Given the following dataframe, is there some way to select only columns starting with a given prefix? I know I could do e.g. pl.col(column) for column in df.columns if column.startswith("prefix_"), but I'm wondering if I can do it as part of a single expression.
df = pl.DataFrame(
{"prefix_a": [1, 2, 3], "prefix_b": [1, 2, 3], "some_column": [3, 2, 1]}
)
df.select(pl.all().<column_name_starts_with>("prefix_"))
Would this be possible to do lazily?
From the documentation for polars.col, the expression can take one of the following arguments:
a single column by name
all columns by using a wildcard “*”
column by regular expression if the regex starts with ^ and ends with $
So in this case, we can use a regex expression to select for the prefix. And this does work in lazy mode.
(
df
.lazy()
.select(pl.col('^prefix_.*$'))
.collect()
)
>>> (
... df
... .lazy()
... .select(pl.col('^prefix_.*$'))
... .collect()
...
... )
shape: (3, 2)
┌──────────┬──────────┐
│ prefix_a ┆ prefix_b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞══════════╪══════════╡
│ 1 ┆ 1 │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 2 │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 3 ┆ 3 │
└──────────┴──────────┘
Note: we can also use polars.exclude with regex expressions:
(
df
.lazy()
.select(pl.exclude('^prefix_.*$'))
.collect()
)
shape: (3, 1)
┌─────────────┐
│ some_column │
│ --- │
│ i64 │
╞═════════════╡
│ 3 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1 │
└─────────────┘
Related
Any idea what I can do to imitate the below pandas code using polars? Polars doesn't have indexes like pandas so I couldn't figure out what I can do .
df = pd.DataFrame(data = ([21,123], [132,412], [23, 43]), columns = ['c1', 'c2']).set_index("c1")
print(df.loc[[23, 132]])
and it prints
c1
c2
23
43
132
412
the only polars conversion I could figure out to do is
df = pl.DataFrame(data = ([21,123], [132,412], [23, 43]), schema = ['c1', 'c2'])
print(df.filter(pl.col("c1").is_in([23, 132])))
but it prints
c1
c2
132
412
23
43
which is okay but the rows are not in the order I gave. I gave [23, 132] and want the output rows to be in the same order, like how pandas' output has.
I can use a sort() later yes, but the original data I use this on has like 30Million rows so I'm looking for something that's as fast as possible.
I suggest using a left join to accomplish this. This will maintain the order corresponding to your list of index values. (And it is quite performant.)
For example, let's start with this shuffled DataFrame.
nbr_rows = 30_000_000
df = pl.DataFrame({
'c1': pl.arange(0, nbr_rows, eager=True).shuffle(2),
'c2': pl.arange(0, nbr_rows, eager=True).shuffle(3),
})
df
shape: (30000000, 2)
┌──────────┬──────────┐
│ c1 ┆ c2 │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞══════════╪══════════╡
│ 4052015 ┆ 20642741 │
│ 7787054 ┆ 17007051 │
│ 20246150 ┆ 19445431 │
│ 1309992 ┆ 6495751 │
│ ... ┆ ... │
│ 10371090 ┆ 4791782 │
│ 26281644 ┆ 12350777 │
│ 6740626 ┆ 24888572 │
│ 22573405 ┆ 14885989 │
└──────────┴──────────┘
And these index values:
nbr_index_values = 10_000
s1 = pl.Series(name='c1', values=pl.arange(0, nbr_index_values, eager=True).shuffle())
s1
shape: (10000,)
Series: 'c1' [i64]
[
1754
6716
3485
7058
7216
1040
1832
3921
1639
6734
5560
7596
...
4243
4455
894
7806
9291
1883
9947
3309
2030
7731
4706
8528
8426
]
We now perform a left join to obtain the rows corresponding to the index values. (Note that the list of index values is the left DataFrame in this join.)
start = time.perf_counter()
df2 = (
s1.to_frame()
.join(
df,
on='c1',
how='left'
)
)
print(time.perf_counter() - start)
df2
>>> print(time.perf_counter() - start)
0.8427023889998964
shape: (10000, 2)
┌──────┬──────────┐
│ c1 ┆ c2 │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞══════╪══════════╡
│ 1754 ┆ 15734441 │
│ 6716 ┆ 20631535 │
│ 3485 ┆ 20199121 │
│ 7058 ┆ 15881128 │
│ ... ┆ ... │
│ 7731 ┆ 19420197 │
│ 4706 ┆ 16918008 │
│ 8528 ┆ 5278904 │
│ 8426 ┆ 18927935 │
└──────┴──────────┘
Notice how the rows are in the same order as the index values. We can verify this:
s1.series_equal(df2.get_column('c1'), strict=True)
>>> s1.series_equal(df2.get_column('c1'), strict=True)
True
And the performance is quite good. On my 32-core system, this takes less than a second.
