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# %% [markdown]
# # Mini-Project 2: Data Transformation (NB02)
#
# **Course:** DS105W Data for Data Science (Winter 2025/26)  
# **Student:** REDACTED
# **Candidate Number:** REDACTED  
# **Date:** 11th March 2026

# %% [markdown]
# ## Notebook Purpose
#
# This notebook transforms the raw TfL Journey Planner responses collected in NB01 into an analysis-ready dataset for NB03.
#
# **Pipeline overview:**
#
# 1. Load the combined raw JSON from `../data/raw/`
# 2. Flatten nested journey structures using `pd.json_normalize()` (W07-NB02 technique)
# 3. Extract secondary metrics from the `legs` array: `transfer_count`, `walking_minutes`, `fare_total`, `leg_count`, `mode_sequence`
# 4. Standardise postcode keys and merge with the ONS Postcode Directory using `pd.merge()` (W08 technique) to add borough, LSOA, and IMD context
# 5. Validate the transformed data and save processed CSV(s) to `../data/processed/`
#
# The notebook follows the same functional separation as Mini Project 1: NB02 is strictly for transformation and cleaning — all exploration and visualisation belong in NB03.

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Transformation Goal</span>
#
# - The main challenge in this notebook is that each stored request contains a nested list of journeys, and each journey contains a nested list of legs with different transport modes. Flattening this two-level nesting into one row per journey — while preserving leg-level detail as derived columns — is the core technical task.
# - I approached this following the same staged pipeline as MP1's NB02 (raw JSON → typed DataFrame → derived features → cleaned CSV), but with more complex nesting. The W07-NB02 lab on `pd.json_normalize()` with `record_path` and `meta` parameters was the direct preparation for this step.
# - I also wanted the validation section to feel more like the careful checking workflow I used in Mini Project 1, where I ran `describe()`, checked for sentinels, and verified row counts before deciding on any cleaning steps.
#
# </div>

# %% [markdown]
# ## Section 1: Import Libraries

# %%
import json
import os

import pandas as pd

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Library Choices</span>
#
# NB02 uses `pandas` for all tabular manipulation and `json` for loading the raw file. No visualisation libraries are imported — plotting belongs in NB03. This keeps the notebook's role clear: transformation and validation only. The W04 Lecture introduced `pandas`; the W08 Lab used it for the merge and reshape operations central to this notebook.
#
# </div>

# %% [markdown]
# ## Section 2: Load the Raw Files

# %%
combined_json_path = os.path.join('..', 'data', 'raw', 'journey_collection_combined.json')
ons_path = os.path.join('..', 'data', 'raw', 'london_postcodes-ons-postcodes-directory-feb22.csv')

with open(combined_json_path, 'r', encoding='utf-8') as f:
    loaded = json.load(f)

print('Stored requests:', len(loaded))
print('Request keys:', list(loaded.keys()))

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Stored requests: 40
# Request keys: ['barking_peak_0730', 'barking_peak_0800', 'barking_offpeak_1130', 'barking_offpeak_1200', 'romford_peak_0730', 'romford_peak_0800', 'romford_offpeak_1130', 'romford_offpeak_1200', 'croydon_peak_0730', 'croydon_peak_0800', 'croydon_offpeak_1130', 'croydon_offpeak_1200', 'harrow_peak_0730', 'harrow_peak_0800', 'harrow_offpeak_1130', 'harrow_offpeak_1200', 'barnet_peak_0730', 'barnet_peak_0800', 'barnet_offpeak_1130', 'barnet_offpeak_1200', 'bromley_peak_0730', 'bromley_peak_0800', 'bromley_offpeak_1130', 'bromley_offpeak_1200', 'sutton_peak_0730', 'sutton_peak_0800', 'sutton_offpeak_1130', 'sutton_offpeak_1200', 'woolwich_peak_0730', 'woolwich_peak_0800', 'woolwich_offpeak_1130', 'woolwich_offpeak_1200', 'hounslow_peak_0730', 'hounslow_peak_0800', 'hounslow_offpeak_1130', 'hounslow_offpeak_1200', 'walthamstow_peak_0730', 'walthamstow_peak_0800', 'walthamstow_offpeak_1130', 'walthamstow_offpeak_1200']
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Loading Raw Data</span>
#
# - The combined JSON was saved by NB01 with collection metadata (origin label, time period, postcode, reason) wrapped around each raw API response. This means I can extract the context directly from the file without needing to parse filenames — a design choice I made in NB01 after learning from MP1 that filename-based metadata extraction is fragile.
# - I load the ONS Postcode Directory path here but do not read it until Section 8, because the merge should happen after flattening and cleaning, not before. This ordering follows the W08 lecture's workflow: prepare your journey data first, then enrich it with external context.
#
# </div>

# %% [markdown]
# ## Section 3: Inspect One Response
#
# Before flattening, I inspect one stored response to confirm the nesting structure and identify which fields to extract.

# %%
first_key = list(loaded.keys())[0]
first_payload = loaded[first_key]

print('First stored key:', first_key)
print('Top-level metadata keys:', [k for k in first_payload.keys() if k != 'response'])
print('Response keys:', list(first_payload['response'].keys()))
print('Returned journeys:', len(first_payload['response']['journeys']))

# Inspect one journey's structure
first_journey = first_payload['response']['journeys'][0]
print('\nFirst journey keys:', list(first_journey.keys()))
print('Duration:', first_journey.get('duration'), 'minutes')
print('Number of legs:', len(first_journey.get('legs', [])))

# Leg summary — vectorised via pd.json_normalize instead of a for-loop
legs_preview = pd.json_normalize(first_journey['legs'])[['mode.name', 'mode.id', 'duration']]
legs_preview.columns = ['mode', 'mode_id', 'duration_min']
display(legs_preview)

# Inspect fare structure
fare = first_journey.get('fare', {})
print(f'\nFare totalCost: {fare.get("totalCost")}')
print(f'Fare chargeLevel: {fare.get("fares", [{}])[0].get("chargeLevel", "N/A")}')

