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How to to arrange a loop in order to loop over columns and then do something

I'm a complete newbie to python, and I'm currently trying to work on a problem that allows me to take the average of each column except the number of columns is unknown.
I figured how to do it if I knew how many columns it is and to do each calculation separate. I'm supposed to do it by creating an empty list and looping the columns back into it.
import numpy as np
#average of all data not including NAN
def average (dataset):
return np.mean (dataset [np.isfinite (dataset)])
#this is how I did it by each column separate
dataset = np.genfromtxt("some file")
print (average(dataset [:,0]))
print (average(dataset [:,1]))
#what I'm trying to do with a loop
def avg (dataset):
for column in dataset:
lst = []
column = #i'm not sure how to define how many columns I have
Avg = average (column)
return Avg

You can use the numpy.mean() function:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html
with:
np.mean(my_data, axis=0)
The axis indicates whether you are taking the average along columns or rows (axis = 0 means you take the average of each column, what you are trying to do). The output will be a vector whose length is the same as the number of columns (or rows) along which you took the average, and each element is the average of the corresponding column (or row). You do not need to know the shape of the matrix in advance to do this.
You CAN do this using a for loop, but it's not a good idea -- looping over matrices in numpy is slow, whereas using vectorized operations like np.mean() is very very fast. So in general when using numpy one tries to use those types of built-in operations instead of looping over everything at least if possible.
Also -- if you want the number of columns in your matrix -- it's
my_matrix.shape[1]
returns number of columns;
my_matrix.shape[0] is number of rows.

Is there any way to do a task for each member of a list without loop in python?

I have a large number of array in pandas with 256 row and 5 columns and I would like to calculate statistical(min, max, mean, ....) features for 4 members of array in each column. i wrote the following code but it is so time-consuming:
for col in array:
for j in range(0,256,1):
min = array[col].iloc[j:j+4].min()
max= array[col].iloc[j:j+4].max()
(other functions)
as I have many array and i would like to do this task for each array it is very time consuming. is there any way to write a simpler code without loop that decreases the time of execution.

You want to calculate min and max for 4 consecutive elements of a pandas.DataFrame?
This can be done using pandas rolling:
df.rolling(4).agg(['min', 'max']).shift(-3)
The shift is necessary as the default for pandas is to have the window right aligned.

Dataframe - Sum Arrays in Cell - Memory Issue

I have a very large dataframe (close to 1 million rows), which has a couple of meta data columns and one single column that contains a long string of triples. One string could look like this:
0,0,123.63;10,360,2736.11;30,270,98.08;...
That is, three values separated by comma and then separated by semicolon. Let us refer to the three values as IN, OUT, MEASURE. Effectively i want to group my data by the original columns + the IN & OUT columns and then sum over the MEASURE column. Since each long string contains roughly 30 triples my dataframe would grow to be ~30 million rows if i simply unstacked the data. Obviously this is not feasible.
So given a set of columns (which may in- or exclude the IN & OUT columns) over which I want to group and then sum my MEASURE data, how would I efficiently strip out the relevant data and sum everything up without blowing up my memory?
My current solution simply loops over each row and then over each triple and keeps a running total of each group I specified. This is very slow, so I am looking for something faster, perhaps vectorised. Any help would be appreciated.
Edit: Sample data below (columns separated by pipe)
DATE|REGION|PRIORITY|PARAMETERS
10-Oct-2016|UK|High|0,0,77.82;30,90,7373.70;
10-Oct-2016|US|Low|0,30,7.82;30,90,733.70;
11-Oct-2016|UK|High|0,0,383.82;40,90,713.75;
12-Oct-2016|NA|Low|40,90,937.11;30,180,98.23;
where PARAMETERS has the form 'IN,OUT,MEASURE;IN,OUT,MEASURE;...'
I basically want to (as an example) create a pivot table where
values=MEASURE
index=DATE, IN
columns=PRIORITY

