maxframe.dataframe.Series.append#

Series.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:

  • other (DataFrame or Series/dict-like object, or list of these) – The data to append.

  • ignore_index (bool, default False) – If True, the resulting axis will be labeled 0, 1, …, n - 1.

  • verify_integrity (bool, default False) – If True, raise ValueError on creating index with duplicates.

  • sort (bool, default False) – Sort columns if the columns of self and other are not aligned.

Returns:

A new DataFrame consisting of the rows of caller and the rows of other.

Return type:

DataFrame

See also

concat

General function to concatenate DataFrame or Series 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 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.

Examples

>>> import maxframe.dataframe as md
>>> df = md.DataFrame([[1, 2], [3, 4]], columns=list('AB'), index=['x', 'y'])
>>> df.execute()
   A  B
x  1  2
y  3  4
>>> df2 = md.DataFrame([[5, 6], [7, 8]], columns=list('AB'), index=['x', 'y'])
>>> df.append(df2).execute()
   A  B
x  1  2
y  3  4
x  5  6
y  7  8

With ignore_index set to True:

>>> df.append(df2, ignore_index=True).execute()
   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 = md.DataFrame(columns=['A'])
>>> for i in range(5):
...     df = df.append({'A': i}, ignore_index=True)
>>> df.execute()
   A
0  0
1  1
2  2
3  3
4  4

More efficient:

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