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Q: How to increase values of polars dataframe column by index I have a data frame as follow
┌────────────┬──────────┬──────────┬──────────┬──────────┐
│ time       ┆ open     ┆ high     ┆ low      ┆ close    │
│ ---        ┆ ---      ┆ ---      ┆ ---      ┆ ---      │
│ i64        ┆ f64      ┆ f64      ┆ f64      ┆ f64      │
╞════════════╪══════════╪══════════╪══════════╪══════════╡
│ 1649016000 ┆ 46405.49 ┆ 47444.11 ┆ 46248.84 ┆ 46407.35 │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 1649030400 ┆ 46407.36 ┆ 46461.14 ┆ 45744.77 ┆ 46005.44 │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 1649044800 ┆ 46005.43 ┆ 46293.38 ┆ 45834.39 ┆ 46173.99 │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 1649059200 ┆ 46174.0  ┆ 46287.97 ┆ 45787.0  ┆ 46160.09 │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ ...        ┆ ...      ┆ ...      ┆ ...      ┆ ...      │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 1653278400 ┆ 30171.32 ┆ 30670.51 ┆ 30101.07 ┆ 30457.01 │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 1653292800 ┆ 30457.01 ┆ 30616.18 ┆ 30281.89 ┆ 30397.11 │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 1653307200 ┆ 30397.12 ┆ 30625.98 ┆ 29967.07 ┆ 30373.53 │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 1653321600 ┆ 30373.53 ┆ 30529.9  ┆ 30042.09 ┆ 30121.02 │
└────────────┴──────────┴──────────┴──────────┴──────────┘

I want to count how many times each price (low and high) were local minimum/maximum in a window range of 2 to 50.
first I add two columns for count of being local min/max for each row and fill by zeros
raw_data["lmin_count"] = np.zeros(len(raw_data), dtype=np.int16)
raw_data["lmax_count"] = np.zeros(len(raw_data), dtype=np.int16)

then I iterate window length from 2 to 50 and find index of each local min/max by using:
for _order in range(2, 51):
    local_minima = argrelextrema(raw_data["low"].to_numpy(), np.less, order=_order)[0]
    local_maxima = argrelextrema(raw_data["high"].to_numpy(), np.greater, order=_order)[0]

which order is window length.
and in each iteration over window length I want to increase value of lmin_count and lmax_count by indices found in local_minima and local_maxima
I tried increasing value by this code:
if len(local_minima) > 1:
    raw_data[local_minima,5] += 1
if len(local_maxima) > 1:
    raw_data[local_minima,6] += 1

which local_minima and local_maxima are array of indices and 5,6 are index of lmin_count and lmax_count columns.
but got error not implemented.
So what is the best way to increase (or assign) value of column by row indices?
Update 2022/05/24
As answers were very helpful now I have other issues.
I changed my code as follow:
min_expr_list = [
    (
        pl.col("price").rolling_min(
            window_size=_order * 2 + 1, min_periods=_order + 2, center=True
        )
        == pl.col("price")
    ).cast(pl.UInt32)
    for _order in range(200, 1001)
]

max_expr_list = [
    (
        pl.col("price").rolling_max(
            window_size=_order * 2 + 1, min_periods=_order + 2, center=True
        )
        == pl.col("price")
    ).cast(pl.UInt32)
    for _order in range(200, 1001)
]
raw_data = raw_data.with_columns(
    [
        pl.sum(min_expr_list).alias("min_freq"),
        pl.sum(max_expr_list).alias("max_freq"),
    ]
)

first: is it possible to merge both min_expr_list and max_expr_list into one list? and if it is possible, in with_columns expression how can I add separate columns based on each element of list?
another issue I am facing is memory usage of this approach.
In previous example _order were limited but in action it is more wider than example.
currently I have datasets with millions of records (some of them have more than 10 million records) and _orders range can be from 2 to 1500 so calculating needs lots of GB of ram.
is there any better way to do that?
and one more side problem. when increasing _order to more than 1000 it seems it doesn't work. is there any limitation in source code?

A: Let me see if we can build on @ritchie46 response and nudge you closer to the finish line.
Data
I've concatenated the 'open', 'high', and 'low' columns in your sample data, just to give us some data to work with.  I've also added a row_nr column, just for discussion.  (It won't be used in any calculations whatsoever, so you don't need to include it in your final code.)
import numpy as np
import polars as pl
from scipy.signal import argrelextrema

df = pl.DataFrame(
    {
        "col1": [
            46405.49, 46407.36, 46005.43, 46174.00, 30171.32, 30457.01, 30397.12, 30373.53,
            47444.11, 46461.14, 46293.38, 46287.97, 30670.51, 30616.18, 30625.98, 30529.90,
            46248.84, 45744.77, 45834.39, 45787.00, 30101.07, 30281.89, 29967.07, 30042.09,
        ]
    }
).with_row_count()
df

shape: (24, 2)
┌────────┬──────────┐
│ row_nr ┆ col1     │
│ ---    ┆ ---      │
│ u32    ┆ f64      │
╞════════╪══════════╡
│ 0      ┆ 46405.49 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 1      ┆ 46407.36 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 2      ┆ 46005.43 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 3      ┆ 46174.0  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 4      ┆ 30171.32 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 5      ┆ 30457.01 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 6      ┆ 30397.12 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 7      ┆ 30373.53 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 8      ┆ 47444.11 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 9      ┆ 46461.14 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 10     ┆ 46293.38 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 11     ┆ 46287.97 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 12     ┆ 30670.51 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 13     ┆ 30616.18 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 14     ┆ 30625.98 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 15     ┆ 30529.9  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 16     ┆ 46248.84 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 17     ┆ 45744.77 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 18     ┆ 45834.39 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 19     ┆ 45787.0  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 20     ┆ 30101.07 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 21     ┆ 30281.89 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 22     ┆ 29967.07 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 23     ┆ 30042.09 │
└────────┴──────────┘

Now, let's run the scipy.signal.argrelextrema code on this data.
for _order in range(1, 7):
    print(
        "order:", _order, ":", argrelextrema(df["col1"].to_numpy(), np.less, order=_order)
    )

order: 1 : (array([ 2,  4,  7, 13, 15, 17, 20, 22]),)
order: 2 : (array([ 4,  7, 15, 22]),)
order: 3 : (array([ 4, 15, 22]),)
order: 4 : (array([ 4, 15, 22]),)
order: 5 : (array([ 4, 22]),)
order: 6 : (array([ 4, 22]),)

From the output, it looks like you're trying to find the index of any row that is the minimum value of a window centered on that row, for various window sizes.
For example, row_nr 2 is a local minimum of a window of size 3, centered on row_nr 2.  (Here, order=1 in the call to argrelextrema means "including one value above and below", and hence "window size" = (order * 2) + 1) = 3.
Let's replicate this in Polars.  We'll take it in steps.
rolling_min
First, let's use the rolling_min expression to calculate rolling minimums corresponding to order from 1 to 6.  Notice that Polars allows us to generate a list of expressions outside of the with_columns context.  (This often helps keep code more readable.)
I'm converting the scipy order keyword to the equivalent window_size for rolling_min.  Also, I'm setting the min_periods to make sure that there is at least one value on each side of the center value of any window (to replicate the scipy calculations).
expr_list = [
        pl.col("col1").rolling_min(
            window_size=_order * 2 + 1,
            min_periods=_order + 2,
            center=True
        ).alias("roll_min" + str(_order))
    for _order in range(1, 7)
]
df.with_columns(expr_list)

