Python Pandas dataframe.append() (original) (raw)

Last Updated : 21 Nov, 2024

Pandas append function is used to add rows of other dataframes to end of existing dataframe, returning a new dataframe object. Columns not in the original data frames are added as new columns and the new cells are populated with NaN value.

**Append Dataframe into another Dataframe

In this example, we are creating two dataframes and append the second to the first one, using df.append().

Python `

Importing pandas as pd

import pandas as pd

Creating the first Dataframe using dictionary

df1 = pd.DataFrame({"a":[1, 2, 3, 4],"b":[5, 6, 7, 8]})

Creating the Second Dataframe using dictionary

df2 = pd.DataFrame({"a":[1, 2, 3],"b":[5, 6, 7]})

to append df2 at the end of df1 dataframe

print(df1.append(df2))

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 Pandas 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.

**Pandas Append Two DataFrames Ignore Index

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.

Python `

A continuous index value will be maintained

across the rows in the new appended data frame.

df1.append(df2, ignore_index=True)

**Output

a b 0 1 5 1 2 6 2 3 7 3 4 8 4 1 5 5 2 6 6 3 7

Append Rows to Dataframe

In this example, we are appending dictionary as row to dataframe.

Python `

import pandas as pd

Creating the first Dataframe using dictionary

df1 = df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})

Append Dict as row to DataFrame

new_row = {"a": 10, "b": 10} df2 = df.append(new_row, ignore_index=True)

print(df2)

**Output

a   b

0 1 5 1 2 6 2 3 7 3 4 8 4 10 10

Append to Dataframe of Different Shapes

In this example, we are appending 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.

Python `

Importing pandas as pd

import pandas as pd

Creating the first Dataframe using dictionary

df1 = pd.DataFrame({"a":[1, 2, 3, 4], "b":[5, 6, 7, 8]})

Creating the Second Dataframe using dictionary

df2 = pd.DataFrame({"a":[1, 2, 3], "b":[5, 6, 7], "c":[1, 5, 4]})

for appending df2 at the end of df1

df1 = df1.append(df2, ignore_index = True) df1

**Output

a b c 0 1 5 NaN 1 2 6 NaN 2 3 7 NaN 3 4 8 NaN 4 1 5 1.0 5 2 6 5.0 6 3 7 4.0

Notice, that the new cells are populated with NaN values.