I am trying to write a code which calculates the HCF of two numbers but I am either getting a error or an empty list as my answer
I was expecting the HCF, My idea was to get the factors of the 2 given numbers and then find the common amongst them then take the max out of that
For future reference, do not attach screenshots. Instead, copy your code and put it into a code block because stack overflow supports code blocks. To start a code block, write three tildes like ``` and to end it write three more tildes to close. If you add a language name like python, or javascript after the first three tildes, syntax highlighting will be enabled. I would also create a more descriptive title that more accurately describes the problem at hand. It would look like so:
Title: How to print from 1-99 in python?
for i in range(1,100):
print(i)
To answer your question, it seems that your HCF list is empty, and the python max function expects the argument to the function to not to be empty (the 'arg' is the HCF list). From inspection of your code, this is because the two if conditions that need to be satisfied before anything is added to HCF is never satisfied.
So it could be that hcf2[x] is not in hcf and hcf[x] is not in hcf[x] 2.
What I would do is extract the logic for the finding of the factors of each number to a function, then use built in python functions to find the common elements between the lists. Like so:
num1 = int(input("Num 1:")) # inputs
num2 = int(input("Num 2:")) # inputs
numberOneFactors = []
numberTwoFactors = []
commonFactors = []
# defining a function that finds the factors and returns it as a list
def findFactors(number):
temp = []
for i in range(1, number+1):
if number%i==0:
temp.append(i)
return temp
numberOneFactors = findFactors(num1) # populating factors 1 list
numberTwoFactors = findFactors(num2) # populating factors 2 list
# to find common factors we can use the inbuilt python set functions.
commonFactors = list(set(numberOneFactors).intersection(numberTwoFactors))
# the intersection method finds the common elements in a set.
Related
I need to select values from a single column in a Julia dataframe based on multiple criteria sourced from an array. Context: I'm attempting to format the data from a large Julia DataFrame to support a PCA (primary component analysis), so I first split the original data into an anlytical matrix and a label array. This is my code, so far (doesn't work):
### Initialize source dataframe for PCA
dfSource=DataFrame(
colDataX=[0,5,10,15,5,20,0,5,10,30],
colDataY=[1,2,3,4,5,6,7,8,9,0],
colRowLabels=[0.2,0.3,0.5,0.6,0.0,0.1,0.2,0.1,0.8,0.0])
### Extract 1/2 of rows into analytical matrix
matSource=convert(Matrix,DataFrame(dfSource[1:2:end,1:2]))'
### Extract last column as labels
arLabels=dfSource[1:2:end,3]
### Select filtered rows
datGet=matSource[:,arLabels>=0.2 & arLabels<0.7][1,:]
print(datGet)
output> MethodError: no method matching...
At the last line before the print(datGet) statement, I get a MethodError indicating a method mismatch related to use of the & logic. What have I done wrong?
A small example of alternative implementation (maybe you will find it useful to see what DataFrames.jl has in-built):
# avoid materialization if dfSource is large
dfSourceHalf = #view dfSource[1:2:end, :]
lazyFilter = Iterators.filter(row -> 0.2 <= row[3] < 0.7, eachrow(dfSourceHalf))
matFiltered = mapreduce(row -> collect(row[1:2]), hcat, lazyFilter)
matFiltered[1, :]
(this is not optimized for speed, but rather as a showcase what is possible, but still it is already several times faster than your code)
This code works:
dfSource=DataFrame(
colDataX=[0,5,10,15,5,20,0,5,10,30],
colDataY=[1,2,3,4,5,6,7,8,9,0],
colRowLabels=[0.2,0.3,0.5,0.6,0.0,0.1,0.2,0.1,0.8,0.0])
matSource=convert(Matrix,DataFrame(dfSource[1:2:end,1:2]))'
arLabels=dfSource[1:2:end,3]
datGet=matSource[:,(arLabels.>=0.2) .& (arLabels.<0.7)][1,:]
print(datGet)
output> [0,10,0]
Note the use of parenthetical enclosures (arLabels.>=0.2) and (arLabels<0.7), as well as the use of the .>= and .< syntax (which forces Julia to iterate through a container/collection). Finally, and most crucially (since it's the part most people miss), note the use of .& in place of just &. The dot operator makes all the difference!
