python - Flatten of dict of lists into a dataframe

Python - Flatten of dict of lists into a dataframe

To flatten a dictionary of lists into a pandas DataFrame in Python, you can use the pd.DataFrame constructor and pd.Series constructor from the pandas library. Here's a step-by-step guide to achieve this:

Example Scenario

Let's say you have a dictionary where each key maps to a list of values:

data = { 'A': [1, 2, 3], 'B': ['a', 'b', 'c'], 'C': [10.5, 20.3, 30.1] }

Steps to Flatten the Dictionary into a DataFrame

  1. Convert Dictionary to Pandas Series:

    • Use pd.Series to convert each list in the dictionary into a Series. This will align each value in the list with its corresponding key.

  2. Combine Series into DataFrame:

    • Use pd.DataFrame to combine the Series into a DataFrame where each Series becomes a column.

Implementation

Here's how you can implement this in Python:

import pandas as pd # Sample data (dictionary of lists) data = { 'A': [1, 2, 3], 'B': ['a', 'b', 'c'], 'C': [10.5, 20.3, 30.1] } # Convert dictionary to pandas Series series_dict = {key: pd.Series(value) for key, value in data.items()} # Create DataFrame from the Series df = pd.DataFrame(series_dict) print(df)

Output

This will output a DataFrame where each key from the original dictionary becomes a column, and the values are flattened into rows:

 A B C 0 1 a 10.5 1 2 b 20.3 2 3 c 30.1

Explanation

  • pd.Series(value): Converts each list in data (e.g., [1, 2, 3], ['a', 'b', 'c'], [10.5, 20.3, 30.1]) into a pandas Series.

  • Dictionary Comprehension: Uses a dictionary comprehension {key: pd.Series(value) for key, value in data.items()} to create a dictionary where each key is mapped to a pandas Series.

  • pd.DataFrame(series_dict): Converts the dictionary of Series into a pandas DataFrame. Each Series becomes a column in the DataFrame.

Notes

  • Ensure that the lists in your original dictionary (data) are of equal length. Pandas expects aligned data when creating a DataFrame.

  • If your lists are of unequal length or if you need to handle missing values, consider preprocessing your data accordingly before creating the DataFrame.

By following these steps, you can easily flatten a dictionary of lists into a pandas DataFrame in Python, making it easier to work with structured data for further analysis or manipulation.

Examples

  1. Flatten Dictionary of Lists into DataFrame

    • Description: Convert a dictionary where values are lists into a pandas DataFrame, flattening the lists into separate columns.
    • Python Code:
      import pandas as pd # Sample dictionary data = { 'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9] } # Convert to DataFrame df = pd.DataFrame(data)
    • Explanation: Creates a DataFrame df from a dictionary data where each key represents a column and its corresponding list values are flattened into columns.

  2. Flatten Nested Dictionary of Lists into DataFrame

    • Description: Convert a nested dictionary where values are lists into a pandas DataFrame, handling nested structures.
    • Python Code:
      import pandas as pd from pandas.json_normalize import json_normalize # Sample nested dictionary data = { 'A': [1, 2, 3], 'B': [{'X': 4, 'Y': 5}, {'X': 6, 'Y': 7}, {'X': 8, 'Y': 9}] } # Normalize nested dictionary df = json_normalize(data, sep='_')
    • Explanation: Uses json_normalize from pandas to flatten nested structures in the dictionary data into a DataFrame df.

  3. Flatten Dictionary of Lists with Nested Keys into DataFrame

    • Description: Convert a dictionary where keys are nested and values are lists into a pandas DataFrame, flattening nested keys and lists into columns.
    • Python Code:
      import pandas as pd # Sample dictionary with nested keys data = { ('A', 'X'): [1, 2, 3], ('A', 'Y'): [4, 5, 6], ('B', 'X'): [7, 8, 9] } # Convert to DataFrame df = pd.DataFrame.from_dict({(i, j): data[i, j] for i in data.keys() for j in data[i]})
    • Explanation: Uses pd.DataFrame.from_dict() to create a DataFrame df from a dictionary data with nested keys, preserving the structure in column names.

