Pandas - Replace values based on index

Question

If I create a dataframe like so:

import pandas as pd, numpy as np

df = pd.DataFrame(np.random.randint(0,100,size=(100, 2)), columns=list('AB'))

How would I change the entry in column A to be the number 16 from row 0 -15, for example? In other words, how do I replace cells based purely on index?

Answer 1

In addition to the other answers, here is what you can do if you have a list of individual indices:

indices = [0,1,3,6,10,15]
df.loc[indices,'A'] = 16

print(df.head(16))

Output:

     A  B
0   16  4
1   16  4
2    4  3
3   16  4
4    1  1
5    3  0
6   16  4
7    2  1
8    4  4
9    3  4
10  16  0
11   3  1
12   4  2
13   2  2
14   2  1
15  16  1

Answer 2

One more solution is

df.at[0:15, 'A']=16

print(df.head(20))

OUTPUT:

     A   B
0   16  44
1   16  86
2   16  97
3   16  79
4   16  94
5   16  24
6   16  88
7   16  43
8   16  64
9   16  39
10  16  84
11  16  42
12  16   8
13  16  72
14  16  23
15  16  28
16  18  11
17  76  15
18  12  38
19  91   6

Answer 3

Very interesting observation, that code below does change the value in the original dataframe

df.loc[0:15,'A'] = 16

But if you use a pretty similar code like this

df.loc[0:15]['A'] = 16

Than it will give back just a copy of your dataframe with changed value and doesn't change the value in the original df object. Hope that this will save some time for someone dealing with this issue.

Answer 4

Could you instead of 16, update the value of that column to -1.0? for me, it returns 255 instead of -1.0.

>>> effect_df.loc[3:5, ['city_SF', 'city_Seattle']] = -1.0

    Rent  city_SF  city_Seattle
0  3999        1             0
1  4000        1             0
2  4001        1             0
3  3499      255           255
4  3500      255           255
5  3501      255           255
6  2499        0             1
7  2500        0             1
8  2501        0             1