Sum Cells of Certain Columns for Each Row

sum cells of certain columns for each row

You could do something like this:

summed <- rowSums(zscore[, c(1, 2, 3, 5)])

summing rows of specific columns then dividing by the sum

Try this way to specify the column (by sub-setting Df), and then indicating the margin as 1

Df_new = t(apply(Df[,c(1:3)], 1, \(x) x/sum(x)))

       lose      draw       win
[1,] 0.5000 0.1428571 0.3571429
[2,] 0.0625 0.1250000 0.8125000

Sum values across a row if they are in certain columns, and if adjacent cells have a specific value

If the blocks are all right next to each as shown in your image, and the value you want to sum is always 1 cell to the right of the time value, and only the time values can be >= 1.5 (all of your MedX values are < 1), then this formula would work for you:

=SUMIF(D3:O3,">=1.5",E3:P3)

If it's possible for the MedX values to be >= 1.5, then this more explicit formula should work for you:

=SUMPRODUCT(--($D$2:$O$2="Time"),--(D3:O3>=1.5),--($E$2:$P$2="Med1"),E3:P3)

Sum if column name is higher than row value

We can check with np.greater_equal.outer, then slice the column mask the unwanted cell with boolean output as NaN

s = pd.to_datetime(df.date).values
m = np.greater_equal.outer(pd.to_datetime(df.columns[:-1]).values,s).T
df = df.append(df.iloc[:,:-1].where(m).sum().to_frame('Total').T)
df
Out[381]: 
       01-01-2020  01-01-2021  01-01-2022        date
1             1.0         3.0         6.0  01-01-2020
2             4.0         4.0         2.0  01-10-2021
3             5.0         1.0         9.0  01-12-2021
Total         1.0         3.0        17.0         NaN

Pandas: sum DataFrame rows for given columns

You can just sum and set param axis=1 to sum the rows, this will ignore none numeric columns:

In [91]:

df = pd.DataFrame({'a': [1,2,3], 'b': [2,3,4], 'c':['dd','ee','ff'], 'd':[5,9,1]})
df['e'] = df.sum(axis=1)
df
Out[91]:
   a  b   c  d   e
0  1  2  dd  5   8
1  2  3  ee  9  14
2  3  4  ff  1   8

If you want to just sum specific columns then you can create a list of the columns and remove the ones you are not interested in:

In [98]:

col_list= list(df)
col_list.remove('d')
col_list
Out[98]:
['a', 'b', 'c']
In [99]:

df['e'] = df[col_list].sum(axis=1)
df
Out[99]:
   a  b   c  d  e
0  1  2  dd  5  3
1  2  3  ee  9  5
2  3  4  ff  1  7

How to set the last column of a pandas dataframe as the sum of certain columns?

One of the advantages of pandas is that you can often use vectorized operations instead of loops. Thus in your case it's possible to sum over a 2-dimensional slice of the dataframe like this:

df['your_score'] = df.loc[:, 'Week1':'Week19'].sum(axis=1)

The loc operator allows indexing and slicing by labels. : selects all the rows, so we get a sub-dataframe. The df.sum() method is based on the NumPy function of the same name, so you can select a dimension to sum over with the axis argument.

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