As per the following data set I want no get the number of unique values and count of the unique values.
My data set:
Account_Type Gold Gold Platinum Gold Output :
no of unique values : 2 unique values : [Gold,Platinum] Gold : 3 Platinum :1 
- you may try set().Shiping– Shiping2017-03-28 09:59:01 +00:00Commented Mar 28, 2017 at 9:59
- Does this answer your question? Counting unique values in a column in pandas dataframe like in Qlik?jdhao– jdhao2022-10-03 06:50:48 +00:00Commented Oct 3, 2022 at 6:50
Add a comment |
3 Answers
Use pd.value_counts
pd.value_counts(df.Account_Type) Gold 3 Platinum 1 Name: Account_Type, dtype: int64 Get number of unique as well
s = pd.value_counts(df.Account_Type) s1 = pd.Series({'nunique': len(s), 'unique values': s.index.tolist()}) s.append(s1) Gold 3 Platinum 1 nunique 2 unique values [Gold, Platinum] dtype: object Alternate Approach
df['col1'].value_counts(sort=True) df['col1'].value_counts(sort=True, normalize=True) -> provides proportion 
1 Comment
NuValue
When applying this nice code to a categorical variable of a df I receive an error: pd.value_counts(df.application_type) s = pd.value_counts(df.application_type) s1 = pd.Series({'nunique': len(s), 'unique values': s.index.tolist()}) s.append(s1) TypeError: cannot append a non-category item to a CategoricalIndex "application_type" is a category column from df.
You can use set() to remove duplicates and then calculate the length:
len(set(data_set))
To count the occurrence:
data_set.count(value)
Comments
df['Account_Type].unique() returns unique values of the specified column (in this case 'Account_Type') as a NumPy array.
All you have to do is use the len() function to find the no of unique values in the array.
len(df['Account_Type].unique()) To find the respective counts of unique values, you can use value_counts()
