diff --git a/README.md b/README.md index 3bdadcd..8825873 100644 --- a/README.md +++ b/README.md @@ -91,11 +91,11 @@ import pandas as pd from naampy import in_rolls_fn_gender, predict_fn_gender # Create a DataFrame with names -names_df = pd.DataFrame({'name': ['Priyanka', 'Rahul', 'Anjali']}) +names_df = pd.DataFrame({"name": ["Priyanka", "Rahul", "Anjali"]}) # Get gender predictions from electoral roll data -result = in_rolls_fn_gender(names_df, 'name') -print(result[['name', 'prop_female', 'prop_male']]) +result = in_rolls_fn_gender(names_df, "name") +print(result[["name", "prop_female", "prop_male"]]) ``` ### Using the ML Model @@ -104,7 +104,7 @@ For names not in the electoral roll database: ```python # Use the neural network model for predictions -names = ['Aadhya', 'Reyansh', 'Kiara'] +names = ["Aadhya", "Reyansh", "Kiara"] predictions = predict_fn_gender(names) print(predictions) ``` @@ -120,11 +120,11 @@ import pandas as pd from naampy import in_rolls_fn_gender # Sample data -names = [{'name': 'gaurav'}, {'name': 'yasmin'}, {'name': 'deepti'}] +names = [{"name": "gaurav"}, {"name": "yasmin"}, {"name": "deepti"}] df = pd.DataFrame(names) -result = in_rolls_fn_gender(df, 'name') -print(result[['name', 'n_male', 'n_female', 'prop_female', 'prop_male']]) +result = in_rolls_fn_gender(df, "name") +print(result[["name", "n_male", "n_female", "prop_female", "prop_male"]]) ``` **Output:** diff --git a/uv.lock b/uv.lock index 2c47b54..54fd9d1 100644 --- a/uv.lock +++ b/uv.lock @@ -103,7 +103,7 @@ name = "cffi" version = "2.0.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "pycparser", marker = "implementation_name != 'PyPy'" }, + { name = "pycparser" }, ] sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" } 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