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: First stored key: barking_peak_0730
# Top-level metadata keys: ['origin_label', 'from_postcode', 'reason', 'request_label', 'time_period', 'journey_date', 'journey_time', 'to_postcode']
# Response keys: ['$type', 'journeys', 'lines', 'stopMessages', 'recommendedMaxAgeMinutes', 'searchCriteria', 'journeyVector']
# Returned journeys: 3
#
# First journey keys: ['$type', 'startDateTime', 'duration', 'arrivalDateTime', 'alternativeRoute', 'legs', 'fare']
# Duration: 51 minutes
# Number of legs: 4
# # Display:       mode  mode_id  duration_min
# 0  walking  walking            10
# 1     tube     tube            14
# 2     tube     tube            13
# 3  walking  walking            13
# # Output: 
# Fare totalCost: 480
# Fare chargeLevel: Peak
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Input Inspection</span>
#
# - I inspected one stored response before writing any flattening code to confirm that the nested list I need is `response['journeys']`, and that each journey contains a `legs` array with per-segment mode and duration information.
# - This small check prevented guesswork. In MP1 I skipped this step and went straight into `pd.json_normalize()` in NB02, which meant I had to repeatedly switch between notebooks to check what fields were available. The W07-NB02 lab reinforced this: the first pass through `json_normalize()` was guided by manually inspecting the JSON tree first.
# - The fare structure is worth noting: TfL reports `totalCost` in pence (e.g., 480 = £4.80), and the `chargeLevel` field indicates whether the fare was charged at peak or off-peak rates. I will handle the pence-to-pounds conversion in the cleaning step.
#
# </div>

# %% [markdown]
# ## Section 4: Flatten the Journey Data
#
# Each stored request contains a `journeys` array with approximately three journey options. I use `pd.json_normalize()` with `record_path='journeys'` to create one row per journey option, attaching the collection metadata via the `meta` parameter.
#
# **Methodology:** `pd.json_normalize()` must be called per-request because each request wraps its journeys with different metadata (origin, time period, etc.). The list comprehension builds all per-request DataFrames in one expression; `pd.concat()` stacks them vertically (W08 Lecture stacking technique). This avoids the explicit `for` + `append` pattern.
#

# %%
# Flatten all requests using a list comprehension — avoids explicit for-loop + append.
# pd.json_normalize() must be called per-request because each request wraps its
# journeys array with different metadata. The comprehension builds all DataFrames
# in one expression; pd.concat() stacks them (W08 Lecture stacking technique).

META_FIELDS = [
    'collection_key', 'origin_label', 'from_postcode', 'reason',
    'request_label', 'time_period', 'journey_date', 'journey_time', 'to_postcode',
]

raw_df = pd.concat(
    [
        pd.json_normalize(
            {**{k: payload[k] for k in META_FIELDS[1:]},  # metadata from stored wrapper
             'collection_key': key,
             'journeys': payload['response']['journeys']},
            record_path='journeys',
            meta=META_FIELDS,
            sep='_',
            errors='ignore',
        )
        for key, payload in loaded.items()
    ],
    ignore_index=True,
)

print(f'Flattened {len(loaded)} requests into {len(raw_df)} journey rows')
print(f'Columns produced by json_normalize: {raw_df.shape[1]}')
raw_df.head(3)

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Flattened 40 requests into 120 journey rows
# Columns produced by json_normalize: 19
# # Result:                                                $type        startDateTime  \
# 0  Tfl.Api.Presentation.Entities.JourneyPlanner.J...  2026-03-18T07:30:00   
# 1  Tfl.Api.Presentation.Entities.JourneyPlanner.J...  2026-03-18T07:32:00   
# 2  Tfl.Api.Presentation.Entities.JourneyPlanner.J...  2026-03-18T07:34:00   
#
#    duration      arrivalDateTime  alternativeRoute  \
# 0        51  2026-03-18T08:21:00             False   
# 1        51  2026-03-18T08:23:00             False   
# 2        52  2026-03-18T08:26:00             False   
#
#                                                 legs  \
# 0  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...   
# 1  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...   
# 2  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...   
#
#                                           fare_$type  fare_totalCost  \
# 0  Tfl.Api.Presentation.Entities.JourneyPlanner.J...             480   
# 1  Tfl.Api.Presentation.Entities.JourneyPlanner.J...             480   
# 2  Tfl.Api.Presentation.Entities.JourneyPlanner.J...             480   
#
#                                           fare_fares  \
# 0  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...   
# 1  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...   
# 2  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...   
#
#                                         fare_caveats     collection_key  \
# 0  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...  barking_peak_0730   
# 1  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...  barking_peak_0730   
# 2  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...  barking_peak_0730   
#
#   origin_label from_postcode  \
# 0      barking      IG11 7LU   
# 1      barking      IG11 7LU   
# 2      barking      IG11 7LU   
#
#                                               reason request_label  \
# 0  East, lower IMD, District/Hammersmith & City c...     peak_0730   
# 1  East, lower IMD, District/Hammersmith & City c...     peak_0730   
# 2  East, lower IMD, District/Hammersmith & City c...     peak_0730   
#
#   time_period journey_date journey_time to_postcode  
# 0        peak     20260318         0730    WC2A 2AE  
# 1        peak     20260318         0730    WC2A 2AE  
# 2        peak     20260318         0730    WC2A 2AE
# ```

# %%
# Post-concat verification: confirm the combined dataset has the expected structure
# (W08 Lecture: "checking that whenever you do a compact transformation... shows you understood")
print(f'Combined: {raw_df.shape[0]} rows from {raw_df["collection_key"].nunique()} requests')
print(f'Columns: {raw_df.shape[1]}')
print('\nRows per origin × period:')
print(raw_df.groupby(['origin_label', 'time_period']).size())

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Combined: 120 rows from 40 requests
# Columns: 19
#
# Rows per origin × period:
# origin_label  time_period
# barking       off_peak       6
#               peak           6
# barnet        off_peak       6
#               peak           6
# bromley       off_peak       6
#               peak           6
# croydon       off_peak       6
#               peak           6
# harrow        off_peak       6
#               peak           6
# hounslow      off_peak       6
#               peak           6
# romford       off_peak       6
#               peak           6
# sutton        off_peak       6
#               peak           6
# walthamstow   off_peak       6
#               peak           6
# woolwich      off_peak       6
#               peak           6
# dtype: int64
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Flattening Logic</span>
#
# I used a list comprehension inside `pd.concat()` rather than an explicit `for` loop with `.append()`. The comprehension is not truly vectorised — `pd.json_normalize()` must still be called per-request because each request wraps its journeys with different metadata — but it avoids the mutable-list anti-pattern and expresses the full flatten-and-stack operation in a single statement. The `META_FIELDS` list is defined once so that the metadata column names are a single source of truth rather than repeated strings.
#
# The W07-NB02 lab on `pd.json_normalize()` with `record_path` and `meta` was the direct preparation for this step. I found that the `sep='_'` parameter is essential to avoid column name collisions when nested JSON keys share names (e.g., `fare.totalCost` becomes `fare_totalCost`).
#
# </div>

# %% [markdown]
# ## Section 5: Select and Rename Useful Columns
#
# `pd.json_normalize()` produces more columns than needed for this project. I select the fields relevant to the research question and rename them for clarity.

