s (diesel)\", \"Miles_per_Gallon\": 36.4, \"Cylinders\": 5, \"Displacement\": 121.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2950, \"Acceleration\": 19.9, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"mercedes-benz 240d\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 146.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 3250, \"Acceleration\": 21.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic 1500 gl\", \"Miles_per_Gallon\": 44.6, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1850, \"Acceleration\": 13.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"renault lecar deluxe\", \"Miles_per_Gallon\": 40.9, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": null, \"Weight_in_lbs\": 1835, \"Acceleration\": 17.3, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"subaru dl\", \"Miles_per_Gallon\": 33.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2145, \"Acceleration\": 18.0, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"vokswagen rabbit\", \"Miles_per_Gallon\": 29.8, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Weight_in_lbs\": 1845, \"Acceleration\": 15.3, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun 280-zx\", \"Miles_per_Gallon\": 32.7, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 132.0, \"Weight_in_lbs\": 2910, \"Acceleration\": 11.4, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda rx-7 gs\", \"Miles_per_Gallon\": 23.7, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2420, \"Acceleration\": 12.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"triumph tr7 coupe\", \"Miles_per_Gallon\": 35.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2500, \"Acceleration\": 15.1, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"ford mustang cobra\", \"Miles_per_Gallon\": 23.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": null, \"Weight_in_lbs\": 2905, \"Acceleration\": 14.3, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"honda Accelerationord\", \"Miles_per_Gallon\": 32.4, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 2290, \"Acceleration\": 17.0, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth reliant\", \"Miles_per_Gallon\": 27.2, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2490, \"Acceleration\": 15.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick skylark\", \"Miles_per_Gallon\": 26.6, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2635, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aries wagon (sw)\", \"Miles_per_Gallon\": 25.8, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2620, \"Acceleration\": 14.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet citation\", \"Miles_per_Gallon\": 23.5, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2725, \"Acceleration\": 12.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth reliant\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2385, \"Acceleration\": 12.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota starlet\", \"Miles_per_Gallon\": 39.1, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Weight_in_lbs\": 1755, \"Acceleration\": 16.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth champ\", \"Miles_per_Gallon\": 39.0, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 64.0, \"Weight_in_lbs\": 1875, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"honda civic 1300\", \"Miles_per_Gallon\": 35.1, \"Cylinders\": 4, \"Displacement\": 81.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 1760, \"Acceleration\": 16.1, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"subaru\", \"Miles_per_Gallon\": 32.3, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2065, \"Acceleration\": 17.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 210\", \"Miles_per_Gallon\": 37.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 1975, \"Acceleration\": 19.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota tercel\", \"Miles_per_Gallon\": 37.7, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Weight_in_lbs\": 2050, \"Acceleration\": 17.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda glc 4\", \"Miles_per_Gallon\": 34.1, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 16.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth horizon 4\", \"Miles_per_Gallon\": 34.7, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Weight_in_lbs\": 2215, \"Acceleration\": 14.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford escort 4w\", \"Miles_per_Gallon\": 34.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2045, \"Acceleration\": 16.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford escort 2h\", \"Miles_per_Gallon\": 29.9, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2380, \"Acceleration\": 20.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen jetta\", \"Miles_per_Gallon\": 33.0, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Weight_in_lbs\": 2190, \"Acceleration\": 14.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"renault 18i\", \"Miles_per_Gallon\": 34.5, \"Cylinders\": 4, \"Displacement\": 100.0, \"Horsepower\": null, \"Weight_in_lbs\": 2320, \"Acceleration\": 15.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda prelude\", \"Miles_per_Gallon\": 33.7, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2210, \"Acceleration\": 14.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 32.4, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2350, \"Acceleration\": 16.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 200sx\", \"Miles_per_Gallon\": 32.9, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2615, \"Acceleration\": 14.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda 626\", \"Miles_per_Gallon\": 31.6, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 74.0, \"Weight_in_lbs\": 2635, \"Acceleration\": 18.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"peugeot 505s turbo diesel\", \"Miles_per_Gallon\": 28.1, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 3230, \"Acceleration\": 20.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"saab 900s\", \"Miles_per_Gallon\": null, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2800, \"Acceleration\": 15.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volvo diesel\", \"Miles_per_Gallon\": 30.7, \"Cylinders\": 6, \"Displacement\": 145.0, \"Horsepower\": 76.0, \"Weight_in_lbs\": 3160, \"Acceleration\": 19.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota cressida\", \"Miles_per_Gallon\": 25.4, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 116.0, \"Weight_in_lbs\": 2900, \"Acceleration\": 12.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 810 maxima\", \"Miles_per_Gallon\": 24.2, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 2930, \"Acceleration\": 13.