Day 1: Part-to-Whole

import geopandas as gpd
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.font_manager as fm
import contextily as ctx
from shapely.geometry import Point

# Font setup for Korean labels
fm._load_fontmanager(try_read_cache=False)
mpl.rcParams["font.family"] = "NanumBarunGothic"
mpl.rcParams["axes.unicode_minus"] = False

# Create GeoDataFrame
gdf = gpd.GeoDataFrame(
    cb_df,
    geometry=[Point(float(x), float(y)) for x, y in zip(cb_df["LOT"], cb_df["LAT"])],
    crs="EPSG:4326",
)

# Convert to Web Mercator for basemap
gdf = gdf.to_crs(epsg=3857)

# Assign colors by district
districts = sorted(gdf["GU_NM"].unique())
cmap = plt.cm.get_cmap("tab20", len(districts))
color_map = {gu: cmap(i) for i, gu in enumerate(districts)}

fig, ax = plt.subplots(figsize=(12, 12))

for gu in districts:
    sub = gdf[gdf["GU_NM"] == gu]
    ax.scatter(
        sub.geometry.x, sub.geometry.y,
        c=[color_map[gu]], label=gu,
        s=5, alpha=0.6, edgecolors="none",
    )

# Add basemap
ctx.add_basemap(ax, source=ctx.providers.CartoDB.Positron)

ax.set_title("서울시 벚나무·왕벚나무 위치 (구별)", fontsize=16)
ax.legend(
    loc="upper left", bbox_to_anchor=(1.01, 1),
    markerscale=3, fontsize=8, title="구",
)
ax.set_axis_off()
plt.tight_layout()
plt.show()

df2 = pd.read_csv("roadside_tree_2026.csv", low_memory=False)

species_row = df2.iloc[1].values  # species name row
cols = df2.columns.tolist()

# Map start column index for each year
year_starts = {}
for i, c in enumerate(cols):
    base = c.split(".")[0]
    if base not in year_starts and base not in ["자치구별(1)", "자치구별(2)"]:
        year_starts[base] = i
years = list(year_starts.keys())

# Filter to actual district names (exclude subtotals)
districts = ["종로구", "중구", "용산구", "성동구", "광진구", "동대문구", "중랑구", "성북구",
             "강북구", "도봉구", "노원구", "은평구", "서대문구", "마포구", "양천구", "강서구",
             "구로구", "금천구", "영등포구", "동작구", "관악구", "서초구", "강남구", "송파구", "강동구"]
data_rows = df2[df2["자치구별(2)"].isin(districts)].copy()

# Extract cherry tree column per year → long format
records = []
for yi, y in enumerate(years):
    start = year_starts[y]
    cherry_col_idx = start + 5  # offset 5 within each year block = cherry/Yoshino cherry
    for _, row in data_rows.iterrows():
        val = row.iloc[cherry_col_idx]
        try:
            val = int(val)
        except (ValueError, TypeError):
            val = 0
        records.append({"year": int(y), "district": row["자치구별(2)"], "cherry_count": val})

cherry_df = pd.DataFrame(records)
cherry_df.head()

import geopandas as gpd
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.font_manager as fm

# Font setup
fm._load_fontmanager(try_read_cache=False)
mpl.rcParams["font.family"] = "NanumBarunGothic"
mpl.rcParams["axes.unicode_minus"] = False

# Seoul district boundaries
seoul = gpd.read_file("seoul_gu.geojson")

# 2024 cherry tree counts
cherry_2024 = cherry_df[cherry_df["year"] == 2024][["district", "cherry_count"]].copy()

# Merge
seoul = seoul.merge(cherry_2024, left_on="name", right_on="district", how="left")
seoul["cherry_count"] = seoul["cherry_count"].fillna(0).astype(int)

fig, ax = plt.subplots(figsize=(10, 10))

seoul.plot(
    column="cherry_count", cmap="PuRd", edgecolor="white", linewidth=1.5,
    legend=True,
    legend_kwds={"label": "왕벚나무 수 (그루)", "shrink": 0.6},
    ax=ax,
)

# Label each district with name + count
for _, row in seoul.iterrows():
    centroid = row.geometry.centroid
    ax.annotate(
        f"{row['name']}\n{row['cherry_count']:,}",
        xy=(centroid.x, centroid.y),
        ha="center", va="center", fontsize=7, fontweight="bold",
    )

ax.set_title("2024년 서울시 자치구별 왕벚나무 수", fontsize=16)
ax.set_axis_off()
plt.tight_layout()
plt.show()

import numpy as np
import geopandas as gpd
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.font_manager as fm
import matplotlib.image as mpimg
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import matplotlib.colors as mcolors

# --- Font Settings ---
# Switching to a standard sans-serif font for English rendering
mpl.rcParams["font.family"] = "NanumBarunGothic"
mpl.rcParams["axes.unicode_minus"] = False

# === 1. Data Preparation ===
seoul_inf = gpd.read_file("seoul_gu.geojson")

# Filtering cherry tree data for 2024
# (Note: Assumes cherry_df is already loaded in your environment)
c2024 = cherry_df[cherry_df["year"] == 2024][["district", "cherry_count"]].copy()

seoul_inf = seoul_inf.merge(c2024, left_on="name", right_on="district", how="left")
seoul_inf["cherry_count"] = seoul_inf["cherry_count"].fillna(0).astype(int)

