kopia lustrzana https://github.com/animator/learn-python
199 wiersze
7.8 KiB
Markdown
199 wiersze
7.8 KiB
Markdown
# Scatter Plots in Plotly
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* A scatter plot is a type of data visualization that uses dots to show values for two variables, with one variable on the x-axis and the other on the y-axis. It's useful for identifying relationships, trends, and correlations, as well as spotting clusters and outliers.
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* The dots on the plot shows how the variables are related. A scatter plot is made with the plotly library's `px.scatter()`.
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## Prerequisites
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Before creating Scatter plots in Plotly you must ensure that you have Python, Plotly and Pandas installed on your system.
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## Introduction
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There are various ways to create Scatter plots in `plotly`. One of the prominent and easiest one is using `plotly.express`. Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. On the other hand you can also use `plotly.graph_objects` to create various plots.
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Here, we'll be using `plotly.express` to create the Scatter Plots. Also we'll be converting our datasets into pandas DataFrames which makes it extremely convenient and easy to create charts.
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Also, note that when you execute the codes in a simple python file, the output plot will be shown in your **browser**, rather than a pop-up window like in matplotlib. If you do not want that, it is **recommended to create the plots in a notebook (like jupyter)**. For this, install an additional library `nbformat`. This way you can see the output on the notebook itself, and can also render its format to png, jpg, etc.
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## Creating a simple Scatter Plot using `plotly.express.scatter`
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In `plotly.express.scatter`, each data point is represented as a marker point, whose location is given by the x and y columns.
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```Python
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import plotly.express as px
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import pandas as pd
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# Creating dataset
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years = ['1998', '1999', '2000', '2001', '2002']
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num_of_cars_sold = [200, 300, 500, 700, 1000]
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# Converting dataset to pandas DataFrame
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dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold}
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df = pd.DataFrame(dataset)
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# Creating scatter plot
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fig = px.scatter(df, x='Years', y='Number of Cars sold')
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# Showing plot
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fig.show()
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```
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Here, we are first creating the dataset and converting it into a pandas DataFrame using a dictionary, with its keys being DataFrame columns. Next, we are plotting the scatter plot by using `px.scatter`. In the `x` and `y` parameters, we have to specify a column name in the DataFrame.
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`px.scatter(df, x='Years', y='Number of Cars sold')` is used to specify that the scatter plot is to be plotted by taking the values from column `Years` for the x-axis and the values from column `Number of Cars sold` for the y-axis.
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Note: When you generate the image using the above code, it will show you an interactive plot. If you want an image, you can download it from the interactive plot itself.
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## Customizing Scatter Plots
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### Adding title to the plot
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Simply pass the title of your plot as a parameter in `px.scatter`.
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```Python
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import plotly.express as px
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import pandas as pd
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# Creating dataset
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years = ['1998', '1999', '2000', '2001', '2002']
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num_of_cars_sold = [200, 300, 500, 700, 1000]
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# Converting dataset to pandas DataFrame
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dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold}
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df = pd.DataFrame(dataset)
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# Creating scatter plot
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fig = px.scatter(df, x='Years', y='Number of Cars sold' ,title='Number of cars sold in various years')
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# Showing plot
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fig.show()
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```
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### Adding bar colors and legends
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* To add different colors to different bars, simply pass the column name of the x-axis or a custom column which groups different bars in `color` parameter.
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* There are a lot of beautiful color scales available in plotly and can be found here [plotly color scales](https://plotly.com/python/builtin-colorscales/). Choose your favourite colorscale apply it like this:
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```Python
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import plotly.express as px
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import pandas as pd
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# Creating dataset
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flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil']
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petals = [11,9,17,4,7]
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# Converting dataset to pandas DataFrame
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dataset = {'flowers':flowers, 'petals':petals}
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df = pd.DataFrame(dataset)
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# Creating pie chart
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fig = px.pie(df, values='petals', names='flowers',
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title='Number of Petals in Flowers',
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color_discrete_sequence=px.colors.sequential.Agsunset)
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# Showing plot
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fig.show()
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```
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You can also set custom colors for each label by passing it as a dictionary(map) in `color_discrete_map`, like this:
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```Python
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import plotly.express as px
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import pandas as pd
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# Creating dataset
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years = ['1998', '1999', '2000', '2001', '2002']
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num_of_cars_sold = [200, 300, 500, 700, 1000]
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# Converting dataset to pandas DataFrame
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dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold}
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df = pd.DataFrame(dataset)
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# Creating scatter plot
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fig = px.scatter(df, x='Years',
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y='Number of Cars sold' ,
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title='Number of cars sold in various years',
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color='Years',
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color_discrete_map={'1998':'red',
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'1999':'magenta',
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'2000':'green',
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'2001':'yellow',
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'2002':'royalblue'})
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# Showing plot
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fig.show()
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```
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### Setting Size of Scatter
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We may want to set the size of different scatters for visibility differences between categories. This can be done by using the `size` parameter in `px.scatter`, where we specify a column in the DataFrame that determines the size of each scatter point.
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```Python
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import plotly.express as px
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import pandas as pd
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# Creating dataset
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years = ['1998', '1999', '2000', '2001', '2002']
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num_of_cars_sold = [200, 300, 500, 700, 1000]
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# Converting dataset to pandas DataFrame
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dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold}
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df = pd.DataFrame(dataset)
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# Creating scatter plot
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fig = px.scatter(df, x='Years',
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y='Number of Cars sold' ,
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title='Number of cars sold in various years',
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color='Years',
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color_discrete_map={'1998':'red',
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'1999':'magenta',
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'2000':'green',
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'2001':'yellow',
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'2002':'royalblue'},
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size='Number of Cars sold')
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# Showing plot
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fig.show()
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```
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### Giving a hover effect
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you can use the `hover_name` and `hover_data` parameters in `px.scatter`. The `hover_name` parameter specifies the column to use for the `hover text`, and the `hover_data` parameter allows you to specify additional data to display when hovering over a point
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```Python
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import plotly.express as px
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import pandas as pd
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# Creating dataset
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years = ['1998', '1999', '2000', '2001', '2002']
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num_of_cars_sold = [200, 300, 500, 700, 1000]
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# Converting dataset to pandas DataFrame
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dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold}
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df = pd.DataFrame(dataset)
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# Creating scatter plot
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fig = px.scatter(df, x='Years',
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y='Number of Cars sold' ,
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title='Number of cars sold in various years',
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color='Years',
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color_discrete_map={'1998':'red',
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'1999':'magenta',
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'2000':'green',
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'2001':'yellow',
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'2002':'royalblue'},
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size='Number of Cars sold',
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hover_name='Years',
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hover_data={'Number of Cars sold': True})
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# Showing plot
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fig.show()
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```
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