# %%
work_df = raw_df.copy()

rename_map = {
    'duration': 'duration_minutes',
    'startDateTime': 'start_time',
    'arrivalDateTime': 'arrival_time',
    'fare_totalCost': 'fare_total',
}

work_df.rename(columns=rename_map, inplace=True)

# Select only the columns needed for analysis
journeys_df = work_df[[
    'collection_key', 'origin_label', 'from_postcode', 'reason', 'request_label', 'time_period',
    'journey_date', 'journey_time', 'to_postcode', 'duration_minutes', 'start_time', 'arrival_time',
    'fare_total', 'legs'
]].copy()

print(f'Selected {journeys_df.shape[1]} columns from {raw_df.shape[1]} produced by json_normalize')
journeys_df.head(3)

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Selected 14 columns from 19 produced by json_normalize
# # Result:       collection_key origin_label from_postcode  \
# 0  barking_peak_0730      barking      IG11 7LU   
# 1  barking_peak_0730      barking      IG11 7LU   
# 2  barking_peak_0730      barking      IG11 7LU   
#
#                                               reason request_label  \
# 0  East, lower IMD, District/Hammersmith & City c...     peak_0730   
# 1  East, lower IMD, District/Hammersmith & City c...     peak_0730   
# 2  East, lower IMD, District/Hammersmith & City c...     peak_0730   
#
#   time_period journey_date journey_time to_postcode  duration_minutes  \
# 0        peak     20260318         0730    WC2A 2AE                51   
# 1        peak     20260318         0730    WC2A 2AE                51   
# 2        peak     20260318         0730    WC2A 2AE                52   
#
#             start_time         arrival_time  fare_total  \
# 0  2026-03-18T07:30:00  2026-03-18T08:21:00         480   
# 1  2026-03-18T07:32:00  2026-03-18T08:23:00         480   
# 2  2026-03-18T07:34:00  2026-03-18T08:26:00         480   
#
#                                                 legs  
# 0  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...  
# 1  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...  
# 2  [{'$type': 'Tfl.Api.Presentation.Entities.Jour...
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Column Selection and Renaming</span>
#
# - `pd.json_normalize()` produced over 20 columns from the nested TfL response, but only a subset is relevant to the research question. I selected the metadata columns (origin, time period, postcode), the core journey metrics (duration, start/arrival time, fare), and the raw `legs` list which I will explode in the next step to extract per-mode durations.
# - I renamed `duration` to `duration_minutes` and `fare_totalCost` to `fare_total` so that column names are self-documenting. This follows the same principle I used in MP1's NB02 where I renamed `components.no2` to `no2` — the W05 lecture on data transformation emphasised that column names should describe their content clearly for downstream readers.
# - I kept the `legs` column at this stage because it contains the nested per-segment information I need to derive `transfer_count`, `walking_minutes`, and `mode_sequence`. It will be dropped after feature engineering.
#
# </div>

# %% [markdown]
# ## Section 6: Feature Engineering from Leg-Level Data
#
# The `legs` column contains a list of dictionaries, each representing one journey segment (walk, tube, bus, etc.).
#
# **Methodology:**
# 1. `explode('legs')` unpacks each leg into its own row (inverse of aggregation)
# 2. `pd.json_normalize()` flattens the leg dictionaries into columns
# 3. `pivot_table(columns='mode_id', values='duration', aggfunc='sum')` reshapes per-mode durations back to journey level (W08 Lecture reshaping technique)
# 4. `groupby(index).agg()` derives `leg_count`, `has_walking_leg`, `mode_sequence`, and `transfer_count`
#
# All operations are vectorised — no explicit iteration over individual legs.
#

# %%
# Parse datetime columns
journeys_df['journey_date'] = pd.to_datetime(journeys_df['journey_date'], format='%Y%m%d')
journeys_df['start_time'] = pd.to_datetime(journeys_df['start_time'])
journeys_df['arrival_time'] = pd.to_datetime(journeys_df['arrival_time'])

# Standardise postcode keys for ONS merge (W08 Lab Part 2 technique)
journeys_df['from_postcode_key'] = (
    journeys_df['from_postcode']
    .str.strip()
    .str.upper()
    .str.replace(' ', '', regex=False)
)
journeys_df['to_postcode_key'] = (
    journeys_df['to_postcode']
    .str.strip()
    .str.upper()
    .str.replace(' ', '', regex=False)
)

# Explode legs and flatten into per-leg rows
legs_exploded = journeys_df[['legs']].explode('legs')
legs_flat = pd.json_normalize(legs_exploded['legs'], sep='_')
legs_flat.index = legs_exploded.index

# W08 technique: pivot_table() to aggregate minutes by transport mode per journey
minutes_by_mode = (
    legs_flat.pivot_table(
        index=legs_flat.index,
        columns='mode_id',
        values='duration',
        aggfunc='sum',
        fill_value=0,
    )
    .rename(columns=lambda x: f'{x}_minutes')
    .reindex(
        columns=['walking_minutes', 'bus_minutes', 'tube_minutes',
                 'elizabeth-line_minutes', 'national-rail_minutes'],
        fill_value=0,
    )
)

# Derive journey-level summary statistics
stats = legs_flat.groupby(legs_flat.index).agg(
    leg_count=('mode_id', 'size'),
    has_walking_leg=('mode_id', lambda x: (x == 'walking').any()),
    mode_sequence=('mode_id', ' -> '.join),
)
stats['transfer_count'] = stats['leg_count'] - 1

# Join derived features back to the journey table and drop the raw legs column
journeys_df = journeys_df.drop(columns='legs').join(minutes_by_mode).join(stats)

print(f'Feature engineering complete. Shape: {journeys_df.shape}')
print(f'Derived columns: walking_minutes, bus_minutes, tube_minutes, elizabeth-line_minutes,')
print(f'  national-rail_minutes, leg_count, has_walking_leg, mode_sequence, transfer_count')
journeys_df[['origin_label', 'time_period', 'duration_minutes', 'transfer_count',
             'walking_minutes', 'mode_sequence']].head(6)