I have a dataframe where a number of the columns only consists of NaNs. I am trying to select only the columns in the dataframe where all the values are not equal to NaNs using Polars.
I have tried seeing if I could use a similar syntax to how I would proceed in Pandas e.g.
df[df.columns[~df.is_null().all()]]
However the syntax doesn't translate.
I also know that you can use pl.filter but this only filters rows and not columns based on the criteria's applied within the filter expression.
So this is basically subsetting columns with a boolean mask.
So first let's create some sample data:
import polars as pl
import numpy as np
df = pl.DataFrame(
{"a": [np.nan, np.nan, np.nan, np.nan],
"b": [3,4, np.nan, 5],
"c": [np.nan, np.nan, np.nan, np.nan]
})
Next we have to get if a column consists completely of NaN Values
df.select(pl.all().is_nan().all().is_not())
shape: (1, 3)
┌───────┬──────┬───────┐
│ a ┆ b ┆ c │
│ --- ┆ --- ┆ --- │
│ bool ┆ bool ┆ bool │
╞═══════╪══════╪═══════╡
│ false ┆ true ┆ false │
└───────┴──────┴───────┘
To get this DataFrame as iterable we use the row function
df.select(pl.all().is_nan().all().is_not()).row(0)
(False, True, False)
This we can now use in the bracket notation
df[:, df.select(pl.all().is_nan().all().is_not()).row(0)]
shape: (4, 1)
┌─────┐
│ b │
│ --- │
│ f64 │
╞═════╡
│ 3.0 │
├╌╌╌╌╌┤
│ 4.0 │
├╌╌╌╌╌┤
│ NaN │
├╌╌╌╌╌┤
│ 5.0 │
└─────┘
Since in general bracket notation is not recommended we can do this also with select: (for looking more concise we use the compress function from itertools)
from itertools import compress
df.select(compress(df.columns, df.select(pl.all().is_nan().all().is_not()).row(0)))
shape: (4, 1)
┌─────┐
│ b │
│ --- │
│ f64 │
╞═════╡
│ 3.0 │
├╌╌╌╌╌┤
│ 4.0 │
├╌╌╌╌╌┤
│ NaN │
├╌╌╌╌╌┤
│ 5.0 │
└─────┘
Suppose we have a polars dataframe like:
df = pl.DataFrame({"a": [1, 2, 3], "b": [3, 4, 5]}).lazy()
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 3 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 4 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 5 │
└─────┴─────┘
I would like to X^TX the matrix while preserving the sparse matrix format for arrow* - in pandas I would do something like:
pdf = df.collect().to_pandas()
numbers = pdf[["a", "b"]]
(numbers.T # numbers).melt(ignore_index=False)
variable value
a a 14
b a 26
a b 26
b b 50
I did something like this in polars:
df.select(
[
(pl.col("a") * pl.col("a")).sum().alias("aa"),
(pl.col("a") * pl.col("b")).sum().alias("ab"),
(pl.col("b") * pl.col("a")).sum().alias("ba"),
(pl.col("b") * pl.col("b")).sum().alias("bb"),
]
).melt().collect()
shape: (4, 2)
┌──────────┬───────┐
│ variable ┆ value │
│ --- ┆ --- │
│ str ┆ i64 │
╞══════════╪═══════╡
│ aa ┆ 14 │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ ab ┆ 26 │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ ba ┆ 26 │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ bb ┆ 50 │
└──────────┴───────┘
Which is almost there but not quite. This is a hack to get around the fact that I can't store lists as the column names (and then I could unnest them to become two different columns representing the x and y axis of the matrix). Is there a way to get the same format as shown in the pandas example?
*arrow is a columnar data format which means it's performant when scaled across rows but not across columns, which is why I think the sparse matrix representation is better if I want to use the results of the gram matrix chained with pl.LazyFrames later down the graph. I could be wrong though!
Polars doesn't have matrix multiplication, but we can tweak your algorithm slightly to accomplish what we need:
use the built-in dot expression
calculate each inner product only once, since <a, b> = <b, a>. We'll use Python's combinations_with_replacement iterator from itertools to accomplish this.
