Sort/Filter Table Data

Sorting and filtering is a common way to manage and inspect table data. These operations are collectively called “proxy” because both of them intermediary map the original data to new indices.

Filtering

Use filter functions

You can set a function that maps a DataFrame to a 1-D boolean array as a filter function. This is a most straightforward way.

def filter_func(df):
    return df["label"] == "A"

table.proxy.filter(filter_func)

# or equivalently, use decorator
@table.proxy.filter
def filter_func(df):
    return df["label"] == "A"

This example is essentially equivalent to slicing a DataFrame by df[df["label"] == "A"].

If the table is

	A	label
0	2	A
1	3	B
2	6	B
3	4	A

then it looks like following after applying the filter.

	A	label
0	2	A
3	4	A

Simple filtering in GUI

In right-click contextmenu, you can select Filter to filter the table by the column. Filter buttons will be anchored at the corner of the column section during the table being filtered. You can update or remove the filter by clicking the button.

Use query-style expression

Instead of a function, you can also set a query-style expression as a filter.

table.proxy.filter("label == 'A'")

See the API reference of pandas.eval for details of the syntax.

If the expression can be interpreted as a simple column-wise filter, button will similarly be added.

Query-style filtering in GUI

You can also open a overlay dialog to filter the table data from the button in the toolbar.

The line edit for filter expression supports auto-completion (Tab) and history browsing (↑, ↓).

Sorting

Use sorting functions

Similar to filtering, you can also set a function for sorting. In this case, the function should map a DataFrame to a 1-D interger array, just like argsort().

def sort_func(df):
    return df["x"].argsort()

table.proxy.sort(sort_func)

# or equivalently, use decorator
@table.proxy.sort
def sort_func(df):
    return df["x"].argsort()

If the table is

	x	y
0	2	a0
1	3	a1
2	1	a2
3	0	a3

then it looks like following after sorting.

	x	y
3	0	a3
2	1	a2
0	2	a0
1	3	a1

Sorting function doesn’t always have to be surjective, i.e. it can return only a subset of the source indices.

@table.proxy.sort
def sort_func(df):
    # return the top 10 rows
    return df["x"].argsort()[:10]

Sort by a column

In most cases, you’d like to sort a table by a column, in ascending or descending order. The sort() method also supports this use case, by passing by argument.

table.proxy.sort(by="x")  # ascending order by default
table.proxy.sort(by="x", ascending=False)  # descending order

Multi-column sorting is also supported.

table.proxy.sort(by=["x", "y"])

Sort in GUI

You can sort selected column(s) by clicking button in the toolbar.

Edit Cells during Proxy

You can edit cells while the table is sorted/filtered.

Suppose you have a table like below

A	B
0	row-0
1	row-1
2	row-2
3	row-3
4	row-4
5	row-5
6	row-6
7	row-7
8	row-8
9	row-9

and applied a filter by

viewer.current_table.proxy.filter("A % 2 == 1")

A	B
1	row-1
3	row-3
5	row-5
7	row-7
9	row-9

Here, you can edit, or paste data directly (The edited cells are highlighted in red).

A	B
1	row-1
3	X-3
5	X-5
7	X-7
9	row-9

After removing filter (viewer.current_table.proxy.reset()), you’ll see the cells are properly edited.

A	B
0	row-0
1	row-1
2	row-2
3	X-3
4	row-4
5	X-5
6	row-6
7	X-7
8	row-8
9	row-9

Column Proxy

New in version 0.5.5.

Another way to sort/filter table data is the column proxy.

def filter_func(s: str):
    return "_mean" in s

table.columns.filter(filter_func)

# or equivalently, use decorator
@table.columns.filter
def filter_func(s: str):
    return "_mean" in s

table.columns.sort(ascending=True)