docs: replace JSON dataset with Parquet to sidestep error (#580)

* Replace JSON dataset with Parquet to sidestep error

* Update data-read stmt in LaTeX examples .qmd file

* Update file path in LaTeX example

* Add .ndjson coffee-sales data (remove parquet file)

* Update LaTeX coffee sales example

* Update coffee sales docs site example

docs/a-latex_examples/index.qmd

@@ -448,7 +448,7 @@ import polars as pl
 import polars.selectors as cs
 from great_tables import GT, loc, style
 
-coffee_sales = pl.DataFrame.deserialize("../examples/_data/coffee-sales.json", format="json")
+coffee_sales = pl.read_ndjson("../examples/_data/coffee-sales.ndjson")
 
 sel_rev = cs.starts_with("revenue")
 sel_prof = cs.starts_with("profit")
@@ -491,7 +491,7 @@ print(
         locations=loc.body(rows=pl.col("product") == "Total"),
     )
     # .fmt_nanoplot("monthly_sales", plot_type="bar")
-    #.fmt_image("icon", path="docs/examples/_data/coffee-table-icons/")
+    # .fmt_image("icon", path="docs/examples/_data/coffee-table-icons/")
     .sub_missing(missing_text="")
     .as_latex()
 )

docs/examples/_data/coffee-sales.ndjson

@@ -0,0 +1,14 @@
+{"icon":"grinder.png","product":"Grinder","revenue_dollars":904500.0,"revenue_pct":0.03,"profit_dollars":567960.0,"profit_pct":0.04,"monthly_sales":[521,494,596,613,667,748,765,686,607,594,568,751]}
+{"icon":"moka-pot.png","product":"Moka pot","revenue_dollars":2045250.0,"revenue_pct":0.07,"profit_dollars":181080.0,"profit_pct":0.01,"monthly_sales":[4726,4741,4791,5506,6156,6619,6868,6026,5304,4884,4648,6283]}
+{"icon":"cold-brew.png","product":"Cold brew","revenue_dollars":288750.0,"revenue_pct":0.01,"profit_dollars":241770.0,"profit_pct":0.02,"monthly_sales":[244,249,438,981,1774,2699,2606,2348,1741,896,499,244]}
+{"icon":"filter.png","product":"Filter","revenue_dollars":404250.0,"revenue_pct":0.01,"profit_dollars":70010.0,"profit_pct":0.0,"monthly_sales":[2067,1809,1836,2123,2252,2631,2562,2367,2164,2195,2070,2744]}
+{"icon":"drip-machine.png","product":"Drip machine","revenue_dollars":2632000.0,"revenue_pct":0.09,"profit_dollars":1374450.0,"profit_pct":0.09,"monthly_sales":[2137,1623,1971,2097,2580,2456,2336,2316,2052,1967,1837,2328]}
+{"icon":"aeropress.png","product":"AeroPress","revenue_dollars":2601500.0,"revenue_pct":0.09,"profit_dollars":1293780.0,"profit_pct":0.09,"monthly_sales":[6332,5199,6367,7024,7906,8704,8693,7797,6828,6963,6877,9270]}
+{"icon":"pour-over.png","product":"Pour over","revenue_dollars":846000.0,"revenue_pct":0.03,"profit_dollars":364530.0,"profit_pct":0.02,"monthly_sales":[1562,1291,1511,1687,1940,2177,2141,1856,1715,1806,1601,2165]}
+{"icon":"french-press.png","product":"French press","revenue_dollars":1113250.0,"revenue_pct":0.04,"profit_dollars":748120.0,"profit_pct":0.05,"monthly_sales":[3507,2880,3346,3792,3905,4095,4184,4428,3279,3420,3297,4819]}
+{"icon":"cezve.png","product":"Cezve","revenue_dollars":2512500.0,"revenue_pct":0.09,"profit_dollars":1969520.0,"profit_pct":0.13,"monthly_sales":[12171,11469,11788,13630,15391,16532,17090,14433,12985,12935,11598,15895]}
+{"icon":"chemex.png","product":"Chemex","revenue_dollars":3137250.0,"revenue_pct":0.11,"profit_dollars":817680.0,"profit_pct":0.06,"monthly_sales":[4938,4167,5235,6000,6358,6768,7112,6249,5605,6076,4980,7220]}
+{"icon":"scale.png","product":"Scale","revenue_dollars":3801000.0,"revenue_pct":0.13,"profit_dollars":2910290.0,"profit_pct":0.2,"monthly_sales":[1542,1566,1681,2028,2425,2549,2569,2232,2036,2089,1693,3180]}
+{"icon":"kettle.png","product":"Kettle","revenue_dollars":756250.0,"revenue_pct":0.03,"profit_dollars":617520.0,"profit_pct":0.04,"monthly_sales":[1139,1023,1087,1131,1414,1478,1456,1304,1140,1233,1193,1529]}
+{"icon":"espresso-machine.png","product":"Espresso Machine","revenue_dollars":8406000.0,"revenue_pct":0.29,"profit_dollars":3636440.0,"profit_pct":0.25,"monthly_sales":[686,840,618,598,2148,533,797,996,1002,668,858,2577]}
+{"icon":null,"product":"Total","revenue_dollars":29448500.0,"revenue_pct":1.0,"profit_dollars":14793150.0,"profit_pct":1.0,"monthly_sales":null}

docs/examples/index.qmd

@@ -361,7 +361,7 @@ import polars as pl
 import polars.selectors as cs
 from great_tables import GT, loc, style
 
-coffee_sales = pl.DataFrame.deserialize("./_data/coffee-sales.json", format="json")
+coffee_sales = pl.read_ndjson("./_data/coffee-sales.ndjson")
 
 sel_rev = cs.starts_with("revenue")
 sel_prof = cs.starts_with("profit")