I am trying to set a value in panda dataframe.
ZEROS = np.zeros((4,4), dtype=np.int)
df = pd.DataFrame(ZEROS, columns=['A1','B1','C1','D1'])
df.at[2,3] = 32
df
I don't want NaN for the entire column, the expected output is below:
Using numpy I am able to set the value like below
ZEROS[1][3] = 44
output:
array([[ 0, 0, 0, 0],
[ 0, 0, 0, 44],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]])
Use pd.DataFrame.iat to reference and/or assign to the ordinal location of a single cell.
ZEROS = np.zeros((4,4), dtype=np.int)
df = pd.DataFrame(ZEROS, columns=['A1','B1','C1','D1'])
df.iat[2,3] = 32
df
A1 B1 C1 D1
0 0 0 0 0
1 0 0 0 0
2 0 0 0 32
3 0 0 0 0
You could also use iloc however, iloc can also take array like input. This makes iloc more flexible but also requires more overhead. Therefore, if it is only a single cell you want to change... use iat
Also see this post for more information
Use iloc:
df.iloc[2,3] = 32
print(df)
# A1 B1 C1 D1
#0 0 0 0 0
#1 0 0 0 0
#2 0 0 0 32
#3 0 0 0 0
Or if you want to modify by index and column name, use loc:
df.loc[2, 'D1'] = 32
