set_value
has been deprecated. You can now use DataFrame.at
to set by label, and DataFrame.iat
to set by integer position.
at
/iat
# Setup
df = pd.DataFrame({'A': [12, 23], 'B': [['a', 'b'], ['c', 'd']]})
df
A B
0 12 [a, b]
1 23 [c, d]
df.dtypes
A int64
B object
dtype: object
If you want to set a value in second row of the "B" to some new list, use DataFrane.at
:
df.at[1, 'B'] = ['m', 'n']
df
A B
0 12 [a, b]
1 23 [m, n]
You can also set by integer position using DataFrame.iat
df.iat[1, df.columns.get_loc('B')] = ['m', 'n']
df
A B
0 12 [a, b]
1 23 [m, n]
ValueError: setting an array element with a sequence
?I'll try to reproduce this with:
df
A B
0 12 NaN
1 23 NaN
df.dtypes
A int64
B float64
dtype: object
df.at[1, 'B'] = ['m', 'n']
# ValueError: setting an array element with a sequence.
This is because of a your object is of float64
dtype, whereas lists are object
s, so there's a mismatch there. What you would have to do in this situation is to convert the column to object first.
df['B'] = df['B'].astype(object)
df.dtypes
A int64
B object
dtype: object
Then, it works:
df.at[1, 'B'] = ['m', 'n']
df
A B
0 12 NaN
1 23 [m, n]
Even more wacky, I've found you can hack through DataFrame.loc
to achieve something similar if you pass nested lists.
df.loc[1, 'B'] = [['m'], ['n'], ['o'], ['p']]
df
A B
0 12 [a, b]
1 23 [m, n, o, p]
You can read more about why this works here.