shape: (24, 8)
┌────────┬──────────┬───────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ row_nr ┆ col1     ┆ roll_min1 ┆ roll_min2 ┆ roll_min3 ┆ roll_min4 ┆ roll_min5 ┆ roll_min6 │
│ ---    ┆ ---      ┆ ---       ┆ ---       ┆ ---       ┆ ---       ┆ ---       ┆ ---       │
│ u32    ┆ f64      ┆ f64       ┆ f64       ┆ f64       ┆ f64       ┆ f64       ┆ f64       │
╞════════╪══════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ 0      ┆ 46405.49 ┆ null      ┆ null      ┆ null      ┆ null      ┆ null      ┆ null      │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 1      ┆ 46407.36 ┆ 46005.43  ┆ 46005.43  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 2      ┆ 46005.43 ┆ 46005.43  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 3      ┆ 46174.0  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 4      ┆ 30171.32 ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 5      ┆ 30457.01 ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 6      ┆ 30397.12 ┆ 30373.53  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 7      ┆ 30373.53 ┆ 30373.53  ┆ 30373.53  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 8      ┆ 47444.11 ┆ 30373.53  ┆ 30373.53  ┆ 30373.53  ┆ 30171.32  ┆ 30171.32  ┆ 30171.32  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 9      ┆ 46461.14 ┆ 46293.38  ┆ 30373.53  ┆ 30373.53  ┆ 30373.53  ┆ 30171.32  ┆ 30171.32  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 10     ┆ 46293.38 ┆ 46287.97  ┆ 30670.51  ┆ 30373.53  ┆ 30373.53  ┆ 30373.53  ┆ 30171.32  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 11     ┆ 46287.97 ┆ 30670.51  ┆ 30616.18  ┆ 30616.18  ┆ 30373.53  ┆ 30373.53  ┆ 30373.53  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 12     ┆ 30670.51 ┆ 30616.18  ┆ 30616.18  ┆ 30529.9   ┆ 30529.9   ┆ 30373.53  ┆ 30373.53  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 13     ┆ 30616.18 ┆ 30616.18  ┆ 30529.9   ┆ 30529.9   ┆ 30529.9   ┆ 30529.9   ┆ 30373.53  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 14     ┆ 30625.98 ┆ 30529.9   ┆ 30529.9   ┆ 30529.9   ┆ 30529.9   ┆ 30529.9   ┆ 30101.07  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 15     ┆ 30529.9  ┆ 30529.9   ┆ 30529.9   ┆ 30529.9   ┆ 30529.9   ┆ 30101.07  ┆ 30101.07  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 16     ┆ 46248.84 ┆ 30529.9   ┆ 30529.9   ┆ 30529.9   ┆ 30101.07  ┆ 30101.07  ┆ 29967.07  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 17     ┆ 45744.77 ┆ 45744.77  ┆ 30529.9   ┆ 30101.07  ┆ 30101.07  ┆ 29967.07  ┆ 29967.07  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 18     ┆ 45834.39 ┆ 45744.77  ┆ 30101.07  ┆ 30101.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 19     ┆ 45787.0  ┆ 30101.07  ┆ 30101.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 20     ┆ 30101.07 ┆ 30101.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 21     ┆ 30281.89 ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 22     ┆ 29967.07 ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  ┆ 29967.07  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 23     ┆ 30042.09 ┆ null      ┆ null      ┆ null      ┆ null      ┆ null      ┆ null      │
└────────┴──────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┘

Looking at the output in roll_min_1 (the equivalent of an order=1 call for argrelextrema), we see that the values in roll_min_1  equal the values in col1 for row_nr 2,  4,  7, 13, 15, 17, 20, 22 ... which corresponds exactly to the output of argrelextrema for order=1.  Likewise, for the other roll_min_X columns.  We'll use this fact in the next step.
Obtaining the row index
As @ritchie46 points out, in Polars, we use conditions (not indexing).  We'll modify the above code to identify whether the value in col1 equals it's rolling min, for each of our window sizes.
expr_list = [
    (
        pl.col("col1").rolling_min(
            window_size=_order * 2 + 1,
            min_periods=_order + 2,
            center=True
        )
        == pl.col("col1")
    ).alias("min_idx_" + str(_order))
    for _order in range(1, 7)
]
df.with_columns(expr_list)

shape: (24, 8)
┌────────┬──────────┬───────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ row_nr ┆ col1     ┆ min_idx_1 ┆ min_idx_2 ┆ min_idx_3 ┆ min_idx_4 ┆ min_idx_5 ┆ min_idx_6 │
│ ---    ┆ ---      ┆ ---       ┆ ---       ┆ ---       ┆ ---       ┆ ---       ┆ ---       │
│ u32    ┆ f64      ┆ bool      ┆ bool      ┆ bool      ┆ bool      ┆ bool      ┆ bool      │
╞════════╪══════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ 0      ┆ 46405.49 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 1      ┆ 46407.36 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 2      ┆ 46005.43 ┆ true      ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 3      ┆ 46174.0  ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 4      ┆ 30171.32 ┆ true      ┆ true      ┆ true      ┆ true      ┆ true      ┆ true      │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 5      ┆ 30457.01 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 6      ┆ 30397.12 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 7      ┆ 30373.53 ┆ true      ┆ true      ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 8      ┆ 47444.11 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 9      ┆ 46461.14 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 10     ┆ 46293.38 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 11     ┆ 46287.97 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 12     ┆ 30670.51 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 13     ┆ 30616.18 ┆ true      ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 14     ┆ 30625.98 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 15     ┆ 30529.9  ┆ true      ┆ true      ┆ true      ┆ true      ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 16     ┆ 46248.84 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 17     ┆ 45744.77 ┆ true      ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 18     ┆ 45834.39 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 19     ┆ 45787.0  ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 20     ┆ 30101.07 ┆ true      ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 21     ┆ 30281.89 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 22     ┆ 29967.07 ┆ true      ┆ true      ┆ true      ┆ true      ┆ true      ┆ true      │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 23     ┆ 30042.09 ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     ┆ false     │
└────────┴──────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┘

Notice that for min_idx_1, the values are true for row_nr 2,  4,  7, 13, 15, 17, 20, 22, which corresponds to the output of argrelextrema for order=1.  Likewise, for the other columns.
Summing
We can now use the cast function and the polars.sum function to sum row-wise across our columns.  (Indeed, we won't keep our rolling min columns -- we'll just keep the sums).
expr_list = [
    (
        pl.col("col1").rolling_min(
            window_size=_order * 2 + 1,
            min_periods=_order + 2,
            center=True
        )
        == pl.col("col1")
    ).cast(pl.UInt32)
    for _order in range(1, 7)
]
df.with_columns(pl.sum(expr_list).alias("min_freq"))

┌────────┬──────────┬──────────┐
│ row_nr ┆ col1     ┆ min_freq │
│ ---    ┆ ---      ┆ ---      │
│ u32    ┆ f64      ┆ u32      │
╞════════╪══════════╪══════════╡
│ 0      ┆ 46405.49 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 1      ┆ 46407.36 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 2      ┆ 46005.43 ┆ 1        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 3      ┆ 46174.0  ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 4      ┆ 30171.32 ┆ 6        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 5      ┆ 30457.01 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 6      ┆ 30397.12 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 7      ┆ 30373.53 ┆ 2        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 8      ┆ 47444.11 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 9      ┆ 46461.14 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 10     ┆ 46293.38 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 11     ┆ 46287.97 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 12     ┆ 30670.51 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 13     ┆ 30616.18 ┆ 1        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 14     ┆ 30625.98 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 15     ┆ 30529.9  ┆ 4        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 16     ┆ 46248.84 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 17     ┆ 45744.77 ┆ 1        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 18     ┆ 45834.39 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 19     ┆ 45787.0  ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 20     ┆ 30101.07 ┆ 1        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 21     ┆ 30281.89 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 22     ┆ 29967.07 ┆ 6        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 23     ┆ 30042.09 ┆ 0        │
└────────┴──────────┴──────────┘

I believe this is the result you were looking to obtain.
From here, I think you can expand the above code for rolling maximums.
Ties
One difference between this code and the argrelextrema code pertains to ties.  If two values tie for the minimum in any window, argrelextrema considers neither to be the minimum for the window.  The code above considers both to be minimum values.
I'm not sure how likely this will be for the size of windows you have, or for the type of data.
Please update Polars to 0.13.38
The latest release of Polars contains some major improvements to the performance of rolling functions.  (The announcement is on this Twitter thread.)  You'll want to take advantage of that by updating to the latest version.
Update - 2022/05/24
Merging all lists into one expression

first: is it possible to merge both min_expr_list and max_expr_list into one list? and if it is possible, in with_columns expression how can I add separate columns based on each element of list?

It is possible to generate all columns (min and max, for each order, for each variable) using a single list in a single with_columns context.  The calculations are independent, and thus can be in the same with_columns context.  Each column would have a name that could be used in a later calculation step.
But in that case, the accumulation steps would need to be in a separate with_columns expression.  The with_columns context assumes all calculations are independent - and that they can be run in any order, without dependencies.  But summarizing columns (by selecting them by name) is dependent on those columns first being created.
You can return multiple Series from a single function if you return them as a Series of struct (e.g., using map) .. but that's generally to be avoided.  (And beyond the scope of our question here.)
And more specifically for this problem, we are dealing with issues of memory pressure.  So, in this case, we'll need to move in the opposite direction - we'll need to break lists into smaller pieces and feed them to the with_columns expression in batches.
Batching the calculations by order

currently I have datasets with millions of records (some of them have more than 10 million records) and _orders range can be from 2 to 1500 so calculating needs lots of GB of ram.
is there any better way to do that?

