dask.dataframe.DataFrame.append

DataFrame.append(other, interleave_partitions=False)[source]

Append rows of other to the end of caller, returning a new object.

This docstring was copied from pandas.core.frame.DataFrame.append.

Some inconsistencies with the Dask version may exist.

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 (Not supported in Dask)

If True, the resulting axis will be labeled 0, 1, …, n - 1.

verify_integritybool, default False (Not supported in Dask)

If True, raise ValueError on creating index with duplicates.

sortbool, default False (Not supported in Dask)

Sort columns if the columns of self and other are not aligned.

Changed in version 1.0.0: Changed to not sort by default.

Returns
DataFrame

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

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

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'), index=['x', 'y'])  
>>> df  
   A  B
x  1  2
y  3  4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'), index=['x', 'y'])  
>>> df.append(df2)  
   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)  
   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 = pd.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:

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