Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. show ( )Įinblick is an AI-native data science platform that provides data teams with an agile workflow to swiftly explore data, build predictive models, and deploy data apps. scatterplot ( x = filtered_df, y = filtered_df, hue = filtered_df, palette = ) plt. # Create the plot using seaborn scatterplot sns. # CHANGE: Gold is the biggest marker, bronze is the smallest # Import the necessary libraries import seaborn as sns Use custom color palette: Gold = gold, Silver = silver, Bronze = bronze. Gold is the biggest marker, bronze is the smallest # PROMPT: Plot height and weight using seaborn, color and size by medal. Here, we added an additional prompt via "Change this cell" to ensure that the sizing of the data points made sense for the use-case. Plot height and weight using seaborn, color and size by medal. If the default color palette or sizing doesn't make sense for a use-case (such as this one, where the groupings are colors, and there is an order that is not alphabetical), you can ask Prompt to use a custom color palette and sizing that makes sense. title ( 'Distribution of Height and Weight among Medal Winners' ) # Show the plot plt. scatterplot ( data = filtered_df, x = 'Height', y = 'Weight', hue = 'Medal', size = 'Medal' ) # Add labels and title to the plot plt. # Create a scatter plot using seaborn's scatterplot function sns. Plot height and weight using seaborn, color and size by medal # PROMPT: Plot height and weight using seaborn, color and size by medal # Import the necessary libraries import seaborn as sns If you want more distinction between the groups, you can double-down, and alter the color and size of the data points. title ( 'Scatterplot of Height and Weight' ) # Display the plot plt. scatterplot ( data = filtered_df, x = 'Height', y = 'Weight', hue = 'Medal', alpha = 0.5 ) # Set alpha value to make data points transparent # Add labels to the x-axis, y-axis, and title of the plot plt. # Create a scatterplot using seaborn's scatterplot function sns. Then with the newly duplicated plot, select Prompt > Change this cell Make the data points transparent # PROMPT: Duplicate the above plot # CHANGE: Make the data points transparent # Import the necessary libraries import seaborn as sns Start by selecting Prompt > Add new cell below Duplicate the above plot This helps in side-by-side comparisons, if you're determining which is the best version to present to stakeholders, for example. Since Prompt is able to take in natural language queries, we can also ask Prompt to duplicate any plot, and then adjust it as we like. But the data points are highly clustered, particularly in the middle, so let's see if adjusting the transparency will help get a clearer image of the data. This plot now allows us to compare different groups, and it seems there is an equal distribution of medals across the height and weight of the athletes. scatterplot ( data = filtered_df, x = 'Height', y = 'Weight', hue = 'Medal' ) # Add labels to the x-axis, y-axis, and title of the plot plt. # PROMPT: Create a scatterplot of height and weight, using seaborn, color by medal # Import the necessary libraries import seaborn as sns In Einblick, Prompt will automatically pip install and import any missing libraries. In this case, we asked Prompt to use a specific library, and to color code the plot. Prompt 2: Color-coded scatter plot Create a scatterplot of height and weight, using seaborn, color by medalĪs you can see, you can add on your requests directly in the prompt. Change the size, shape, or color of the data pointsĮinblick Prompt has you covered-no need to fuss with finnicky syntax!.Color parts of the graph differently based on a different variable.This is a great first plot, but there is a lot more information that a simple scatter plot can show, and a lot more ways you may want to customize your plot. scatter ( filtered_df, filtered_df ) plt. # Plotting height against weight using scatter plot plt. # PROMPT: Plot height vs weight import matplotlib. Prompt will infer that you want a plot a scatter plot based on the context. NOTE: replace height and weight with whatever numerical variables are in your particular dataset. Since the most basic scatter plots are used for visualizing the relationship between two numerical variables, you can simply ask Prompt the following: Plot height vs weight
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