In pandas, you can count duplicates by using the duplicated() function followed by the sum() function.
For example:
1 2 3 4 5 6 |
import pandas as pd data = {'A': [1, 2, 2, 3, 4, 4, 4]} df = pd.DataFrame(data) print(df.duplicated().sum()) |
This will output the number of duplicates in the DataFrame df. You can also pass specific columns to the duplicated() function if you only want to check for duplicates in those columns.
Best Python Books to Read In November 2024
How to count duplicates in pandas and return the duplicate rows as a separate DataFrame?
You can count duplicates in a pandas DataFrame and return the duplicate rows as a separate DataFrame by using the duplicated() method along with boolean indexing.
Here's an example code snippet that demonstrates how to achieve this:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
import pandas as pd # Sample DataFrame data = {'A': [1, 1, 2, 3, 3, 4], 'B': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar']} df = pd.DataFrame(data) # Count duplicates duplicate_mask = df.duplicated() num_duplicates = duplicate_mask.sum() print(f'Number of duplicates: {num_duplicates}') # Get duplicate rows as a separate DataFrame duplicate_rows = df[duplicate_mask] print('\nDuplicate rows:\n', duplicate_rows) |
In this code snippet, we first create a sample DataFrame with some duplicate rows. We then use the duplicated() method to create a boolean mask indicating whether each row is a duplicate or not. We calculate the total number of duplicates by summing up the values in the mask. Finally, we use boolean indexing to filter out the duplicate rows and store them in a separate DataFrame called duplicate_rows.
You can customize this code to suit your specific DataFrame and requirements.
How to count duplicates in pandas and count the number of occurrences of each duplicate row?
One way to count duplicates in a pandas dataframe and the number of occurrences of each duplicate row is to use the duplicated() function along with value_counts().
Here is an example to demonstrate this:
- First, import the pandas library:
1
|
import pandas as pd
|
- Create a sample dataframe with some duplicate rows:
1 2 3 |
data = {'A': [1, 2, 3, 1, 2, 3, 4], 'B': ['X', 'Y', 'Z', 'X', 'Y', 'Z', 'W']} df = pd.DataFrame(data) |
- Use the duplicated() function to identify duplicate rows in the dataframe:
1
|
duplicates = df[df.duplicated()]
|
- Then, use the value_counts() function on the duplicates dataframe to count the number of occurrences of each duplicate row:
1
|
duplicate_counts = duplicates.value_counts()
|
- Finally, print the duplicate_counts to see the number of occurrences of each duplicate row:
1
|
print(duplicate_counts)
|
This will output a series showing the number of occurrences of each duplicate row in the dataframe.
Note: You can also use the keep parameter in the duplicated() function to specify whether to mark the first or last occurrence of a duplicate row as not a duplicate.
What is the syntax to count duplicates in pandas using the duplicated() method?
To count duplicates in pandas using the duplicated() method, you can use the following syntax:
1
|
df.duplicated().sum()
|
This will return the total count of duplicates in the DataFrame df.
How to count duplicates in pandas and mark duplicate rows with a specific value in a new column?
You can count duplicates in pandas and mark duplicate rows with a specific value in a new column using the following steps:
- Use the duplicated() function in pandas to identify duplicate rows in a dataframe.
- Use the sum() function to count the duplicate rows.
- Use the loc function to mark duplicate rows with a specific value in a new column.
Here's a step-by-step guide:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |
import pandas as pd # Create a sample dataframe data = {'A': [1, 2, 2, 3, 4, 4], 'B': ['one', 'two', 'two', 'three', 'four', 'four']} df = pd.DataFrame(data) # Count duplicates df['is_duplicate'] = df.duplicated() df['count_duplicates'] = df.duplicated(keep=False) # Mark duplicate rows with a specific value in a new column df.loc[df['is_duplicate'], 'mark_duplicate'] = 'duplicate' df.loc[~df['is_duplicate'], 'mark_duplicate'] = 'not duplicate' print(df) |
In this code snippet, we first create a sample dataframe with duplicate rows. We then use the duplicated() function to identify duplicate rows and count them. We create a new column 'is_duplicate' to mark the duplicate rows.
Lastly, we use the loc function to mark the duplicate rows with a specific value ('duplicate') in a new column 'mark_duplicate', and mark the non-duplicate rows with a different value ('not duplicate').