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Feature engineering complete. Shape: (120, 24)
# Derived columns: walking_minutes, bus_minutes, tube_minutes, elizabeth-line_minutes,
#   national-rail_minutes, leg_count, has_walking_leg, mode_sequence, transfer_count
# # Result:   origin_label time_period  duration_minutes  transfer_count  walking_minutes  \
# 0      barking        peak                51               3               23   
# 1      barking        peak                51               3               23   
# 2      barking        peak                52               3               23   
# 3      barking        peak                53               3               23   
# 4      barking        peak                52               3               23   
# 5      barking        peak                51               3               23   
#
#                         mode_sequence  
# 0  walking -> tube -> tube -> walking  
# 1  walking -> tube -> tube -> walking  
# 2  walking -> tube -> tube -> walking  
# 3  walking -> tube -> tube -> walking  
# 4  walking -> tube -> tube -> walking  
# 5  walking -> tube -> tube -> walking
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Feature Engineering</span>
#
# - This cell applies several pandas techniques from the course in sequence. The `explode('legs')` call unpacks the nested list so that each leg becomes its own row — this is the inverse of aggregation, and I first encountered it when exploring how to handle one-to-many relationships in the W08 Lecture on reshaping.
# - The `pivot_table()` call with `aggfunc='sum'` is directly from W08 Lecture Section 3, where it was used to aggregate journey durations by destination and time band. Here I use it to sum leg durations by transport mode, producing columns like `walking_minutes` and `tube_minutes` for each journey. The `fill_value=0` ensures that journeys without a particular mode get zero rather than NaN.
# - The postcode key standardisation (`.str.strip().str.upper().str.replace(' ', '')`) follows the pattern from W08 Lab Part 2, where the practice data had messy postcodes like `"ig11 0ab"` and `" IG11 0AB "` that would fail to match without cleaning. I applied the same cleaning here to ensure the ONS merge in Section 8 succeeds.
# - I defined `transfer_count` as `leg_count - 1` because each transfer corresponds to a change between journey segments. A journey with 4 legs (walk → tube → tube → walk) has 3 transfers. This is a simplification — not all leg transitions are meaningful transfers — but it provides a consistent comparative metric across journeys.
# - The `mode_sequence` column (e.g., `'walking -> tube -> tube -> walking'`) captures the qualitative route structure. This will be useful in NB03 for the second insight about journey experience changes, even when duration stays similar.
#
# </div>

# %% [markdown]
# ## Section 7: Data Validation and Quality Handling
#
# ### 7.1 Pre-Handling Checks
#
# Following the same validation-first approach as Mini Project 1, I run structure and quality checks *before* deciding on any cleaning steps. The W08 Lab Part 2 emphasised checking row counts and null values before and after each transformation to catch silent issues.
#
# **Checks performed:** `.describe()` on all numeric columns, duplicate count, missing value count, negative value count, and row coverage by origin x period.
#

# %%
df_precheck = journeys_df.copy()

numeric_check_cols = [
    'duration_minutes', 'fare_total', 'leg_count', 'transfer_count',
    'walking_minutes', 'bus_minutes', 'tube_minutes',
    'elizabeth-line_minutes', 'national-rail_minutes',
]

display(df_precheck[numeric_check_cols].describe().T)

print(f'Shape: {df_precheck.shape}')
print(f'Journey date: {df_precheck["journey_date"].min().date()}')
print(f'Duplicates: {df_precheck.duplicated().sum()}')
print(f'Missing durations: {df_precheck["duration_minutes"].isna().sum()}')
print(f'Missing fares: {df_precheck["fare_total"].isna().sum()}')
print(f'Negative durations: {(df_precheck["duration_minutes"] < 0).sum()}')
print(f'Fare range (raw): {df_precheck["fare_total"].min()} → {df_precheck["fare_total"].max()}')

print('\nRows by origin and time period:')
coverage = (
    df_precheck.groupby(['origin_label', 'time_period'], as_index=False)
    .agg(journey_rows=('duration_minutes', 'size'))
)
display(coverage)

# %% [markdown]
# [OUTPUT]
#
# ```
# # Display:                         count        mean         std    min     25%    50%  \
# duration_minutes        120.0   56.166667    8.139883   42.0   50.00   53.0   
# fare_total              120.0  554.083333  167.330729  330.0  397.50  555.0   
# leg_count               120.0    4.708333    1.024046    3.0    4.00    4.0   
# transfer_count          120.0    3.708333    1.024046    2.0    3.00    3.0   
# walking_minutes         120.0   19.175000    3.446256   10.0   16.00   19.5   
# bus_minutes             120.0    2.783333    5.009543    0.0    0.00    0.0   
# tube_minutes            120.0   17.733333   13.596671    0.0    3.75   22.0   
# elizabeth-line_minutes  120.0    3.341667    7.427516    0.0    0.00    0.0   
# national-rail_minutes   120.0    7.958333   12.972755    0.0    0.00    0.0   
#
#                           75%     max  
# duration_minutes         64.0    72.0  
# fare_total              655.0  1055.0  
# leg_count                 5.0     8.0  
# transfer_count            4.0     7.0  
# walking_minutes          22.0    31.0  
# bus_minutes               5.0    30.0  
# tube_minutes             27.0    45.0  
# elizabeth-line_minutes    0.0    26.0  
# national-rail_minutes    18.0    40.0
# # Output: Shape: (120, 24)
# Journey date: 2026-03-18
# Duplicates: 0
# Missing durations: 0
# Missing fares: 0
# Negative durations: 0
# Fare range (raw): 330 → 1055
#
# Rows by origin and time period:
# # Display:    origin_label time_period  journey_rows
# 0       barking    off_peak             6
# 1       barking        peak             6
# 2        barnet    off_peak             6
# 3        barnet        peak             6
# 4       bromley    off_peak             6
# 5       bromley        peak             6
# 6       croydon    off_peak             6
# 7       croydon        peak             6
# 8        harrow    off_peak             6
# 9        harrow        peak             6
# 10     hounslow    off_peak             6
# 11     hounslow        peak             6
# 12      romford    off_peak             6
# 13      romford        peak             6
# 14       sutton    off_peak             6
# 15       sutton        peak             6
# 16  walthamstow    off_peak             6
# 17  walthamstow        peak             6
# 18     woolwich    off_peak             6
# 19     woolwich        peak             6
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Pre-Handling Checks</span>
#
# - The W08 Lab Part 2 emphasised checking row counts and null values before and after each merge step to catch silent row duplication. Following the same logic as Mini Project 1, I ran explicit checks before deciding whether any cleaning was needed. In MP1, `describe()` revealed sentinel values of -9999 in pollutant columns. Here, the checks confirm no missing values, no duplicates, and no negative entries — the TfL API appears to return cleaner data than the OpenWeather historical endpoint.
# - This is the point where I noticed the fare field looked abnormal. Values like 480 and 615 did not make sense as pounds. The Section 3 response inspection had already shown that TfL labels this field as `totalCost` in pence, and the fare caveat in the raw response confirms these are single adult pay-as-you-go fares. The conversion to pounds belongs in the cleaning step, not here.
# - The coverage table confirms that each origin-period combination has exactly 3 journey rows (one per journey option returned by the API). This is consistent with what I noted in NB01's response structure preview, and it means each destination-period group has the same sample size — a clean baseline for NB03 comparisons, though the small n of 3 per group will need to be acknowledged as a limitation.
#
# </div>

# %% [markdown]
# ### 7.2 Lightweight Quality Handling
#
# Based on the pre-handling checks, the data requires no row removal. The only transformation is the fare unit correction (pence → pounds).

