I want to dynamically extend an empty pandas DataFrame in the following way:
df=pd.DataFrame()
indices=['A','B','C']
colums=['C1','C2','C3']
for colum in colums:
for index in indices:
#df[index,column] = anyValue
Where both indices and colums can have arbitrary sizes which are not known in advance, i.e. I cannot create a DataFrame with the correct size in advance.
Which pandas function can I use for
#df[index,column] = anyValue
?
I think you can use loc:
df = pd.DataFrame()
df.loc[0,1] = 10
df.loc[2,8] = 100
print(df)
1 8
0 10.0 NaN
2 NaN 100.0
Faster solution with DataFrame.set_value:
df = pd.DataFrame()
indices = ['A', 'B', 'C']
columns = ['C1', 'C2', 'C3']
for column in columns:
for index in indices:
df.set_value(index, column, 1)
print(df)
C1 C2 C3
A 1.0 1.0 1.0
B 1.0 1.0 1.0
C 1.0 1.0 1.0
loc works very well, but...
For single assignments use at
df = pd.DataFrame()
indices = ['A', 'B', 'C']
columns = ['C1', 'C2', 'C3']
for column in columns:
for index in indices:
df.at[index, column] = 1
df
.at vs .loc vs .set_value timing