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"buick century\", \"Miles_per_Gallon\": 22.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3415, \"Acceleration\": 15.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile cutlass ls\", \"Miles_per_Gallon\": 26.6, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3725, \"Acceleration\": 19.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada gl\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 3060, \"Acceleration\": 17.1, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler lebaron salon\", \"Miles_per_Gallon\": 17.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 3465, \"Acceleration\": 16.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet cavalier\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2605, \"Acceleration\": 19.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet cavalier wagon\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2640, \"Acceleration\": 18.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet cavalier 2-door\", \"Miles_per_Gallon\": 34.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2395, \"Acceleration\": 18.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac j2000 se hatchback\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2575, \"Acceleration\": 16.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aries se\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2525, \"Acceleration\": 16.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac phoenix\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2735, \"Acceleration\": 18.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont futura\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2865, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord dl\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": null, \"Weight_in_lbs\": 3035, \"Acceleration\": 20.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen rabbit l\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Weight_in_lbs\": 1980, \"Acceleration\": 15.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"mazda glc custom l\", \"Miles_per_Gallon\": 37.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2025, \"Acceleration\": 18.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda glc custom\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 1970, \"Acceleration\": 17.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth horizon miser\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Weight_in_lbs\": 2125, \"Acceleration\": 14.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury lynx l\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2125, \"Acceleration\": 17.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"nissan stanza xe\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2160, \"Acceleration\": 14.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda Accelerationord\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2205, \"Acceleration\": 14.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 34.0, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2245, \"Acceleration\": 16.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda civic\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1965, \"Acceleration\": 15.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda civic (auto)\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1965, \"Acceleration\": 15.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 310 gx\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1995, \"Acceleration\": 16.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"buick century limited\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 6, \"Displacement\": 181.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2945, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile cutlass ciera (diesel)\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 6, \"Displacement\": 262.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 3015, \"Acceleration\": 17.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler lebaron medallion\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2585, \"Acceleration\": 14.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada l\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 112.0, \"Weight_in_lbs\": 2835, \"Acceleration\": 14.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota celica gt\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 144.0, \"Horsepower\": 96.0, \"Weight_in_lbs\": 2665, \"Acceleration\": 13.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge charger 2.2\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2370, \"Acceleration\": 13.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet camaro\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2950, \"Acceleration\": 17.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford mustang gl\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 86.0, \"Weight_in_lbs\": 2790, \"Acceleration\": 15.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw pickup\", \"Miles_per_Gallon\": 44.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 2130, \"Acceleration\": 24.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"dodge rampage\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2295, \"Acceleration\": 11.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford ranger\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 79.0, \"Weight_in_lbs\": 2625, \"Acceleration\": 18.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevy s-10\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 82.0, \"Weight_in_lbs\": 2720, \"Acceleration\": 19.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import altair as alt\n",
"from vega_datasets import data\n",
"cars = data.cars()\n",
"\n",
"alt.Chart(cars).mark_point().encode(\n",
" x='Horsepower',\n",
" y='Miles_per_Gallon',\n",
" color='Origin',\n",
").interactive()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotly"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2021-03-03T10:48:21.802558Z",
"iopub.status.busy": "2021-03-03T10:48:21.801990Z",
"iopub.status.idle": "2021-03-03T10:48:21.965127Z",
"shell.execute_reply": "2021-03-03T10:48:21.964083Z",
"shell.execute_reply.started": "2021-03-03T10:48:21.802497Z"
}
},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"linkText": "Export to plot.ly",
"plotlyServerURL": "https://plot.ly",
"showLink": false
},
"data": [
{
"type": "contour",
"z": [
[
10,
10.625,
12.5,
15.625,
20
],
[
5.625,
6.25,
8.125,
11.25,
15.625
],
[
2.5,
3.125,
5,
8.125,
12.5
],
[
0.625,
1.25,
3.125,
6.25,
10.625
],
[
0,
0.625,
2.5,
5.625,
10
]
]
}
],
"layout": {
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},