Reading a Text File of Dictionaries into a DataFrame

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In this article, we will explore how to read a text file containing dictionaries in Python into a pandas DataFrame. We’ll use the provided Kaggle dataset as an example and walk through the steps necessary to transform it from a list of dictionaries into a structured DataFrame.

Introduction

The dataset consists of dictionaries representing matches between two players. Each dictionary contains information about the match, including player characteristics and general match details. Our goal is to read this text file into a pandas DataFrame, which will allow us to easily manipulate and analyze the data.

Step 1: Store Match Dictionaries in a List

The first step is to store all the match dictionaries from the Kaggle dataset into a single list. This can be done using a simple loop:

matches = [
    {'players': {'right': {'deck': [['Mega Minion', '9'], ['Electro Wizard', '3'], ['Arrows', '11'], ['Lightning', '5'], ['Tombstone', '9'], ['The Log', '2'], ['Giant', '9'], ['Bowler', '5']], 'trophy': '4258', 'clan': 'TwoFiveOne', 'name': 'gpa raid'}, 
    'left': {'deck': [['Fireball', '9'], ['Archers', '12'], ['Goblins', '12'], ['Minions', '11'], ['Bomber', '12'], ['The Log', '2'], ['Barbarians', '12'], ['Royal Giant', '13']], 
    'trophy': '4325', 'clan': 'battusai', 'name': 'Supr4'}, 
    'type': 'ladder', 'result': ['2', '0'], 'time': '2017-07-12'},
    # ... and so on for the rest of the matches
]

Step 2: Create a DataFrame from the List of Dictionaries

Next, we create a pandas DataFrame from the list of dictionaries. This can be done using the pd.DataFrame() function:

df = pd.DataFrame(matches)

This will automatically populate columns for the match type, time, and result.

Step 3: Populate Columns with Player Information

To populate columns containing information about the deck, trophy, clan, and name of both players in each match, we use a nested loop structure. We iterate over each side (left and right) and then over each key (deck, trophy, clan, and name). For each dictionary in the list, we apply a lambda function to extract the corresponding information.

sides = ['right', 'left']
player_keys = ['deck', 'trophy', 'clan', 'name']

for side in sides:
    for key in player_keys:
        for i, row in df.iterrows():
            df[side + '_' + key] = df['players'].apply(lambda x: x[side][key])

df = df.drop('players', axis=1)  # no longer need this after populating the other columns

Step 4: Rearrange Columns for Better Readability

Finally, we rearrange the columns to display player information from left to right, followed by general match details at the far right. We can do this using iloc:

df = df.iloc[:, ::-1]  # made sense to display columns in order of player info from left to right,
                       # followed by general match info at the far right of the dataframe

Conclusion

In this article, we demonstrated how to read a text file containing dictionaries into a pandas DataFrame. By following these steps and using Python’s powerful data manipulation libraries, you can easily transform unstructured data into a structured, analyzable format.

Example Use Cases

• Data Analysis: With the resulting DataFrame in hand, you can perform various analyses on player characteristics, match outcomes, and other relevant metrics.
• Visualization: Utilize visualization libraries like Matplotlib or Seaborn to create informative plots that reveal insights into the data.
• Machine Learning: If desired, you could use this dataset as input for machine learning models trained on classification or regression tasks.

By applying these steps and techniques, you’ll be well-equipped to handle a wide range of text-based datasets containing dictionaries in Python.

Last modified on 2025-01-03