I am trying to figure out a calculation I can perform in C# to determine the rows per column. Let's say I know I am going to have 3 columns and my record count is 46. I know that I can mod the results to get a remainder, but I would like something more efficient than what I have tried. So I know I will have 16 rows per column with a remainder of 14 for the last column, but what is the best way to loop through the resutls and keep counts.
Integer divsion will give you the number of complete rows (46 / 3 = 15). You then check the modulus to see if you have any leftover (46 Mod 3 = 1; yep, you have one column to put in a final extra row.)
To loop through, just check the modulus of the current record index (zero-based) with your column count. That modulus is the (zero-based) column index. If it equals 0, you start a new row.
But from your question, it sounds like you already got this far. So am I misunderstanding the question?
Related
total beginner here. If my question is irrelevant, apologies in advance, I'll remove it. So, I have a question : using pandas, I want to calculate an evolution ratio for a week data compared with the previous rolling 4 weeks mean data.
df['rolling_mean_fourweeks'] = df.rolling(4).mean().round(decimals=1)
from here I wanna create a new column for the evolution ratio based on the week data compared with the row of the rolling mean at the previous week.
what is the best way to go here? (I don't have big data) I have tried unsuccessfully with .shift() but am very foreign to .shift()... I should get NAN for week 3 (fourth week) and ~47% for fifth week.
Any suggestion for retrieving the value at row with step -1?
Thanks and have a good day!
Your idea about using shift can perfectly work. The shift(x) function simply shifts a series (a full column in your case) of x steps.
A simple way to check if the rolling_mean_fourweeks is a good predictor can be to shift Column1 and then check how it differs from rolling_mean_fourweeks:
df['column1_shifted'] = df['Column1'].shift(-1)
df['rolling_accuracy'] = ((df['column1_shifted']-df['rolling_mean_fourweeks'])
/df['rolling_mean_fourweeks'])
resulting in:
I've been learning TSQL and need some help with a conversion CPU MIPS into PERCENTAGE.
I've built my code to get some data that I'm expecting. In addition to this, I want to add a column to my code which is to get the CPU%. I have a column that gives me TOTALCPU MIPS and want to use this in the code but in the form of percentage. Example, I have these values in my TOTAL CPU Column:
1623453.66897
0
0
2148441.01573933
3048946.946314
I want to convert these values into percentage and use them. I couldn't find much info on the internet.
Appreciate your response.
I assume that you have 5 numeric quantities (2 of them being zero) and you want to find the percentage that corresponds to each of them out of the addition of the five quantities. Is it so?
To find the percentage of a particular number in the addition you multiply the number by 100 and divide by the addition, the result is the percentage that that number is in relation with the addition.
The sum: 6820841.631023
The percentage of the first number (of MIPS):
1623453.668970 * 100 / 6820841.631023 = 23.80136876 =>
23.80136876% is the percentage of CPU used by the first program.
To give the answer some SQL looking, refering to Mips_Table as the view/table that contains the MIPs data:
select mips, mips/totMips*100 Pct_CPU
from Mips_Table,
(select sum(mips) TotMips from Mips_Table) k
I have a companies dataset with 35 columns. The companies can belong to one of 8 different groups. How do I for each group create a new dataframe which subtract the mean of the column for that group away from the original value?
Here is an example of part of the dataset.
So for example for row 1 I want to subtract the mean of BANK_AND_DEP for Consumer Markets away from the value of 7204.400207. I need to do this for each column.
I assume this is some kind of combination of a transform and a lambda - but cannot hit the syntax.
Although it might seem counter-intuitive for this to involve a loop at all, looping through the columns themselves allows you to do this as a vectorized operation, which will be quicker than .apply(). For what to subtract by, you'll combine .groupby() and .transform() to get the value you need to subtract from a column. Then, just subtract it.
for column in df.columns:
df['new_'+column] = df[column]-df.groupby('Cluster')['column'].transform('mean')
I have just tried my first sqlite select-statement and got a result (an iterator over tuples). So, in other words, every row is represented by a tuple and I can access value in the cells of the row like this: r[7] or r[3] (get value from the column 7 or column 3). But I would like to access columns not by their positions but by their names. Let us say, I would like to know the value in the column user_name. What is the way to do it?
I found the answer on my question here:
cursor.execute("PRAGMA table_info(tablename)")
print cursor.fetchall()
What is the fastest way to lookup the index of a value in sorted vector in MATLAB?
That is, is there a fast find(vector == myNumber, 1, 'first') for when vector is sorted?
I have a large matrix (200,000 x 4) of locations each with a unique integer ID recorded in the first column. I want to find the right the location of a known ID but thousands of searches can take me a little bit to find.
If you use ismembc2, the loc output should give you what you need. See this for more details:
http://www.mathworks.com/support/solutions/en/data/1-9NIE1N/index.html?product=ML&solution=1-9NIE1N
There are a number of submissions for this on FEX: http://www.mathworks.com/matlabcentral/fileexchange/?term=binary+search+vector
I do not know if it is faster but you may want to try
result=vector(vector(:,1)==myNumber,:)
result will contain the 4 elements row for which vector first column == myNumber