I have a dataframe like the following.
| i | j | element |
|---|---|---|
| 0 | 0 | 1 |
| 0 | 1 | 2 |
| 0 | 2 | 3 |
| 1 | 0 | 4 |
| 1 | 1 | 5 |
| 1 | 2 | 6 |
| 2 | 0 | 7 |
| 2 | 1 | 8 |
| 2 | 2 | 9 |
How can I convert it to the 3*3 array below?
| 1 | 2 | 3 |
| 4 | 5 | 6 |
| 7 | 8 | 9 |
-
Does this answer your question? Convert pandas dataframe to NumPy arraymsanford– msanford2022-11-04 15:20:00 +00:00Commented Nov 4, 2022 at 15:20
Add a comment
|
2 Answers
Assuming that the dataframe is called df, one can use pandas.DataFrame.pivot as follows, with .to_numpy() (recommended) or .values as follows
array = df.pivot(index='i', columns='j', values='element').to_numpy()
# or
array = df.pivot(index='i', columns='j', values='element').values
[Out]:
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]], dtype=int64)
Comments
If you transform your dataframe into three lists where the first is containing "i" values, the second - j and the third is data, you can create NumPy array "manually":
i, j, v = zip(*[x for x in df.itertuples(index=False, name=None)])
arr = np.zeros(df.shape)
arr[i, j] = v
