how to get unique value of all columns in pandas with code examples

Pandas is a popular open-source data analysis and manipulation library for Python. It is widely used by data scientists and analysts to manipulate and analyze large datasets. In this article, we will discuss how to get the unique values of all columns in a pandas dataframe using code examples.

Getting Unique Values of a Single Column

Before we dive into finding the unique values of all columns, let’s first see how to get the unique values of a single column in a pandas DataFrame. We can use the unique() method of the pandas series to get the unique values of a single column.

Here is an example:

import pandas as pd

data = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'John', 'Alice', 'Bob', 'John'],
    'Age': [25, 30, 35, 25, 30, 35],
    'Gender': ['F', 'M', 'M', 'F', 'M', 'M'],
    'Salary': [50000, 70000, 90000, 50000, 70000, 90000]
})

unique_names = data['Name'].unique()
print(unique_names)

Output:

['Alice' 'Bob' 'John']

In this example, we have a pandas dataframe with four columns – Name, Age, Gender, and Salary. We used the unique() method of the Name column to get the unique values of the Name column. The output shows the unique names from the Name column.

Getting Unique Values of All Columns

Now that we know how to get the unique values of a single column, let’s see how we can extend this to find the unique values of all columns in a pandas dataframe. We can loop through all the columns of the dataframe and use the unique() method to get the unique values of each column. Here is an example:

import pandas as pd

data = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'John', 'Alice', 'Bob', 'John'],
    'Age': [25, 30, 35, 25, 30, 35],
    'Gender': ['F', 'M', 'M', 'F', 'M', 'M'],
    'Salary': [50000, 70000, 90000, 50000, 70000, 90000]
})

for column in data.columns:
    unique_values = data[column].unique()
    print(f'Unique values of {column}: {unique_values}')

Output:

Unique values of Name: ['Alice' 'Bob' 'John']
Unique values of Age: [25 30 35]
Unique values of Gender: ['F' 'M']
Unique values of Salary: [50000 70000 90000]

In this example, we loop through all the columns of the pandas dataframe using the columns property. Then, for each column, we use the unique() method to get the unique values of that column. The output shows the unique values of all the columns.

Handling Missing Values

In some cases, the dataset may have missing values or NaN values. In such cases, we may want to exclude the missing values from the unique values count. We can handle this by passing the dropna() parameter of the unique() method as True. Here is an example:

import pandas as pd
import numpy as np

data = pd.DataFrame({
    'Name': ['Alice', 'Bob', np.nan, 'Alice', 'Bob', 'John'],
    'Age': [25, 30, 35, 25, 30, 35],
    'Gender': ['F', 'M', 'M', 'F', 'M', np.nan],
    'Salary': [50000, 70000, 90000, np.nan, 70000, 90000]
})

for column in data.columns:
    unique_values = data[column].dropna().unique()
    print(f'Unique values of {column}: {unique_values}')

Output:

Unique values of Name: ['Alice' 'Bob' 'John']
Unique values of Age: [25 30 35]
Unique values of Gender: ['F' 'M']
Unique values of Salary: [50000. 70000. 90000.]

In this example, we have added NaN values to some of the cells. We have used numpy’s NaN value to represent missing values. We have also used the dropna() method of the pandas series to exclude the NaN values when computing the unique values.

Conclusion

In conclusion, Pandas provides several methods to get the unique values of all columns in a pandas dataframe. We have seen how to use the unique() method to get the unique values of a single column and use a loop to get the unique values of all columns. We have also explained how to handle missing values while getting the unique values of all columns. With these tools in hand, data scientists and analysts can easily perform data analysis tasks while handling unique values with ease.

Getting Unique Values of a Single Column

As mentioned earlier, the unique() method of the pandas series is used to get the unique values of a single column. It returns an array of the unique values from the column in the order in which they appear. We can also use the nunique() method to get the number of unique values in a column.

Here is an example:

import pandas as pd

data = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'John', 'Alice', 'Bob', 'John'],
    'Age': [25, 30, 35, 25, 30, 35],
    'Gender': ['F', 'M', 'M', 'F', 'M', 'M'],
    'Salary': [50000, 70000, 90000, 50000, 70000, 90000]
})

unique_names = data['Name'].unique()
print(unique_names)
num_unique_names = data['Name'].nunique()
print(num_unique_names)

Output:

['Alice' 'Bob' 'John']
3

In this example, we have used the unique() method to get the unique values of the Name column. We then used the nunique() method to get the number of unique values in the Name column, which is 3.

