cudf.DataFrame.append#

DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=False)#

Append rows of other to the end of caller, returning a new object. Columns in other that are not in the caller are added as new columns.

Parameters:

otherDataFrame or Series/dict-like object, or list of these: The data to append.
ignore_indexbool, default False: If True, do not use the index labels.
sortbool, default False: Sort columns ordering if the columns of self and other are not aligned.
verify_integritybool, default False: This Parameter is currently not supported.

Returns:

DataFrame

See also

cudf.concat: General function to concatenate DataFrame or objects.

Notes

If a list of dict/series is passed and the keys are all contained in the DataFrame’s index, the order of the columns in the resulting DataFrame will be unchanged. Iteratively appending rows to a cudf DataFrame can be more computationally intensive than a single concatenate. A better solution is to append those rows to a list and then concatenate the list with the original DataFrame all at once. verify_integrity parameter is not supported yet.

Examples

>>> import cudf
>>> df = cudf.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
>>> df
   A  B
0  1  2
1  3  4
>>> df2 = cudf.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
>>> df2
   A  B
0  5  6
1  7  8
>>> df.append(df2)
   A  B
0  1  2
1  3  4
0  5  6
1  7  8

With ignore_index set to True:

>>> df.append(df2, ignore_index=True)
   A  B
0  1  2
1  3  4
2  5  6
3  7  8

The following, while not recommended methods for generating DataFrames, show two ways to generate a DataFrame from multiple data sources. Less efficient:

>>> df = cudf.DataFrame(columns=['A'])
>>> for i in range(5):
...     df = df.append({'A': i}, ignore_index=True)
>>> df
   A
0  0
1  1
2  2
3  3
4  4

More efficient than above:

>>> cudf.concat([cudf.DataFrame([i], columns=['A']) for i in range(5)],
...           ignore_index=True)
   A
0  0
1  1
2  2
3  3
4  4