Working with Julia 1.1:
The following minimal code works and does what I want:
function test()
df = DataFrame(NbAlternative = Int[], NbMonteCarlo = Int[], Similarity = Float64[])
append!(df.NbAlternative, ones(Int, 5))
df
end
Appending a vector to one column of df. Note: in my whole code, I add a more complicated Vector{Int} than ones' return.
However, #code_warntype test() does return:
%8 = invoke DataFrames.getindex(%7::DataFrame, :NbAlternative::Symbol)::AbstractArray{T,1} where T
Which means I suppose, thisn't efficient. I can't manage to get what this #code_warntype error means. More generally, how can I understand errors returned by #code_warntype and fix them, this is a recurrent unclear issue for me.
EDIT: #BogumiłKamiński's answer
Then how one would do the following code ?
for na in arr_nb_alternative
#show na
for mt in arr_nb_montecarlo
println("...$mt")
append!(df.NbAlternative, ones(Int, nb_simulations)*na)
append!(df.NbMonteCarlo, ones(Int, nb_simulations)*mt)
append!(df.Similarity, compare_smaa(na, nb_criteria, nb_simulations, mt))
end
end
compare_smaa returns a nb_simulations length vector.
You should never do such things as it will cause many functions from DataFrames.jl to stop working properly. Actually such code will soon throw an error, see https://github.com/JuliaData/DataFrames.jl/issues/1844 that is exactly trying to patch this hole in DataFrames.jl design.
What you should do is appending a data frame-like object to a DataFrame using append! function (this guarantees that the result has consistent column lengths) or using push! to add a single row to a DataFrame.
Now the reason you have type instability is that DataFrame can hold vector of any type (technically columns are held in a Vector{AbstractVector}) so it is not possible to determine in compile time what will be the type of vector under a given name.
EDIT
What you ask for is a typical scenario that DataFrames.jl supports well and I do it almost every day (as I do a lot of simulations). As I have indicated - you can use either push! or append!. Use push! to add a single run of a simulation (this is not your case, but I add it as it is also very common):
for na in arr_nb_alternative
#show na
for mt in arr_nb_montecarlo
println("...$mt")
for i in 1:nb_simulations
# here you have to make sure that compare_smaa returns a scalar
# if it is passed 1 in nb_simulations
push!(df, (na, mt, compare_smaa(na, nb_criteria, 1, mt)))
end
end
end
And this is how you can use append!:
for na in arr_nb_alternative
#show na
for mt in arr_nb_montecarlo
println("...$mt")
# here you have to make sure that compare_smaa returns a vector
append!(df, (NbAlternative=ones(Int, nb_simulations)*na,
NbMonteCarlo=ones(Int, nb_simulations)*mt,
Similarity=compare_smaa(na, nb_criteria, nb_simulations, mt)))
end
end
Note that I append here a NamedTuple. As I have written earlier you can append a DataFrame or any data frame-like object this way. What "data frame-like object" means is a broad class of things - in general anything that you can pass to DataFrame constructor (so e.g. it can also be a Vector of NamedTuples).
Note that append! adds columns to a DataFrame using name matching so column names must be consistent between the target and appended object.
This is different in push! which also allows to push a row that does not specify column names (in my example above I show that a Tuple can be pushed).
I'm running summary statistics for a group of standard OLS regressions. The code was written by my professor and I'm trying to figure out what's going on specifically in a portion of the code.
summary_col(
[reg0,reg1,reg2,reg3],
stars=True,
float_format='%0.2f',
info_dict = {
'N':lambda x: "{0:d}".format(int(x.nobs)),
'R2':lambda x: "{:.2f}".format(x.rsquared)
})
I looked up lambda functions. I have a fairly decent understanding of how they work. Aspects of the code that I do understand:
info_dict is a dictionary of values that can be called if you wish to include them in your summary statistics
lambda function work by calling an anonymous function "lambda x" then you place the : and list what operation you want to take place (i.e. x + 5) and then if you already know what parameters you want it to run you can put in a list after a second ":".
