#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import lux

url = 'https://github.com/lux-org/lux-datasets/blob/master/data/olympic.csv?raw=true'
df = pd.read_csv(url)
df["Year"] = pd.to_datetime(df["Year"], format='%Y') # change pandas dtype for the column "Year" to datetype


# In the earlier tutorials, we have seen how Lux recommends visualizations automatically to the user. Often, the user might have a particular visualizations in mind that they want to specify. In this case, users can quickly define their own visualizations using Lux and visualize their data on-demand.
# 
# In this tutorial, we will introduce how to create a visualization via the `Vis` object and a collection of visualization via the `VisList` object. 

# # Creating Desired Visualizations On-Demand using `Vis`

# A `Vis` object represents an individual visualization displayed in Lux, which can either be automatically generated or defined by the user.
# 
# To generate a `Vis`, users should specify their intent and a source dataframe as inputs. The intent is expressed using the same intent specification language described in the last tutorial. 
# 
# For example, here we indicate our intent for visualizing the `Weight` attribute on the dataframe `df`.

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from lux.vis.Vis import Vis
intent = ["Weight"]
vis = Vis(intent,df)
vis


# We can very easily replace the Vis's source data without changing the `Vis` definition, which is useful for comparing differences across different datasets with the same schema. 
# 
# For example, we might be interested in the same `Weight` distribution, but plotted only on the subset of data with female athletes.

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vis.refresh_source(df[df["Sex"]=='F'])
vis


# Likewise, we can modify the intent of the query, in this case, to increase the bin size of the histogram and to indicate the filtered source:

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new_intent = [lux.Clause("Weight",bin_size=50),"Sex=F"]
vis.set_intent(new_intent)
vis


# `Vis` objects are powerful programmatic representations of visualizations that can be exported into visualization code (more in the next tutorial) or be composed into a `VisList` collection.

# # Working with Collections of Visualization with `VisList`

# `VisList` objects represent collections of visualizations in Lux.
# 
# There are two ways to specify lists of visualization in Lux: 1) by specifying intent or 2) by manually composing `Vis` object into a list.

# ### Approach #1: Specifying `VisList` using intent syntax

# First, we look at an example of a `VisList` created through a user intent. Here, we create a vis collection of `Weight` with respect to all other attributes, using the wildcard "?" symbol.

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from lux.vis.VisList import VisList
vc = VisList(["Weight","?"],df)
vc


# Alternatively, we can specify desired attributes via a list with respect to `Weight`: 

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vc = VisList(["Weight",['Sport','Year','Height','HostRegion','SportType']],df)
vc


# ### Approach #2: Specifying `VisList` by constructing `Vis` objects

# `VisList` can be manually constructed by individually specifying the content of each `Vis`, then finally putting the entire list into a `VisList` object.
# 
# Here is the equivalent `VisList` example constructed using this approach:

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from lux.vis.VisList import VisList

vcLst = []
for attribute in ['Sport','Year','Height','HostRegion','SportType']: 
    vis = Vis([lux.Clause("Weight"), lux.Clause(attribute)])
    vcLst.append(vis)
vc = VisList(vcLst,df)

vc