Merge pull request animator#1323 from manasdeshpande/ScatterplotPlotly

Introduction to Scatter Plots in Plotly

Author: ashitaprasad

contrib/plotting-visualization/images/plotly-basic-scatter-plot.png

@@ -6,9 +6,10 @@
 - [Pie Charts in Matplotlib](matplotlib-pie-charts.md)
 - [Line Charts in Matplotlib](matplotlib-line-plots.md)
 - [Scatter Plots in Matplotlib](matplotlib-scatter-plot.md)
+- [Violin Plots in Matplotlib](matplotlib-violin-plots.md)
 - [Introduction to Seaborn and Installation](seaborn-intro.md)
 - [Seaborn Plotting Functions](seaborn-plotting.md)
 - [Getting started with Seaborn](seaborn-basics.md)
 - [Bar Plots in Plotly](plotly-bar-plots.md)
 - [Pie Charts in Plotly](plotly-pie-charts.md)
-- [Violin Plots in Matplotlib](matplotlib-violin_plots.md)
+- [Scatter Plots in Plotly](plotly-scatter-plots.md)

contrib/plotting-visualization/images/plotly-scatter-colour-2.png

@@ -0,0 +1,198 @@
+# Scatter Plots in Plotly
+
+* 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.
+* The dots on the plot shows how the variables are related. A scatter plot is made with the plotly library's `px.scatter()`.
+
+## Prerequisites
+
+Before creating Scatter plots in Plotly you must ensure that you have Python, Plotly and Pandas installed on your system.
+
+## Introduction
+
+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.
+
+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.
+
+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.
+
+## Creating a simple Scatter Plot using `plotly.express.scatter`
+
+In `plotly.express.scatter`, each data point is represented as a marker point, whose location is given by the x and y columns.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold}
+df = pd.DataFrame(dataset)
+
+# Creating scatter plot
+fig = px.scatter(df, x='Years', y='Number of Cars sold')
+
+# Showing plot
+fig.show()
+```
+![Basic Scatter Plot](images/plotly-basic-scatter-plot.png)
+
+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.
+
+`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.
+
+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.
+
+## Customizing Scatter Plots
+
+### Adding title to the plot
+
+Simply pass the title of your plot as a parameter in `px.scatter`.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold}
+df = pd.DataFrame(dataset)
+
+# Creating scatter plot
+fig = px.scatter(df, x='Years', y='Number of Cars sold' ,title='Number of cars sold in various years')
+
+# Showing plot
+fig.show()
+```
+![Scatter Plot title](images/plotly-scatter-title.png)
+
+### Adding bar colors and legends
+
+* 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.
+* 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:
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil']
+petals = [11,9,17,4,7]
+
+# Converting dataset to pandas DataFrame
+dataset = {'flowers':flowers, 'petals':petals}
+df = pd.DataFrame(dataset)
+
+# Creating pie chart
+fig = px.pie(df, values='petals', names='flowers',
+ title='Number of Petals in Flowers',
+ color_discrete_sequence=px.colors.sequential.Agsunset)
+
+# Showing plot
+fig.show()
+```
+![Scatter Plot Colors-1](images/plotly-scatter-colour.png)
+
+You can also set custom colors for each label by passing it as a dictionary(map) in `color_discrete_map`, like this:
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold}
+df = pd.DataFrame(dataset)
+
+# Creating scatter plot
+fig = px.scatter(df, x='Years', 
+ y='Number of Cars sold' ,
+ title='Number of cars sold in various years',
+ color='Years',
+ color_discrete_map={'1998':'red',
+ '1999':'magenta',
+ '2000':'green',
+ '2001':'yellow',
+ '2002':'royalblue'})
+
+# Showing plot
+fig.show()
+```
+![Scatter Plot Colors-1](images/plotly-scatter-colour-2.png)
+
+### Setting Size of Scatter
+
+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.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold}
+df = pd.DataFrame(dataset)
+
+# Creating scatter plot
+fig = px.scatter(df, x='Years', 
+ y='Number of Cars sold' ,
+ title='Number of cars sold in various years',
+ color='Years',
+ color_discrete_map={'1998':'red',
+ '1999':'magenta',
+ '2000':'green',
+ '2001':'yellow',
+ '2002':'royalblue'},
+ size='Number of Cars sold')
+
+# Showing plot
+fig.show()
+```
+![Scatter plot size](images/plotly-scatter-size.png)
+
+### Giving a hover effect 
+
+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
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold}
+df = pd.DataFrame(dataset)
+
+# Creating scatter plot
+fig = px.scatter(df, x='Years', 
+ y='Number of Cars sold' ,
+ title='Number of cars sold in various years',
+ color='Years',
+ color_discrete_map={'1998':'red',
+ '1999':'magenta',
+ '2000':'green',
+ '2001':'yellow',
+ '2002':'royalblue'},
+ size='Number of Cars sold',
+ hover_name='Years', 
+ hover_data={'Number of Cars sold': True})
+
+# Showing plot
+fig.show()
+```
+![Scatter Hover](images/plotly-scatter-hover.png)
+