How to add data in dataframe in python

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.

The data to append.

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

If True, raise ValueError on creating index with duplicates.

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

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

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

See also

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