# Converting a specific range of binary bits in a char array that contains 4 byte binary instruction

A relatively trivial question today Suppose I have a the following code: int extract_bits_21_to_25(unsigned char* instruction) { int instruction_to_int = *(int*)instruction; int bits_21_to_25 = (instruction_to_int >> 20) & 0x1F; return bits_21_to_25; } unsigned char* reverse(unsigned char* instruction){ int length = strlen(instruction); for (int i = 0; i < length / 2; i++) { char temp… Read More Converting a specific range of binary bits in a char array that contains 4 byte binary instruction

# How to shift a column by 1 year in Python

With the python shift function, you are able to offset values by the number of rows. I’m looking to offset values by a specified time, which is 1 year in this case. Here is my sample data frame. The value_py column is what I’m trying to return with a shift function. This is an over… Read More How to shift a column by 1 year in Python

# Bitwise shift operator and file pointer

Can you please explain why, whenever I call this function, the file pointer is shifted? How does this piece of code work? … static long fget32le(FILE* fp) { long answer; answer = fgetc(fp); answer |= (fgetc(fp) << 8); answer |= (fgetc(fp) << 16); answer |= (fgetc(fp) << 24); /* check for negative */ if (answer… Read More Bitwise shift operator and file pointer

# Julia DataFrame: shift a column by more rows

I am looking for a way how to shift DataFrame column by more rows. Shifting by one row works fine: df = DataFrame(A=[1,2,3,4], B=[9,8,7,6]) julia> transform(df, "A" => ShiftedArrays.lag => :A1) 4×3 DataFrame Row │ A B A1 │ Int64 Int64 Int64? ─────┼─────────────────────── 1 │ 1 9 missing 2 │ 2 8 1 3 │… Read More Julia DataFrame: shift a column by more rows

# Converting Python code to pyspark environment

import pandas as pd temp = pd.DataFrame(data=[[‘a’,0],[‘a’,0],[‘a’,0],[‘b’,0],[‘b’,1],[‘b’,1],[‘c’,1],[‘c’,0],[‘c’,0]], columns=[‘ID’,’X’]) temp[‘transformed’] = temp.groupby(‘ID’).apply(lambda x: (x["X"].shift() != x["X"]).cumsum()).reset_index()[‘X’] print(temp) My question is how to achieve in pyspark. Thanks in advance >Solution : Pyspark have handle these type of queries with Windows utility functions. you can read its documentation here Your pyspark code would be something like this :… Read More Converting Python code to pyspark environment

# Sum two columns in a grouped data frame using shift()

I have a data frame df where I would like to create new column ID which is a diagonal combination of two other columns ID1 & ID2. This is the data frame: import pandas as pd df = pd.DataFrame({‘Employee’:[5,5,5,20,20], ‘Department’:[4,4,4,6,6], ‘ID’:[‘AB’,’CD’,’EF’,’XY’,’AA’], ‘ID2’:[‘CD’,’EF’,’GH’,’AA’,’ZW’]},) This is how the initial data frame looks like: Employee Department ID1 ID2… Read More Sum two columns in a grouped data frame using shift()

# How to replace jquery variable values with php array values

I am working with php and i want to change value of dropdown using jquery, i want to pass php array into jquery variable(make dynamic dropdown value) Right now i am getting array(\$data) in following format Array ( [0] => stdClass Object ( [UserId] => 8 [name] => web1 ) [1] => stdClass Object (… Read More How to replace jquery variable values with php array values

# rank for nan values based on group

I have dataframe with column d1 and now i am trying calculate ‘out’ column after ranking that column when there in ‘nan’ value with in a column. data_input = {‘Name’:[‘Renault’, ‘Renault’, ‘Renault’, ‘Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’], ‘type’:[‘Duster’, ‘Duster’, ‘Duster’,’Duster’,’Duster’,’Duster’,’Duster’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’], ‘d1’:[‘nan’,’10’,’10’,’10’,’nan’,’nan’,’20’,’20’,’nan’,’nan’,’30’,’30’,’30’,’nan’]} df_input = pd.DataFrame(data_input) data_out = {‘Name’:[‘Renault’, ‘Renault’, ‘Renault’, ‘Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’,’Renault’], ‘type’:[‘Duster’, ‘Duster’, ‘Duster’,’Duster’,’Duster’,’Duster’,’Duster’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’,’Triber’], ‘d1’:[‘nan’,’10’,’10’,’10’,’nan’,’nan’,’20’,’20’,’nan’,’nan’,’30’,’30’,’30’,’nan’], ‘out’:[1,np.NaN,np.NaN,np.NaN,2,2,np.NaN,np.NaN,1,1,np.NaN,np.NaN,np.NaN,2]} df_out = pd.DataFrame(data_out) If… Read More rank for nan values based on group

# Delete nth node from both beginning and end of singly linked list

I’m just starting to learn linkedlist. I can come up with an approach to delete a nth node from the beginning and end separately but I couldn’t take care of the checks needed to perform both at the same time. Delete nth node from both beginning and end INPUT: 1->2->3->4->5->6->7->8->9->10->NULL N =4 OUTPUT: 1->2->3->5->6->8->9->10->NULL C++… Read More Delete nth node from both beginning and end of singly linked list