We'll try a couple of techniques to reduce memory pressure ... and still achieve good performance.
One technique will be to use the fold method to accumulate values.  This allows us to sum boolean values without having to cast every intermediate column to integers.  This should reduce memory pressure during the intermediate calculations.
We'll also batch our calculations by breaking the expression lists into sub-lists, calculating intermediate results, and accumulating into an accumulator column using the fold method.
First, let's eliminate the cast to integer in your min_expr_list.
min_expr_list = [
    (
        pl.col("price").rolling_min(
            window_size=_order * 2 + 1, min_periods=_order + 2, center=True
        )
        == pl.col("price")
    )
    for _order in range(1, 20)
]

Next we'll need to pick a batch_size and initialize an accumulator column.  I would experiment with different batch_size numbers until you find one that seems to work well for your computing platform and size of dataset.  Since we have limited data in this example, I'll pick a batch_size of 5 - just to demonstrate the algorithm.
batch_size = 5
df = df.with_column(pl.lit(0, dtype=pl.UInt32).alias("min_freq"))

Next, we'll iterate through the batches of sub-lists, and accumulate as we go.
while(min_expr_list):
    next_batch, min_expr_list = min_expr_list[0: batch_size], min_expr_list[batch_size:]
    df=(
        df
        .with_column(
            pl.fold(
                pl.col("min_freq"),
                lambda acc, x: acc + x,
                next_batch,
            )
        )
    )

print(df)

shape: (24, 3)
┌────────┬──────────┬──────────┐
│ row_nr ┆ price    ┆ min_freq │
│ ---    ┆ ---      ┆ ---      │
│ u32    ┆ f64      ┆ u32      │
╞════════╪══════════╪══════════╡
│ 0      ┆ 46405.49 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 1      ┆ 46407.36 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 2      ┆ 46005.43 ┆ 1        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 3      ┆ 46174.0  ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 4      ┆ 30171.32 ┆ 15       │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 5      ┆ 30457.01 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 6      ┆ 30397.12 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 7      ┆ 30373.53 ┆ 2        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 8      ┆ 47444.11 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 9      ┆ 46461.14 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 10     ┆ 46293.38 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 11     ┆ 46287.97 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 12     ┆ 30670.51 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 13     ┆ 30616.18 ┆ 1        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 14     ┆ 30625.98 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 15     ┆ 30529.9  ┆ 4        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 16     ┆ 46248.84 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 17     ┆ 45744.77 ┆ 1        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 18     ┆ 45834.39 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 19     ┆ 45787.0  ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 20     ┆ 30101.07 ┆ 1        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 21     ┆ 30281.89 ┆ 0        │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 22     ┆ 29967.07 ┆ 19       │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 23     ┆ 30042.09 ┆ 0        │
└────────┴──────────┴──────────┘

Problems with rolling_min when order is 1,000 or more

and one more side problem. when increasing _order to more than 1000 it seems it doesn't work. is there any limitation in source code?

I generated datasets of 50 million random numbers and tested the rolling_min for order sizes of 1,500 .. and found no problems.  Indeed, I replicated the algorithm using a rolling slice and found no errors.
But I have a hunch about what might be happening.  Let's start with this dataset of 10 records:
df = pl.DataFrame(
    {
        "col1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    }
)
df

shape: (10, 1)
┌──────┐
│ col1 │
│ ---  │
│ i64  │
╞══════╡
│ 1    │
├╌╌╌╌╌╌┤
│ 2    │
├╌╌╌╌╌╌┤
│ 3    │
├╌╌╌╌╌╌┤
│ 4    │
├╌╌╌╌╌╌┤
│ 5    │
├╌╌╌╌╌╌┤
│ 6    │
├╌╌╌╌╌╌┤
│ 7    │
├╌╌╌╌╌╌┤
│ 8    │
├╌╌╌╌╌╌┤
│ 9    │
├╌╌╌╌╌╌┤
│ 10   │
└──────┘

If we set _order = 8 and run the algorithm, we get a result.
_order = 8
df = df.with_column(
    pl.col("col1")
    .rolling_min(window_size=(2 * _order) + 1, min_periods=(_order + 2), center=True)
    .alias("rolling_min")
)
df

shape: (10, 2)
┌──────┬─────────────┐
│ col1 ┆ rolling_min │
│ ---  ┆ ---         │
│ i64  ┆ i64         │
╞══════╪═════════════╡
│ 1    ┆ null        │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2    ┆ 1           │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 3    ┆ 1           │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 4    ┆ 1           │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 5    ┆ 1           │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 6    ┆ 1           │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 7    ┆ 1           │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 8    ┆ 1           │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 9    ┆ 1           │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 10   ┆ null        │
└──────┴─────────────┘

However, if we set _order=9, we get all null values:
shape: (10, 2)
┌──────┬─────────────┐
│ col1 ┆ rolling_min │
│ ---  ┆ ---         │
│ i64  ┆ i64         │
╞══════╪═════════════╡
│ 1    ┆ null        │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2    ┆ null        │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 3    ┆ null        │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 4    ┆ null        │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 5    ┆ null        │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 6    ┆ null        │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 7    ┆ null        │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 8    ┆ null        │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 9    ┆ null        │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 10   ┆ null        │
└──────┴─────────────┘

Is this what you're seeing?  The reason for the null values is the min_period value.  We set min_period = _order + 2, which in this case is 11.  However, there are only 10 values in the dataset.  Thus, we get all null values.
Perhaps this is what is happening in your data?

A: You write very imperative code which is not really idiomatic polars. You typically should not even know where the index of a value is. Instead you assign by conditions, for instance with when(condition) -> then(value) -> otherwise(value) expressions.
Your condition in a when can still refer to an index. This snippet for instance is equal to assigning to a specific index, but then written more functional:
pl.DataFrame({
    "letters": ["a", "b", "c", "d"]
}).with_column(
    # use a condition to determine the index location
    pl.when(pl.arange(0, pl.count()) == 2)
      .then("idx_2")
      .otherwise("letters").alias("letters")
)

shape: (4, 1)
┌─────────┐
│ letters │
│ ---     │
│ str     │
╞═════════╡
│ letters │
├╌╌╌╌╌╌╌╌╌┤
│ letters │
├╌╌╌╌╌╌╌╌╌┤
│ idx_2   │
├╌╌╌╌╌╌╌╌╌┤
│ letters │
└─────────┘

Your intent, counting local minima/maxima
To help you in your cause, I'd like to show how you can find your local minima in an idiomatic polars way.
The local minima/maxima can be found by:

*

*taking the derivative dy/dx of a function x.

*computing the sign of that derivative tells us where the functions slope is increasing and decreasing.

*if we take the derivative of the sign of dy/dx we know where the signs are changing and thus where we have local minima/maxima.

Let's try this on a dummy DataFrame.
df = pl.DataFrame({
    "x": [8, 4, 2, 7, 9, 6, 3, 0]
})

# find the local minima/maxima
df = df.with_columns([
    (pl.col("x").diff().sign().diff().shift(-1) == -2).alias("local_maximum"),
    (pl.col("x").diff().sign().diff().shift(-1) == 2).alias("local_minimum")
])
print(df)

shape: (8, 3)
┌─────┬───────────────┬───────────────┐
│ x   ┆ local_maximum ┆ local_minimum │
│ --- ┆ ---           ┆ ---           │
│ i64 ┆ bool          ┆ bool          │
╞═════╪═══════════════╪═══════════════╡
│ 8   ┆ false         ┆ false         │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 4   ┆ false         ┆ false         │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2   ┆ false         ┆ true          │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 7   ┆ false         ┆ false         │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 9   ┆ true          ┆ false         │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 6   ┆ false         ┆ false         │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 3   ┆ false         ┆ false         │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 0   ┆ false         ┆ false         │
└─────┴───────────────┴───────────────┘

Next we can take the cumulative sum to count the total seen local minima and maxima.
df.with_columns([
    pl.col("local_maximum").cumsum().alias("local_max_count"),
    pl.col("local_minimum").cumsum().alias("local_min_count")
])

shape: (8, 5)
┌─────┬───────────────┬───────────────┬─────────────────┬─────────────────┐
│ x   ┆ local_maximum ┆ local_minimum ┆ local_max_count ┆ local_min_count │
│ --- ┆ ---           ┆ ---           ┆ ---             ┆ ---             │
│ i64 ┆ bool          ┆ bool          ┆ u32             ┆ u32             │
╞═════╪═══════════════╪═══════════════╪═════════════════╪═════════════════╡
│ 8   ┆ false         ┆ false         ┆ 0               ┆ 0               │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 4   ┆ false         ┆ false         ┆ 0               ┆ 0               │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2   ┆ false         ┆ true          ┆ 0               ┆ 1               │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 7   ┆ false         ┆ false         ┆ 0               ┆ 1               │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 9   ┆ true          ┆ false         ┆ 1               ┆ 1               │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 6   ┆ false         ┆ false         ┆ 1               ┆ 1               │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 3   ┆ false         ┆ false         ┆ 1               ┆ 1               │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 0   ┆ false         ┆ false         ┆ 1               ┆ 1               │
└─────┴───────────────┴───────────────┴─────────────────┴─────────────────┘

I hope this helps nudge you in the right direction.


text:
require 'net/http'
require 'json'

def show_size(endpoint)
	uri = URI("https://api.github.com/repos/#{endpoint}")
	Net::HTTP.get_response(uri)
end

ARGV.each do |endpoint|
	res = show_size(endpoint)
	puts "#{endpoint} : #{JSON.parse(res.body)['size'] / 1024.0} MB"
end

redpajama_set_name: RedPajamaGithub


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text:
Q: Fit a square root function with basic R I am quite new to R. I have my data already plotted and I just want to fit a nice model to it. That's it.
I think I can fit a square root model here (at least to the red data points). Or do you have another suggestion? I don't know how to do this.
My code looks like this:
plot(gbl$g_aH_upper~gbl$windspeed,
 main="Boundary Layer Conductance",
 ylab="Boundary Layer Conductance [m/s]",ylim=lmts, #adj=1,
 xlab="wind speed [m/s]",
 xaxt='n',
 cex.axis=0.8,
 pch=19,
 las=1,
 col="black",type="p",cex=0.7)
axis(side=1,las=1, at=c(seq(from=0,to=2.0,by=0.1), tick=F))
legend('bottomright', legend=c("Upper leaves","Lower leaves")[-50],
   lty=1, col=c('black', 'red'), bty='n', cex=1.0, y.intersp = 0.6, x.intersp = 0.4, seg.len = 0.6,lwd=1.5, text.width = 0.8)
par(new = TRUE)
plot(gbl$g_aH_lower~gbl$windspeed, axes = FALSE, xlab = ' ', ylab = ' ', col="red" ,type="p",cex=0.7,las=1, pch=16)

As you can see the linear models don't fit nicely. I think I can make a better one.

