It takes an argument subset, which is the column we want to find or duplicates based on - in this case, we want all the unique names. You would do this using the drop_duplicates method. To achieve this, you would remove rows that contain a dog name already listed earlier, or in other words, you will extract a dog with each name from the dataset once. Hence, you cannot just count the number of each breed in the breed column. However, there are dogs like Max and Stella, who have visited the vet more than once in your dataset. Let's say you have a dataframe that contains vet visits, and the vet's office wants to know how many dogs of each breed have visited their office. Let's understand how to use it with the help of a few examples. In Python, this could be accomplished by using the Pandas module, which has a method known as drop_duplicates. Please check out the notebook for the source code and stay tuned if you are interested in the practical aspect of machine learning.Removing duplicates is an essential skill to get accurate counts because you often don't want to count the same thing multiple times. I recommend you to check out the documentation for the duplicated() and drop_duplicates() API and to know about other things you can do. I hope this article will help you to save time in learning Pandas. ![]() It is important to know them as we often need to use them during the data preprocessing and analysis. Pandas duplicated() and drop_duplicates() are two quick and convenient methods to find and remove duplicates. Similarly, to consider certain columns for dropping duplicates, we can pass a list of columns to the argument subset: # Considering certain columns for dropping duplicates df.drop_duplicates( subset=) Conclusion # To drop all duplicates df.drop_duplicates( keep=False) Considering certain columns for dropping duplicates # Use keep='last' to keep the last occurrence df.drop_duplicates( keep='last')Īnd we can set keep to False to drop all duplicates. Similarly, we can set keep to 'last' to keep the last occurrence and drop other duplicates. ![]() It defaults to 'first' to keep the first occurrence and drop all other duplicates. The argument keep can be set for drop_duplicates() as well to determine which duplicates to keep. df.drop_duplicates( inplace=True) Determining which duplicate to keep We can set the argument inplace=True to remove duplicates from the original DataFrame. By default, this method returns a new DataFrame with duplicate rows removed. Note that we started out as 80 rows, now it’s 77. We can Pandas loc data selector to extract those duplicate rows: # Extract duplicate rows df. However, it is not practical to see a list of True and False when we need to perform some data analysis. Pandas duplicated() returns a boolean Series. If you want to count the number of non-duplicates (The number of False), you can invert it with negation ( ~)and then call sum(): # Count the number of non-duplicates > ( ~df.duplicated()).sum() 77 3. duplicated().sum() 3 # Count duplicate on certain columns > df. Just like before, we can count the duplicate in a DataFrame and on certain columns. Behind the theme, True get converted to 1 and False get converted to 0, then it adds them up. The result of the duplicated() is a boolean Series, and we can add them up to count the number of duplicates. 271 True 278 True 286 True 299 True 300 True Length: 80, dtype: bool 2. To consider certain columns for identifying duplicates, we can pass a list of columns to the argument subset: > df.duplicated( subset=) 0 False 1 False 9 False 10 True 14 True. 271 False 278 False 286 False 299 False 300 False Length: 80, dtype: bool duplicated() 0 False 1 False 9 False 10 False 14 False. It outputs True if an entire row is identical to a previous row. To take a look at the duplication in the DataFrame as a whole, just call the duplicated() method on the DataFrame. In other words, the value True means the entry is identical to a previous one. The result is a boolean Series with the value True denoting duplicate. 271 False 278 False 286 False 299 False 300 False Name: Cabin, Length: 80, dtype: bool To find duplicates on a specific column, we can simply call duplicated() method on the column.
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