  4. Flatten Dictionary of Lists with Index into DataFrame

    • Description: Convert a dictionary where values are lists with an index into a pandas DataFrame, flattening the index and lists into columns.
    • Python Code:
      import pandas as pd # Sample dictionary with index data = { 'A': {0: 1, 1: 2, 2: 3}, 'B': {0: 4, 1: 5, 2: 6} } # Convert to DataFrame df = pd.DataFrame.from_dict(data, orient='index').transpose()
    • Explanation: Uses pd.DataFrame.from_dict() with orient='index' to convert a dictionary data with an index into a DataFrame df, transposing the result.

  5. Flatten Dictionary of Lists into DataFrame with Missing Values

    • Description: Convert a dictionary where values are lists of different lengths into a pandas DataFrame, handling missing values.
    • Python Code:
      import pandas as pd # Sample dictionary with uneven lists data = { 'A': [1, 2, 3], 'B': [4, 5], 'C': [6] } # Convert to DataFrame df = pd.DataFrame.from_dict({k: pd.Series(v) for k, v in data.items()})
    • Explanation: Uses pd.DataFrame.from_dict() with pd.Series() to create a DataFrame df from a dictionary data with uneven lists, filling missing values with NaN.

  6. Flatten Dictionary of Lists into Long Form DataFrame

    • Description: Convert a dictionary where values are lists into a long-form pandas DataFrame, preserving index associations.
    • Python Code:
      import pandas as pd # Sample dictionary data = { 'Index': [1, 2, 3], 'A': [4, 5, 6], 'B': [7, 8, 9] } # Convert to long-form DataFrame df = pd.melt(pd.DataFrame(data), id_vars=['Index'], var_name='Category', value_name='Value')
    • Explanation: Uses pd.melt() to convert a wide-form DataFrame into a long-form DataFrame df, preserving index associations with id_vars and labeling columns with var_name and value_name.

  7. Flatten Dictionary of Lists into DataFrame with Custom Column Names

    • Description: Convert a dictionary where values are lists into a pandas DataFrame with custom column names.
    • Python Code:
      import pandas as pd # Sample dictionary data = { 'First': [1, 2, 3], 'Second': [4, 5, 6], 'Third': [7, 8, 9] } # Convert to DataFrame with custom column names df = pd.DataFrame(data, columns=['First', 'Second', 'Third'])
    • Explanation: Uses pd.DataFrame() to create a DataFrame df from a dictionary data with custom column names specified in columns.

  8. Flatten Dictionary of Lists into DataFrame with Multi-level Index

    • Description: Convert a dictionary where values are lists into a pandas DataFrame with a multi-level index.
    • Python Code:
      import pandas as pd # Sample dictionary with multi-level index data = { ('A', 'X'): [1, 2, 3], ('A', 'Y'): [4, 5, 6], ('B', 'X'): [7, 8, 9] } # Convert to DataFrame with multi-level index df = pd.DataFrame.from_dict(data, orient='index').transpose()
    • Explanation: Uses pd.DataFrame.from_dict() with orient='index' to create a DataFrame df from a dictionary data with a multi-level index.

  9. Flatten Dictionary of Lists into DataFrame with Index and Columns

    • Description: Convert a dictionary where values are lists into a pandas DataFrame with specified index and column names.
    • Python Code:
      import pandas as pd # Sample dictionary data = { 'A': [1, 2, 3], 'B': [4, 5, 6] } # Convert to DataFrame with index and columns df = pd.DataFrame(data, index=['X', 'Y', 'Z'], columns=['A', 'B'])
    • Explanation: Uses pd.DataFrame() to create a DataFrame df from a dictionary data with specified index and column names.

  10. Flatten Dictionary of Lists into DataFrame with Reset Index

    • Description: Convert a dictionary where values are lists into a pandas DataFrame and reset the index.
    • Python Code:
      import pandas as pd # Sample dictionary data = { 'A': [1, 2, 3], 'B': [4, 5, 6] } # Convert to DataFrame and reset index df = pd.DataFrame(data).reset_index(drop=True)
    • Explanation: Uses pd.DataFrame().reset_index() to create a DataFrame df from a dictionary data and reset the index to default numeric values.

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