automatically generate the list of expressions that will run in parallel
Let's expand your data a bit:
from itertools import combinations_with_replacement
import polars as pl
df = pl.DataFrame(
{"a": [1, 2, 3, 4, 5], "b": [3, 4, 5, 6, 7], "c": [5, 6, 7, 8, 9]}
).lazy()
df.collect()
shape: (5, 3)
┌─────┬─────┬─────┐
│ a ┆ b ┆ c │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1 ┆ 3 ┆ 5 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 4 ┆ 6 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 5 ┆ 7 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 4 ┆ 6 ┆ 8 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 5 ┆ 7 ┆ 9 │
└─────┴─────┴─────┘
The algorithm would be as follows:
expr_list = [
pl.col(col1).dot(pl.col(col2)).alias(col1 + "|" + col2)
for col1, col2 in combinations_with_replacement(df.columns, 2)
]
dot_prods = (
df
.select(expr_list)
.melt()
.with_column(
pl.col('variable').str.split_exact('|', 1)
)
.unnest('variable')
.cache()
)
result = (
pl.concat([
dot_prods,
dot_prods
.filter(pl.col('field_0') != pl.col('field_1'))
.select(['field_1', 'field_0', 'value'])
.rename({'field_0':'field_1', 'field_1': 'field_0'})
],
)
.sort(['field_0', 'field_1'])
)
result.collect()
shape: (9, 3)
┌─────────┬─────────┬───────┐
│ field_0 ┆ field_1 ┆ value │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ i64 │
╞═════════╪═════════╪═══════╡
│ a ┆ a ┆ 55 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ a ┆ b ┆ 85 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ a ┆ c ┆ 115 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ b ┆ a ┆ 85 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ b ┆ b ┆ 135 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ b ┆ c ┆ 185 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ c ┆ a ┆ 115 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ c ┆ b ┆ 185 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ c ┆ c ┆ 255 │
└─────────┴─────────┴───────┘
Couple of notes:
I'm assuming that a pipe would be an appropriate delimiter for your column names.
The use of Python bytecode and iterator will not significantly impair performance. It is only used to generate the list of expressions, not run any calculations.
I have a huge dataframe. Following a groupby operation, I have a list of strings corresponding to every element of the first column. What I need is to be able to quickly find common strings between some particular i'th row with all the other rows. I could do that in Pandas by saving the above dataframe as a pickle file. The solution was suboptimal as loading takes a very long time.
I then found polars to be promising, except that I cannot store the dataframe with column of sets in any format that it supports for quick loading. So that leaves the alternate solution of storing as a list but quickly converting the grouped column to sets after loading from parquet. (I faced the same problems with datatables and vaex too.)
The solution with polars that I found was to use .apply. But it works in a single thread and is very slow. The code I used was as follows:
>>> df = pl.read_csv('test.csv')
>>> df
shape: (4, 2)
┌────────┬────────┐
│ ColA ┆ ColB │
│ --- ┆ --- │
│ str ┆ str │
╞════════╪════════╡
│ apple ┆ boy │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ orange ┆ ball │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ apple ┆ bamboo │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ orange ┆ bull │
└────────┴────────┘
>>> df = (df.lazy().groupby('ColA').agg([pl.col('ColB').list()])).collect()
>>> df
shape: (2, 2)
┌────────┬───────────────────┐
│ ColA ┆ ColB │
│ --- ┆ --- │
│ str ┆ list[str] │
╞════════╪═══════════════════╡
│ orange ┆ ["ball", "bull"] │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ apple ┆ ["boy", "bamboo"] │
└────────┴───────────────────┘
>>> df['ColB'] = df['ColB'].apply(set)
>>> df
shape: (2, 2)
┌────────┬───────────────────┐
│ ColA ┆ ColB │
│ --- ┆ --- │
│ str ┆ object │
╞════════╪═══════════════════╡
│ orange ┆ {'ball', 'bull'} │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ apple ┆ {'boy', 'bamboo'} │
└────────┴───────────────────┘
>>>
I found discussion on using map, but it works on series only. Unlike in that example that worked on per element basis, when I used np.asarray to convert to numpy array (to apply intersect on them later), entire columns collpased into arrays!
>>> df = (df.lazy().groupby('ColA').agg([pl.col('ColB').list()])).collect()
>>> df
shape: (2, 2)
┌────────┬─────────────────────────┐
│ ColA ┆ ColB │
│ --- ┆ --- │
│ str ┆ list[str] │
╞════════╪═════════════════════════╡
│ orange ┆ ["ball", "bull", "boy"] │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ apple ┆ ["boy", "bamboo"] │
└────────┴─────────────────────────┘
>>> df.select([pl.all().map(np.asarray)])
shape: (1, 2)
┌────────────────────┬─────────────────────────────────────┐
│ ColA ┆ ColB │
│ --- ┆ --- │
│ object ┆ object │
╞════════════════════╪═════════════════════════════════════╡
│ ['orange' 'apple'] ┆ [array(['ball', 'bull', 'boy'], ... │
└────────────────────┴─────────────────────────────────────┘
>>>
I would like to know where I went wrong, and how to use multi-threads (as with map) to convert a column of list to a column of numpy array (or preferably sets).
Not perhaps the best approach, but the following worked reasonably well.
