To draw a Seaborn plot from a dictionary data make sure to follow the steps outlined below
Step 1: Import Pandas and Seaborn
Out first step will be to import the 3rd party packages we will be working with today: pandas and Seaborn. Type the following command into your Jupyter Notebook , Colab, PyCharm or other Python dev environment you are using:
import pandas as pd import seaborn as sns
Make sure both are correctly installed in your development environment before moving on.
Step 2: Build your dictionary
Next, we will quickly define a dictionary object . In this example, we will create a few Python lists and use them to build the campaign dictionary:
month = ['March', 'July', 'July', 'September', 'November', 'December'] language = ['R', 'R', 'Python', 'Python', 'R', 'R'] interviews = [13, 12, 8, 14, 19, 15] campaign = dict(month = month, language = language, interviews = interviews)
Steps 3: Create a DataFrame
Now that we have our dictionary, we can go ahead and create a pandas DataFrame that we will use to feed Seaborn with:
hrdf = pd.DataFrame(data=campaign)
The DataFrame contains and index column and 3 data columns:
Step 4: Plot a Seaborn chart
Next, we will go ahead and render a bar chart using Seaborn.
bar = sns.barplot(data = hrdf, x = 'language', y= 'interviews', hue = 'month', palette = 'Paired'); #1 bar.set_title("Interview metrics"); #2 bar.legend(bbox_to_anchor= (1.01, 1)); #3
- Row #1 defines the barplot object. It assigns the DataFrame that we have created to the chart, links the respective DataFrame columns to the axis and define a color palette.
- Row #2 defines a title for the plot
- Row #3 adjusts the legend placement to the right hand side of the chart.
This will create the following bar plot.
Note: We can use similar techniques to build any kind of Searborn charts such as histograms, column charts, box plots etc’.