A: Since you all ready have a theory on the behavior of the data, that theory should be the basis of the model.  In this case y=beta0 + beta1*sqrt(x).
This model can be fitted with the standard lm function as opposed to the glm function.
Here is an example using your approximate data:
#create limited data
x<-c(0,  1.1, 1.4, 1.7)
y<-c(0.06, 0.115, 0.115, 0.125)

plot(y~x)
model<-lm(y~x)
#plot a linear fit
  abline(model, col="blue")
  print(summary(model))

#model with the square of x
#  I() inhibit interpretation see help(I) for more information
  modelsr<-lm(y~I(sqrt(x)))
  print(summary(modelsr))

#generate the data to the model
  xbar<-seq(0, 1.7, 0.03)
  ybar<-modelsr$coefficients[1]+sqrt(xbar)*modelsr$coefficients[2]
#plot model
lines(y=ybar, x=xbar, pch=19, col="green")

Yes the square root (green line) does fit the data much better than the simple linear regression(blue line):

redpajama_set_name: RedPajamaStackExchange


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text:
David Robert Wills (born 1953) is a noted translator of Jacques Derrida, including The Gift of Death, Right of Inspection, Counterpath, and The Animal That Therefore I Am. Currently, Wills is a professor of French at Brown University.

To date much of Wills's own original and published work "has concentrated on literary theory, especially the work of Derrida, film theory, comparative literature" with an emphasis on how and where we think through (and with) technology and politics. As noted, Wills's writing "rethinks not only our nature before all technology but also what we understand to be technology."

Wills began teaching at University at Albany, SUNY in 1998 and moved to Brown University in 2013. He has degrees from the University of Auckland, and received his doctorate from the Université de Paris III-Sorbonne Nouvelle.

Selected bibliography
Translator
Works by Jacques Derrida:
Derrida/Marie-Françoise Plissart, Right of Inspection (Monacelli, 1998).
Catherine Malabou/Derrida, Counterpath (Stanford, 2004).
The Gift of Death (2nd edition) & Literature in Secret (Chicago, 2008, [1st edition, 1995])
The Animal That Therefore I Am (Fordham, 2008)
Theory and Practice (Chicago, 2018)
Single author
Self (De)construct: Writing and the Surrealist Text (James Cook University Press, 1985).
Prosthesis (Stanford, 1995; Editions Galilée, 1997, 1998 [author's translation]).
Matchbook: Essays in Deconstruction (Stanford, 2005).
Dorsality: Thinking Back Through Technology and Politics (Minnesota, 2008)

Co-author
Screen/Play: Derrida and Film Theory (with Peter Brunette, Princeton, 1989).
Writing Pynchon: Strategies in Textual Analysis (with Alec McHoul, Illinois, 1990).

Editor/co-editor
Deconstruction and the Visual Arts (with Peter Brunette, Cambridge, 1994)
Jean-Luc Godard's Pierrot le fou (Cambridge, 2000)

Essays and articles
"Techneology or the Discourse of Speed," in Marquard Smith and Joanne Morra (eds.), The Prosthetic Impulse (MIT, 2006).
"Notes Toward a Requiem or the Music of Memory," Mosaic 39, 3 (2006).
"Dorsal Chances: An Interview with David Wills," Parallax 13, 4 (2007).

References

External links
Will's review of Derrida's The Beast and the Sovereign Volume 1
Two Words Pro-Derrida Wills describes this piece as "adapted from - and translated from - my participation in two roundtable discussions on translation"
B. Buseyne et al, An Interview with David Wills (2001) as published in: N. Roelens and W. Strauven, Homo Orthopedicus (Paris: Harmattan, 2001)

1953 births
Living people
French–English translators
State University of New York faculty
University at Albany, SUNY faculty
University of Auckland alumni
University of Paris alumni
Translators of Jacques Derrida
American translators

Brown University faculty

redpajama_set_name: RedPajamaWikipedia


text:
Fossil Free Europe
About Fossil Free
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Find a local campaign near you
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Online Skill-Ups
Liberate Le Louvre #FossilFreeCulture
Share on Facebook Tweet @MuséeLouvre
Why target Total?
Our demand
12 March: the art-activists collective @LiberonsleLouvre staged a choreography of falling bodies, for the global victims of fossil-fuel polluter Total, which is one of the museum's major sponsor:
Together let's kick fossil fuel sponsors like Total out of the Louvre and end its partnerships with companies that wreck the climate.
The Louvre Museum is a vital institution — it's world-famous for its role in helping us to better understand the span of human history and civilisation.
By accepting corporate donations from fossil fuel companies like Total, the Louvre Museum not only damages its own reputation, but it helps foster the idea that we can continue to use fossil fuels without harm.
We, therefore, ask the Louvre Museum to immediately end any partnerships with the fossil fuel industry.
Thank you for signing the petition. Please will you click below to share it too? Share on Facebook Tweet @MuséeLouvre
The Louvre museum is a unique place. It plays a vital role in helping us to better understand the vast span of human history and civilisation.
From tablets in cuneiform script to "Liberty leading the people", from the Victory of Samothrace to the works of Leonardo da Vinci, the Louvre explores history in the making, in all its complexity. The history of the birth and collapse of entire civilisations. History that is as emancipatory as it is domineering, turbulent; as belligerent as it is harmonious and peaceful; and as violent as it is enlightening.
The antiquities and works exhibited in the Louvre are not only a demonstration of human creativity. They are also symbols bearing witness to a great shift, a change of era: through the galleries of the Louvre, the museum takes us from the relative climate stability of the Holocene, to accelerated global warming, marking the start of the Anthropocene.
The Louvre is not simply an external witness to this shift, it is, indeed, one of its victims. Global warming emphasises the frequency and force of violent climatic events, and there is an increasing risk of flooding from the Seine. Last May, the Louvre had to evacuate its reserves to save them from the rising water levels. Some artworks have come through more than 10 millennia of history, and are threatened by the intense coal, gas and oil combustion humanity has been submitting itself to for just under two centuries.
The Paris Agreement, adopted at the conclusion of COP21, draws a clear, red line: keep global warming to as close to 1.5 °C as possible to avoid plunging into climate chaos. To do so, the scientific consensus is clear: we need to stop using 80% of known reserves of coal, gas and oil if we want to avoid the collapse of our societies.
We are able to identify the victims of climate change. But we can also point out those who are responsible. And we can object to the organisational and strategic inability of fossil fuel companies to make the link between scientific knowledge and their activities.
The Louvre is not, in fact, a passive victim. It was directly affected given the risks borne by the works on display: by agreeing to accept corporate donations from companies such as Total, the Louvre museum is helping to foster the idea that we can continue burning fossil fuels without consequence.
The Louvre, by regarding the activities of Total as legitimate, is also condemning our future. By drilling even further and deeper, these companies are contributing to the destruction of the planet, of entire ecosystems and reducing the likelihood that current and future generations will live in a climate of justice and peace. Hundreds of millions of lives worldwide have already been devastated by climate change, and whole segments of life on earth and our common heritage could continue to rapidly disappear.
We believe that the Louvre Museum has a fundamental moral responsibility regarding the climate crisis, as a communication channel between civilisations and cultures, but also as a place of education. Its partnerships with Total completely undermine its role and its ambition to build bridges between past civilisations and current as well as future generations.
We now know: our common future is not compatible with the fossil fuel industry. We, therefore, ask the Louvre museum to put an immediate and complete end to its partnership with the fossil fuel industry.
Latest Campaign News
Disobedient art can win the fight against artwashing
In this guest blog, Fossil Free Culture NL share their reflections on their successful campaign that made the Van Gogh museum end its sponsorship with oil and gas company Shell
Dutch museums refuse Shell sponsorship
Disruptive, artistic interventions shunning oil and gas company Shell have created ripple effects among Dutch museums. The fossil fuel industry's image has taken a big hit – and not just in the Netherlands.
« Libérons le Louvre » denounces museum's partnership with Total, by occupying room
On Monday 13th of March 2018, art-activist collective 'Libérons le Louvre' staged a theatrical performance inside the Louvre museum in Paris in protest of the museum's sponsorship deal with oil and gas company Total.
Only people can power change. Join us 8-9 September
Put this date in your diaries now. Ahead of the next round of global climate talks in Poland in November,…
#zerofossile Tweets

redpajama_set_name: RedPajamaCommonCrawl


text:
WEB DESIGNS & DEVELOPMENT
OPEN SOURCE CUSTOMIZATION
2D/3D ANIMATION
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Celebrate Black History Month with a rich offering of culture, food, history and tours. You'll find events in Harlem, Central Park and Riverside Church.
From a map courtesy of NYU students, here are 10 monuments, memorials, parks, and historic locations throughout NYC to celebrate International Day of Peace.
Black History Month will offer trolley tours of Green-Wood Cemetery & Weeksville, Art at Riverside Church, Studio Museum & Schomburg Center, food, gospel & jazz.
Here's our top 10 list of our favorite secrets in Central Park, from a survey bolt, a fully loaded cannon, a hidden cave, remnants of old villages and more.


