def test_stocks_column_names():
stocks = data.stocks()
assert type(stocks) is pd.DataFrame
assert sorted(stocks.columns) == ["date", "price", "symbol"]
stocks = data.stocks.raw()
assert type(stocks) is bytes
def test_stocks_column_names():
stocks = data.stocks()
assert type(stocks) is pd.DataFrame
assert tuple(stocks.columns) == ('symbol', 'date', 'price')
stocks = data.stocks.raw()
assert type(stocks) is bytes
def test_stocks_column_names():
stocks = data.stocks()
assert type(stocks) is pd.DataFrame
assert sorted(stocks.columns) == ['date', 'price', 'symbol']
stocks = data.stocks.raw()
assert type(stocks) is bytes
'''
Trellis Area Sort Chart
-----------------------
This example shows small multiples of an area chart.
Stock prices of four large companies
sorted by `['MSFT', 'AAPL', 'IBM', 'AMZN']`
'''
# category: area charts
import altair as alt
from vega_datasets import data
source = data.stocks()
alt.Chart(source).transform_filter(
alt.datum.symbol != 'GOOG'
).mark_area().encode(
x='date:T',
y='price:Q',
color='symbol:N',
row=alt.Row('symbol:N', sort=['MSFT', 'AAPL', 'IBM', 'AMZN'])
).properties(height=50, width=400)
import altair as alt from vega_datasets import data stocks = data.stocks() ___ = alt.Chart(stocks).mark_line().encode(x='date', y='price', color='symbol') ___ + ___
def test_download_stock_parsing():
stocks = data.stocks(use_local=False)
assert all(stocks.dtypes == ['object', 'datetime64[ns]', 'float64'])
def test_stock_pivoted():
stocks = data.stocks(pivoted=True)
assert stocks.index.name == 'date'
assert all(stocks.columns == ['AAPL', 'AMZN', 'GOOG', 'IBM', 'MSFT'])
def test_stock_date_parsing():
stocks = data.stocks()
assert all(stocks.dtypes == ['object', 'datetime64[ns]', 'float64'])
def test_download_stock_parsing():
stocks = data.stocks(use_local=False)
assert all(stocks.dtypes == ["object", "datetime64[ns]", "float64"])
def test_stock_pivoted():
stocks = data.stocks(pivoted=True)
assert stocks.index.name == "date"
assert sorted(stocks.columns) == ["AAPL", "AMZN", "GOOG", "IBM", "MSFT"]
def test_stock_date_parsing():
stocks = data.stocks()
assert all(stocks.dtypes == ["object", "datetime64[ns]", "float64"])