pandas - Python: Assign Labels to values in an array

Pandas - Python: Assign Labels to values in an array

In Pandas, you can assign labels to values in a DataFrame using the map() function. If you have a NumPy array, you can convert it to a Pandas Series and then use the map() function to assign labels to each value. Here's an example:

import pandas as pd # Sample NumPy array values = [1, 2, 3, 1, 2, 3] # Define a dictionary to map values to labels label_mapping = {1: 'Label_A', 2: 'Label_B', 3: 'Label_C'} # Convert the NumPy array to a Pandas Series series_values = pd.Series(values) # Use the map function to assign labels series_with_labels = series_values.map(label_mapping) # Create a DataFrame for better visualization df = pd.DataFrame({'Original Values': series_values, 'Labels': series_with_labels}) # Display the DataFrame print(df)

In this example:

  • We have a NumPy array named values.
  • We define a dictionary label_mapping to map each unique value to its corresponding label.
  • We convert the NumPy array to a Pandas Series using pd.Series().
  • We use the map() function to assign labels to each value based on the mapping dictionary.
  • Finally, we create a DataFrame to display the original values and their corresponding labels.

Adjust the values and label_mapping based on your specific data and label requirements.

Examples

  1. "Assign labels to unique values in a pandas Series"

    • Code:
      import pandas as pd # Create a pandas Series series = pd.Series([1, 2, 1, 3, 2, 1]) # Assign labels to unique values labels = {1: 'LabelA', 2: 'LabelB', 3: 'LabelC'} series_with_labels = series.map(labels)
    • Description: Creates a pandas Series and assigns labels to unique values using the map function.

  2. "Assign labels based on conditions in a pandas DataFrame column"

    • Code:
      import pandas as pd # Create a pandas DataFrame df = pd.DataFrame({'ColumnA': [10, 25, 18, 30]}) # Assign labels based on conditions in a DataFrame column conditions = [ (df['ColumnA'] < 20), (df['ColumnA'] >= 20) & (df['ColumnA'] < 30), (df['ColumnA'] >= 30) ] labels = ['LabelA', 'LabelB', 'LabelC'] df['Labels'] = pd.cut(df['ColumnA'], bins=[0, 20, 30, float('inf')], labels=labels, right=False)
    • Description: Creates a pandas DataFrame and assigns labels based on conditions in a specific column using the cut function.

  3. "Assign labels to bins in a pandas DataFrame column"

    • Code:
      import pandas as pd # Create a pandas DataFrame df = pd.DataFrame({'ColumnA': [15, 22, 35, 10, 28, 40]}) # Assign labels to bins in a DataFrame column bins = [0, 20, 30, float('inf')] labels = ['LabelA', 'LabelB', 'LabelC'] df['Labels'] = pd.cut(df['ColumnA'], bins=bins, labels=labels, right=False)
    • Description: Creates a pandas DataFrame and assigns labels to bins in a specific column using the cut function.

  4. "Assign labels based on a custom function in a pandas DataFrame"

    • Code:
      import pandas as pd # Create a pandas DataFrame df = pd.DataFrame({'ColumnA': [10, 25, 18, 30]}) # Define a custom function to assign labels def label_function(value): if value < 20: return 'LabelA' elif value >= 20 and value < 30: return 'LabelB' else: return 'LabelC' # Apply the custom function to assign labels df['Labels'] = df['ColumnA'].apply(label_function)
    • Description: Creates a pandas DataFrame and assigns labels based on a custom function applied to a specific column.

  5. "Assign labels to values in a pandas Series using a dictionary"

    • Code:
      import pandas as pd # Create a pandas Series series = pd.Series(['A', 'B', 'A', 'C', 'B', 'A']) # Assign labels to values using a dictionary labels_mapping = {'A': 'LabelX', 'B': 'LabelY', 'C': 'LabelZ'} series_with_labels = series.map(labels_mapping)
    • Description: Creates a pandas Series and assigns labels to values using a dictionary with the map function.

  6. "Assign labels based on a range of values in a pandas DataFrame column"

    • Code:
      import pandas as pd # Create a pandas DataFrame df = pd.DataFrame({'ColumnA': [15, 22, 35, 10, 28, 40]}) # Assign labels based on a range of values in a DataFrame column df['Labels'] = pd.cut(df['ColumnA'], bins=[0, 20, 30, float('inf')], labels=['LabelA', 'LabelB', 'LabelC'], right=False)
    • Description: Creates a pandas DataFrame and assigns labels based on a range of values in a specific column using the cut function.

  7. "Assign labels to values in a pandas DataFrame column using a lambda function"

    • Code:
      import pandas as pd # Create a pandas DataFrame df = pd.DataFrame({'ColumnA': [10, 25, 18, 30]}) # Assign labels to values using a lambda function label_function = lambda value: 'LabelA' if value < 20 else ('LabelB' if value >= 20 and value < 30 else 'LabelC') df['Labels'] = df['ColumnA'].apply(label_function)
    • Description: Creates a pandas DataFrame and assigns labels to values in a specific column using a lambda function with the apply method.

  8. "Assign labels to values in a pandas DataFrame column based on a mapping function"

    • Code:
      import pandas as pd # Create a pandas DataFrame df = pd.DataFrame({'ColumnA': [10, 25, 18, 30]}) # Define a mapping function to assign labels def map_labels(value): if value < 20: return 'LabelA' elif value >= 20 and value < 30: return 'LabelB' else: return 'LabelC' # Apply the mapping function to assign labels df['Labels'] = df['ColumnA'].map(map_labels)
    • Description: Creates a pandas DataFrame and assigns labels to values in a specific column based on a mapping function using the map method.

  9. "Assign labels to values in a pandas DataFrame column based on conditions with numpy.select"

    • Code:
      import pandas as pd import numpy as np # Create a pandas DataFrame df = pd.DataFrame({'ColumnA': [10, 25, 18, 30]}) # Define conditions and corresponding labels conditions = [ df['ColumnA'] < 20, (df['ColumnA'] >= 20) & (df['ColumnA'] < 30), df['ColumnA'] >= 30 ] labels = ['LabelA', 'LabelB', 'LabelC'] # Use numpy.select to assign labels based on conditions df['Labels'] = np.select(conditions, labels, default='Unknown')
    • Description: Creates a pandas DataFrame and assigns labels to values in a specific column based on conditions using numpy.select.

  10. "Assign labels to values in a pandas DataFrame column based on a function with np.vectorize"

    • Code:
      import pandas as pd import numpy as np # Create a pandas DataFrame df = pd.DataFrame({'ColumnA': [10, 25, 18, 30]}) # Define a function to assign labels def label_function(value): if value < 20: return 'LabelA' elif value >= 20 and value < 30: return 'LabelB' else: return 'LabelC' # Use np.vectorize to apply the function to assign labels df['Labels'] = np.vectorize(label_function)(df['ColumnA'])
    • Description: Creates a pandas DataFrame and assigns labels to values in a specific column based on a function using np.vectorize.

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