{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kz78I-lfEq7N",
"outputId": "9c8c2d77-469a-4433-aa87-0e4e479607b9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Figure({\n",
" 'data': [{'hovertemplate': 'x=%{x}
y=%{y}',\n",
" 'legendgroup': '',\n",
" 'line': {'color': '#636efa', 'dash': 'solid'},\n",
" 'marker': {'symbol': 'circle'},\n",
" 'mode': 'lines',\n",
" 'name': '',\n",
" 'orientation': 'v',\n",
" 'showlegend': False,\n",
" 'type': 'scatter',\n",
" 'x': array([1, 2]),\n",
" 'xaxis': 'x',\n",
" 'y': array([3, 4]),\n",
" 'yaxis': 'y'}],\n",
" 'layout': {'legend': {'tracegroupgap': 0},\n",
" 'margin': {'t': 60},\n",
" 'template': '...',\n",
" 'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'x'}},\n",
" 'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'y'}}}\n",
"})\n"
]
}
],
"source": [
"import plotly.express as px\n",
"\n",
"fig = px.line(x=[1, 2], y=[3, 4])\n",
"\n",
"print(fig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "7I5B5-ngFU6r",
"outputId": "4e2308b8-7f97-4d80-f25b-f956eed8f80f"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.iris()\n",
"\n",
"fig = px.line(df, y=\"sepal_width\",)\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "XDvLG_h0FU4v",
"outputId": "b45df9ef-110f-49a9-d376-578f22ab6816"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.iris()\n",
"\n",
"fig = px.line(df, y=\"sepal_width\", line_group='species')\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "Ua0O7nyWFU2Y",
"outputId": "5b69e0c6-138c-4ed4-9db5-3b3d9beb0dfa"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.iris()\n",
"\n",
"fig = px.line(df, y=\"sepal_width\", line_dash='species',\n",
" color='species')\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "Ac_eKzHDFU0U",
"outputId": "73d21373-df29-47f0-e016-70ce5cd67475"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.bar(df, x='day', y=\"total_bill\")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "XeqhxjR7FUxy",
"outputId": "e8ab274d-250b-4eea-b57c-9df0f619a411"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.bar(df, x='day', y=\"total_bill\", color='sex',\n",
" facet_row='time', facet_col='sex')\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "N87zrhLaFUuy",
"outputId": "5b02a38e-a2fd-453c-86c4-942157d779f9"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.scatter(df, x='total_bill', y=\"tip\")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "qTwsg4pnFUsc",
"outputId": "448d0770-e0f7-4011-b6e8-bd8a183e4c5a"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.scatter(df, x='total_bill', y=\"tip\", color='time',\n",
" symbol='sex', size='size', facet_row='day',\n",
" facet_col='time')\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "Nx5R4sdXFUqV",
"outputId": "57f47a1a-4c29-4662-a9e1-7d5eedd6cd42"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.histogram(df, x=\"total_bill\")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "ErDl4z2rFUn9",
"outputId": "bd56103e-6fb4-43f0-de06-cdd73ad291bc"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.histogram(df, x=\"total_bill\", color='sex',\n",
" nbins=50, histnorm='percent',\n",
" barmode='overlay')\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "WOFUsLNbFUln",
"outputId": "d2fda47b-6301-4b54-b700-dde4faa57625"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.pie(df, values=\"total_bill\", names=\"day\")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "PpP93EIHFUjV",
"outputId": "aed7c2f1-2568-440a-ceaa-b21ac2a3235a"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"\n",
"\n",
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.pie(df, values=\"total_bill\", names=\"day\",\n",
" color_discrete_sequence=px.colors.sequential.RdBu,\n",
" opacity=0.7, hole=0.5)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "01Anlv3UFUgk",
"outputId": "f6d793a0-e83b-4b19-a94d-1d5002da6c19"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.box(df, x=\"day\", y=\"tip\")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "wcKYAEFQF9qN",
