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from plotly.offline import plot, iplot, init_notebook_mode

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import plotly.graph_objects as go

from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import plotly
import plotly.graph_objects as go
import plotly.express as px
values = [['District', 'District', 'District', 'District', 'District', 'District', 'District', 'Product', 'Product', 'Product', 'Product', 'Product', 'Product', 'Engagement', 'Engagement', 'Engagement', 'Engagement'], ['district_id', 'state', 'locale', 'pct_black/hispanic', 'pct_free/reduced', 'countyconnectionsratio', 'pptotalraw', 'LP ID', 'URL', 'Product Name', 'Provider/Company Name', 'Sector(s)', 'Primary Essential Function', 'time', 'lp_id', 'pct_access', 'engagement_index'], ['The unique identifier of the school district', 'The state where the district resides in', 'NCES locale classification that categorizes U.S. territory into four types of areas: City, Suburban, Town, and Rural.', 'Percentage of students in the districts identified as Black or Hispanic based on 2018-19 NCES data', 'Percentage of students in the districts eligible for free or reduced-price lunch based on 2018-19 NCES data', 'ratio (residential fixed high-speed connections over 200 kbps in at least one direction/households) based on the county level data from FCC From 477 (December 2018 version)', "Per-pupil total expenditure (sum of local and federal expenditure) from Edunomics Lab's National Education Resource Database on Schools (NERD$) project. The expenditure data are school-by-school, and we use the median value to represent the expenditure of a given school district.", 'The unique identifier of the product', 'Web Link to the specific product', 'Name of the specific product', 'Name of the product provider', 'Sector of education where the product is used', 'The basic function of the product. There are two layers of labels here. Products are first labeled as one of these three categories: ,<b>LC = Learning & Curriculum, CM = Classroom Management<b>, and <b>SDO = School & District Operations<b>. Each of these categories have multiple sub-categories with which the products were labeled', 'date in YYYY-MM-DD', 'The unique identifier of the product', 'Percentage of students in the district have at least one page-load event of a given product and on a given day', 'Total page-load events per one thousand students of a given product and on a given day']]
fig = go.Figure(data=[go.Table(columnorder=[1, 2, 3], columnwidth=[60, 80, 400], header=dict(values=[['<b>Dataset</b>'], ['<b>Columns Names</b>'], ['<b>Description</b>']], line_color='darkslategray', fill_color='royalblue', align=['left', 'center'], font=dict(color='white', size=15), height=40), cells=dict(values=values, line_color='darkslategray', fill=dict(color=['paleturquoise', 'white']), align=['left', 'center'], font_size=12, height=30))])

import pandas as pd
district_data = pd.read_csv('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
fig = go.Figure(data=[go.Table(header=dict(values=list(district_data.columns), fill_color='paleturquoise', align='left'), cells=dict(values=[district_data['district_id'], district_data['state'], district_data['locale'], district_data['pct_black/hispanic'], district_data['pct_free/reduced'], district_data['county_connections_ratio'], district_data['pp_total_raw']], fill_color=[['white', 'lavender'] * len(district_data)], align='left'))])
fig.show()
product_data = pd.read_csv('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
fig = go.Figure(data=[go.Table(header=dict(values=list(product_data.columns), fill_color='paleturquoise', align='left'), cells=dict(values=[product_data['LP ID'], product_data['URL'], product_data['Product Name'], product_data['Provider/Company Name'], product_data['Sector(s)'], product_data['Primary Essential Function']], fill_color=[['white', 'lavender'] * len(product_data)], align='left'))])
fig.show()