Pandas - add value at specific iloc into new dataframe column

There are two steps to created & populate a new column using only a row number... (in this approach iloc is not used)

First, get the row index value by using the row number

rowIndex = df.index[someRowNumber]

Then, use row index with the loc function to reference the specific row and add the new column / value

df.loc[rowIndex, 'New Column Title'] = "some value"

These two steps can be combine into one line as follows

df.loc[df.index[someRowNumber], 'New Column Title'] = "some value"

If you want to add values to certain rows in a new column, depending on values in other cells of the dataframe you can do it like this:

import pandas as pd
df = pd.DataFrame(data={"A":[1,1,2,2], "B":[1,2,3,4]})

Add value in a new column based on the values in cloumn "A":

df.loc[df.A == 2, "C"] = 100

This creates the column "C" and addes the value 100 to it, if column "A" is 2.

Output:

   A  B  C
0  1  1  NaN
1  1  2  NaN
2  2  3  100
3  2  4  100

It is not necessary to initialise the column first.

If you have a dataframe like

import pandas as pd
df = pd.DataFrame(data={'X': [1.5, 6.777, 2.444, pd.np.NaN], 'Y': [1.111, pd.np.NaN, 8.77, pd.np.NaN], 'Z': [5.0, 2.333, 10, 6.6666]})

Instead of iloc,you can use .loc with row index and column name like df.loc[row_indexer,column_indexer]=value

df.loc[[0,3],'Z'] = 3

Output:

       X      Y       Z
0  1.500  1.111   3.000
1  6.777    NaN   2.333
2  2.444  8.770  10.000
3    NaN    NaN   3.000