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Get memory address of an R data frame


By : FinleyMa
Date : September 15 2020, 05:00 AM
Does that help Rcpp passes by reference. I.e. it passes pointers to R objects, not their values. So you can manipulate them in C++ in the same way as any pointer.
example
code :
library(Rcpp)

cppFunction('
void f1(DataFrame x) {
  IntegerVector V1 = x["V1"];
  V1 = V1 * 2;
  }
')

x = data.frame(V1 = 1:5, V2 = 1:5)
f1(x)
x
#   V1 V2
# 1  2  1
# 2  4  2
# 3  6  3
# 4  8  4
# 5 10  5


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If a frame is 4KB then a system with 4 byte entries can address 2^36 bytes of physical memory. How is it 2^36?


By : user1285874
Date : March 29 2020, 07:55 AM
I wish this help you It looks like you are bit wrong with the quote. The book says: "A 32-bit entry can point to one of 2^32 physical page frames. If frame size is 4 KB (2^12), then a system with 4-byte entries can address 2^44 bytes (or 16 TB) of physical memory"
in such representation - the 2^44 is logical. We have 2^32 frames, where each frame, on x32 system, can hold 2^12 entries (4 KB frame size. The entry is 4 bytes - pointer size on x32 systems). So having that: 2^32 * 2^14 = 2^44 - total amount of bytes that frames can hold.

How to generate data frame variable name on the fly and address existing data frame variable with it?


By : clement
Date : March 29 2020, 07:55 AM
I hope this helps you . Short answer: use [[ instead of $, e.g. df[[paste0("v",a)]].
get() is not generally recommended, but if you need to use it, you'll have to do this in two steps:
code :
get("df")[[paste0("v",a)]]
eval(parse(text=paste0("df$v",a)))

how to copy PyObject * memory data to another memory address?


By : darshana chauhan
Date : March 29 2020, 07:55 AM
With these it helps The easiest way would be to pickle your Python object into a string and copy the string into the shared memory. Then, the other process would unpickle it back into a Python object. From manual:

In a call stack that grows from high to low address, are the data elements saved from low memory address to high memory


By : Johey
Date : March 29 2020, 07:55 AM
I wish this help you Endianness is completely irrelevant, why is that even being mentioned.
Why didnt you just try it and find out? Of course it is compiler/calling convention specific, but using the compiler you care about easy to figure out.

How can I divide sub-selections of a data frame by another data frame using minimal memory usage in python?


By : Mai Abo-Elmaaty
Date : March 29 2020, 07:55 AM
To fix the issue you can do I have a dataframe with many columns and I want to divide it by another data frame at regular column intervals with minimal memory usage. , Using pd.concat:
code :
res = pd.concat([df2]*5, 1)
res.columns = df1.columns

df1/res
          0         1         2         3    ...            6         7         8         9
0  1.000000  0.666667  3.000000  1.333333    ...     7.000000  2.666667  9.000000  3.333333
1  1.428571  1.500000  1.142857  1.166667    ...     0.571429  0.500000  0.285714  0.166667
2  0.222222  1.333333  0.333333  0.333333    ...     0.777778  2.666667  1.000000  1.333333
res = pd.concat([df2]*(df1.shape[1]//df2.shape[1]), 1)
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