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Get the sum of absolutes of columns for a dataframe

If I have a dataframe and I want to sum the values of the columns I could do something like
import pandas as pd
studentdetails = {
"studentname":["Ram","Sam","Scott","Ann","John","Bobo"],
"mathantics" :[80,90,85,70,95,100],
"science" :[85,95,80,90,75,100],
"english" :[90,85,80,70,95,100]
}
index_labels=['r1','r2','r3','r4','r5','r6']
df = pd.DataFrame(studentdetails ,index=index_labels)
print(df)
df3 = df.sum()
print(df3)
col_list= ['studentname', 'mathantics', 'science']
print( df[col_list].sum())
How can I do something similar but instead of getting only the sum, getting the sum of absolute values (which in this particular case would be the same though) of some columns?
I tried abs in several way but it did not work
Edit:
studentname mathantics science english
r1 Ram 80 85 90
r2 Sam 90 95 -85
r3 Scott -85 80 80
r4 Ann 70 90 70
r5 John 95 -75 95
r6 Bobo 100 100 100
Expected output
mathantics 520
science 525
english 520
Edit2:
The col_list cannot include string value columns

You need numeric columns for absolute values:
col_list = df.columns.difference(['studentname'])
df[col_list].abs().sum()
df.set_index('studentname').abs().sum()
df.select_dtypes(np.number).abs().sum()

Extract information from an Excel (by updating arrays) with Excel / Python

I have an Excel file with thousands of columns on the following format:
Member No.
X
Y
Z
1000
25
60
-30
-69
38
68
45
2
43
1001
24
55
79
4
-7
89
78
51
-2
1002
45
-55
149
94
77
-985
-2
559
56
I need a way such that I shall get a new table with the absolute maximum value from each column. In this example, something like:
Member No.
X
Y
Z
1000
69
60
68
1001
78
55
89
1002
94
559
985
I have tried it in Excel (with using VLOOKUP for finding the "Member Number" in the first row and then using HLOOKUP for finding the values from the rows thereafter), but the problem is that the HLOOKUP command is not automatically updated with the new array (the array in which Member number 1001 is) (so my solution works for member 1000, but not for 1001 and 1002), and hence it always searches for the new value ONLY in the 1st Row (i.e. the row with the member number 1000).
I also tried reading the file with Python, but I am not well-versed enough to make much of a headway - once the dataset has been read, how do I tell excel to read the next 3 rows and get the (absolute) maximum in each column?
Can someone please help? Solution required in Python 3 or Excel (ideally, Excel 2014).

The below solution will get you your desired output using Python.
I first ffill to fill in the blanks in your Member No column (axis=0 means row-wise). Then convert your dataframe values to +ve using abs. Lastly, using pandas.DataFrame.agg, I get the max value for all the columns in your dataframe.
Assuming your dataframe is called data:
import pandas as pd
data['Member No.'] = data['Member No.'].ffill(axis=0).astype(int)
df = abs(df)
res = (data.groupby('Member No.').apply(lambda x: x.max())).drop('Member No.',axis=1).reset_index()
Which will print you:
Member No. X Y Z A B C
0 1000 69 60 68 60 74 69
1 1001 78 55 89 78 92 87
2 1002 94 559 985 985 971 976
Note that I added extra columns in your sample data to make sure that all the columns will return their max() value.

pandas group by multiple columns and remove rows based on multiple conditions

I have a dataframe which is as follows:
imagename,locationName,brandname,x,y,w,h,xdiff,ydiff
95-20180407-215120-235505-00050.jpg,Shirt,SAMSUNG,0,490,177,82,0,0
95-20180407-215120-235505-00050.jpg,Shirt,SAMSUNG,1,491,182,78,1,1
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,3,450,94,45,2,-41
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,5,451,95,48,2,1
95-20180407-215120-235505-00050.jpg,DUGOUT,VIVO,167,319,36,38,162,-132
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,446,349,99,90,279,30
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,455,342,84,93,9,-7
95-20180407-215120-235505-00050.jpg,Shirt,GOIBIBO,559,212,70,106,104,-130
Its a csv dump. From this I want to group by imagename and brandname. Wherever the values in xdiff and ydiff is less than 10 then remove the second line.
For example, from the first two lines I want to delete the second line, similarly from lines 3 and 4 I want to delete line 4.
I could do this quickly in R using dplyr group by, lag and lead functions. However, I am not sure how to combine different functions in python to achieve this. This is what I have tried so far:
df[df.groupby(['imagename','brandname']).xdiff.transform() <= 10]
Not sure what function should I call within transform and how to include ydiff too.
The expected output is as follows:
imagename,locationName,brandname,x,y,w,h,xdiff,ydiff
95-20180407-215120-235505-00050.jpg,Shirt,SAMSUNG,0,490,177,82,0,0
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,3,450,94,45,2,-41
95-20180407-215120-235505-00050.jpg,DUGOUT,VIVO,167,319,36,38,162,-132
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,446,349,99,90,279,30
95-20180407-215120-235505-00050.jpg,Shirt,GOIBIBO,559,212,70,106,104,-130

You can take individual groupby frames and apply the conditions through apply function
#df.groupby(['imagename','brandname'],group_keys=False).apply(lambda x: x.iloc[range(0,len(x),2)] if x['xdiff'].lt(10).any() else x)
df.groupby(['imagename','brandname'],group_keys=False).apply(lambda x: x.iloc[range(0,len(x),2)] if (x['xdiff'].lt(10).any() and x['ydiff'].lt(10).any()) else x)
Out:
imagename locationName brandname x y w h xdiff ydiff
2 95-20180407-215120-235505-00050.jpg Shirt DHFL 3 450 94 45 2 -41
5 95-20180407-215120-235505-00050.jpg Shirt DHFL 446 349 99 90 279 30
7 95-20180407-215120-235505-00050.jpg Shirt GOIBIBO 559 212 70 106 104 -130
0 95-20180407-215120-235505-00050.jpg Shirt SAMSUNG 0 490 177 82 0 0
4 95-20180407-215120-235505-00050.jpg DUGOUT VIVO 167 319 36 38 162 -132

