70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
import pandas as pd
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class TemporalAnalysis:
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def __init__(self, df: pd.DataFrame):
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self.df = df
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def avg_reply_time_per_emotion(self) -> dict:
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df = self.df.copy()
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replies = df[
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(df["type"] == "comment") &
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(df["reply_to"].notna()) &
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(df["reply_to"] != "")
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]
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id_to_time = df.set_index("id")["dt"].to_dict()
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def compute_reply_time(row):
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reply_id = row["reply_to"]
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parent_time = id_to_time.get(reply_id)
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if parent_time is None:
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return None
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return (row["dt"] - parent_time).total_seconds()
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replies["reply_time"] = replies.apply(compute_reply_time, axis=1)
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emotion_cols = [col for col in df.columns if col.startswith("emotion_") and col not in ("emotion_neutral", "emotion_surprise")]
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replies["dominant_emotion"] = replies[emotion_cols].idxmax(axis=1)
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grouped = (
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replies
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.groupby("dominant_emotion")["reply_time"]
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.agg(["mean", "count"])
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.reset_index()
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)
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return grouped.to_dict(orient="records")
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def posts_per_day(self) -> dict:
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per_day = (
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self.df.groupby("date")
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.size()
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.reset_index(name="count")
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)
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return per_day.to_dict(orient="records")
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def heatmap(self) -> dict:
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weekday_order = [
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"Monday", "Tuesday", "Wednesday",
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"Thursday", "Friday", "Saturday", "Sunday"
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]
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self.df["weekday"] = pd.Categorical(
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self.df["weekday"],
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categories=weekday_order,
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ordered=True
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)
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heatmap = (
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self.df
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.groupby(["weekday", "hour"], observed=True)
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.size()
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.unstack(fill_value=0)
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.reindex(columns=range(24), fill_value=0)
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)
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heatmap.columns = heatmap.columns.map(str)
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return heatmap.to_dict(orient="records") |