Create plots using plotly library in Python

Create plots using plotly library in Python

1. Create a basic scatter plot with Plotly to display the data using x and y axes.

For example, this code shows how to plot a basic scatter plot where x and y are given as array_like objects: # x and y given as array_like objects import plotly.express as px fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16]) fig.show() Add labels to the scatter plot to show the x-axis and y-axis data. For example: # x and y given as DataFrame columns import plotly.express as px df = px.data.iris() # iris is a pandas DataFrame fig = px.scatter(df, x="sepal_width", y="sepal_length") fig.show()

2. Use the iris method within the dataframe and plotly library to display data in various colors to represent the type of species from the sample data.

In the example below, the iris method is represented by data.iris() and is used to set the size, color, and line widths for the different columns representing different species: import plotly.express as px df = px.data.iris() fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", size='petal_length', hover_data=['petal_width']) fig.show()

3. Use the prepackaged functions available in the Plotly library to create a line plot with Plotly Express.

This is a line graph plotted with the prepackaged functions available in the Plotly library: import plotly.express as px import numpy as np t = np.linspace(0, 2*np.pi, 100) fig = px.line(x=t, y=np.cos(t), labels={'x':'t', 'y':'cos(t)'}) fig.show()

4. Use Plotly graph objects and the sample dataset available in the Plotly library to create line and scatter plots.

The example below showes a line and scatter plot created using Plotly graph objects: import plotly.graph_objects as go # Create random data with numpy import numpy as np np.random.seed(1) N = 100 random_x = np.linspace(0, 1, N) random_y0 = np.random.randn(N) + 5 random_y1 = np.random.randn(N) random_y2 = np.random.randn(N) - 5 fig = go.Figure() # Add traces fig.add_trace(go.Scatter(x=random_x, y=random_y0, mode='markers', name='markers')) fig.add_trace(go.Scatter(x=random_x, y=random_y1, mode='lines+markers', name='lines+markers')) fig.add_trace(go.Scatter(x=random_x, y=random_y2, mode='lines', name='lines')) fig.show()

5. Use Plotly Express built-in functions to create statistical charts such as box plots, histograms and distribution plots for statistical analysis.

For example, the following is a histogram with a predefined dataframe showing the total_bill (x axis) and count (y axis) created using Plotly Express’ built-in functions: import plotly.express as px df = px.data.tips() fig = px.histogram(df, x="total_bill") fig.show() Output: The next example is a histogram with columns populated with categorical data and displayed by day: import plotly.express as px df = px.data.tips() fig = px.histogram(df, x="day") fig.show()

6. Create histograms using the Dash library and sample dataset within the Plotly library.

The following example is a histogram created using Dash with parameters to adjust mean and standard deviation: import dash import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output import plotly.express as px import numpy as np np.random.seed(2020) app = dash.Dash(__name__) app.layout = html.Div([ dcc.Graph(id="graph"), html.P("Mean:"), dcc.Slider(id="mean", min=-3, max=3, value=0, marks={-3: '-3', 3: '3'}), html.P("Standard Deviation:"), dcc.Slider(id="std", min=1, max=3, value=1, marks={1: '1', 3: '3'}), ]) @app.callback( Output("graph", "figure"), [Input("mean", "value"), Input("std", "value")]) def display_color(mean, std): data = np.random.normal(mean, std, size=500) fig = px.histogram(data, nbins=30, range_x=[-10, 10]) return fig app.run_server(debug=True) Output:

7. Use the sample dataset within the Plotly library to create funnel charts.

Funnel charts are extremely useful to marketers, as they can be used to better illustrate lead conversion metrics. Below is an example of a funnel chart showing website visits (shown in numbers) per funnel stage (shown as variable stages): import plotly.express as px data = dict( number=[39, 27.4, 20.6, 11, 2], stage=["Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent"]) fig = px.funnel(data, x='number', y='stage') fig.show() Output: The texposition and textinfo libraries within Plotly’s funnel chart library can also be used to show different funnel steps in different colors. Below is an example of how you could customize a funnel chart: from plotly import graph_objects as go fig = go.Figure(go.Funnel( y = ["Website visit", "Downloads", "Potential customers", "Requested price", "Finalized"], x = [39, 27.4, 20.6, 11, 2], textposition = "inside", textinfo = "value+percent initial", opacity = 0.65, marker = {"color": ["deepskyblue", "lightsalmon", "tan", "teal", "silver"], "line": {"width": [4, 2, 2, 3, 1, 1], "color": ["wheat", "wheat", "blue", "wheat", "wheat"]}}, connector = {"line": {"color": "royalblue", "dash": "dot", "width": 3}}) ) fig.show() Output: