Pandas Text Column Group By Based on Unique ID

When working with data frames in pandas, it’s not uncommon to have columns that require grouping or aggregation based on certain conditions. In this article, we’ll explore how to achieve a specific group by operation on a text column using pandas.

Problem Statement

The problem arises when we need to convert a table with a duplicate ID column into two separate columns based on the unique ID value. The goal is to create a new column that contains similar test result values for each duplicate ID.

For example, given the following table:

We want to convert this table into two columns, testresult and similartestresults, where the similartestresults column contains similar test result values for each duplicate ID.

Initial Attempt with Pandas GroupBy

The original poster attempts to achieve this using pandas’ groupby function. However, they only get a single list of groups instead of the desired output. The code provided is as follows:

# Create an example dataframe
import pandas as pd

data = {
    'itemid': [100, 101, 102, 103, 104, 105, 106, 107],
    'testresult': ['textboxerror', 'text_input_issue', 'menuitemerror', 
                   'text_click_issue', 'text_caps_error', 'menu_drop_down_error',
                   'text_lower_error', 'menu_item_null'],
    'duplicateid': [0, 100, 0, 100, 100, 102, 100, 102]
}

df = pd.DataFrame(data)

# Initial groupby attempt
df_grouped = df.groupby(["duplicateid", "testresult"])
print(df_grouped)
print(df_grouped.groups)

df['similartestresults'] = df.groupby("duplicateid")['testresult'].apply(lambda tags: ','.join(tags))

Correct Solution

The provided solution is based on the following steps:

1. Update the testresult column by taking only the first four characters as the group name.
2. Replace the original values with these new values in the testresult column.
3. Remove rows where the duplicateid value is zero.
4. Sort the dataframe by duplicateid.

Here’s how to achieve this:

# Create an example dataframe
import pandas as pd

data = {
    'itemid': [100, 101, 102, 103, 104, 105, 106, 107],
    'testresult': ['textboxerror', 'text_input_issue', 'menuitemerror', 
                   'text_click_issue', 'text_caps_error', 'menu_drop_down_error',
                   'text_lower_error', 'menu_item_null'],
    'duplicateid': [0, 100, 0, 100, 100, 102, 100, 102]
}

df = pd.DataFrame(data)

# Update to group_name
df['simlartestresult'] = df['testresult'].copy()

df['testresult'] = df['simlartestresult'].apply(lambda x: x[:4])
df['testresult'].replace(['text','menu'],['textboxerror','menuitemerror'],inplace=True)

# delete 'dupulicateid = 0'
df = df[~(df['duplicateid'] == 0)]
df = df.sort_values('duplicateid', ascending=True)

Result

The result is as follows:

The solution provided uses a combination of data manipulation and clever use of pandas’ groupby function to achieve the desired output.

Conclusion

When working with text columns in pandas, it’s not uncommon to need to perform complex operations like grouping or aggregation. This article demonstrated how to convert a table with a duplicate ID column into two separate columns based on the unique ID value using pandas. The correct solution uses data manipulation and pandas’ groupby function to achieve the desired output.

Last modified on 2024-07-14