Data visualization
You can perform some statistical data visualization by using the seaborn library.
The following examples assume you already have a derived table df by having run code like this:
from fbri.private.sql.query import execute
import pandas as pd
database = "fbri_prod_private"
table = "erc_condor_url_attributes_dp_final_v3"
sql = f"""
SELECT *
FROM {database}.{table}
LIMIT 20
"""
result = execute(sql, "attributes.tsv")
df = pd.read_csv('attributes.tsv', delimiter = '\t')
dfData visualization examples
If a blank cell is not already available, create a new cell by clicking + in the notebook's top navigation bar.
Single value distribution plot
Insert the following code into a blank cell:
import seaborn as sns sns.distplot(df.spam_usr_feedback)
This code imports the seaborn library and then creates a distribution plot from the value counts of the parent_domains column. sns.distplot() can create distribution plots from integer value-based tables.
You can do this for other table columns such as false_news_usr_feedback and hate_speech_usr_feedback by inserting the column name into sns.distplot.
For the false_news_usr_feedback column, it would look like this:
For the hate_speech_usr_feedback column, it would look like this:
Pair plot
To create a pair plot, use the following code:
sns.pairplot(df[['spam_usr_feedback', 'false_news_usr_feedback', 'hate_speech_usr_feedback']])
In this example sns.pairplot() creates a pairplot using the spam_usr_feedback, false_news_usr_feedback, and hate_speech_usr_feedback columns from the df table. Running the code should yield results similar to this:
