The error “iloc cannot enlarge its target object” usually occurs when using the iloc method in pandas to access elements in a DataFrame or Series but attempting to enlarge the size of the target object.
The iloc function is used for integer-based indexing in pandas. It allows you to select specific rows or columns based on their integer position rather than by label.
It is important to note that iloc is not designed to add or modify elements in a DataFrame or Series. Therefore, you may encounter the mentioned error if you try to assign a value to an index that doesn’t exist or if you try to append rows or columns using iloc.
Here is an example to better understand the issue:
import pandas as pd
# Create a sample DataFrame
data = {'Column1': [1, 2, 3]}
df = pd.DataFrame(data)
# Attempt to assign a value to a non-existing index
df.iloc[3] = 4
In the above example, the DataFrame df has only one column named ‘Column1’ with three rows. When we try to assign the value 4 to the index 3 using iloc, it will throw the error “iloc cannot enlarge its target object” because the index 3 doesn’t exist in the DataFrame.
To fix the error, you need to ensure that the index you are accessing with iloc is valid and already exists. If you want to add new rows or columns to a DataFrame, you can use other methods like loc or append instead.
Here is an example of appending a new row to a DataFrame:
import pandas as pd
# Create a sample DataFrame
data = {'Column1': [1, 2, 3]}
df = pd.DataFrame(data)
# Create a new row to append
new_row = pd.DataFrame({'Column1': [4]})
# Append the new row to the DataFrame
df = df.append(new_row, ignore_index=True)
In this example, we create a new row named new_row using a separate DataFrame. Then, we append the new row to the original DataFrame using the append function. The resulting DataFrame will have four rows including the newly added row.