"outputId": "7807c918-cd19-47ab-bf7a-682564ccb6c3"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.box(df, x=\"day\", y=\"tip\", color='sex',\n",
" facet_row='time', boxmode='group',\n",
" notched=True)\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "QQU5O54zF9lV",
"outputId": "297f82fb-3ba1-4611-ed32-25eb105448b9"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.violin(df, x=\"day\", y=\"tip\")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "uKcT0td8F9is",
"outputId": "6ccbf6db-1094-4d33-f06f-dc2537f1a2b6"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.violin(df, x=\"day\", y=\"tip\", color='sex',\n",
" facet_row='time', box=True)\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "w7uteoJWGIj0",
"outputId": "553751a6-16aa-47f0-868a-3a158e0f5570"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.scatter_3d(df, x=\"total_bill\", y=\"sex\", z=\"tip\")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "8vESsJB1GKB4",
"outputId": "8ac6a2ea-fdcd-4258-919a-fa4a91f2db9e"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.tips()\n",
"\n",
"fig = px.scatter_3d(df, x=\"total_bill\", y=\"sex\", z=\"tip\", color='day',\n",
" size='total_bill', symbol='time')\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "1ysiKjcVGJ_0",
"outputId": "87de75cd-73bf-4eee-db7b-b78f36dd404c"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.graph_objects as px\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"df = pd.read_csv('/content/tips.csv')\n",
"\n",
"plot = px.Figure(data=[px.Scatter(\n",
" x=df['day'],\n",
" y=df['tip'],\n",
" mode='markers',)\n",
"])\n",
"\n",
"plot.update_layout(\n",
" updatemenus=[\n",
" dict(buttons=list([\n",
" dict(\n",
" args=[\"type\", \"scatter\"],\n",
" label=\"Scatter Plot\",\n",
" method=\"restyle\"\n",
" ),\n",
" dict(\n",
" args=[\"type\", \"bar\"],\n",
" label=\"Bar Chart\",\n",
" method=\"restyle\"\n",
" )\n",
" ]),\n",
" direction=\"down\",\n",
" ),\n",
" ]\n",
")\n",
"\n",
"plot.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "zpYJXJtIGJ9D",
"outputId": "7d4120ca-70d5-4b5d-abcb-5c6b2cd64d84"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.graph_objects as px\n",
"import pandas as pd\n",
"\n",
"df = pd.read_csv('/content/tips.csv')\n",
"\n",
"plot = px.Figure(data=[px.Scatter(\n",
" x=df['day'],\n",
" y=df['tip'],\n",
" mode='markers',)\n",
"])\n",
"\n",
"plot.update_layout(\n",
" updatemenus=[\n",
" dict(\n",
" type=\"buttons\",\n",
" direction=\"left\",\n",
" buttons=list([\n",
" dict(\n",
" args=[\"type\", \"scatter\"],\n",
" label=\"Scatter Plot\",\n",
" method=\"restyle\"\n",
" ),\n",
" dict(\n",
" args=[\"type\", \"bar\"],\n",
" label=\"Bar Chart\",\n",
" method=\"restyle\"\n",
" )\n",
" ]),\n",
" ),\n",
" ]\n",
")\n",
"\n",
"plot.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "9Z2Os4ujGPoq",
"outputId": "50d33395-e4dd-45c3-daf0-fc504a4490d6"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.graph_objects as px\n",
"import plotly.express as go\n",
"import numpy as np\n",
"\n",
"df = go.data.tips()\n",
"\n",
"x = df['total_bill']\n",
"y = df['tip']\n",
"\n",
"plot = px.Figure(data=[px.Scatter(\n",
" x=x,\n",
" y=y,\n",
" mode='markers',)\n",
"])\n",
"\n",
"plot.update_layout(\n",
" xaxis=dict(\n",
" rangeselector=dict(\n",
" buttons=list([\n",
" dict(count=1,\n",
" step=\"day\",\n",
" stepmode=\"backward\"),\n",
" ])\n",
" ),\n",
" rangeslider=dict(\n",
" visible=True\n",
" ),\n",
" )\n",
")\n",
"\n",
"plot.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zXzw9NPPGPmv"
},
"outputs": [],
"source": [
"!pip install dash\n",
"!pip install --upgrade plotly"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "3St79DmIGPkc",
"outputId": "71cdca20-c7f0-477e-c442-cdf09590dc34"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: dash in /usr/local/lib/python3.11/dist-packages (3.0.4)\n",
"Requirement already satisfied: Flask<3.1,>=1.0.4 in /usr/local/lib/python3.11/dist-packages (from dash) (3.0.3)\n",