Performing multiple calculations on a Python Pandas group from CSV data

I have daily csv's that are automatically created for work that average about 1000 rows and exactly 630 columns. I've been trying to work with pandas to create a summary report that I can write to a new txt.file each day.
The problem that I'm facing is that I don't know how to group the data by 'provider', while also performing my own calculations based on the unique values within that group.
After 'Start', the rest of the columns(-2000 to 300000) are profit/loss data based on time(milliseconds). The file is usually between 700-1000 lines and I usually don't use any data past column heading '20000' (not shown).
I am trying to make an output text file that will summarize the csv file by 'provider'(there are usually 5-15 unique providers per file and they are different each day). The calculations I would like to perform are:
Provider = df.group('providers')
total tickets = sum of 'filled' (filled column: 1=filled, 0=reject)
share % = a providers total tickets / sum of all filled tickets in file
fill rate = sum of filled / (sum of filled + sum of rejected)
Size = Sum of 'fill_size'
1s Loss = (count how many times column '1000' < $0) / total_tickets
1s Avg = average of column '1000'
10s Loss = (count how many times MIN of range ('1000':'10000') < $0) / total_tickets
10s Avg = average of range ('1000':'10000')
Ideally, my output file will have these headings transposed across the top and the 5-15 unique providers underneath
While I still don't understand the proper format to write all of these custom calculations, my biggest hurdle is referencing one of my calculations in the new dataframe (ie. total_tickets) and applying it to the next calculation (ie. 1s loss)
I'm looking for someone to tell me the best way to perform these calculations and maybe provide an example of at least 2 or 3 of my metrics. I think that if I have the proper format, I'll be able to run with the rest of this project.
Thanks for the help.

The function you want is DataFrame.groupby, with more examples in the documentation here.
Usage is fairly straightforward.
You have a field called 'provider' in your dataframe, so to create groups, you simple call grouped = df.groupby('provider'). Note that this does no calculations, just tells pandas how to find groups.
To apply functions to this object, you can do a few things:
If it's an existing function (like sum), tell the grouped object which columns you want and then call .sum(), e.g., grouped['filled'].sum() will give the sum of 'filled' for each group. If you want the sum of every column, grouped.sum() will do that. For your second example, you could divide this resulting series by df['filled'].sum() to get your percentages.
If you want to pass a custom function, you can call grouped.apply(func) to apply that function to each group.
To store your values (e.g., for total tickets), you can just assign them to a variable, to total_tickets = df['filled'].sum(), and tickets_by_provider = grouped['filled'].sum(). You can then use these in other calculations.
Update:
For one second loss (and for the other losses), you need two things:
The number of times for each provider df['1000'] < 0
The total number of records for each provider
These both fit within groupby.
For the first, you can use grouped.apply with a lambda function. It could look like this:
_1s_loss_freq = grouped.apply(lambda x: x['fill'][x['1000'] < 0].sum())
For group totals, you just need to pick a column and get counts. This is done with the count() function.
records_per_group = grouped['1000'].count()
Then, because pandas aligns on indices, you can get your percentages with _1s_loss_freq / records_per_group.
This analogizes to the 10s Loss question.
The last question about the average over a range of columns relies on pandas understanding of how it should apply functions. If you take a dataframe and call dataframe.mean(), pandas returns the mean of each column. There's a default argument in mean() that is axis=0. If you change that to axis=1, pandas will instead take the mean of each row.
For your last question, 10s Avg, I'm assuming you've aggregated to the provider level already, so that each provider has one row. I'll do that with sum() below but any aggregation will do. Assuming the columns you want the mean over are stored in a list called cols, you want:
one_rec_per_provider = grouped[cols].sum()
provider_means_over_cols = one_rec_per_provider.mean(axis=1)

Return subset/slice of Pandas dataframe based on matching column of other dataframe, for each element in column?