{0:d} will round to integers which makes perfect sense for observations. Although I don't know why you can't just say {%.f}. Maybe it's because the former returns an explicit int and the latter returns a float that looks like an int.
{:.2f} will return a float with 2 decimal places
What I don't fully understand is what somestring.format() does. Somehow x is getting defined as the results from the regression I believe and x.nobs is the variable "number of observations". Similar for x.rsquared.
Could someone fill in the gaps for me about what's going on in the formula? What exactly about the lambda function is enabling it to fetch data for each individual regression?
Let's break this out a little bit to make it obvious what is happening:
summary_col(
[reg0,reg1,reg2,reg3],
stars=True,
float_format='%0.2f',
info_dict={
'N':lambda x: "{0:d}".format(int(x.nobs)),
'R2':lambda x: "{:.2f}".format(x.rsquared)
}
)
The summary_col object is taking in some input, the first argument being a list of regression objects, [reg0,reg1,reg2,reg3]. Then there are three named arguments, stars, float_format, and info_dict. When we pass in the list of regression objects as the first argument, I believe that the lambda function knows to apply the anonymous function to each object. So all info_dict is doing is creating a dictionary with two keys, N and R2 which map to strings. When the member x.nobs and x.rsquared are referenced in the lambda functions they are applied against the regression objects due to the context in which these are used.
If you try to use lambda in that line of code on something that does not exist in the regression objects, you'll almost certainly get an error. The key is in the context against which the lambda is applied.
A good example on the context of lambda functions is iterating over a dictionary and sorting by key and value.
# sort the dict by value first, and key second...
# x is inferred from the context (my_dict.items())
for key, value in sorted(my_dict.items(), key=lambda x: (x[1], x[0]):
print(key, value)
I have a Dataset that consists of four classes, meaning the target variable has 4 different classes (like 0,1,2,3)
as we know we can obtain most correlated features to our target by pandas using this snippet.
# Find correlations with the target and sort
correlations = train.corr()['Target'].sort_values()
# Display correlations
print('Most Positive Correlations:\n', correlations.tail(15))
print('\nMost Negative Correlations:\n', correlations.head(15))
but my question is, i need to obtain most correlated features to specific target class. for example i want to get which features have high effect(correlated) on target class 3. I have tried this,
correlations = train.corr()[(train['Target'] == 3)].sort_values()
but it gives this error
IndexingError: Unalignable boolean Series provided as indexer (index of the
boolean Series and of the indexed object do not match
my expected output
You haven't given us anything to work with but I'm assuming your problem is calling .corr() before masking. You need to call:
correlations = train[(train['Target'] == 3)].corr().sort_values()
Edit:
A more elegant solution is probably groupby. Try something along the lines of:
train.groupby('Target').apply(lambda grp: grp.corr())
To me, adding "Dump" to the end of an expression doesn't seem to do anything different, at least for seeing rows in a table. Can you point me to an example of where it is handy?
If you are just working with an expression, there is no reason to call Dump—it's called automatically. But, in the language selection box, LINQPad allows allows the selection of Statements and Program. Once you select one of those, you don't get any Dump output unless you call it.
With Statements or Programs, you might want to call Dump on multiple times. In those cases, it is handy to pass the Description parameters so you can distinguish the output.
There are also other parameters you can use to shape the output, such as depth, which limits the substructure details.
Simple example (Language=C# Statements):
var integers = Enumerable.Range(1,10);
integers.Select(i => new { i, v = i * i}).Dump("Squares");
integers.Select(i => new { i, v = i * i * i}).Dump("Cubes");
var output = "λ is awesome";
Encoding.UTF8.GetBytes(output)
.Dump("UTF-8");
Encoding.GetEncoding("Windows-1252").GetBytes(output)
.Dump("Windows-1252 (lossy)");