text:
John Cairns the Presbyterian
This is the day that …John Cairns was ordained in the United Presbyterian Church – one of the three branches of Presbyterianism that existed in Scotland in 1845.
Born on 23 August, 1818, John Cairns was to become "their outstanding leader" – 33 years ministering at Berwick-on-Tweed, and then serving many years as principal of their theological college and professor of systematic theology and apologetics at the United Presbyterian Divinity Hall in Edinburgh.
It was not learned until after his death that he had received an invitation, at the age of 40, to the principalship of Edinburgh University, and had turned it down.
Alexander Gammie, in his Preachers I Have Heard, tells of Principal Cairns' pulpit style: "His arms seem to give him the most trouble. It was all utterly ungainly. It would have been enough to wreck the pulpit popularity of most men. But in his case it was quite otherwise. People would have walked miles just to hear John Cairns say: 'Let us pray …'" (page 58). "His transparent goodness, his simplicity of character, his forgetfulness of self, shone through every utterance. He was a saint who was unconscious of his saintliness …" (page 59).
And in The Christian Portrait Gallery we read: "He was an orator, and swayed his hearers with the passion and pathos of his words! He was fond of illustrations, and used similes never beyond the comprehension of the illiterate, but instinct with a fire that set the blood tingling through the veins" (page 52).
All of this was combined with a massive intellect.
Near the end of his ministry he exclaimed: "I have now preached for 43 years and I have been a professor of theology for more than 20, and I find every year how much grander the gospel of the grace of God becomes, and how much deeper, vaster and more unsearchable the riches of Christ, which it is the function of theology to explore …" (Fathers of the Kirk, by R. Wright, page 213).
Principal John Cairns died in 1892 at the age of 74.
This post is based on the work of my late friend Donald Prout whose love for books and Christian history led him to collate a daily Christian calendar. I continue to work with Don's wife, Barbara, to share his life work with the world. I have updated some of these historical posts and will hopefully draw from Don's huge files of clippings to continue this series beyond Don's original work. More of Don's work can be found at www.donaldprout.com.
Tags: edinburgh, edinburgh university, john cairns, presbyterian, presbyterianism, principal, scotland, theologian, united presbyterian church
Filed Under: Church History Tagged With: edinburgh, edinburgh university, john cairns, presbyterian, presbyterianism, principal, scotland, theologian, united presbyterian church


According to the latest report on crypto market by the Canaccord Genuity company that is focused on digital asset management, the decision on licensing Bitcoin-ETF traded on the platform is likely to be taken in 2019.
One of the leading decentralized investment companies of Canada named Canaccord Genuity has published a quarterly report which covers some unresolved issues the company is busy with. Those mentioned include "security-token" as well as the most significant price fluctuations for the main cryptocurrencies over the last months.
The document also describes problems of bitcoin-ETF and its licensing the crypto community began putting high hopes on after the companies like "VanEck" and "SolidX" claimed to present their digital project at the securities platform of СВОЕ.
The Canaccord Genuity spokesmen told that they clearly understand huge demand for bitcoin-ETF existing today in the crypto community and note that the SEC seems to delay the decision for as much as possible till early spring 2019. Meanwhile there are some odds that the regulator make take it this Friday.


KELOLAND News first broke the story of the class action lawsuit filed by a Native American student against Mid Central Educational Cooperative over the GEAR UP program.
Tonight that lawsuit is growing in size.
KELOLAND's Angela Kennecke has been investigating the GEAR UP grant for the last 8 months and tonight spoke exclusively with the attorney representing the student in the class action lawsuit.
Alyssa Black Bear is suing Mid Central saying educational co-op breached its contract to serve Native American students in the state by mishandling and mismanaging the GEAR UP grant money.
Since KELOLAND News Investigates first brought you the story of this lawsuit, several other parents, students and non-profit groups working on the reservation have contacted the attorney representing Black Bear.
While Black Bear is staying out of the spotlight for now, her Sioux Falls attorney spoke to us about the case.
While Alyssa Black Bear was the first student to file suit, this is a class action suit that could potentially have thousands of plaintiffs. Black Bear was a student at Todd County High School, but says she never got to take part in the college readiness program.
"She was eligible and was told the dollars weren't sufficient for her to have the opportunity to go through this program," Heidepriem said.
While it's too late for Black Bear and other Native American students who've already graduated, Attorney Scott Heidepriem says someone must be held accountable for the program's failure.
"After all, this is a program that is really supposed to turn the tide of poverty on the reservation. That's the tragedy of this. It existed for a reason and that reason never got carried out for thousands of kids," Heidepriem said.
Mid Central was supposed to administer it according to the plan laid out in the state's grant application to the federal government. But as our KELOLAND News investigation has revealed, what the state promised to do and what actually happened were very different.
"There was a contract here between Mid Central and the State of South Dakota and there were third party beneficiaries. Those are the thousands of kids who were supposed to be helped up to the starting line of life and were deprived of those services. So if there are any resources available we'd like to see if we can't get them to compensate those kids," Heidepriem said.
Kelsey Walking Eagle and her mother spoke with KELOLAND News last fall. They told us about many problems they experienced with the GEAR UP program and services they were supposed to get, but did not. Walking Eagle is one of the first students to join in on the class action suit with Black Bear against Mid Central.
"Treating these kids justly is really, really important. It's important to them, but it's important to all of us that actually happens. We have a chance to do that we think," Heidepriem said.
The lawsuit outlines many of the promises the state of South Dakota made about administering the grant to the federal government. Those include safeguards to prevent fraud and conflict of interest. Heidepriem says naming the state in the lawsuit hasn't been ruled out.
No court date has been set yet for Black Bear's lawsuit, which is filed in Charles Mix County.


I would say that fantasy is one of the hardest thing to write for me. I have to close my eyes and turn my mind into a film camera and project the vivid world of imagination. I see my characters. I haven not figured out what they would look like maybe something beautiful, maybe something grotesque or maybe something just plain. Once I know their visage I work on their personalities and the adventures they are going to embark.
It's all in a hard days work. I end up scrapping two to three stories before I am satisfied with the finished product. My first story I wrote had 12 chapters and I still had not gotten to the climax or the development of my characters. It was about a young girl that lived with her grandmother, a witch. Her parents had abandon her years earlier and she had not seen them since. They lived in a cottage far from the town, near the woods. She had no friends to speak to or anyone she socialized with. Her grandmother was not rich but she was not poor either. Grandma had a regal demeanor and made her living by baking breads, desserts and other tasty dishes. I got pretty good at writing down the foods Grandma made. I could have written a cook book with all the foods I described in my book.
Well, the only time the girl got to go into town is when her grandmother delivered her foods to the many patrons that paid her. It took Grandma all day to do the deliveries. Sometimes the girl got to wander the town, sometimes she had to help her grandmother but she never complained. You could say, I made the little girl placid and content with how things were. She wasn't rebellious or restless, seeking to see the world or have adventures. She accepted how things were.
She was not aware that her grandmother was a witch. How else would they bake 30 pies and cakes and still have time to knead the dough for different kinds of breads, then make delicious soups, casseroles, and meats of all kinds. That was my problem again. I wrote more about the food than the story. Anyhow, the grandmother was all very watchful of the girl especially when she turned twelve.
Along the way she meets a friend who just moved into town. His father had just died and his grieving mother wanted to start anew in this town. He moved into with an Uncle everybody in town avoided. He was mean and evil and hated children, especially those that passed by his house. He would chase them away very often.
Then there was the Mayor who had his own malicious agenda. He had a daughter who befriended them both. In the story they were going to stop the Mayor and his plans but how could they when they were friends with his daughter. The daughter did not believe her father was mean just doing his job that bought him many enemies.
I was just unraveling one moment to another without a climax. I hope I can rewrite the story in the future.


