Better Dict

Contents

• Description
• Installation
• Documentation
• Quickstart
• Q&A
• Contributing
• License

Better Dict

Python dictionary on steroids. The custom dictionary is inspired in by the functionalities that pandas offers in their DataFrame and Series classes. Head over to the Quickstart section for examples of what it can do.

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Installation

Install directly from PyPi

To install Better Dict, execute the command:

$ pip install better-dict

Install Manually

Alternatively, you can also install the package by cloning this repository and performing a local pip install:

$ git clone https://github.com/erik-ingwersen-ey/better-dict.git

Then, navigate to the cloned repository:

$ cd better-dict

Finally, install the package executing the following command:

$ pip install .

Or, to install it in development mode, include the optional -e tag to the previous command:

$ pip install -e .

Documentation

Read the full documentation at Better-dict documentation

Quickstart

Here's a quick example of how to use Better Dict:

import better_dict as bd

d = bd.BetterDict({"a": 1, "b": 2, "c": 3})

# == Accessing values ==========================================================
# Access multiple keys at once:
d[["a", "b"]]  # returns {"a": 1, "b": 2}

# Access dictionary values using item indexes:
d.iloc[0]       # returns 1
d.iloc[[0, 2]]  # returns [1, 3]
d.iloc[1:]      # returns [2, 3]

# Access dictionary keys using their values:
d.vloc[1]       # returns "a"
d.vloc[[1, 3]]  # returns ["a", "c"]

# == Key Translations ==========================================================
# Rename dictionary keys:
d.rename({"a": "A", "b": "B", "c": "C"})    # returns {"A": 1, "B": 2, "C": 3}

# == Apply Function ============================================================
# Apply a function to all dictionary values:
d.apply(lambda x: x + 1)                    # returns {"a": 2, "b": 3, "c": 4}

# Apply a function to all dictionary keys:
d.apply_keys(lambda x: x.upper(), axis=0)   # returns {"A": 1, "B": 2, "C": 3}

# == I/O Operations ============================================================
# Save dictionary to a Pickle file:
d.to_pickle("d.pkl")

# Load dictionary from a Pickle file:
d = bd.BetterDict.from_pickle("d.pkl")

# Save dictionary to a joblib file:
d.to_joblib("d.joblib")

# Load dictionary from a joblib file:
d = bd.BetterDict.from_joblib("d.joblib")

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Q&A

Here are the answers for some common questions about better-dict that you might have:

1. What is the BetterDict class and what additional functionality does it provide?

The BetterDict class is a custom subclass of Python's built-in dict class, designed to provide additional functionality for easier and more flexible manipulation of dictionaries. The main enhancements include:

• Accessing dictionary keys by value.
• Manipulating dictionary keys and values using index notation.
• Accessing and manipulating dictionary values using dot notation.
• Other features include saving/loading dictionaries to/from files, creating dictionaries from various data structures, applying functions to dictionary values and keys, fuzzy key matching, and renaming dictionary keys.

2. How can I access and set values in a BetterDict instance?

Accessing and setting values in a BetterDict instance is made easy through a variety of methods:

• Get/Set values by key: Use the standard dictionary syntax with square brackets (e.g., d["key"] and d["key"] = value).
• Get/Set multiple values at once: Supply an iterable of keys (e.g., d["key1", "key2"] and d["key1", "key2"] = value1, value2).
• Index notation: Use the iloc property to access and set values by index (e.g., d.iloc[index] and d.iloc[index1, index2] = value1, value2).
• Additionally, dot notation can be used to access and set values (e.g., d.key and d.key = value).

3. What are the available I/O operations for BetterDict and how can I use them?

BetterDict supports I/O operations using the pickle and joblib libraries, allowing you to easily save and load dictionaries to/from files. The main methods for I/O operations are:

• Save with pickle: Use the save_pickle method, supplying the file path (e.g., d.save_pickle("file_path.pkl")).
• Load with pickle: Use the load_pickle method, supplying the file path (e.g., d = BetterDict.load_pickle("file_path.pkl")).
• Save with joblib: Use the save_joblib method, supplying the file path (e.g., d.save_joblib("file_path.joblib")).
• Load with joblib: Use the load_joblib method, supplying the file path (e.g., d = BetterDict.load_joblib("file_path.joblib")).

4. How can I create a BetterDict from different data structures like pandas.DataFrame or numpy.ndarray?

BetterDict offers class methods to create instances from various data structures, such as pandas DataFrames, pandas Series, numpy arrays, and lists:

• From pandas.DataFrame: Use the from_frame method (e.g., d = BetterDict.from_frame(data_frame)).
• From pandas.Series: Use the from_series method (e.g., d = BetterDict.from_series(data_series)).
• From numpy.ndarray: No direct method is available, but you can first convert the array to a pandas DataFrame and then use from_frame (e.g., d = BetterDict.from_frame(pd.DataFrame(array))).
• From list: Use the from_list method (e.g., d = BetterDict.from_list(list_obj)).

These methods facilitate easy conversion between different data structures and BetterDict.

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Contributing

If you want to contribute to Better Dict, please read the Contributor Guide for additional information on how you can contribute to this project.

License

Distributed under the terms of the MIT License, Better Dict is free and open source software. We do not provide guarantees nor custom support for it, use it at your own risk.