So I think this is a relatively simple question:
I have a Pandas data frame (A) that has a key column (which is not unique/will have repeats of the key)
I have another Pandas data frame (B) that has a key column, which may have many matching entries/repeats.
So what I'd like is a bunch of data frames (a list, or a bunch of slice parameters, etc.), one for each key in A (regardless of whether it's unique or not)
In [bad] pseudocode:
for each key in A:
resultDF[] = Rows in B where B.key = key
I can easily do this iteratively with loops, but I've read that you're supposed to slice/merge/join data frames holistically, so I'm trying to see if I can find a better way of doing this.
A join will give me all the stuff that matches, but that's not exactly what I'm looking for, since I need a resulting dataframe for each key (i.e. for every row) in A.
Thanks!
EDIT:
I was trying to be brief, but here are some more details:
Eventually, what I need to do is generate some simple statistical metrics for elements in the columns of each row.
In other words, I have a DF, call it A, and it has a r rows, with c columns, one of which is a key. There may be repeats on the key.
I want to "match" that key with another [set of?] dataframe, returning however many rows match the key. Then, for that set of rows, I want to, say, determine the min and max of certain element (and std. dev, variance, etc.) and then determine if the corresponding element in A falls within that range.
You're absolutely right that it's possible that if row 1 and row 3 of DF A have the same key -- but potentially DIFFERENT elements -- they'd be checked against the same result set (the ranges of which obviously won't change). That's fine. These won't likely ever be big enough to make that an issue (but if there's the better way of doing it, that's great).
The point is that I need to be able to do the "in range" and stat summary computation for EACH key in A.
Again, I can easily do all of this iteratively. But this seems like the sort of thing pandas could do well, and I'm just getting into using it.
Thanks again!
FURTHER EDIT
The DF looks like this:
df = pd.DataFrame([[1,2,3,4,1,2,3,4], [28,15,13,11,12,23,21,15],['keyA','keyB','keyC','keyD', 'keyA','keyB','keyC','keyD']]).T
df.columns = ['SEQ','VAL','KEY']
SEQ VAL KEY
0 1 28 keyA
1 2 15 keyB
2 3 13 keyC
3 4 11 keyD
4 1 12 keyA
5 2 23 keyB
6 3 21 keyC
7 4 15 keyD
Both DF's A and B are of this format.
I can iterative get the resultant sets by:
loop_iter = len(A) / max(A['SEQ_NUM'])
for start in range(0, loop_iter):
matchA = A.iloc[start::loop_iter, :]['KEY']
That's simple. But I guess I'm wondering if I can do this "inline". Also, if for some reason the numeric ordering breaks (i.e. the SEQ get out of order) this this won't work. There seems to be no reason NOT to do it explicitly splitting on the keys, right? So perhaps I have TWO questions: 1). How to split on keys, iteratively (i.e. accessing a DF one row at a time), and 2). How to match a DF and do summary statistics, etc., on a DF that matches on the key.
So, once again:
1). Iterate through DF A, going one at a time, and grabbing a key.
2). Match the key to the SET (matchB) of keys in B that match
3). Do some stats on "values" of matchB, check to see if val.A is in range, etc.
4). Profit!

Ok, from what I understand, the problem at its most simple is that you have a pd.Series of values (i.e. a["key"], which let's just call keys), which correspond to the rows of a pd.DataFrame (the df called b), such that set(b["key"]).issuperset(set(keys)). You then want to apply some function to each group of rows in b where the b["key"] is one of the values in keys.
I'm purposefully disregarding the other df -- a -- that you mention in your prompt, because it doesn't seem to bear any significance to the problem, other than being the source of keys.
Anyway, this is a fairly standard sort of operation -- it's a groupby-apply.
def descriptive_func(df):
"""
Takes a df where key is always equal and returns some summary.
:type df: pd.DataFrame
:rtype: pd.Series|pd.DataFrame
"""
pass
# filter down to those rows we're interested in
valid_rows = b[b["key"].isin(set(keys))]
# this groups by the value and applies the descriptive func to each sub df in turn
summary = valid_rows.groupby("key").apply(descriptive_func)
There are a few built in methods on the groupby object that are useful. For example, check out valid_rows.groupby("key").sum() or valid_rows.groupby("key").describe(). Under the covers, these are really similar uses of apply. The shape of the returned summary is determined by the applied function. The unique grouped-by values -- those of b["key"] -- always constitute the index, but if the applied function returns a scalar, summary is a Series; if the applied function returns a Series, then summary constituted of the return Series as rows; if the applied function returns a DataFrame, then the result is a multiindex DataFrame. This is a core pattern in Pandas, and there's a whole, whole lot to explore here.