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add creating plots in plotly tutorial
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‎README.md

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@@ -55,6 +55,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
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- [How to Perform Voice Gender Recognition using TensorFlow in Python](https://www.thepythoncode.com/article/gender-recognition-by-voice-using-tensorflow-in-python). ([code](https://github.com/x4nth055/gender-recognition-by-voice))
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- [Introduction to Finance and Technical Indicators with Python](https://www.thepythoncode.com/article/introduction-to-finance-and-technical-indicators-with-python). ([code](machine-learning/technical-indicators))
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- [Algorithmic Trading with FXCM Broker in Python](https://www.thepythoncode.com/article/trading-with-fxcm-broker-using-fxcmpy-library-in-python). ([code](machine-learning/trading-with-fxcm))
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- [How to Create Plots With Plotly In Python](https://www.thepythoncode.com/article/creating-dynamic-plots-with-plotly-visualization-tool-in-python). ([code](machine-learning/plotly-visualization))
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- ### [General Python Topics](https://www.thepythoncode.com/topic/general-python-topics)
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- [How to Make Facebook Messenger bot in Python](https://www.thepythoncode.com/article/make-bot-fbchat-python). ([code](general/messenger-bot))

‎machine-learning/plotly-visualization/Plotly_Viz.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import plotly.offline as py\n",
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"import plotly.graph_objs as go\n",
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"import plotly.figure_factory as ff\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import yfinance as yf\n",
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"import pandas_datareader as pdr\n",
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"\n",
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"py.init_notebook_mode()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = [ i for i in range(-10,10) ]\n",
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"\n",
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"y = [ i*2 for i in range(-10,10) ]\n",
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"\n",
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"xaxis = go.layout.XAxis(title=\"X Axis\")\n",
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"yaxis = go.layout.YAxis(title=\"Y Axis\")\n",
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"\n",
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"fig = go.Figure(layout=go.Layout(title=\"Simple Line Plot\", xaxis=xaxis, yaxis=yaxis))\n",
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"fig.add_trace(go.Scatter(x=x, y=y))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def sigmoid(x):\n",
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" return 1 / (1 + np.exp((-1) * x))\n",
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"\n",
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"x = sorted(np.random.random(100) * 10 - 5)\n",
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"y = [ sigmoid(i) for i in x ]\n",
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"\n",
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"xaxis = go.layout.XAxis(title=\"X Axis\")\n",
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"yaxis = go.layout.YAxis(title=\"Y Axis\")\n",
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"\n",
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"fig=go.Figure(layout=go.Layout(title=\"Sigmoid Plot\",xaxis=xaxis, yaxis=yaxis))\n",
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"fig.add_trace(go.Scatter(x=x, y=y, marker=dict(color=\"red\")))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"l = []\n",
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"\n",
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"for _ in range(5):\n",
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" l.append([ sorted(np.random.randint(low=0, high=10000, size=50)), sorted(np.random.randint(low=0, high=10000, size=50)) ])\n",
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"\n",
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"l = np.array(l)\n",
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"\n",
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"figure = go.Figure(layout=go.Layout(title=\"Simple Scatter Example\", xaxis=go.layout.XAxis(title=\"X\"), yaxis=go.layout.YAxis(title=\"Y\")))\n",
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"for i in range(len(l)):\n",
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" figure.add_trace(go.Scatter(x=l[i][0],y=l[i][1], mode=\"markers\", name=f\" Distribution {i+1} \"))\n",
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"figure.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dist = np.random.normal(loc=0, scale=1, size=50000)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"figure = go.Figure()\n",
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"figure.add_trace(go.Histogram(x=dist,))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"d=[{\"values\":np.random.normal(0,0.5,10000), \"information\": \" Normal Distribution with mean 0 and std= 0.5\"},\n",
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" {\"values\":np.random.normal(0,1,10000), \"information\": \" Normal Distribution with mean 0 and std= 1\"},\n",
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" {\"values\":np.random.normal(0,1.5,10000), \"information\": \" Normal Distribution with mean 0 and std= 1.5\"},\n",
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" {\"values\":np.random.normal(0,2,10000), \"information\": \" Normal Distribution with mean 0 and std= 2\"},\n",
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" {\"values\":np.random.normal(0,5,10000), \"information\": \" Normal Distribution with mean 0 and std= 5\"}]\n",
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"\n",
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"ff.create_distplot([ele[\"values\"] for ele in d], group_labels=[ele[\"information\"] for ele in d], show_hist=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.random.randint(low=5, high=100, size=15)\n",
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"y = np.random.randint(low=5, high=100 ,size=15)\n",
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"z = np.random.randint(low=5, high=100, size=15)\n",
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"\n",
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"fig = go.Figure()\n",
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"fig.add_trace(go.Scatter3d(x=x, y=y, z=z, mode=\"markers\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_iris = pd.read_csv(\"iris.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig = go.Figure()\n",
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"species_types = df_iris.species.unique().tolist()\n",
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"\n",
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"for specie in species_types:\n",
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" b = df_iris.species == specie\n",
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" fig.add_trace(go.Scatter3d(x=df_iris[\"sepal_length\"][b], y=df_iris[\"sepal_width\"][b], z=df_iris[\"petal_width\"][b], name=specie, mode=\"markers\"))\n",
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"\n",
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"\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"yf.pdr_override()\n",
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"\n",
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"symbols = [\"AAPL\",\"MSFT\"]\n",
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"stocks = []\n",
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"for symbol in symbols:\n",
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" stocks.append(pdr.get_data_yahoo(symbol, start=\"2020-01-01\", end=\"2020-05-31\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig = go.Figure()\n",
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"\n",
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"for stock,symbol in zip(stocks,symbols):\n",
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" fig.add_trace(go.Scatter(x=stock.index, y=stock.Close, name=symbol))\n",
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"\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_aapl = pdr.get_data_yahoo(symbol, start=\"2020-01-01\", end=\"2020-05-31\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"ff.create_candlestick(dates=df_aapl.index, open=df_aapl.Open, high=df_aapl.High, low=df_aapl.Low, close=df_aapl.Close)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎machine-learning/plotly-visualization/README.md

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# [How to Create Plots With Plotly In Python](https://www.thepythoncode.com/article/creating-dynamic-plots-with-plotly-visualization-tool-in-python)
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To run this on a jupyter lab:
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- Install Jupyter Lab
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- Install plotly extension:
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```bash
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$ jupyter labextension install jupyterlab-plotly
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```
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- `pip3 install -r requirements.txt`

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