87 lines
1.9 KiB
Python
87 lines
1.9 KiB
Python
import marimo
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__generated_with = "0.18.3"
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app = marimo.App(width="medium")
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@app.cell
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def _():
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import marimo as mo
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import pandas as pd
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from pathlib import Path
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INPUT_DIR = Path("./data/processing/03_sentiment_analysis")
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WORKING_DIR = Path('./data/processing/04_sentiment_aggregation')
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if not WORKING_DIR.exists():
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WORKING_DIR.mkdir(parents=True)
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return INPUT_DIR, mo, pd
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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# Voices
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""")
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return
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@app.cell
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def _(INPUT_DIR, mo):
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voice_csv_files = list(INPUT_DIR.glob("*voice*.csv"))
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file_options = {f.stem: str(f) for f in voice_csv_files}
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voice_multiselect = mo.ui.multiselect(options=file_options, label="Select Voice CSV Files for Aggregation")
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return (voice_multiselect,)
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@app.cell
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def _(mo, voice_multiselect):
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mo.hstack([voice_multiselect, mo.md(f"Has value: {voice_multiselect.value}")])
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return
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@app.cell
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def _(pd, voice_multiselect):
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# Load all voice CSV files and aggregate them so that each row-column pair is summed
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KEY_COL = "_context"
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def _read_voice_csv(path: str) -> pd.DataFrame:
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df = pd.read_csv(path).set_index(KEY_COL)
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df = df.apply(pd.to_numeric, errors="coerce")
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return df
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def aggregate_voice_data(files: list[str]) -> pd.DataFrame:
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if not files:
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return pd.DataFrame()
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master = _read_voice_csv(files[0])
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for path in files[1:]:
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master = master.add(_read_voice_csv(path), fill_value=0)
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return master.reset_index()
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master_df = aggregate_voice_data(voice_multiselect.value)
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master_df
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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# Characters
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""")
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return
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@app.cell
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def _(INPUT_DIR):
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char_csv_files = list(INPUT_DIR.glob("*character*.csv"))
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char_csv_files
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return
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if __name__ == "__main__":
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app.run()
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