# %%
df = df_precheck.copy()

# Fare unit correction: TfL reports totalCost in pence
df = df.rename(columns={'fare_total': 'fare_total_pence'})
df['fare_total_gbp'] = df['fare_total_pence'] / 100

# Defensive cleaning — single vectorised boolean mask instead of chained filters.
# Pre-handling checks showed no issues; these guards catch edge cases if data changes.
rows_before = len(df)
valid_mask = (
    df['duration_minutes'].notna()
    & df['fare_total_pence'].notna()
    & (df['duration_minutes'] >= 0)
    & (df['fare_total_pence'] >= 0)
    & (df['transfer_count'] >= 0)
    & ~df.duplicated()
)
df = df.loc[valid_mask].reset_index(drop=True)

print(f'Rows before cleaning: {rows_before}')
print(f'Rows after cleaning:  {len(df)} ({rows_before - len(df)} removed)')
print(f'Fare range converted to pounds: {df["fare_total_gbp"].min():.2f} to {df["fare_total_gbp"].max():.2f}')

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Rows before cleaning: 120
# Rows after cleaning:  120 (0 removed)
# Fare range converted to pounds: 3.30 to 10.55
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Quality Handling</span>
#
# The W05 Lecture on data transformation stressed that cleaning decisions should be driven by evidence, not routine. The pre-handling checks showed no issues requiring row removal, so the defensive filters are guards rather than active cleaners. I combined all conditions into a single boolean mask (`valid_mask`) rather than chaining five separate `df = df[...]` calls — this is both more efficient (one pass through the data) and clearer to audit, since all conditions are visible in one place. In MP1 I used chained filters and only realised during review that each intermediate step created a copy; the single-mask approach avoids that.
#
# </div>

# %% [markdown]
# ### 7.3 Final Preview
#
# Quick manual spot checks on the cleaned output before moving to the postcode merge.

# %%
print('Head:')
display(df[['origin_label', 'time_period', 'duration_minutes', 'fare_total_gbp',
            'transfer_count', 'walking_minutes', 'mode_sequence']].head())

print('\nTail:')
display(df[['origin_label', 'time_period', 'duration_minutes', 'fare_total_gbp',
            'transfer_count', 'walking_minutes', 'mode_sequence']].tail())

print('\nSample (random_state=42):')
display(df[['origin_label', 'time_period', 'duration_minutes', 'fare_total_gbp',
            'transfer_count', 'walking_minutes', 'mode_sequence']].sample(5, random_state=42))

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Head:
# # Display:   origin_label time_period  duration_minutes  fare_total_gbp  transfer_count  \
# 0      barking        peak                51             4.8               3   
# 1      barking        peak                51             4.8               3   
# 2      barking        peak                52             4.8               3   
# 3      barking        peak                53             4.8               3   
# 4      barking        peak                52             4.8               3   
#
#    walking_minutes                       mode_sequence  
# 0               23  walking -> tube -> tube -> walking  
# 1               23  walking -> tube -> tube -> walking  
# 2               23  walking -> tube -> tube -> walking  
# 3               23  walking -> tube -> tube -> walking  
# 4               23  walking -> tube -> tube -> walking
# # Output: 
# Tail:
# # Display:     origin_label time_period  duration_minutes  fare_total_gbp  \
# 115  walthamstow    off_peak                44             3.3   
# 116  walthamstow    off_peak                46             3.3   
# 117  walthamstow    off_peak                46             3.3   
# 118  walthamstow    off_peak                46             3.3   
# 119  walthamstow    off_peak                46             3.3   
#
#      transfer_count  walking_minutes                       mode_sequence  
# 115               3               22  walking -> tube -> tube -> walking  
# 116               3               22  walking -> tube -> tube -> walking  
# 117               3               22  walking -> tube -> tube -> walking  
# 118               3               22  walking -> tube -> tube -> walking  
# 119               3               20  walking -> tube -> tube -> walking
# # Output: 
# Sample (random_state=42):
# # Display:    origin_label time_period  duration_minutes  fare_total_gbp  transfer_count  \
# 44       harrow    off_peak                52            5.55               5   
# 47       harrow    off_peak                51            5.55               5   
# 4       barking        peak                52            4.80               3   
# 55       barnet    off_peak                65            5.55               4   
# 26      croydon        peak                45            6.60               2   
#
#     walking_minutes                                      mode_sequence  
# 44               16  walking -> tube -> tube -> walking -> bus -> w...  
# 47               16  walking -> tube -> tube -> walking -> bus -> w...  
# 4                23                 walking -> tube -> tube -> walking  
# 55               17          walking -> bus -> tube -> tube -> walking  
# 26               25                walking -> national-rail -> walking
# ```

# %%
# .info() confirms dtypes and non-null counts (W08 Lecture: "I would also recommend you always do info", also reinforced in W09 Lecture)
df.info()

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: <class 'pandas.core.frame.DataFrame'>
# RangeIndex: 120 entries, 0 to 119
# Data columns (total 25 columns):
#  #   Column                  Non-Null Count  Dtype         
# ---  ------                  --------------  -----         
#  0   collection_key          120 non-null    object        
#  1   origin_label            120 non-null    object        
#  2   from_postcode           120 non-null    object        
#  3   reason                  120 non-null    object        
#  4   request_label           120 non-null    object        
#  5   time_period             120 non-null    object        
#  6   journey_date            120 non-null    datetime64[ns]
#  7   journey_time            120 non-null    object        
#  8   to_postcode             120 non-null    object        
#  9   duration_minutes        120 non-null    int64         
#  10  start_time              120 non-null    datetime64[ns]
#  11  arrival_time            120 non-null    datetime64[ns]
#  12  fare_total_pence        120 non-null    int64         
#  13  from_postcode_key       120 non-null    object        
#  14  to_postcode_key         120 non-null    object        
#  15  walking_minutes         120 non-null    int64         
#  16  bus_minutes             120 non-null    int64         
#  17  tube_minutes            120 non-null    int64         
#  18  elizabeth-line_minutes  120 non-null    int64         
#  19  national-rail_minutes   120 non-null    int64         
#  20  leg_count               120 non-null    int64         
#  21  has_walking_leg         120 non-null    bool          
#  22  mode_sequence           120 non-null    object        
#  23  transfer_count          120 non-null    int64         
#  24  fare_total_gbp          120 non-null    float64       
# dtypes: bool(1), datetime64[ns](3), float64(1), int64(9), object(11)
# memory usage: 22.7+ KB
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Final Preview</span>
#
# - The W04-NB02 Lab emphasised using `head()`, `tail()`, and `sample()` as complementary checks to `describe()`. The manual preview is not redundant with `describe()` — it catches structural issues (wrong column types, misaligned rows, unexpected values) that summary statistics miss. In MP1 I found a timezone-related formatting issue only through `head()` / `tail()` inspection.
# - Using `sample(5, random_state=42)` makes the spot check reproducible when the marker reruns the notebook, following the same practice I adopted in MP1's NB02.
#
# </div>

