RangeIndex object is not callable – Python

The Problem:

While processing a text file using Pandas in Python, you want to find the index of specific substrings in the file. You read the text file into a DataFrame and try to use the .index() method to locate the indices of the substrings. However, you encounter an error message stating that the ‘RangeIndex’ object is not callable. Additionally, converting the index to a list results in a similar error with ‘Int64Index’ not being callable. Your goal is to find a way to obtain the indices of the substrings in the DataFrame efficiently.

The Solutions:

Solution 1: Use the `drop()` method with `index=` parameter

In your code, you are trying to drop a row from the DataFrame using the `drop()` method with the `index()` method as an argument. However, the `index()` method returns a `RangeIndex` object, which is not callable. To drop a row by its index, you need to use the `drop()` method with the `index=` parameter. Here’s the corrected code:

df.drop(index=string, inplace=True)

This will drop the row from the DataFrame where the `sent` column is equal to the `string` variable.

Solution 2: Use the `df.index` Method on the DataFrame

To fix the error "RangeIndex object is not callable", you need to use the df.index method on the dataframe, rather than on your search string.

Here’s a modified version of your code:

matched_rows = df.index[df1.sent.str.match(string)]

if len(matched_rows) > 2:
    df.drop(matched_rows, inplace=True)
    df.reset_index(level=None, drop=True, inplace=True)

Explanation:

1. matched_rows = df.index[df1.sent.str.match(string)]: This line finds the indices of the rows in df1 where the column sent matches the search string.

2. if len(matched_rows) > 2:: This condition checks if there are more than two rows that match the search string. If there are more than two matches, it means the substring and its matching parent string are present, and you want to eliminate the substring from the dataframe.

3. df.drop(matched_rows, inplace=True): This line drops the rows with the matching indices from the df dataframe. The inplace parameter ensures that the changes are made directly to df, rather than creating a new object.

4. df.reset_index(level=None, drop=True, inplace=True): This line resets the index of df to start from 0 and drops the old index column. This is done to ensure that the indices are consistent after deleting rows.

With this corrected code, you should be able to successfully find and remove substrings that match a given string from your dataframe.