Iterating over pandas rows to get minimum

Here is my dataframe:
Date cell tumor_size(mm)
25/10/2015 113 51
22/10/2015 222 50
22/10/2015 883 45
20/10/2015 334 35
19/10/2015 564 47
19/10/2015 123 56
22/10/2014 345 36
13/12/2013 456 44
What I want to do is compare the size of the tumors detected on the different days. Let's consider the cell 222 as an example; I want to compare its size to different cells but detected on earlier days e.g. I will not compare its size with cell 883, because they were detected on the same day. Or I will not compare it with cell 113, because it was detected later on.
As my dataset is too large, I have iterate over the rows. If I explain it in a non-pythonic way:
for the cell 222:
get_size_distance(absolute value):
(50 - 35 = 15), (50 - 47 = 3), (50 - 56 = 6), (50 - 36 = 14), (44 - 36 = 8)
get_minumum = 3, I got this value when I compared it with 564, so I will name it as a pait for the cell 222
Then do it for the cell 883
The resulting output should look like this:
Date cell tumor_size(mm) pair size_difference
25/10/2015 113 51 222 1
22/10/2015 222 50 123 6
22/10/2015 883 45 456 1
20/10/2015 334 35 345 1
19/10/2015 564 47 456 3
19/10/2015 123 56 456 12
22/10/2014 345 36 456 8
13/12/2013 456 44 NaN NaN
I will really appreciate your help

It's not pretty, but I believe it does the trick
a = pd.read_clipboard()
# Cut off last row since it was a faulty date. You can skip this.
df = a.copy().iloc[:-1]
# Convert to dates and order just in case (not really needed I guess).
df['Date'] = df.Date.apply(lambda x: datetime.strptime(x, '%d/%m/%Y'))
df.sort_values('Date', ascending=False)
# Rename column
df = df.rename(columns={"tumor_size(mm)": 'tumor_size'})
# These will be our lists of pairs and size differences.
pairs = []
diffs = []
# Loop over all unique dates
for date in df.Date.unique():
# Only take dates earlier then current date.
compare_df = df.loc[df.Date < date].copy()
# Loop over each cell for this date and find the minimum
for row in df.loc[df.Date == date].itertuples():
# If no cells earlier are available use nans.
if compare_df.empty:
pairs.append(float('nan'))
diffs.append(float('nan'))
# Take lowest absolute value and fill in otherwise
else:
compare_df['size_diff'] = abs(compare_df.tumor_size - row.tumor_size)
row_of_interest = compare_df.loc[compare_df.size_diff == compare_df.size_diff.min()]
pairs.append(row_of_interest.cell.values[0])
diffs.append(row_of_interest.size_diff.values[0])
df['pair'] = pairs
df['size_difference'] = diffs
returns:
Date cell tumor_size pair size_difference
0 2015-10-25 113 51 222.0 1.0
1 2015-10-22 222 50 564.0 3.0
2 2015-10-22 883 45 564.0 2.0
3 2015-10-20 334 35 345.0 1.0
4 2015-10-19 564 47 345.0 11.0
5 2015-10-19 123 56 345.0 20.0
6 2014-10-22 345 36 NaN NaN

filter pandas dataframe based in another column

this might be a basic question, but I have not being able to find a solution. I have two dataframes, with identical rows and columns, called Volumes and Prices, which are like this
Volumes
Index ProductA ProductB ProductC ProductD Limit
0 100 300 400 78 100
1 110 370 20 30 100
2 90 320 200 121 100
3 150 320 410 99 100
....
Prices
Index ProductA ProductB ProductC ProductD Limit
0 50 110 30 90 0
1 51 110 29 99 0
2 49 120 25 88 0
3 51 110 22 96 0
....
I want to assign 0 to the "cell" of the Prices dataframe which correspond to Volumes less than what it is on the Limit column
so, the ideal output would be
Prices
Index ProductA ProductB ProductC ProductD Limit
0 50 110 30 0 0
1 51 110 0 0 0
2 0 120 25 88 0
3 51 110 22 0 0
....
I tried
import pandas as pd
import numpy as np
d_price = {'ProductA' : [50, 51, 49, 51], 'ProductB' : [110,110,120,110],
'ProductC' : [30,29,25,22],'ProductD' : [90,99,88,96], 'Limit': [0]*4}
d_volume = {'ProductA' : [100,110,90,150], 'ProductB' : [300,370,320,320],
'ProductC' : [400,20,200,410],'ProductD' : [78,30,121,99], 'Limit': [100]*4}
Prices = pd.DataFrame(d_price)
Volumes = pd.DataFrame(d_volume)
Prices[Volumes > Volumes.Limit]=0
but I do not obtain any changes to the Prices dataframe... obviously I'm having a hard time understanding boolean slicing, any help would be great

The problem is in
Prices[Volumes > Volumes.Limit]=0
Since Limit varies on each row, you should use, for example, apply like following:
Prices[Volumes.apply(lambda x : x>x.Limit, axis=1)]=0

you can use mask to solve this problem, I am not an expert either but this solutions does what you want to do.
test=(Volumes.ix[:,'ProductA':'ProductD'] >= Volumes.Limit.values)
final = Prices[test].fillna(0)