text:
PAN-Utopia
Great Utopians
Collective Imagination
Valongo, Cidade Utópica
Pedagogical resources
Utopia 500 Team
Guestbook Project
ARUS
Pan Utopia
Sounds of Utopia
Solidarity Movements and Organizati
Sara Conesa
Portuguese Immigration Measures
How much do we know about refugees and immigrants? This issue is a major problem of the last decades and, although you can find all types of data about refugees arriving to countries like the UK and Germany, there is very little to no information about this subject in minor countries like Portugal. Although this Iberian country has not received as many immigrants and asylum seekers, it is undoubtedly their problem too. In fact, Portugal offered to accept 10.000 people, more than 3 times the amount it was assigned. Thanks to Refugees Welcome Porto and an interview with a volunteer in the Portuguese Immigration Centre, I have been able to collect some important information that will be useful to understand the procedures immigrants have to go through and how it is to live without papers.
We should always keep in mind that the terms "refugee" and "asylum seeker" do not refer to the same thing. Refugee refers to an individual that has fled from his country and has a permit (i.e. legal papers) to stay in a different country from his. On the other hand, asylum seekers are those in the process to become refugees. As such, when they arrive to Portugal to seek asylum, they have to present themselves to the authorities or the SEF (Serviço de Estrangeiros e Fronteiras), who has 20 days to decide on the admissibility or inadmissibility of the asylum claim. If the decision is negative, the applicant can solicit a re-appreciation of its case. If it is approved, the applicant is either recognised as a Refugee or issued a Temporary Residence Permit valid for two months and which can be renewed for periods of 30 days until the final decision. With this permit, asylum seekers are allowed to work, to study, to attend professional training courses, and to apply for a tax contributor card. In other words, if you are not a refugee and do not have this permit, you do not have access to any of these rights. Concerning health care, asylum seekers, stateless persons and refugees, regardless of whether they are holding a Residence Permit (temporary or not), are entitled to medical assistance and medication.
But who exactly is being detained? As detailed by Portuguese immigration laws, asylum seekers lodging or asking for an application at ports of entry and any foreign citizen who unlawfully enters or stays in national territory may be detained. But this does not end here. It seems very easy for immigrants to become undocumented and illegal, and be detained by the authorities, for instance, if your passport expires and you do not renew it. Moreover, immigrants can only be held at airports or other ports of entry for a maximum of 7 days, and the detention order needs to be validated by a judge. Nevertheless, they can be held beyond that time frame if there is a risk of absconding or a failure to comply with the voluntary removal order. At detention centres this time frame is extended to 60 days, but if you are an immigrant already living in Portugal, you are offered an alternative to immigration detention centres: the obligation to report to immigration or police authorities, and home confinement using electronic surveillance. To what extent this is granted we do not know yet.
Naturally, all detainees have a high chance of being deported if their documents are not valid or approved by a judge, but this is more complicated than it seems at first. Countries are not allowed to deport people whose countries of birth are at war or have been declared unstable by the UN. For instance, Afghanistan has become more peaceful in the last fifteen years and, although being still one of the most unstable states in the world, the UN has declared it stable and Afghan asylum seekers can now be deported. However, deportation involves a lot of costs and agreements. As a case in point, if Portugal wants to deport someone to Morocco, first they have to contact them in order to confirm if they will accept him. If Morocco refuses, then Portugal is obliged to maintain the person in the country. On the other hand, if you are deported, you are allowed to apply for a visa again to that same country after a period of 3 years.
On top of that, while their documents are being processed, asylum seekers and illegal immigrants are detained at the Immigration Detention Centre in Porto, namely Unidade Habitacional de Santo António, for a maximum of 60 days, after which they are released for 21 days and can be detained back again after that time frame. The problem is that even when they are released, they still do not have legal papers, which means that they cannot get jobs nor rent a house, and they go homeless. Sometimes, the only way for them to get food and a place to sleep is by actually being detained to the detention centre, where they may find both good and bad conditions.
The Unidade Habitacional de Santo António is managed by SEF, which collaborates in delivering services at the facility with IOM (International Organisation for Migration), JRS (Jesuit Refugee Services), and Médicos do Mundo (Doctors of the World). SEF also manages short-term holding facilities located at the airports of Faro, Lisbon, Funchal, and Ponta Delgada, and other border central stations. The facilities include an outdoor green space; common areas with tables, sofas and two TVs; a child-friendly zone equipped with toys and cribs; a canteen; and the rooms, which are divided by gender and located in different floors. It has been reported, nevertheless, that the facilities lack adequate recreational facilities. Moreover, the total capacity of the centre is 36 people (30 adults and 6 children), which is barely enough space for the increasing influx of immigrants and asylum seekers in Portugal. This means that sometimes detainees have to sleep on the floor because there is not enough room in the centre. Many of them are even sent back to the detention facilities at the airports, which can be more fittingly described as jails. On the other hand, detainees at the centre have access to the Internet and telephone cards (to personal phones in some instances) for a certain price, and are allowed two hours of phone calls and four cigarettes per day. It is nevertheless true that the centre has certain security rules. For instance, journalists are not allowed in the facilities, detainees have to be together at all times, and there are some objects that they are not allowed to have, such as scissors, belts, shoe laces, pens or pencils, and anything that could be used as a weapon.
All in all, there are still many things beyond our understanding and we cannot claim to comprehend the entire journey of becoming stateless, but we can help make these people's lives a little bit better, and a great example of this is Gail, a volunteer at the Immigration Detention Centre in Porto that helped me understand this strange world of politics and immigration laws. She pointed out several times throughout our conversation how important it is to help this minority directly in order to make an impact: "I think that when you are doing volunteering, it is important that you have a connection with the people you are helping. It is not like putting money in an envelope every month or something like that. You need to see what you are doing so that you can either adjust your behaviour or your attitude, or be able to talk about it at first hand. You can get reports on how well your money is doing in a volunteering, donation or charity situation, but your reward should be seeing the people you are helping. If you see someone who needs help, you can do something about it straightaway. I cannot help these people become legal, but I can help them feel better. Many of these people have a lot of problems, and sometimes it is just being there and remembering their names and where they come from that makes a difference." So, if you feel like you are not doing enough and want to collaborate in a solution, helping those who arrive to your country, and showing them your language and culture, may be a fun and life-enhancing way to do it. You can always count on associations like Refugees Welcome or Programa Mentores Para Migrantes to help you through this process and make the most out of it (see the link below for more information).
"The challenge of talking about sex with young people"
Burrneshat & 101 Intro to Women rights in Kosovo
Porto Innovation Hub


Study the new language (Hindi or Bengali)for 30 minutes every day morning. Try to fit it in your daily routine.
Go over old lessons several times .Always take notes when you are learning .It helps you to understand the language better. Don't start a new chapter if you are not confident about the old lesson.
Make sure you're comfortable with the fundamentals of the language. The basic grammar rules, tenses etc.It might take a while to get used to the pronunciation and orthography of a new language.But don't worry, it's very normal and with everyday practise it's not hard to learn these things.
Give yourself a certain time to reach a level, suppose 2-3 months to start basic conversation. Next 2 months to learn the script etc…Setting reasonable targets is good to motivate yourself. And by this you will reach a certain level of proficiency soon.
When you start basic conversation in a language try to talk about what you are interested in. Learn new words on the topic and try start a discussion on the topic and participate.Don't hesitate.
Learning a new language always takes time.And that varies person to person.Just be regular and don't think about the progress .After 2-3 months just compare what you have learn than the time you have just started it.
Don't worry about it.But try to speak the language aloud.
Learn the language in a funny way.Don't be so serious or try to learn so fast.Because this way you will lose interest to learn the language.
Try to find persons also learning the same language.Attend meet ups to meet new learners and learn in a funway.
Ask your teacher or a native language speaker to test your language level.It helps to grow your confidence.
Learn Bengali numbers with talkingbees.Here I will first tell you the English numbers then Bengali numbers from 1-10 .Then I will say only Bengali numbers one after one ,and I want you to repeat after me.
If you are thinking to learn a new language then why not give it a try.Hindi is one of the most spoken languages in the world.There are many Indians through out the world may be few are your friends.There are a lot more reasons for learning Hindi .Just ask me.I listed the cause people showed then they came to learn Hindi from me.
Check it " Reasons for Learning Hindi"
You like to travel a lot and India is so beautiful .Its nature variation ,different geographical places.
People like our culture and our warm welcome for visitors.
People easily make friends here and keep coming often to meet their friends and gradually they like our country a lot.
If some one fall in love with a guy or a girl or already married to an Indian ,then they want to understand their partner and they start learning Hindi.
Few want to start learning Hindi as they are interested in Hinduism or for other religious purposes.
Few want to learn Hindi to get a job in American Consulate as a Foreign Service Officer (FSO) and they might get a good rank if they pass a language test,such as Hindi language test.So its also important for their career.