# %% [markdown]
# ## Section 8: Merge ONS Postcode Context
#
# The ONS Postcode Directory provides geographic and socioeconomic context for each origin.
#
# **Methodology (W08 Lecture Section 4, W08 Lab Part 2):**
# 1. Load ONS with `usecols` to minimise memory
# 2. Filter terminated postcodes: `doterm.isna()`
# 3. Standardise merge key: `.str.strip().str.upper().str.replace(' ', '')`  (W08 Slide 29 — silent merge failure prevention)
# 4. `pd.merge(how='left')` on the standardised key
# 5. Verify: row count unchanged, zero unmatched postcodes
# 6. Map borough codes to human-readable names via ONS Open Geography Portal
#

# %%
# Step 1: Load ONS with selected columns only
ons_columns = ['pcds', 'doterm', 'oslaua', 'lsoa11', 'msoa11', 'lat', 'long', 'imd', 'usertype']
ons_df = pd.read_csv(ons_path, usecols=ons_columns, low_memory=False)
print(f'ONS Postcode Directory loaded: {ons_df.shape}')

# Step 2: Filter to active postcodes (W08 Lecture Section 4 technique)
print(f'Terminated postcodes: {ons_df["doterm"].notna().sum()}')
ons_active_df = ons_df[ons_df['doterm'].isna()].drop(columns='doterm').copy()
print(f'Active postcodes after filtering: {len(ons_active_df)}')

# Step 3: Standardise postcode key (W08 Lab Part 2 technique)
ons_active_df['postcode_key'] = (
    ons_active_df['pcds']
    .str.strip()
    .str.upper()
    .str.replace(' ', '', regex=False)
)
ons_active_df = ons_active_df.drop_duplicates(subset='postcode_key')

# Step 4: Merge journey data with ONS context (W08 left join technique)
ons_slim = ons_active_df[['postcode_key', 'pcds', 'oslaua', 'lsoa11', 'msoa11', 'lat', 'long', 'imd', 'usertype']]

print(f'\nRows before merge: {len(df)}')
df = pd.merge(
    df,
    ons_slim,
    left_on='from_postcode_key',
    right_on='postcode_key',
    how='left',
)
print(f'Rows after merge: {len(df)}')
print(f'Unmatched postcodes: {df["oslaua"].isna().sum()}')

# Rename ONS columns with 'from_' prefix for clarity
df = df.rename(columns={
    'pcds': 'from_postcode_ons',
    'oslaua': 'from_borough_code',
    'lsoa11': 'from_lsoa11',
    'msoa11': 'from_msoa11',
    'lat': 'from_lat',
    'long': 'from_long',
    'imd': 'from_imd',
    'usertype': 'from_usertype',
}).drop(columns='postcode_key')

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: ONS Postcode Directory loaded: (326214, 9)
# Terminated postcodes: 146088
# Active postcodes after filtering: 180126
#
# Rows before merge: 120
# Rows after merge: 120
# Unmatched postcodes: 0
# ```

# %%
# Step 5: Map borough codes to human-readable names
# Borough names verified via the ONS Open Geography Portal:
# https://geoportal.statistics.gov.uk/
borough_map = {
    # Original 5 origins
    'E09000002': 'Barking and Dagenham',
    'E09000016': 'Havering',          # Romford
    'E09000008': 'Croydon',
    'E09000015': 'Harrow',
    'E09000003': 'Barnet',
    'E09000006': 'Bromley',
    'E09000029': 'Sutton',
    'E09000011': 'Greenwich',          # Woolwich
    'E09000018': 'Hounslow',
    'E09000031': 'Waltham Forest',     # Walthamstow
}

df['from_borough'] = df['from_borough_code'].map(borough_map)

# Verify mapping worked for all origins
borough_check = df[['origin_label', 'from_postcode', 'from_borough_code', 'from_borough', 'from_imd']].drop_duplicates()
print('Borough mapping for each origin:')
display(borough_check)

# Confirm no unmapped codes
unmapped = df['from_borough'].isna().sum()
print(f'Unmapped borough codes: {unmapped}')

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Borough mapping for each origin:
# # Display:     origin_label from_postcode from_borough_code          from_borough  \
# 0        barking      IG11 7LU         E09000002  Barking and Dagenham   
# 12       romford       RM1 1AU         E09000016              Havering   
# 24       croydon       CR0 1LF         E09000008               Croydon   
# 36        harrow       HA1 1AR         E09000015                Harrow   
# 48        barnet       EN5 5XY         E09000003                Barnet   
# 60       bromley       BR1 1AA         E09000006               Bromley   
# 72        sutton       SM1 1EA         E09000029                Sutton   
# 84      woolwich      SE18 6HQ         E09000011             Greenwich   
# 96      hounslow       TW3 1HA         E09000018              Hounslow   
# 108  walthamstow       E17 7JN         E09000031        Waltham Forest   
#
#      from_imd  
# 0        6900  
# 12       6369  
# 24       5615  
# 36      19272  
# 48      27938  
# 60      24305  
# 72      10196  
# 84       5270  
# 96      14649  
# 108      9666
# # Output: Unmapped borough codes: 0
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: ONS Merge and Borough Mapping</span>
#
# - The ONS merge follows the three-step pattern from the W08 Lecture and Lab: filter terminated postcodes, standardise the key, then `pd.merge()` with a left join. The W08 Lab Part 2 specifically practised this with messy postcodes — `.str.strip().str.upper()` was needed there because the practice data had inconsistent casing and whitespace. I applied the same defensive cleaning here.
# - I used a left join (`how='left'`) so that all journey rows are preserved even if an ONS match is missing. The post-merge check confirms zero unmatched postcodes, which means all ten Outer London origins were found in the ONS directory.
# - The ONS Postcode Directory provides borough *codes* (e.g., `E09000002`) but not borough *names*. After the merge returned ten unique codes, I looked up their corresponding names on the [ONS Open Geography Portal](https://geoportal.statistics.gov.uk/) and wrote a ten-entry mapping dictionary. I considered downloading a full local authority lookup table, but for ten codes a manual lookup with a cited source was simpler and more transparent.
# - The row count should stay the same before and after the merge, confirming no row duplication. In the W08 lecture, the instructor warned that merges can cause row explosion if the right table has duplicate keys — filtering the ONS data to unique postcode keys before merging prevented this.
# - The IMD column from ONS will be used analytically in NB03 to check whether peak fare premiums correlate with neighbourhood deprivation. The latitude and longitude columns will also be used to compute straight-line distances from each origin to LSE — this geographic analysis is one of the higher-scoring uses of ONS data identified in the assignment requirements.
#
# </div>

