Drop Duplicates In A Subset Of Columns Per Row, Rowwise, Only Keeping The First Copy, Rowwise Only If There Are 3 Or More Duplicates Update Mới

To remove duplicate values based on two columns in pandas, you can use the drop_duplicates() method with the subset parameter set to a list of the two column names. Here’s an example:

python
import pandas as pd

# create a sample DataFrame
data = {
    'col1': ['a', 'b', 'c', 'a', 'b', 'c', 'd'],
    'col2': [1, 2, 3, 1, 2, 3, 4],
    'col3': [10, 20, 30, 40, 50, 60, 70]
}
df = pd.DataFrame(data)

# remove duplicates based on col1 and col2
df.drop_duplicates(subset=['col1', 'col2'], inplace=True)

print(df)

Output:

css
  col1  col2  col3
0    a     1    10
1    b     2    20
2    c     3    30
6    d     4    70

In this example, the drop_duplicates() method removes rows 3, 4, and 5, which are duplicates based on the values in columns ‘col1’ and ‘col2’. The resulting DataFrame only contains unique combinations of values in ‘col1’ and ‘col2’.

In pandas, the drop_duplicates() method is used to remove duplicate rows from a DataFrame. It returns a new DataFrame with the duplicate rows removed.

By default, the method considers all columns of the DataFrame to determine whether two rows are duplicates. You can also specify a subset of columns to consider for duplicate detection using the subset parameter.

Here is an example of using drop_duplicates() method:

python
import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 1, 2, 3],
                   'B': [4, 5, 6, 7, 8, 9]})

# Print the original DataFrame
print(df)

# Drop the duplicate rows
df = df.drop_duplicates()

# Print the DataFrame after removing duplicates
print(df)

The output of this code will be:

css
   A  B
0  1  4
1  2  5
2  3  6
3  1  7
4  2  8
5  3  9

   A  B
0  1  4
1  2  5
2  3  6
3  1  7
4  2  8
5  3  9

As you can see, the original DataFrame had two duplicate rows (rows 0 and 3), and they were removed from the new DataFrame returned by drop_duplicates().

To remove duplicates in a Pandas column, you can use the drop_duplicates() method.

Here’s an example:

python
import pandas as pd

# create a sample dataframe
data = {'name': ['John', 'Alice', 'Bob', 'John', 'Bob'],
        'age': [28, 24, 22, 28, 22]}
df = pd.DataFrame(data)

# display the original dataframe
print("Original dataframe:\n", df)

# remove duplicates from the 'name' column
df['name'] = df['name'].drop_duplicates()

# display the dataframe after removing duplicates
print("Dataframe after removing duplicates:\n", df)

In the example above, the drop_duplicates() method is called on the ‘name’ column of the dataframe df. This removes any duplicate values in the ‘name’ column, and the resulting column is assigned back to the ‘name’ column of df.

Note that the drop_duplicates() method by default keeps the first occurrence of each unique value and removes any subsequent occurrences. If you want to keep the last occurrence of each value instead, you can pass the argument keep='last' to the method.