{"id":26289,"date":"2022-01-03T09:18:41","date_gmt":"2022-01-03T03:48:41","guid":{"rendered":"https:\/\/python-programs.com\/?p=26289"},"modified":"2022-01-03T09:18:41","modified_gmt":"2022-01-03T03:48:41","slug":"in-python-how-do-you-get-unique-values-from-a-dataframe","status":"publish","type":"post","link":"https:\/\/python-programs.com\/in-python-how-do-you-get-unique-values-from-a-dataframe\/","title":{"rendered":"In Python, How do you get Unique Values from a Dataframe?"},"content":{"rendered":"

Pandas DataFrames really amazing. DataFrames in Python makes data manipulation very user-friendly.<\/p>\n

Pandas allow you to import large datasets and then manipulate them effectively. CSV data can be easily imported into a Pandas DataFrame.<\/p>\n

What are Python Dataframes?<\/strong><\/p>\n

Dataframes are two-dimensional labeled data structures with columns of various types.
\nDataFrames can be used for a wide range of analyses.<\/p>\n

Often, the dataset is too large, and it is impossible to examine the entire dataset at once. Instead, we’d like to see the Dataframe’s summary.
\nWe can get the first five rows of the dataset as well as a quick statistical summary of the data. Aside from that, we can gain information\u00a0about the types of columns in our dataset.<\/p>\n

DataFrame is a data structure offered by the Pandas module to cope with large datasets with several dimensions, such as large csv or excel files.<\/p>\n

Because we may store a huge volume of data in a data frame, we frequently encounter situations where we need to find the unique data values from a dataset that may contain redundant or repeated values.<\/p>\n

This is where the pandas.dataframe.unique()<\/strong> function comes in.<\/p>\n

pandas.unique() Function in Python<\/h4>\n

The pandas.unique() function returns the dataset’s unique values.<\/p>\n

It basically employs a hash table-based technique to return the non-redundant values from the set of values existing in the data frame\/series data structure.<\/p>\n

For Example:<\/p>\n

Let dataset values = 5, 6, 7, 5, 2, 6<\/p>\n

The output we get by applying unique function = 5, 6, 7,2<\/p>\n

We were able to readily find the dataset’s unique values this way.<\/p>\n

Syntax:<\/strong><\/p>\n

pandas.unique(data)<\/pre>\n
When dealing with 1-Dimensional data, the above syntax comes in handy. It symbolizes or represents the unique value among the 1-Dimensional data values (Series data structure).<\/p>\n
But what if the data has more than one dimension, such as rows and columns? Yes, we have a solution for it in the syntax below\u2013<\/p>\n
Syntax For Multidimensional data:<\/strong><\/p>\n
pandas.dataframe.column-name.unique()<\/pre>\n
The above\u00a0syntax allows us to extract unique values from a specific column of a dataset.<\/p>\n
It is preferable for the data to be of the categorical type in order for the unique function to produce accurate results. Furthermore, the data is displayed in the order in which it appears in the dataset.<\/p>\n
unique() function with Pandas Series<\/h5>\n
Example<\/strong><\/p>\n
Approach:<\/strong><\/p>\n