# %% [markdown]
# ### 8.2 Validate Origin Selection Criteria
#
# Before merging, I confirm that the ten selected origins meet the criteria documented in NB01: all should be active (no termination date) and span a diverse IMD and borough range. The `usertype` column is displayed openly — four postcodes are classified as `usertype = 1` ("large user"), which reflects their town-centre location near stations rather than a residential/commercial distinction, as documented in NB01's origin selection framework.

# %%
# Validate NB01 origin selection against ONS data
selected_check = (
    ons_active_df[ons_active_df['pcds'].isin(df['from_postcode'].unique())]
    [['pcds', 'oslaua', 'imd', 'usertype']]
    .drop_duplicates()
    .sort_values('imd')
)

print(f'Postcodes found in active ONS data: {len(selected_check)} of {df["from_postcode"].nunique()}')
print(f'IMD range: {selected_check["imd"].min()} (most deprived) to {selected_check["imd"].max()} (least deprived)')
print(f'Usertype: {(selected_check["usertype"] == 0).sum()} residential, {(selected_check["usertype"] == 1).sum()} large user')
display(selected_check)

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Postcodes found in active ONS data: 10 of 10
# IMD range: 5270 (most deprived) to 27938 (least deprived)
# Usertype: 6 residential, 4 large user
# # Display:             pcds     oslaua    imd  usertype
# 190779  SE18 6HQ  E09000011   5270         1
# 10314    CR0 1LF  E09000008   5615         0
# 164295   RM1 1AU  E09000016   6369         0
# 97364   IG11 7LU  E09000002   6900         1
# 43651    E17 7JN  E09000031   9666         1
# 209839   SM1 1EA  E09000029  10196         1
# 260091   TW3 1HA  E09000018  14649         0
# 78660    HA1 1AR  E09000015  19272         0
# 0        BR1 1AA  E09000006  24305         0
# 76645    EN5 5XY  E09000003  27938         0
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Origin Selection Validation</span>
#
# - Four origins show `usertype = 1` in the ONS data. As the [ONS Postcode Directory user guide](https://geoportal.statistics.gov.uk/) explains, the large-user/small-user classification is based on mail volume, not on whether a postcode is residential or commercial. These are town-centre postcodes near major stations — the same kind of location a real commuter would start from. I documented this openly in NB01's origin selection framework rather than filtering them out.
# - The W08 Lecture on merging emphasised that a left join should preserve the row count of the left table. The validation above confirms that all ten postcodes matched the ONS active subset and that no rows were lost or duplicated — the same row-count discipline the W08 Lab Part 2 applied to each merge step.
#
# </div>

# %% [markdown]
# ## Section 9: Summary and Validation Tables
#
# These outputs are displayed here for audit purposes but are **not saved as separate files** — NB03 will compute its own aggregations directly from the journey-level CSV, which makes the analytical work visible in the analysis notebook.

# %%
# Summary with both mean and median (NB03 will recompute these visibly)
request_summary = (
    df.groupby(['origin_label', 'time_period', 'from_borough', 'from_imd'], as_index=False)
    .agg(
        median_duration=('duration_minutes', 'median'),
        mean_duration=('duration_minutes', 'mean'),
        median_transfers=('transfer_count', 'median'),
        median_walking=('walking_minutes', 'median'),
        median_fare_gbp=('fare_total_gbp', 'median'),
        n_options=('duration_minutes', 'size'),
    )
)

print('Summary preview (for reference — NB03 will aggregate fresh from the journey-level data):')
display(request_summary)

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Summary preview (for reference — NB03 will aggregate fresh from the journey-level data):
# # Display:    origin_label time_period          from_borough  from_imd  median_duration  \
# 0       barking    off_peak  Barking and Dagenham      6900             51.5   
# 1       barking        peak  Barking and Dagenham      6900             51.5   
# 2        barnet    off_peak                Barnet     27938             65.5   
# 3        barnet        peak                Barnet     27938             65.0   
# 4       bromley    off_peak               Bromley     24305             59.5   
# 5       bromley        peak               Bromley     24305             56.0   
# 6       croydon    off_peak               Croydon      5615             44.0   
# 7       croydon        peak               Croydon      5615             44.5   
# 8        harrow    off_peak                Harrow     19272             52.0   
# 9        harrow        peak                Harrow     19272             51.0   
# 10     hounslow    off_peak              Hounslow     14649             63.0   
# 11     hounslow        peak              Hounslow     14649             64.0   
# 12      romford    off_peak              Havering      6369             62.5   
# 13      romford        peak              Havering      6369             64.5   
# 14       sutton    off_peak                Sutton     10196             66.5   
# 15       sutton        peak                Sutton     10196             69.5   
# 16  walthamstow    off_peak        Waltham Forest      9666             46.0   
# 17  walthamstow        peak        Waltham Forest      9666             46.5   
# 18     woolwich    off_peak             Greenwich      5270             51.5   
# 19     woolwich        peak             Greenwich      5270             50.0   
#
#     mean_duration  median_transfers  median_walking  median_fare_gbp  \
# 0       51.500000               3.0            23.0             3.60   
# 1       51.666667               3.0            23.0             4.80   
# 2       65.500000               4.0            17.0             5.55   
# 3       65.500000               4.0            16.0             7.05   
# 4       58.333333               3.0            17.0             6.15   
# 5       56.166667               3.0            17.0             8.80   
# 6       45.500000               4.0            15.0             6.15   
# 7       45.000000               4.0            15.0             8.35   
# 8       52.166667               5.0            16.0             5.55   
# 9       50.833333               4.0            17.0             5.30   
# 10      63.000000               3.0            19.0             3.60   
# 11      64.500000               3.0            19.0             4.80   
# 12      59.500000               3.0            21.0             4.00   
# 13      64.500000               3.0            22.0             5.90   
# 14      66.666667               6.0            18.0             5.75   
# 15      69.000000               4.5            20.5             7.70   
# 16      45.500000               3.0            22.0             3.30   
# 17      46.500000               3.0            22.0             3.90   
# 18      51.500000               3.0            22.0             3.60   
# 19      50.500000               4.0            21.0             6.55   
#
#     n_options  
# 0           6  
# 1           6  
# 2           6  
# 3           6  
# 4           6  
# 5           6  
# 6           6  
# 7           6  
# 8           6  
# 9           6  
# 10          6  
# 11          6  
# 12          6  
# 13          6  
# 14          6  
# 15          6  
# 16          6  
# 17          6  
# 18          6  
# 19          6
# ```

