Counting Unique Values in Pandas DataFrames: Pythonic and Qlik-like Approaches


Using nunique() method:

The most direct way in pandas is to use the nunique() method on the desired column. This method efficiently counts the number of distinct elements in the column.

Here's an example:

import pandas as pd

data = {'col1': [1, 2, 2, 3, 3, 4, 1]}
df = pd.DataFrame(data)

# Count the number of unique values in 'col1'
num_unique_values = df['col1'].nunique()


This code will output:


As you can see, nunique() effectively counts the number of unique values, which is 4 in this case.

Using set() method (similar to Qlik):

Another approach, resembling Qlik's functionality, is to convert the column into a set and then use the len() function to get the length. Sets, by definition, eliminate duplicates, so the length represents the number of unique values.

Here's how you can achieve this:

num_unique_values_qlik = len(set(df['col1']))


Both methods achieve the same result of counting the unique values in the column. nunique() is generally considered more pandas-specific and might be slightly more efficient, while the set() approach might be more familiar to Qlik users.

import pandas as pd

# Create a DataFrame
data = {'col1': [1, 2, 2, 3, 3, 4, 1]}
df = pd.DataFrame(data)

# Count the number of unique values in 'col1' using nunique()
num_unique_values = df['col1'].nunique()

print("Number of Unique Values (nunique):", num_unique_values)


  1. We import the pandas library as pd.
  2. We create a sample DataFrame df with a column named col1 containing some data.
  3. We use the nunique() method on the col1 column to directly count the number of unique values.
  4. Finally, we print the result with a descriptive message.
# Same data definition from Example 1

# Count the number of unique values using set()
num_unique_values_qlik = len(set(df['col1']))

print("Number of Unique Values (set):", num_unique_values_qlik)
  1. We reuse the previously defined DataFrame df.
  2. We convert the col1 column into a set using set(). Sets eliminate duplicates.
  3. We use the len() function on the set to get the number of elements, which represents the unique values.

Both examples showcase ways to count unique values in a pandas DataFrame column. Choose the method that best suits your needs or coding style.

Using value_counts() with size:

The value_counts() method provides a Series containing the counts of each unique value in the column. We can then use the size attribute on the resulting Series to get the total number of unique values.

import pandas as pd

data = {'col1': [1, 2, 2, 3, 3, 4, 1]}
df = pd.DataFrame(data)

# Get the value counts and use size to get the number of unique values
value_counts = df['col1'].value_counts()
num_unique_values = value_counts.size

print("Number of Unique Values (value_counts):", num_unique_values)

Using a loop (Less efficient for large datasets):

This method involves iterating through the column and keeping track of unique values encountered. It's generally less efficient for larger datasets compared to the other methods.

Here's an example (for educational purposes):

data = {'col1': [1, 2, 2, 3, 3, 4, 1]}
df = pd.DataFrame(data)

seen = set()  # Set to store unique values seen so far
num_unique_values = 0

for value in df['col1']:
  if value not in seen:
    num_unique_values += 1

print("Number of Unique Values (loop):", num_unique_values)

Choosing the right method:

  • For most cases, nunique() is the recommended approach due to its efficiency and clarity.
  • If you also need the individual value counts alongside the unique count, value_counts() with size can be useful.
  • The loop method is generally discouraged for large datasets due to its slower execution. It's mainly for understanding the concept.

python pandas numpy

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python pandas numpy