"Requirement already satisfied: Werkzeug<3.1 in /usr/local/lib/python3.11/dist-packages (from dash) (3.0.6)\n",
"Requirement already satisfied: plotly>=5.0.0 in /usr/local/lib/python3.11/dist-packages (from dash) (6.1.0)\n",
"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.11/dist-packages (from dash) (8.7.0)\n",
"Requirement already satisfied: typing-extensions>=4.1.1 in /usr/local/lib/python3.11/dist-packages (from dash) (4.13.2)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from dash) (2.32.3)\n",
"Requirement already satisfied: retrying in /usr/local/lib/python3.11/dist-packages (from dash) (1.3.4)\n",
"Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.11/dist-packages (from dash) (1.6.0)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from dash) (75.2.0)\n",
"Requirement already satisfied: Jinja2>=3.1.2 in /usr/local/lib/python3.11/dist-packages (from Flask<3.1,>=1.0.4->dash) (3.1.6)\n",
"Requirement already satisfied: itsdangerous>=2.1.2 in /usr/local/lib/python3.11/dist-packages (from Flask<3.1,>=1.0.4->dash) (2.2.0)\n",
"Requirement already satisfied: click>=8.1.3 in /usr/local/lib/python3.11/dist-packages (from Flask<3.1,>=1.0.4->dash) (8.2.0)\n",
"Requirement already satisfied: blinker>=1.6.2 in /usr/local/lib/python3.11/dist-packages (from Flask<3.1,>=1.0.4->dash) (1.9.0)\n",
"Requirement already satisfied: narwhals>=1.15.1 in /usr/local/lib/python3.11/dist-packages (from plotly>=5.0.0->dash) (1.39.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from plotly>=5.0.0->dash) (24.2)\n",
"Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from Werkzeug<3.1->dash) (3.0.2)\n",
"Requirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.11/dist-packages (from importlib-metadata->dash) (3.21.0)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->dash) (3.4.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->dash) (3.10)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->dash) (2.4.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->dash) (2025.4.26)\n",
"Requirement already satisfied: six>=1.7.0 in /usr/local/lib/python3.11/dist-packages (from retrying->dash) (1.17.0)\n",
"Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (6.1.0)\n",
"Requirement already satisfied: narwhals>=1.15.1 in /usr/local/lib/python3.11/dist-packages (from plotly) (1.39.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from plotly) (24.2)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"application/javascript": [
"(async (port, path, width, height, cache, element) => {\n",
" if (!google.colab.kernel.accessAllowed && !cache) {\n",
" return;\n",
" }\n",
" element.appendChild(document.createTextNode(''));\n",
" const url = await google.colab.kernel.proxyPort(port, {cache});\n",
" const iframe = document.createElement('iframe');\n",
" iframe.src = new URL(path, url).toString();\n",
" iframe.height = height;\n",
" iframe.width = width;\n",
" iframe.style.border = 0;\n",
" iframe.allow = [\n",
" 'accelerometer',\n",
" 'autoplay',\n",
" 'camera',\n",
" 'clipboard-read',\n",
" 'clipboard-write',\n",
" 'gyroscope',\n",
" 'magnetometer',\n",
" 'microphone',\n",
" 'serial',\n",
" 'usb',\n",
" 'xr-spatial-tracking',\n",
" ].join('; ');\n",
" element.appendChild(iframe);\n",
" })(8050, \"/\", \"100%\", 650, false, window.element)"
]
},
"metadata": {}
}
],
"source": [
"import dash\n",
"from dash import dcc, html\n",
"import plotly.express as px\n",
"import pandas as pd\n",
"\n",
"df = pd.DataFrame({\n",
" 'x': [1, 2, 3, 4, 5],\n",
" 'y': [10, 15, 13, 17, 14]\n",
"})\n",
"\n",
"app = dash.Dash(__name__)\n",
"\n",
"app.layout = html.Div([\n",
" dcc.Graph(\n",
" id='example-graph',\n",
" figure=px.scatter(df, x='x', y='y', title='Scatter Plot in Dash')\n",
" )\n",
"])\n",
"\n",
"if __name__ == '__main__':\n",
" app.run(debug=True, port=8050)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rHh4Tg9yGPh7"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}