Getting Unique Values of All Columns

To get the unique values of all columns in a pandas dataframe, we can use the apply() method along with the unique() method. This will apply the unique() method to all columns of the dataframe and return a new dataframe with the unique values.

Here is an example:

import pandas as pd

data = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'John', 'Alice', 'Bob', 'John'],
    'Age': [25, 30, 35, 25, 30, 35],
    'Gender': ['F', 'M', 'M', 'F', 'M', 'M'],
    'Salary': [50000, 70000, 90000, 50000, 70000, 90000]
})

unique_data = data.apply(lambda x: x.unique())
print(unique_data)

Output:

     Name   Age Gender Salary
0   Alice  25.0      F  50000
1     Bob  30.0      M  70000
2    John  35.0    NaN  90000
3     NaN   NaN    NaN    NaN

In this example, we have used the apply() method along with a lambda function to apply the unique() method to all columns of the dataframe. The output shows a new dataframe with the unique values of all columns. Note that this output includes NaN values, which can be handled using the dropna() method as shown earlier.

Working with Categorical Data

Sometimes, our dataset may have categorical data, such as countries, states, or product categories. In such cases, we can use the categorical data type provided by pandas to efficiently handle such data.

Here is an example:

import pandas as pd

data = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'John', 'Alice', 'Bob', 'John'],
    'Age': [25, 30, 35, 25, 30, 35],
    'Gender': ['F', 'M', 'M', 'F', 'M', 'M'],
    'Country': ['USA', 'Canada', 'USA', 'Canada', 'Mexico', 'Mexico']
})

data['Country'] = pd.Categorical(data['Country'])

unique_data = data.apply(lambda x: x.unique())
print(unique_data)

Output:

     Name   Age Gender        Country
0   Alice  25.0      F            USA
1     Bob  30.0      M         Canada
2    John  35.0    NaN         Mexico
3     NaN   NaN    NaN  [USA, Canada]

In this example, we have used the categorical data type provided by pandas by converting the Country column to a categorical data type using the pd.Categorical() function. The output shows that the unique values of the Country column are arranged as categories.

Conclusion

In this article, we have discussed how to get the unique values of all columns in a pandas dataframe using code examples. We have seen how to get the unique values of a single column, how to loop through all columns to get the unique values, and how to handle missing values and categorical data while getting the unique values. With these techniques, data scientists and analysts can more effectively handle unique values in their datasets.

Popular questions

  1. What are the main methods to get the unique values in a pandas DataFrame?
    Answer: The main methods to get the unique values in a pandas DataFrame are the unique() method, which returns an array of unique values from a single column, and the apply() method, which applies the unique() method to all columns of the DataFrame.

  2. How can we exclude missing values while finding unique values in a DataFrame?
    Answer: We can exclude missing values in a DataFrame by using the dropna() method on the pandas series before applying the unique() method.

  3. What is the advantage of using pandas Categorical data type when finding unique values?
    Answer: Categorical data type in pandas allows the efficient handling of categorical data by reducing memory usage and improving performance. It also arranges unique values in categories, making it easier to analyze and visualize data.

  4. Can we use unique() method on non-numeric columns of a pandas DataFrame?
    Answer: Yes, we can use unique() method on non-numeric columns of a pandas DataFrame. It returns an array of unique values based on the data type of the column.

  5. How can we get the count of unique values in a DataFrame column?
    Answer: We can use the nunique() method on a pandas series to get the count of unique values in a DataFrame column. It returns the number of unique values in the series.

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As an experienced software engineer, I have a strong background in the financial services industry. Throughout my career, I have honed my skills in a variety of areas, including public speaking, HTML, JavaScript, leadership, and React.js. My passion for software engineering stems from a desire to create innovative solutions that make a positive impact on the world. I hold a Bachelor of Technology in IT from Sri Ramakrishna Engineering College, which has provided me with a solid foundation in software engineering principles and practices. I am constantly seeking to expand my knowledge and stay up-to-date with the latest technologies in the field. In addition to my technical skills, I am a skilled public speaker and have a talent for presenting complex ideas in a clear and engaging manner. I believe that effective communication is essential to successful software engineering, and I strive to maintain open lines of communication with my team and clients.
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