PythonSTB
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-pandas — USER GUIDE (Android Python STB)
-=========================================
-Generated by RIMI
-Version: pandas 2.3.3
-Python: 3.12.14
-WHAT IS PANDAS?
----------------
-pandas is the most popular Python library for data analysis and manipulation.
-It provides fast, expressive DataFrames (tabular data) and Series (1D data)
-that make working with structured data easy and intuitive.
-In PythonSTB, pandas is used for:
-  - EPG (Electronic Program Guide) data parsing and querying
-  - Channel list management and filtering
-  - Playlist data transformation
-  - Analytics and statistics
-  - CSV/JSON data processing
-QUICK START
------------
-import pandas as pd
-# Create a DataFrame
-df = pd.DataFrame({
-    "channel": ["BBC One", "CNN", "Sky News"],
-    "category": ["entertainment", "news", "news"],
-    "rating": [4.5, 4.2, 4.0]
-})
-# Filter
-news = df[df["category"] == "news"]
-# Sort
-top = df.sort_values("rating", ascending=False)
-# Save / Load
-df.to_csv("channels.csv", index=False)
-df = pd.read_csv("channels.csv")
-CORE CONCEPTS
--------------
-1. DataFrame: 2D table (like a spreadsheet or SQL table)
-   df = pd.DataFrame({"col1": [1,2,3], "col2": ["a","b","c"]})
-2. Series: 1D column or row
-   s = df["col1"]
-3. Indexing:
-   df.loc[row_label, col_label]     # label-based
-   df.iloc[row_int, col_int]        # position-based
-   df[df["col"] > value]            # boolean mask
-4. GroupBy:
-   df.groupby("category")["rating"].mean()
-5. Merge/Join:
-   pd.merge(df1, df2, on="key")
-COMMON OPERATIONS
------------------
-# Filtering
-df[df["rating"] > 4.0]
-df.query("rating > 4.0")
-df.nlargest(5, "rating")
-# Aggregation
-df.groupby("category").agg({"rating": "mean", "channel": "count"})
-# Transform
-df["normalized"] = (df["rating"] - df["rating"].min()) / (df["rating"].max() - df["rating"].min())
-# Pivot
-pd.pivot_table(df, values="rating", index="category", aggfunc="mean")
-# Time series
-df["date"] = pd.to_datetime(df["date"])
-df.set_index("date").resample("D").mean()
-FILE I/O
---------
-# CSV
-df.to_csv("data.csv", index=False)
-df = pd.read_csv("data.csv")
-df = pd.read_csv("data.csv", parse_dates=["date"])
-# JSON
-df.to_json("data.json", orient="records")
-df = pd.read_json("data.json", orient="records")
-# Excel (requires openpyxl)
-df.to_excel("data.xlsx", index=False)
-df = pd.read_excel("data.xlsx")
-EPG DATA EXAMPLE
-----------------
-import pandas as pd
-from lxml import etree
-def parse_epg(xml_content):
-    root = etree.fromstring(xml_content.encode())
-    rows = []
-    for prog in root.findall(".//programme"):
-        rows.append({
-            "channel": prog.get("channel"),
-            "start": pd.to_datetime(prog.get("start"), format="%Y%m%d%H%M%S %z"),
-            "stop": pd.to_datetime(prog.get("stop"), format="%Y%m%d%H%M%S %z"),
-            "title": prog.findtext("title", ""),
-        })
-    return pd.DataFrame(rows)
-# Query: what's on now?
-now = pd.Timestamp.now(tz="UTC")
-on_now = epg[(epg["start"] <= now) & (epg["stop"] > now)]
-# Query: shows longer than 1 hour
-long_shows = epg[(epg["stop"] - epg["start"]) > pd.Timedelta(hours=1)]
-TIPS FOR ANDROID
-----------------
-- pandas on Android is compiled with norelro + 16KB page alignment
-- Full wheel bundles numpy — no separate numpy install needed
-- Use zipfile-based installer (pip doesn't work from run-as)
-- pandas + numpy together use ~15MB installed
-- All DataFrame operations work the same as desktop Python
-TROUBLESHOOTING
----------------
-# ImportError: numpy required
-# Make sure pandas wheel with bundled numpy is installed (Full_Wheel)
-# Slow performance
-# Use vectorized operations instead of Python loops:
-# BAD:  for i in range(len(df)): df.loc[i, "new"] = df.loc[i, "old"] * 2
-# GOOD: df["new"] = df["old"] * 2
-# Memory issues with large datasets
-# Use chunked reading:
-# for chunk in pd.read_csv("big.csv", chunksize=1000):
-#     process(chunk)
-Generated by RIMI