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Phnom Penh Post - Irish aid fuels library renaissance
Irish aid fuels library renaissance
Tarek Bazley | Publication date 05 January 2001 | 07:00 ICT
Fri, 5 January 2001
Tarek Bazley
irish.jpg
A student peruses one of the library's newer acquisitions.
ames Molloy moves easily between a stack of books and the glass-fronted shelves.
He brushes his fringe back and then examines the spine of an archaeology text with
a studious air.
"I don't mind the fact there's more shelving to do - at least it means the students
are using the library more than before," he said.
For the past two years Molloy has been part of an Irish government-supported initiative
to preserve and restore the contents of the Phnom Penh Fine Arts University Library,
one of Cambodia's most valuable but less well known collections of books.
Hidden behind the National Museum in central Phnom Penh, the library boasts a multi-lingual
collection of more than 16,000 titles.
But the real challenge, according to Molloy, has been to get the students to actually
read them.
"Students don't seem to have books as part of their culture. So my job has been
to encourage them actively, show them how to find the information and what resources
are available," he said.
Not yet a lending library, the library is reputedly one of the best of its kind in
the country, rivaled only by the Royal University of Phnom Penh's library.
Founded in 1965, the Fine Arts library originally focused on books and journals for
the 1000 students enrolled in the university's 4-year degree programs in Archaeology,
Architecture and Fine Arts.
By the time of the Khmer Rouge takeover in April 1975, the library had a substantial
collection of mainly French language texts.
According to Molloy, 90% of those books survived the Khmer Rouge regime (the other
10% deteriorated with the heat and the dust). The library staff, however, were less
fortunate, with none of the original librarians alive when the library re-opened
As a result, one of Molloy's biggest challenges in getting the library running has
been to train its three government-paid staff members and to get them conversant
with the mores of library culture.
"We had to get regular opening times and the simple cataloguing and shelving
systems going. I didn't want to be doing all this myself because the point of the
project is to make it a library run by Cambodians for Cambodians.
"We wanted it to be self supporting and although there will still be some NGO
involvement on a part-time basis, I think we've achieved that," he said.
More than 20% of the collection is now catalogued on computer, which along with photocopiers,
audio-visual equipment and new acquisitions are provided by donors.
"We rely to a large degree on Book Aid International and The Asia Foundation
for most of our new acquisitions. While the donated books are not always the ones
we'd choose for the library they are often unusual and interesting," he said.
Molloy notes that the demands of the student users of the library have also changed
with the times, with greater numbers of English language texts joining the shelves
of French and Cambodian-language books.
"Increasingly now the students want books in English and if we want them to
use the library we can't ignore that," he said.
However, Molloy stressed the importance of quality over quantity in terms of the
library's need for new acquisitions.
"Unless books are useful and up-to-date they may never get used and will be
a waste of space on the shelves," he said.
Although his official involvement with libraries in Cambodia ended in December, from
his new base in Australia Molloy intends to set up an internet web site to encourage
authors and academics in the West to donate their books to Phnom Penh's university
libraries.
The Fine Arts University Library is open to the public for a small subscription fee
weekdays 7.30-11am and 2-5pm.
Tarek Bazley is in Cambodia with the help of Aisa 2000 (NZ).


text:
BTW Articles
House Antitrust Subcommittee Releases Long-Awaited Big Tech Report
By David Grogan
The subcommittee proposed changes to antitrust law to rein in Big Tech's power. Among additional recommendations were ways to make it easier to go after dominant platforms for predatory pricing.
Disasters/Relief
Preparing for an SBA Loan Application
Any small business that is planning to seek an SBA forgivable loan or grant during the COVID-19 crisis should be gathering its financial documentation now.
ABA Joins Broad Coalition of Trade Associations in Urging Congress to Create COVID-19 Business Relief Fund
The Business and Employees Continuity and Recovery Fund would look to distribute federal funds and liquidity to impacted businesses and their employees.
About Bookselling
Music Licensing for the Uninitiated
If you play music in your bookstore, you need to understand what your legal responsibilities — and rights — are. Here's an up-to-date look at the major performing rights organizations, what they do, and the factors that dictate their licensing fees.
Sales Tax Initiative
Amazon to Begin Collecting Sales Tax July 1
On Monday, July 1, Amazon.com will begin collecting and remitting sales tax to New Jersey for purchases made by New Jersey residents.
Health Plans Legislation
White House Updates HealthCare.gov in Advance of Health Exchanges
On Monday, June 24, the Obama Administration announced it had kicked off its Health Insurance Marketplace (also known as exchanges) education effort with a new "consumer-focused" HealthCare.gov website, as well as a 24/7 consumer call center designed to assist Americans in preparing for enrollment in the health exchanges.
Marketplace Fairness Coalition Urges House to Consider Sales Tax Fairness
This week, the Marketplace Fairness Coalition (MFC) is urging the U.S. House of Representatives Committee on the Judiciary to hold a hearing on sales tax fairness. MFC includes among its members the American Booksellers Association, the International Council of Shopping Centers (ICSC), and the Retail Industry Leaders Association (RILA).
Amazon.com Drops Its Minnesota Affiliates to Avoid Sales Tax
Amazon.com and other online retailers are terminating their affiliate programs in Minnesota to avoid triggering nexus, as reported by the Minneapolis Star Tribune.
Booksellers Urged to Help Fight for Sales Tax Fairness in Face of Strong Opposition
Given the intensity of opponents' campaign against The Marketplace Fairness Act of 2013, ABA is urging its members to contact their Congressperson.
ABA Recommends Members Opt Out of Swipe Fee Settlement
In an April 19 e-mail to bookstore members regarding the class action settlement over rapidly rising Visa and MasterCard swipe fees, ABA recommended that members opt out and object to the settlement.


Apple had earlier promised to get music to the wrist by getting LTE service, with which music can be directly streamed from the watch itself. But Apple launched the watch without the feature a month ago at the Steve Jobs Theater. Now Apple has delivered the promise and has issued notifications for music streaming for Apple Watch Series 3s with LTE to its customers.
This feature only works with Apple's own music streaming service, and users can't stream Spotify or Google Play Music directly from the Apple Watch. The watch has two separate native music apps and streaming on it requires vocal interaction with Siri to access the library.


WE WILL ROCK YOU takes place in a future age on a planet once called Earth that is now controlled by a mighty corporation … rock music is unheard and all musical instruments are banned. The hope of breaking free rests with an unlikely resistance – an alliance of rebel Bohemians. The show features many of Queen's chart topping hits including "We Will Rock You," "Bohemian Rhapsody," "Radio Gaga," "Another One Bites the Dust," "We are the Champions," and more. It's the world champion of musicals and the show that rocks harder than any other, so don't miss the party!


text:
Q: iPod Touch 3rd gen I'm looking to buy a 3rd gen iPod Touch, largely for the portable music aspect. However, playing games is fun, so I wanted to check how out of date the iOS on the 3rd gen version will be. How far can the iOS be updated?

A: iOS 5.1.1
https://discussions.apple.com/thread/4393895?start=0&tstart=0
Hardware limitation.


