How to assigne a dataframe mean to specific rows of dataframe?

Question

I have a data frame like this

df_a = pd.DataFrame({'a': [2, 4, 5, 6, 12],
                 'b': [3, 5, 7, 9, 15]})

    Out[112]: 
    a   b
0   2   3
1   4   5
2   5   7
3   6   9
4  12  15

and mean out

df_a.mean()

Out[118]: 
a   5.800
b   7.800
dtype: float64

I want this;

df_a[df_a.index.isin([3, 4])] = df.mean()

But I'm getting an error. How do I achieve this? I gave an example here. There are observations that I need to change a lot in the data that I am working with. And I keep their index values in a list

If you don't want to overwrite data that went into the calculation `df_a.append(df_a.mean().rename('mean'))` OR `df_a.append(df_a.mean().rename(len(df_a)))` — piRSquared, Mar 25 '21 at 20:25
Does this answer your question? [What does axis in pandas mean?](https://stackoverflow.com/questions/22149584/what-does-axis-in-pandas-mean) — Joe Ferndz, Mar 25 '21 at 20:25

Max Pierini · Accepted Answer · 2021-03-25T22:11:20.920

0

If you want to overwrite the values of rows in a list, you can do it with iloc

df_a = pd.DataFrame({'a': [2, 4, 5, 6, 12], 'b': [3, 5, 7, 9, 15]})

idx_list = [3, 4]
df_a.iloc[idx_list,:] = df_a.mean()

Output

     a    b
0  2.0  3.0
1  4.0  5.0
2  5.0  7.0
3  5.8  7.8
4  5.8  7.8

edit

If you're using an older version of pandas and see NaNs instead of wanted values, you can use a for loop

df_a_mean = df_a.mean()
for i in idx_list:
    df_a.iloc[i,:] = df_a_mean

edited Mar 25 '21 at 22:11

answered Mar 25 '21 at 20:41

Max Pierini

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But I must use df_a[df_a.index.isin([3, 4])] in my data There are a lot of indexes – Hikmet Selim Talu Mar 25 '21 at 20:52
@HikmetSelimTalu do you want to impute the mean to outliers? – Max Pierini Mar 25 '21 at 20:56
Yes, but I gave an example here. There are observations that I need to change a lot in the data that I am working with. And I keep their index values in a list – Hikmet Selim Talu Mar 25 '21 at 21:00
I've tried this before, but that's how I get the result in python. – Hikmet Selim Talu Mar 25 '21 at 21:11
You wrote `df_a[df_a.index.isin([3, 4])]`, try `iloc` solution I wrote instead. If you have got the indexes `iloc` is the best method – Max Pierini Mar 25 '21 at 21:28
I shared a photo – Hikmet Selim Talu Mar 25 '21 at 21:47
`pandas` version? – Max Pierini Mar 25 '21 at 21:57
1.1.3 version of pandas – Hikmet Selim Talu Mar 25 '21 at 22:03
This is the issue: I'm using 1.2.3 and `iloc` is working correctly. I tried with your version and I get `NaN`s. Update `pandas` if you can. – Max Pierini Mar 25 '21 at 22:08
I added solution for older pandas versions – Max Pierini Mar 25 '21 at 22:14
Thanks Max Pierini I fixed this problem, df_a.loc[idx, df.columns] = df[df.columns].mean().tolist() it worked – Hikmet Selim Talu Mar 26 '21 at 00:22

How to assigne a dataframe mean to specific rows of dataframe?

1 Answers1

edit