Getting Unique values from a column in Pandas dataframe Decimal Functions in Python | Set 2 (logical_and(), normalize(), quantize(), rotate() … NetworkX : Python software package for study of complex networks Let's say this is my data: A B C 0 foo 0 A 1 foo 1 A 2 foo 1 B 3 bar 1 A I would like to drop the rows when A, and B are unique, i.e. Timestamps also include the first and last items. Step 1 - Import the library import pandas as pd We have only imported pandas which is required for this. Generally it … But Series.unique() works only for a single column. We will use unique() method to get unique value from Department column. You can use Pandas unique() method to get unique Values from a Column in Pandas DataFrame. The unique() function is based on hash-table. I would like to keep only the rows 1 and 2. Examples Let’s look at the some of the different use cases of getting unique counts through some examples. So if Pandas Series unique() Pandas unique() function extracts a unique data from the dataset. Often while working with a big data frame in pandas, you might have a column with string/characters and you want to find the number of unique elements present in the column. I am trying to determine whether there is an entry in a Pandas column that has a particular value. So this is the recipe on How we can make a list of unique values in a Pandas DataFrame. List unique values in a pandas column. Pandas library in Python easily let you find the unique values. So if we need to find unique values or categories in the feature then what to do ? # get the unique values (rows) df.drop_duplicates() The above drop_duplicates() function removes all the duplicate rows and returns only unique rows. I have a DataFrame in Pandas. drop_duplicates() function is used to get the unique values (rows) of the dataframe in python pandas. To simulate the select unique col_1, col_2 of SQL you can use DataFrame.drop_duplicates(): df.drop_duplicates() # col_1 col_2 # 0 A 3 # 1 B 4 # 3 B 5 # 4 C 6 This will get you all the unique rows in the dataframe. strings or timestamps), the result’s index will include count, unique, top, and freq. Using unique() method. Basically, the opposite of drop_duplicates(). Step 2 - Setting up the Data By default, the pandas dataframe nunique() function counts the distinct values along axis=0, that is, row-wise which gives you the count of distinct values in each column. PRICE Name PER CATEGORY STORENAME 0 9.99 MF gram Indica Store1 1 9.99 HY gram Herb Store2 2 9.99 FF gram Herb Store2 What I want to do is split these into multiple data frames to have unique names, then in those split to category. Uniques are returned in order of their appearance in the data set. In this tutorial, we will see examples of getting unique values of a column using two Pandas functions. pandas.DataFrame.describe ... For object data (e.g. Here is an example. The top is the most common value. I am stuck with a seemingly easy problem: dropping unique rows in a pandas dataframe. Special thanks to Bob Haffner for pointing out a better way of doing it. Pandas unique() function has an edge advantage over numpy.unique as here we can also have NA values, and it is comparatively faster. Current code: The freq is the most common value’s frequency.