The 8th Annual Texas Monthly BBQ Fest took place this weekend at the Long Center in honor of their June BBQ issue where the staff names the Top 50 BBQ joints in Texas with a heavy emphasis on the Top 10. Luckily, this year, the sun wasn't blazing and 90 degrees like last year (which gave me sun poisoning on my first day back to living in the state of Texas). I also might have had something to do with the fact that I waited in line for Franklin's for 45 min in the heat on that hot pavement. Anyway, this year's weather was much more bleak (although still warm), and it felt much better and easier to indulge in heavy, fatty, meats on the lawn. With over 30 restaurants out there, and only a few hours to attack, my first stop was, again, Franklin's. While this was not my plan, I scored big time as my friend @TheSmokingHo was behind the table with Aaron taking photos, so he snagged me a plate of brisket and pulled pork. I usually skip pulled pork as I'm a ribs, brisket, and turkey kinda gal but this jolt of pepper was a great way to kick it off. My brisket was the most lean I've ever had from Franklin's BBQ which I did know was even an option here. When I had my first sit down meal at the restaurant during SXSW, I didn't know what hit me. It was the richest, moistest, bark-iest brisket I'd ever had, it kind of knocked me out for a day— but in a good way, of course. While I really enjoyed that, this leaner option I had at the festival was still flavorful with a good seasoning around the edges and just a tiny layer of fat. This caused me to rethink my whole philosophy about what makes the best BBQ brisket.
I've realized that it can be pretty rare to get the same consistency out of a brisket every time you go back to your favorite spot. While you can request a preference of fatty or moist vs. lean, it really depends what part of the brisket is being cut off for you (is it the end or middle?), who is cutting it, how long it was cooked that day, etc. Because I will say I have had "my favorite" BBQ at one spot one day and then when I've gone back at night, it hasn't been the same. That being said, after eating at 10 of these spots today, some of the brisket really all started to taste the same, but when something stood out, it REALLY stood out among the rest. BBQ is an art and so much blood, sweat, and bones really goes into perfecting the perfect meat for our state. You never know what you're going to get, but when you get that rich, juicy piece of brisket, you forget everything else that is going on in the world, for at least a second.
2M Smokehouse (San Antonio) – Beef Barbacoa tacos and brisket. It's always fun to see some of the creative options besides the standard brisket, ribs, sausage situation. The tacos were rich and spicy as hell, and the brisket was comforting and good enough to make me want to drive to SA to check out the spot now. Especially that chicharron macaroni.
la BBQ (Austin) – I haven't been to la BBQ since I moved back to Austin. This was the moistest, fattiest brisket I had all day, and also made me dance a little. Thank you for having some pickles to off set the meat!
Franklin BBQ (Austin) – I really loved trying a leaner piece of Franklin's and STILL thinking it was legit. Lean brisket that still tastes tender and not dried out is hard to find.
Tejas Chocolate Craftory (Tomball) – This was the most creative and most fun selection of the day. One plate got you Pastrami Burnt Ends with Mustard, Brisket, a mini Danish Pork Rib, Carrot Souffle , and a Chocolate Mousse Panna Cotta situation. A whole meal in one WITH dessert. In case you are wondering, yes I loved the meat as much as the fact that there was dessert there. Pastrami seasoning is always fun with BBQ especially when it's fall of the bone style meat.
Valentina's Tex-Mex (Austin)– Valentina's is one of my favorite restaurants in Austin. Their flour tortillas are the best I've ever had and they make them fresh to order. I tried their ribs for the first time here which ended in approval and the brisket was moist and standard.
Pinkerton's (Houston) – I've read great things about this Houston joint, and I have to say I am so happy with the options this year as there was NO dry meat. However, at this point, I think I overdid it and hit a meat wall. While the meat was cooked well, the rub was not as peppery compared to everything I had already eaten.
Bodacious BBQ (Longview) – They had boudin: a soft, beef sausage stuffed with rice.
Truth BBQ (Brenham) – No. 10 on the magazine's list. If only they had brought their chocolate cake I had read so much about! Can't wait to go here.
Cattelack Barbeque (Dallas) – They had some pastrami brisket, sausage, and actual bologna!
The Texas Monthly BBQ Fest is a fun family-friendly event where all the best pitmasters in Texas come to us in one day! With 4 hours of meat eating activity, you're bound to be full the rest of the day. This year was such an improvement from last year with open picnic table seatings with umbrellas vs. long communal tables under a tent, the Peterson Brother's performed, and all of the meat was superb!! This event is the best way to eat around the world of Texas BBQ in just a few hours without having to drive anywhere.


text:
How much does a summer wedding with 100 to 150 guests cost? The ceremony is at the beach and reception is at a resort or hotel. The type of wedding is Beach, Romantic. The wedding color theme is Pinks. The estimated cost for this wedding is $35,515.
How much does a spring wedding in Duluth MN with 100 to 150 guests cost? The ceremony is at the beach and reception is at a resort or hotel. The type of wedding is Casual, DIY, Fun, Rustic, Simple, Farm/Barn, Intimate. The wedding color theme is Greens. The estimated cost for this wedding is $31,438.
How much does a summer wedding in Milwaukee WI with 100 to 150 guests cost? The ceremony is at the beach and reception is at a resort or hotel. The type of wedding is Rustic, Vintage, Modern, Farm/Barn. The wedding color theme is White. The estimated cost for this wedding is $34,981.
How much does a spring wedding in Fredericksburg with 100 to 150 guests cost? The ceremony is at the beach and reception is at a banquet hall. The type of wedding is Beach. The wedding color theme is Champagne. The estimated cost for this wedding is $47,103.
How much does a summer wedding in Nantucket MA with 100 to 150 guests cost? The ceremony is at the beach and reception is at some other location. The type of wedding is Beach, Elegant, Romantic, Simple. The wedding color theme is White. The estimated cost for this wedding is $31,412.
How much does a spring wedding in 08204 with 100 to 150 guests cost? The ceremony is at the beach and reception is at an event facility. The type of wedding is Beach, Casual, Romantic, Simple, Modern. The wedding color theme is Greens. The estimated cost for this wedding is $21,783.
How much does a winter wedding in Los Angeles CA with 100 to 150 guests cost? The ceremony is at the beach and reception is at a family or friend's home. The type of wedding is Beach, Elegant, Modern. The wedding color theme is Pinks. The estimated cost for this wedding is $66,501.
How much does a summer wedding in Panama City Beach FL with 100 to 150 guests cost? The ceremony is at the beach and reception is at a country club or golf course. The type of wedding is Beach, Casual, Elegant, Fun, Romantic, Simple, Theme, Modern. The wedding color theme is Pinks. The estimated cost for this wedding is $48,108.
How much does a spring wedding in Marco Island FL with 100 to 150 guests cost? The ceremony is at the beach and reception is at a restaurant. The type of wedding is Beach, Casual, Destination, Fun, Romantic. The wedding color theme is Purples. The estimated cost for this wedding is $33,293.
How much does a spring wedding with 100 to 150 guests cost? The ceremony is at the beach and reception is at a restaurant. The type of wedding is Beach, Fun, Traditional, Experience. The wedding color theme is Other. The estimated cost for this wedding is $29,291.
How much does a winter wedding in Seneca SC with 100 to 150 guests cost? The ceremony is at the beach and reception is at a garden or park. The type of wedding is Beach, Fun, Simple, Unique. The wedding color theme is Reds. The estimated cost for this wedding is $35,932.
How much does a wedding cost in st. petersburg florida with 100 to 150 guests? The ceremony is at the beach and reception is at an event facility. The type of wedding is Beach, Simple. The wedding color theme is Pinks. The estimated cost for this wedding is $26,152.
How much does a wedding cost in Pawleys Island SC with 100 to 150 guests? The ceremony is at the beach and reception is at a country club or golf course. The type of wedding is Beach, Destination, Traditional. The wedding color theme is Blues. The estimated cost for this wedding is $34,245.
How much does a wedding cost in Honolulu HI with 100 to 150 guests? The ceremony is at the beach and reception is at a resort or hotel. The type of wedding is Beach, Destination, Elegant, Formal, Fun, Romantic, Theme, Unique, Modern, Intimate, Experience. The wedding color theme is Blues. The estimated cost for this wedding is $59,564.
How much does a wedding cost in Carpinteria CA with 100 to 150 guests? The ceremony is at the beach and reception is at a resort or hotel. The type of wedding is Beach, Fun, Experience. The wedding color theme is White. The estimated cost for this wedding is $35,035.
How much does a wedding cost in Long Beach CA with 100 to 150 guests? The ceremony is at the beach and reception is at a resort or hotel. The type of wedding is Beach, Fun, Romantic, Unique, Vintage, Modern. The wedding color theme is Blues. The estimated cost for this wedding is $36,494.
How much does a wedding cost in Santa Monica CA with 100 to 150 guests? The ceremony is at the beach and reception is at a resort or hotel. The type of wedding is Beach, Destination, Elegant, Formal, Fun, Romantic, Unique, Intimate, Experience. The wedding color theme is Champagne. The estimated cost for this wedding is $87,805.
How much does a wedding cost in San Diego CA with 100 to 150 guests? The ceremony is at the beach and reception is at a garden or park. The type of wedding is Beach, Elegant, Formal, Fun, Romantic, Unique, Modern. The wedding color theme is Yellows. The estimated cost for this wedding is $52,569.
How much does a wedding cost in South Padre Island TX with 100 to 150 guests? The ceremony is at the beach and reception is at an event facility. The type of wedding is Beach, Fun, Romantic, Simple, Theme, Intimate. The wedding color theme is Blues. The estimated cost for this wedding is $28,432.
How much does a wedding cost in Orlando FL with 100 to 150 guests? The ceremony is at the beach and reception is at a resort or hotel. The type of wedding is Beach. The wedding color theme is Pinks. The estimated cost for this wedding is $24,932.
How much does a wedding cost in Seaside Park NJ with 100 to 150 guests? The ceremony is at the beach and reception is at a family or friend's home. The type of wedding is Beach, DIY, Fun, Garden. The wedding color theme is White. The estimated cost for this wedding is $37,191.
How much does a wedding cost in Newark NJ with 100 to 150 guests? The ceremony is at the beach and reception is at a restaurant. The type of wedding is Beach, Fun, Garden, Traditional, Modern, Experience. The wedding color theme is Silver. The estimated cost for this wedding is $46,200.
How much does a wedding cost in Seattle WA with 100 to 150 guests? The ceremony is at the beach and reception is at a resort or hotel. The type of wedding is Beach, Romantic. The wedding color theme is Blues. The estimated cost for this wedding is $38,026.
How much does a wedding cost in Seattle WA with 100 to 150 guests? The ceremony is at the beach and reception is at a resort or hotel. The type of wedding is Beach, Romantic, Theme. The wedding color theme is White. The estimated cost for this wedding is $50,449.
How much does a wedding cost in Long Beach Island NJ with 100 to 150 guests? The ceremony is at the beach and reception is at a banquet hall. The type of wedding is Beach, Elegant, Unique. The wedding color theme is Purples. The estimated cost for this wedding is $71,980.