binner | A tool for bin packing and estimations
kandi X-RAY | binner Summary
kandi X-RAY | binner Summary
Binner - Quick And Dirty bin allocation.
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- Run the SMALLEST algorithm
- Return the next item .
- Show the results of the run .
- Initialize API .
- Return the index of the bin .
- Checks if the given position is occupied .
- Initialize the widget .
- Print help .
- Create a Slot from a space and item .
- Rotate the mesh .
binner Key Features
binner Examples and Code Snippets
Community Discussions
Trending Discussions on binner
QUESTION
So I have a dataframe, milk_countries_exports, that consists of columns of:
- The 'Period', the year and month for a particular row (the dataset is month by month for a year)
- The 'Reporter' country, that is doing the exporting
- The 'Partner' countries that are importing from the 'reporter'
- The 'Commodity', which consists of 2 items, 'Milk and cream, neither concentrated nor sweetened', and 'Milk and cream, concentrated or sweetened'
- The 'Commodity Code', which is the number assigned to the item types, so '0401' and '0402' respectively
- 'Trade Values (US$)', the amount of trade that took place for the given month, country, and commodity type for that row
I have been asked to:
Filter the dataset so that it only contains rows where the total exports across all the milk products for a particular country are at least two million dollars in any given monthly period. (HINT: group on partner and period and filter against a function that tests the minimum trade value exceeds the required value.)
To me that seems to mean for each given month and partner, if the sum of 0401 and 0402 for that month (e.g. 201401) and partner (e.g. Germany) is greater than 2 million dollars, keep those 2 rows , otherwise filter them out. So if for one month-partner pair you have the values 900,000 and 2,000,000 for 0401 and 0402 respectively, the sum is 2,900,000, so both rows are kept, even though one of them is less than the 2 million threshold, because it's going off the sum of 0401 and 0402, not their values individually.
As a reference point in the data I used the first few rows of 'Germany', because as a partner it contains examples of both 0401 and 0402 (many partners had only one or the other), and crucially the first month contains a case of 0401 being below the 2 million threshold at 982240, and an 0402 of 3187636, over the threshold. So, if one or both of these rows is missing after filtering, I know I've done something wrong.
I attempt to apply it in this way:
I define a function that tests if a dataset meets the criteria for being greater than or equal to 2 million:
...ANSWER
Answered 2020-Sep-01 at 22:16g['Trade Value (US$)'].min() >= 2000000
filters everything out, because it means the minimum must be greater than 2000000.- Use
pandas.Grouper
to groupbyPeriod
with a specified frequency. pandas.core.groupby.DataFrameGroupBy.filter
to filter based on the sum of'Trade Value (US$)'
.x['Trade Value (US$)'].sum() > 2000000
is the filter function. It can be put into an externaldef
function, but it's not necessary.
Commodity Code
can also be added to the groupby:groupby(['Partner', 'Commodity Code', pd.Grouper(key='Period', freq='1M')])
QUESTION
I'm new to Haskell and still don't understand how to deal with their type system. My problem is that I'm playing around with the sequenceA function from the book Learn You a Haskell For Great Good. Here's the function:
...ANSWER
Answered 2020-Apr-10 at 02:24You tried to use the type of sequenceA (binner 4)
with the body of essentially \n -> sequenceA (binner n)
. Since what you wrote takes an Int
that what you gave to :t
doesn't, you need to add an Int ->
to the beginning of the type signature to represent the n
:
QUESTION
I'm attempting to have list items stored in a variable populate cells of a spreadsheet until the list runs out of items (so it works on a list of any size). I have tried a variety of for loops and slices with no luck.
...ANSWER
Answered 2020-Jan-28 at 19:35What you really want to do is add each element of the list into it's own cell as in A1, A2, A3 and so on. Try it like this and see if that gives you the results that you're looking for.
QUESTION
I have the following 2 files:
1.jsonnet
...ANSWER
Answered 2019-Dec-17 at 18:17Note I'm assuming that the user will need somehow to know where in the tree they'd want to overload the field(s).
You can use helpers.jsonnet
from https://github.com/bitnami/kube-prod-runtime/blob/master/manifests/contrib/helpers.jsonnet as:
QUESTION
Does anyone could help me to understand why the option to hide markers with using getZoom doesnt work in my script?
I have updated the code by adding another solution that works and seems simpler
Part of code:
...ANSWER
Answered 2019-May-06 at 04:54You have two issues with your code:
- there is no (documented and useful)
.visible
property of agoogle.maps.Marker
. The documented way to change its visibility is with the.setVisibility
method. marker
is a global variable, so once you have fixed the above, it only works on the lastmarker
. Need to define it inside the function withvar
.
Fixed code:
QUESTION
Able to finish the function and for loop below? Could not figure out how to bin the following columns and then 1) place the binned values into new columns, and 2) .add_prefix() to each of those new 7 columns with the prefix 'bin_'? Couldn't figure out how to get the function and for loop working.
...ANSWER
Answered 2019-Mar-19 at 07:27If I understood you correctly, you are trying to create new column for every column you have in your data frame that contains the bin in which each cell is in.
QUESTION
My python code is pretty simple. I'm running a sql query which gives me datetime column value of when a file has been committed. In the end what I want is the aggregate of commit count by month for e.g
...ANSWER
Answered 2019-Feb-26 at 11:03# Create DataFrame and Month column
df = pd.DataFrame({
'Date': ['2018-04-06 14:00:08', '2017-03-15 00:51:20',
'2017-12-07 05:38:22', '2017-05-12 16:40:05',
'2017-05-03 10:13:47']
})
df['Date'] = df['Date'].astype('datetime64')
df['Month'] = df['Date'].dt.strftime('%Y-%m')
>>> df
Date Month
0 2018-04-06 14:00:08 2018-04
1 2017-03-15 00:51:20 2017-03
2 2017-12-07 05:38:22 2017-12
3 2017-05-12 16:40:05 2017-05
4 2017-05-03 10:13:47 2017-05
QUESTION
I was looking at the following pandas source code:
...ANSWER
Answered 2019-Feb-04 at 11:52Note that outside the class there is another _get_grouper
function, which is precisely the one that is being called in this code snippet.
If it were the same _get_grouper
within the class that was called, it should instead be self._get_grouper
given that it is an attribute of the class.
Here's a simple example to illustrate this:
QUESTION
I need to create 10 bins with the most approximate frequency each; for this, I am using the function "ClassInvervals" from the library (ClassInt) with the style 'quantile' for binning some data. This is working for must columns; but, when I have a column that has 1 number repeated too many times, it appears an error that says that some brackets are not unique, which makes sense assuming the last +30% of the column data is the same number so the function doesn't know how to split the bins.
What I would like to do is that if a number is greater than the 10% of the length of the column, then treat it as a different bin, and if not, then use the function as it is.
For example, let's assume we have this DF:
...ANSWER
Answered 2018-Oct-10 at 20:17You could use cutr::smart_cut
:
QUESTION
A() function return promises after 1000
ms and throws an error. So, the next execution should be into catch
. But, .then()
function gets executed even after the main function throws an error.
ANSWER
Answered 2018-Aug-02 at 12:29Issue is, you are invoking Test1.B(value, value1)
inside then
, rather than keeping it as callback. Because, once you used it as invocation, it started it's own promise chain. To fix it, change it to:
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Install binner
You can use binner like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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