# %%
# Validation audit (displayed for transparency, not saved as a separate file)
validation_checks = pd.DataFrame({
    'check': [
        'Stored API requests',
        'Flattened journey rows',
        'Rows after cleaning',
        'Rows removed by cleaning',
        'Duplicate rows',
        'Missing durations',
        'Missing fares',
        'Negative values',
        'Unmatched ONS postcodes',
        'Distinct origins',
        'Distinct time periods',
        'Journeys per origin-period group',
    ],
    'result': [
        len(loaded),
        len(df_precheck),
        len(df),
        len(df_precheck) - len(df),
        int(df_precheck.duplicated().sum()),
        int(df_precheck['duration_minutes'].isna().sum()),
        int(df_precheck['fare_total'].isna().sum()),
        int((df_precheck['duration_minutes'] < 0).sum() + (df_precheck['fare_total'] < 0).sum()),
        int(df['from_postcode_ons'].isna().sum()),
        df['origin_label'].nunique(),
        df['time_period'].nunique(),
        int(df.groupby(['origin_label', 'time_period']).size().min()),
    ]
})

print('Validation audit:')
display(validation_checks)

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Validation audit:
# # Display:                                check  result
# 0                Stored API requests      40
# 1             Flattened journey rows     120
# 2                Rows after cleaning     120
# 3           Rows removed by cleaning       0
# 4                     Duplicate rows       0
# 5                  Missing durations       0
# 6                      Missing fares       0
# 7                    Negative values       0
# 8            Unmatched ONS postcodes       0
# 9                   Distinct origins      10
# 10             Distinct time periods       2
# 11  Journeys per origin-period group       6
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Summary and Validation</span>
#
# - I chose not to save the summary or validation tables as separate CSV files because NB03 should compute its own aggregations visibly. If I pre-computed summaries here and just loaded them in NB03, the marker would not see the `groupby()` and statistical reasoning work in the analysis notebook. The aggregation skill itself is part of what NB03 needs to demonstrate.
# - The summary includes both mean and median duration. The W07-NB01 methodology scoping notebook cited DfT and TfL journey-time reliability reports that use median and percentile-based measures for skewed transport data. In NB03, I will compare both and justify choosing median — this directly targets the 'Very Good' rubric criterion for justified statistical choices.
# - The validation table is displayed here so that the transformation pipeline is auditable. A marker can see at a glance that no rows were lost, no values were missing, and all postcodes matched the ONS directory. This mirrors the validation approach from MP1, where I ran `describe()` and explicit checks before and after cleaning.
#
# </div>

# %% [markdown]
# ## Section 10: Save the Processed Outputs

# %%
processed_dir = os.path.join('..', 'data', 'processed')
os.makedirs(processed_dir, exist_ok=True)

journey_options_path = os.path.join(processed_dir, 'journey_options.csv')
df.to_csv(journey_options_path, index=False)

print(f'Saved: {journey_options_path} ({len(df)} rows, {df.shape[1]} columns)')

# %% [markdown]
# [OUTPUT]
#
# ```
# # Output: Saved: ..\data\processed\journey_options.csv (120 rows, 34 columns)
# ```

# %% [markdown]
# <div style="font-family: 'Segoe UI', sans-serif; padding: 18px 22px; background-color: #F8FAFC; border-left: 6px solid #0F766E; border-radius: 8px; box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); max-width: 920px; color: #0F172A;">
#
# <span style="color: #0F766E; font-size: 1.05em; font-weight: 700;">💭 Personal Reflection: Processed File Output</span>
#
# - Saving to `data/processed/` follows the NB01→raw, NB02→processed pipeline structure introduced in the W07 lab. I saved one CSV file: the full journey-level dataset with derived metrics and ONS context. This is the only file NB03 needs to load — all aggregation, plotting, and statistical reasoning will happen visibly in the analysis notebook.
# - I deliberately did not save the summary or validation tables as separate files. Summaries computed in NB02 and silently loaded in NB03 would hide the analytical work. The assignment rewards visible data manipulation skills, so NB03 should demonstrate its own `groupby()`, `pivot_table()`, and `melt()` calls.
# - This follows a principle I learned from MP1: keep the processed layer as simple as possible. In MP1 I saved one hourly CSV, and NB03 did all the aggregation from there. The same logic applies here.
#
# </div>

# %% [markdown]
# ## Output of NB02
#
# This notebook transforms the raw JSON from NB01 into one processed file:
#
# - **`../data/processed/journey_options.csv`** — Full journey-level dataset (120 rows, 10 origins x 4 departure times x ~3 journey options) with derived metrics (duration, transfers, walking minutes, fare, mode sequence) and ONS context (borough, LSOA, IMD, lat/long).
#
# Summary and validation tables are displayed in this notebook for transparency but are not saved as separate files. NB03 will compute its own aggregations directly from the journey-level data.
#
# **Key transformations applied:**
# - Nested JSON flattened using `pd.json_normalize()` with `record_path` and `meta`
# - Per-request DataFrames stacked using `pd.concat()` (W08 stacking technique)
# - Leg-level data reshaped using `pivot_table()` (W08 reshaping technique) to derive per-mode durations
# - Postcode keys standardised using `.str.strip().str.upper()` (W08 Lab postcode cleaning)
# - ONS context merged using `pd.merge()` left join (W08 merge technique)
# - Borough codes mapped to names via [ONS Open Geography Portal](https://geoportal.statistics.gov.uk/)
# - Fare corrected from pence to pounds
#
# **Next in NB03-Exploratory-Data-Analysis.ipynb:**
# - Load `journey_options.csv`
# - Check data coverage (journeys per origin x period)
# - Explore duration, transfers, walking, and fare across peak vs off-peak
# - Apply the three-layer strip+box+point visualisation (W08 technique)
# - Use `melt()` for multi-metric faceted comparisons (W08 reshaping technique)
# - Investigate geographic context using ONS lat/long (distance to LSE) and IMD
# - Select the two strongest insights for `REPORT.md`
