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I want every element divided by sum of row inplace,code below always go wrong.
pandas newbie, thanks!
df = pd.DataFrame(np.random.rand(12).reshape(3,4),columns=list('abcd'))
df_row_sum = df.apply(lambda x: x.mean(),axis=1)
df / df_row_sum
691
Steps needed: 1 Create or import the data frame 2 Sum the rows: This can be done using the .sum () function and passing the parameter axis=1 3 Sum the columns: By using the .sum () function and passing the parameter axis=0 4 Filtering on the basis of required conditions
We can find the sum of each row in the DataFrame by using the following syntax: df. sum (axis=1) 0 128.0 1 112.0 2 113.0 3 118.0 4 132.0 5 126.0 6 100.0 7 109.0 8 120.0 9 117.0 dtype: float64. The output tells us: The sum of values in the first row is 128. The sum of values in the second row is 112. The sum of values in the third row is 113.
Other method to get the row sum in R is by using apply () function. row wise sum of the dataframe is also calculated using dplyr package. rowwise () function of dplyr package along with the sum function is used to calculate row wise sum. we will be looking at the following examples Row wise sum in R dataframe using rowSums ()
On calculating the sum of all rows for these three columns, we find the sum to be zero for indexes 2 and 3. If you want to remove any of the columns from a list of columns that has sum equals to zero. We only sum those columns and apply the condition on them.
I think you need sum or maybe mean per rows (axis=1) with division by DataFrame.div :
np.random.seed(123)
df = pd.DataFrame(np.random.randint(10, size=12).reshape(3,4),columns=list('abcd'))
print (df)
a b c d
0 2 2 6 1
1 3 9 6 1
2 0 1 9 0
print (df.sum(axis=1))
0 11
1 19
2 10
dtype: int64
print (df.div(df.sum(axis=1), axis=0))
a b c d
0 0.181818 0.181818 0.545455 0.090909
1 0.157895 0.473684 0.315789 0.052632
2 0.000000 0.100000 0.900000 0.000000
print (df.mean(axis=1))
0 2.75
1 4.75
2 2.50
dtype: float64
print (df.div(df.mean(axis=1), axis=0))
a b c d
0 0.727273 0.727273 2.181818 0.363636
1 0.631579 1.894737 1.263158 0.210526
2 0.000000 0.400000 3.600000 0.000000
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