Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.append() function is used to append rows of other data frames to the end of the given data frame, returning a new data frame object. Columns not in the original data frames are added as new columns and the new cells are populated with NaN value.
Pandas dataframe.append() Syntax
Syntax: DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=None)
Parameters:
- other: DataFrame or Series/dict-like object, or list of these
- ignore_index: If True, do not use the index labels.
- verify_integrity: If True, raise ValueError on creating an index with duplicates.
- sortPandas: Sort columns if the columns of self and other are not aligned. The default sorting is deprecated and will change to not-sorting in a future version of pandas. Explicitly pass sort=True to silence the warning and sort. Explicitly pass sort=False to silence the warning and not sort.
Return Type: appended : DataFrame
NOTE: As of Pandas version 2.0, the .append() method is no longer in use. It is important to keep this in mind while working with Pandas. More efficient alternatives for concatenating DataFrames are the .concat() function from the pandas.DataFrame module.
dataframe.append() in Pandas Examples
Example 1: Create two data frames and append the second to the first one.
Python3
# Importing pandas as pdimport pandas as pd# Creating the first Dataframe using dictionarydf1 = df = pd.DataFrame({"a":[1, 2, 3, 4], "b":[5, 6, 7, 8]})# Creating the Second Dataframe using dictionarydf2 = pd.DataFrame({"a":[1, 2, 3], "b":[5, 6, 7]})# Print df1print(df1, "\n")# Print df2df2 |
Output:
Now append df2 at the end of df1.
Python3
# to append df2 at the end of df1 dataframedf1.append(df2) |
Output:
Notice the index value of the second data frame is maintained in the appended data frame. If we do not want it to happen then we can set ignore_index=True.
Python3
# A continuous index value will be maintained# across the rows in the new appended data frame.df1.append(df2, ignore_index=True) |
Output:
Example 2: Append dataframe of different shapes. For unequal no. of columns in the data frame, a non-existent value in one of the dataframe will be filled with NaN values.
Python3
# Importing pandas as pdimport pandas as pd# Creating the first Dataframe using dictionarydf1 = pd.DataFrame({"a":[1, 2, 3, 4], "b":[5, 6, 7, 8]})# Creating the Second Dataframe using dictionarydf2 = pd.DataFrame({"a":[1, 2, 3], "b":[5, 6, 7], "c":[1, 5, 4]})# for appending df2 at the end of df1df1 = df1.append(df2, ignore_index = True)df1 |
Output:
Notice, that the new cells are populated with NaN values.
Note: pandas.DataFrame.append is deprecated since version 1.4.0 . Rather use concat() .