>>> my_dict = dict(df.to_numpy().tolist())
>>> my_dict
{'orange': array(['ball', 'bull', 'boy'], dtype=object), 'apple': array(['boy', 'bamboo'], dtype=object)}
>>> for i in my_dict:
... my_dict[i] = set(my_dict[i])
...
>>> my_dict
{'orange': {'ball', 'boy', 'bull'}, 'apple': {'bamboo', 'boy'}}
I was trying to search whether there would be a way to change the dtypes for the strings with numbers easily. For example, the problem I face is as follows:
df = pl.Dataframe({"foo": ["100CT pen", "pencils 250CT", "what "125CT soever", "this is a thing"]})
I could extract and create a new column named {"bar": ["100", "250", "125", ""]}. But then I couldn't find a handy function that converts this column to Int64 or float dtypes so that the result is [100, 250, 125, null].
Also, vice versa. Sometimes it would be useful to have a handy function that converts the column of [100, 250, 125, 0] to ["100", "250", "125", "0"]. Is it something that already exists?
Thanks!
The easiest way to accomplish this is with the cast expression.
String to Int/Float
To cast from a string to an integer (or float):
import polars as pl
df = pl.DataFrame({"bar": ["100", "250", "125", ""]})
df.with_column(pl.col('bar').cast(pl.Int64, strict=False).alias('bar_int'))
shape: (4, 2)
┌─────┬─────────┐
│ bar ┆ bar_int │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════════╡
│ 100 ┆ 100 │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 250 ┆ 250 │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 125 ┆ 125 │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ ┆ null │
└─────┴─────────┘
A handy list of available datatypes is here. These are all aliased under polars, so you can refer to them easily (e.g., pl.UInt64).
For the data you describe, I recommend using strict=False to avoid having one mangled number among millions of records result in an exception that halts everything.
Int/Float to String
The same process can be used to convert numbers to strings - in this case, the utf8 datatype.
Let me modify your dataset slightly:
df = pl.DataFrame({"bar": [100.5, 250.25, 1250000, None]})
df.with_column(pl.col("bar").cast(pl.Utf8, strict=False).alias("bar_string"))
shape: (4, 2)
┌────────┬────────────┐
│ bar ┆ bar_string │
│ --- ┆ --- │
│ f64 ┆ str │
╞════════╪════════════╡
│ 100.5 ┆ 100.5 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 250.25 ┆ 250.25 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1.25e6 ┆ 1250000.0 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ null ┆ null │
└────────┴────────────┘
If you need more control over the formatting, you can use the apply method and Python's new f-string formatting.
df.with_column(
pl.col("bar").apply(lambda x: f"This is ${x:,.2f}!").alias("bar_fstring")
)
shape: (4, 2)
┌────────┬────────────────────────┐
│ bar ┆ bar_fstring │
│ --- ┆ --- │
│ f64 ┆ str │
╞════════╪════════════════════════╡
│ 100.5 ┆ This is $100.50! │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 250.25 ┆ This is $250.25! │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1.25e6 ┆ This is $1,250,000.00! │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ null ┆ null │
└────────┴────────────────────────┘
I found this web page to be a handy reference for those unfamiliar with f-string formatting.
As an addition to #cbilot 's answer.
You don't need to use slow python lambda functions to use special string formatting of expressions. Polars has a format function for this purpose:
df = pl.DataFrame({"bar": ["100", "250", "125", ""]})
df.with_columns([
pl.format("This is {}!", pl.col("bar"))
])
shape: (4, 2)
┌─────┬──────────────┐
│ bar ┆ literal │
│ --- ┆ --- │
│ str ┆ str │
╞═════╪══════════════╡
│ 100 ┆ This is 100! │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 250 ┆ This is 250! │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 125 ┆ This is 125! │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ ┆ This is ! │
└─────┴──────────────┘
For other data manipulation in polars, like string to datetime, use strptime().
import polars as pl
df = pl.DataFrame(df_pandas)
df
shape: (100, 2)
┌────────────┬────────┐
│ dates_col ┆ ticker │
│ --- ┆ --- │
│ str ┆ str │
╞════════════╪════════╡
│ 2022-02-25 ┆ RDW │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2008-05-28 ┆ ARTX │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2015-05-21 ┆ CBAT │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2009-02-09 ┆ ANNB │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
Use it like this, converting the column to string:
df.with_column(pl.col("dates_col").str.strptime(pl.Datetime, fmt="%Y-%m-%d").cast(pl.Datetime))
shape: (100, 2)
┌─────────────────────┬────────┐
│ dates_col ┆ ticker │
│ --- ┆ --- │
│ datetime[μs] ┆ str │
╞═════════════════════╪════════╡
│ 2022-02-25 00:00:00 ┆ RDW │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2008-05-28 00:00:00 ┆ ARTX │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2015-05-21 00:00:00 ┆ CBAT │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2009-02-09 00:00:00 ┆ ANNB │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