Upgrading to 1.0.0. Here's some tips.

[KingWebsites]

Hi, I've just upgraded to 1.0.0 and I thought I'd list a couple of the changes I did to get my project working again. Hopefully it will save some of you some time. Please feel free to list more below.

Please read the official migration guide here, which has lots of advice.

1. camelCase. These were a few of the things I had to change:

Concatenating: "df_list" to "dfList"

const newDF = dfd.concat({ df_list: [dataFrameA.column("COL A"), dataFrameB.column("COL B")], axis: 1}) // old
const newDF = dfd.concat({ dfList: [dataFrameA.column("COL A"), dataFrameB.column("COL B")], axis: 1}) // new

*note. I'm not sure why my "axis: 1" worked before.
**note. Concat now removes index names so you have to store and add them back in with setIndex.

Counts of nUnique items: "nunique" to "nUnique" & "value_counts()" to "valueCounts()"

const count = dataFrame.nunique(0).value_counts().values[0]; // old
const count = dataFrame.nUnique(1).valueCounts().values[0]; // new

*note. axis value also changed from 0 to 1.

setIndex

dataFrame.set_index({
dataFrame.setIndex({

2. loc column and row arguments don't accept basic variables like before. They need to be wrapped as a "template literal".

const date = "2021-12-14"
const rowDataFrame = dataFrame.loc({ rows: [date] }); // old
const rowDataFrame = dataFrame.loc({ rows: [`"${date}"`] }); // new

const date = "2021-12-14"
const cellValue = dataFrame.loc({ rows: [date], columns: ["Col B"]} ).values.flat()[0]; // old
const cellValue = dataFrame.loc({ rows: [`"${date}"`], columns: ["Col B"]} ).values.flat()[0]; // new

*note. Should I be using a different method just to get the single value of cell X/Y?

[steveoni]

@KingWebsites thanks for the notice. what do you mean by this *note. I'm not sure why my "axis: 1" worked before. ?

and for the index name, it will be added back

[KingWebsites]

Hi.

Re point 1, I was comparing my new commit and I noticed that I hadn't changed the axis value yet it still worked. Strange.

As for the index being removed when you concat maybe this is a bug? The row names get lost when I concat but they never used to. Here's some some test code which shows the problem:

let data_a = {
	"Name": ["Apples", "Mango", "Banana", "Pear"],
	"Count": [21, 5, 30, 10],
	"Price": [200, 300, 40, 250]
}
let df_a = new dfd.DataFrame(data_a, { index: ["Row A", "Row B", "Row C", "Row D"] })
df_a.print();

let data_b = {
	"Colour": ["Green", "Mango", "Yellow", "Green"],
	"Taste": [8, 9, 7, 6]
}
let df_b = new dfd.DataFrame(data_b, { index: ["Row A", "Row B", "Row C", "Row D"] })
df_b.print();

let df_joined = dfd.concat({ dfList: [df_a, df_b], axis: 1})
df_joined.print();

╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║            │ Name              │ Count             │ Price             ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ Row A      │ Apples            │ 21                │ 200               ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ Row B      │ Mango             │ 5                 │ 300               ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ Row C      │ Banana            │ 30                │ 40                ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ Row D      │ Pear              │ 10                │ 250               ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
╔════════════╤═══════════════════╤═══════════════════╗
║            │ Colour            │ Taste             ║
╟────────────┼───────────────────┼───────────────────╢
║ Row A      │ Green             │ 8                 ║
╟────────────┼───────────────────┼───────────────────╢
║ Row B      │ Mango             │ 9                 ║
╟────────────┼───────────────────┼───────────────────╢
║ Row C      │ Yellow            │ 7                 ║
╟────────────┼───────────────────┼───────────────────╢
║ Row D      │ Green             │ 6                 ║
╚════════════╧═══════════════════╧═══════════════════╝
╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║            │ Name              │ Count             │ Price             │ Colour            │ Taste             ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0          │ Apples            │ 21                │ 200               │ Green             │ 8                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1          │ Mango             │ 5                 │ 300               │ Mango             │ 9                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2          │ Banana            │ 30                │ 40                │ Yellow            │ 7                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 3          │ Pear              │ 10                │ 250               │ Green             │ 6                 ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

[risenW]

Hi.

Re point 1, I was comparing my new commit and I noticed that I hadn't changed the axis value yet it still worked. Strange.

As for the index being removed when you concat maybe this is a bug? The row names get lost when I concat but they never used to. Here's some some test code which shows the problem:

let data_a = {
	"Name": ["Apples", "Mango", "Banana", "Pear"],
	"Count": [21, 5, 30, 10],
	"Price": [200, 300, 40, 250]
}
let df_a = new dfd.DataFrame(data_a, { index: ["Row A", "Row B", "Row C", "Row D"] })
df_a.print();

let data_b = {
	"Colour": ["Green", "Mango", "Yellow", "Green"],
	"Taste": [8, 9, 7, 6]
}
let df_b = new dfd.DataFrame(data_b, { index: ["Row A", "Row B", "Row C", "Row D"] })
df_b.print();

let df_joined = dfd.concat({ dfList: [df_a, df_b], axis: 1})
df_joined.print();

╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║            │ Name              │ Count             │ Price             ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ Row A      │ Apples            │ 21                │ 200               ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ Row B      │ Mango             │ 5                 │ 300               ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ Row C      │ Banana            │ 30                │ 40                ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ Row D      │ Pear              │ 10                │ 250               ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
╔════════════╤═══════════════════╤═══════════════════╗
║            │ Colour            │ Taste             ║
╟────────────┼───────────────────┼───────────────────╢
║ Row A      │ Green             │ 8                 ║
╟────────────┼───────────────────┼───────────────────╢
║ Row B      │ Mango             │ 9                 ║
╟────────────┼───────────────────┼───────────────────╢
║ Row C      │ Yellow            │ 7                 ║
╟────────────┼───────────────────┼───────────────────╢
║ Row D      │ Green             │ 6                 ║
╚════════════╧═══════════════════╧═══════════════════╝
╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║            │ Name              │ Count             │ Price             │ Colour            │ Taste             ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0          │ Apples            │ 21                │ 200               │ Green             │ 8                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1          │ Mango             │ 5                 │ 300               │ Mango             │ 9                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2          │ Banana            │ 30                │ 40                │ Yellow            │ 7                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 3          │ Pear              │ 10                │ 250               │ Green             │ 6                 ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

Yeah, that works because some methods (concat) already used the correct axis.

Overall, thanks for sharing this!

[KingWebsites]

FYI: my concat code example above was to show how the row names (indexes) disappear when you join two dataframes together.

[risenW]

Oh I see, it's a bug. @steveoni is working on it

Issue created here: #349.

@KingWebsites Are you interested in adding this new feature?

[risenW]

Regarding your point on:

Should I be using a different method just to get the single value of cell X/Y?

I think we need to add a new method for that. Something like iat in pandas. I'll create an issue for it.