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Printing and counting unique values from an .xlsx file

I'm fairly new to Python and still learning the ropes, so I need help with a step by step program without using any functions. I understand how to count through an unknown column range and output the quantity. However, for this program, I'm trying to loop through a column, picking out unique numbers and counting its frequency.
So I have an excel file with random numbers down column A. I only put in 20 numbers but let's pretend the range is unknown. How would I go about extracting the unique numbers and inputting them into a separate column along with how many times they appeared in the list?
I'm not really sure how to go about this. :/
unique = 1
while xw.Range((unique,1)).value != None:
frequency = 0
if unique != unique: break
quantity += 1
"end"

I presume as you can't use functions this may be homework...so, high level:
You could first go through the column and then put all the values in a list?
Secondly take the first value from the list and go through the rest of the list - is it in there? If so then it is not unique. Now remove the value where you have found the duplicate from the list. Keep going if you find another remove that too.
Take the second value and so on?
You would just need list comprehension, some loops and perhaps .pop()

Using pandas library would be the easiest way to do. I created a sample excel sheet having only one column called "Random_num"
import pandas
data = pandas.read_excel("sample.xlsx", sheet_name = "Sheet1")
print(data.head()) # This would give you a sneak peek of your data
print(data['Random_num'].value_counts()) # This would solve the problem you asked for
# Make sure to pass your column name within the quotation marks
#eg: data['your_column'].value_counts()
Thanks

Best way to fuzzy match values in a data frame and then replace the value?

I'm working with a dataframe containing various datapoints of customer data. I'm looking to essentially replace any junk phone numbers as a blank value, right now I'm struggling to find an efficient way to find potential junk values such as a phone number like 111-111-1111 and replace that specific value with a blank entry.
I currently have a fairly ugly solution where I'm going through 3 fields; home phone, cell phone and work phone, locating the index values of the rows in question and respective column and then am replacing those,
with regards to actually finding junk values in a dataframe, is there a better approach to this than what I am currently doing?
row_index = dataset[dataset['phone'].str.contains('11111')].index
column_index = dataset.columns.get_loc('phone')
Afterwards, I would zip these up and cycle through a for loop, using dataset.iat[row_index, column_index] = ''. The row and column index variables would also have the junk values in the 'cellphone' and 'workphone' columns appended on as well.

Pandas 'where' function tends to be quick:
dataset['phone'] = dataset['phone'].where(~dataset['phone'].str.contains('11111'),
None)

Pandas: How to check if any of a list in a dataframe column is present in a range in another dataframe?

I'm trying to compare two bioinformatic DataFrames (one with transcription start and end genomic locations, and one with expression data). I need to check if any of a list of locations in one DataFrame is present within ranges defined by the start and end locations in the other DataFrame, returning rows/ids where they match.
I have tried a number of built-in methods (.isin, .where, .query,), but usually get stuck because the lists are nonhashable. I've also tried a nested for loop with iterrows and itertuples, which is exceedingly slow (my actual datasets are thousands of entries).
tss_df = pd.DataFrame(data={'id':['gene1','gene2'],
'locs':[[21,23],[34,39]]})
exp_df = pd.DataFrame(data={'gene':['geneA','geneB'],
'start': [15,31], 'end': [25,42]})
I'm looking to find that the row with id 'gene1' in tss_df has locations (locs) that match 'geneA' in exp_df.
The output would be something like:
output = pd.DataFrame(data={'id':['gene1','gene2'],
'locs': [[21,23],[34,39]],
'match': ['geneA','geneB']})
Edit: Based on a comment below, I tried playing with merge_asof:
pd.merge_asof(tss_df,exp_df,left_on='locs',right_on='start')
This gave me an incompatible merge keys error, I suspect because I'm comparing a list to integer; so I split out the first value in locs:
tss_df['loc1'] = tss_df['locs'][0]
pd.merge_asof(tss_df,exp_df,left_on='loc1',right_on='start')
This appears to have worked for my test data, but I'll need to try it with my actual data!

Based on a comment below, I tried playing with merge_asof:
pd.merge_asof(tss_df,exp_df,left_on='locs',right_on='start')
This gave me an incompatible merge keys error, I suspect because I'm comparing a list to integer; so I split out the first value in locs:
tss_df['loc1'] = tss_df['locs'][0]
pd.merge_asof(tss_df,exp_df,left_on='loc1',right_on='start')
This appears to have worked for my test data!

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.

Correct way to deal with a list of associated data items associated with several index values with pandas/pytables

I was wondering what the correct way to deal with storing/reading through a list of items such as the following example dealing with a rockstar, where the list is known to hold a maximum number of values to hdf5:
Date_of_Birth
Bands[] - where the maximum number of bands is 10
Siblings[] - where the maximum number of siblings is 6
Date_of_Death
All of these would be column names.
One way I had considered, but turned out to give an error (ValueError: cannot reindex from a duplicate axis) was to have duplicate column names. Otherwise, what I could do is have Bands 1, Bands 2 etc... but that would make retrieval and querying bothersome. Is there a better way? Any help would be very much appreciated!

For something like this where you actually want to list out each column of bands and siblings I would try to use a multiindex
lets say you have a dataframe you call df with these columns the call
df.columns spits out something like Int64Index([dob, band_1, band_2], dtype='int64'). You can reconstruct your index into something that will grab all the bands at once by doing this...
edited found a way to do 'partial' MultiIndex
df.columns = pd.MultiIndex.from_tuples([('dob',''),('bands','band_1'),('bands','band_2')])
Also a tip for constructing the list of tuples - You can add up a bunch of list comprehensions on the existing columns....
[('band',each) for each in df.columns[df.columns>1].apply(lambda x: re.search("band",x)]
#etc