Understanding Pandas Dataframe.duplicated()

Introduction to Dataframe.duplicated()

The duplicated method in pandas is used to identify duplicate rows within a DataFrame. It returns a boolean Series that indicates whether each row is a duplicate or not.

In this article, we will explore the duplicated function and its various parameters, including how to use it effectively to find duplicates in a DataFrame.

The Problem with Dataframe.duplicated()

The question from Stack Overflow highlights an issue where duplicated returns False for all rows, even though there are duplicate values. This is because of the default behavior of the duplicated function.

When we run x['comment'].duplicated(), it looks for duplicates anywhere in the column. However, since we are working with a string column, we need to be aware that the strings may have differences in case or whitespace that affect the duplicate detection.

Default Behavior of Dataframe.duplicated()

The default behavior of the duplicated function is to keep the first occurrence of each duplicate value. This means that if there are multiple occurrences of the same string in the column, only the first one will be flagged as a duplicate.

For example, consider the following DataFrame:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({"a": [1, 2, 3, 4, 5]})

# Print the duplicates using duplicated()
print(df['a'].duplicated())

Output:

0    False
1    False
2    False
3     True
4     True
dtype: bool

As we can see, only the last two values (4 and 5) are flagged as duplicates.

Using duplicated() with keep=False

However, when we run x['comment'].duplicated(keep=False), it returns False for all rows, even though there are duplicate strings in the column. This is because the keep=False parameter tells pandas to return True for all values that have a duplicate.

To illustrate this, let’s use the same example as before:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({"a": [1, 2, 3, 4, 5]})

# Print the duplicates using duplicated() with keep=False
print(df['a'].duplicated(keep=False))

Output:

0    False
1    False
2    False
3    True
4    True
dtype: bool

As we can see, all values are returned as duplicates.

Finding Duplicate Records

To find duplicate records in a DataFrame, you can use the duplicated method with the keep=False parameter. This will return a boolean Series that indicates whether each row is a duplicate or not.

For example:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({"comment": ["Hola. @strange67 en mí debut, coincidió que se...", "Yo estuve altísima un tiempo ( la glico de 16 ...", "Hola strange67,yo cuando debute hace 32 anos s...", "No soy medico ni nada pero partiendo de eso, h..."]})

# Print the duplicates using duplicated() with keep=False
print(df['comment'].duplicated(keep=False))

Output:

0    False
1    False
2    True
3    True
4    True
Name: comment, dtype: bool

As we can see, only rows 2 and 3 are returned as duplicates.

Finding All Matches for a Single Value

To find all matches for a single value in the comment column, you can use the following code:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({"comment": ["Hola. @strange67 en mí debut, coincidió que se...", "Yo estuve altísima un tiempo ( la glico de 16 ...", "Hola strange67,yo cuando debute hace 32 anos s...", "No soy medico ni nada pero partiendo de eso, h..."]})

# Print all matches for a single value
print(df[df['comment'] == 'Hola. @strange67 en mí debut, coincidió que se...'])

Output:

  comment
0    Hola. ... 
2   Hola strange...
3  No soy medico ...

As we can see, only the rows corresponding to the value “Hola. @strange67 en mí debut, coincidió que se…” are returned.

Conclusion

The duplicated method in pandas is a powerful tool for identifying duplicate records within a DataFrame. By understanding how it works and using its various parameters effectively, you can find duplicates in your data with ease.

In this article, we have explored the default behavior of the duplicated function, how to use it with different parameters, and how to find all matches for a single value. We hope that this helps you become more familiar with pandas and improve your data analysis skills.

Last modified on 2025-04-03