# --- 💡 NEW: Korean to English District Mapping ---
district_mapping = {
    '강남구': 'Gangnam-gu', '강동구': 'Gangdong-gu', '강북구': 'Gangbuk-gu',
    '강서구': 'Gangseo-gu', '관악구': 'Gwanak-gu', '광진구': 'Gwangjin-gu',
    '구로구': 'Guro-gu', '금천구': 'Geumcheon-gu', '노원구': 'Nowon-gu',
    '도봉구': 'Dobong-gu', '동대문구': 'Dongdaemun-gu', '동작구': 'Dongjak-gu',
    '마포구': 'Mapo-gu', '서대문구': 'Seodaemun-gu', '서초구': 'Seocho-gu',
    '성동구': 'Seongdong-gu', '성북구': 'Seongbuk-gu', '송파구': 'Songpa-gu',
    '양천구': 'Yangcheon-gu', '영등포구': 'Yeongdeungpo-gu', '용산구': 'Yongsan-gu',
    '은평구': 'Eunpyeong-gu', '종로구': 'Jongno-gu', '중구': 'Jung-gu',
    '중랑구': 'Jungnang-gu'
}
# Create a new column for English names
seoul_inf["name_eng"] = seoul_inf["name"].map(district_mapping)

# Calculate percentages and number of petals
total = seoul_inf["cherry_count"].sum()
seoul_inf["pct"] = seoul_inf["cherry_count"] / total * 100
seoul_inf["n_petals"] = np.maximum(1, seoul_inf["cherry_count"] // 200)

seoul_inf["cx"] = seoul_inf.geometry.centroid.x
seoul_inf["cy"] = seoul_inf.geometry.centroid.y
mcx = seoul_inf["cx"].mean()

# Split districts into Left/Right groups
left_mask = seoul_inf["cx"] < mcx
left_df = seoul_inf[left_mask].sort_values("cy", ascending=False).reset_index(drop=True)
right_df = seoul_inf[~left_mask].sort_values("cy", ascending=False).reset_index(drop=True)

# === 2. Cherry Blossom Petal Image Function ===
petal_img = mpimg.imread(
    "/home/jonghwan/git/jonghwanyoon.github.io/src/content/blog/30daychartchallenge/day1-part-to-whole/cherry-blossom.png"
)

def make_petal_grid(img, count, cols=10):
    count = max(1, count)
    n_rows = int(np.ceil(count / cols))
    h, w = img.shape[:2]
    grid = np.zeros((n_rows * h, min(count, cols) * w, 4))
    for i in range(count):
        r, c = divmod(i, cols)
        grid[r * h:(r + 1) * h, c * w:(c + 1) * w] = img[:, :, :4]
    return grid

# === 3. Plotting ===
fig, ax = plt.subplots(figsize=(22, 16))
fig.patch.set_facecolor("#fafafa")
ax.set_facecolor("#fafafa")

# 3.1 Draw Choropleth Map
cmap = mcolors.LinearSegmentedColormap.from_list("cherry_grad", ["#fff0f0", "#ffb3b3", "#ff4d6d"])
seoul_inf.plot(
    ax=ax, 
    column="pct", 
    cmap=cmap, 
    edgecolor="white", 
    linewidth=2, 
    zorder=2,
    vmin=0,
    vmax=seoul_inf["pct"].max()
)

bounds = seoul_inf.geometry.total_bounds
map_w = bounds[2] - bounds[0]
map_h = bounds[3] - bounds[1]

left_x = bounds[0] - (map_w * 0.1)
right_x = bounds[2] + (map_w * 0.1)
y_start = bounds[3] + (map_h * 0.05)
y_end = bounds[1] - (map_h * 0.05)
left_y_coords = np.linspace(y_start, y_end, len(left_df))
right_y_coords = np.linspace(y_start, y_end, len(right_df))

# 3.2 Draw Left-side Labels
for i, row in left_df.iterrows():
    cx, cy = row["cx"], row["cy"]
    lx, ly = left_x, left_y_coords[i]
    
    rad = 0.15 if cy > ly else -0.15
    ax.annotate("", xy=(cx, cy), xytext=(lx, ly),
                arrowprops=dict(arrowstyle="-", color="#e8aeb7", connectionstyle=f"arc3,rad={rad}", lw=1.2), zorder=3)
    ax.plot(cx, cy, "o", color="#d67c8b", markersize=5, zorder=4)
    
    # 💡 CHANGED: row['name'] -> row['name_eng']
    ax.text(lx - 0.01, ly, f"{row['name_eng']} {row['pct']:.1f}% ", 
            fontsize=13, ha="right", va="bottom", fontweight="bold", color="#333", zorder=5)
    
    grid = make_petal_grid(petal_img, row["n_petals"], cols=10)
    im = OffsetImage(grid, zoom=0.08)
    ab = AnnotationBbox(im, (lx - 0.01, ly), frameon=False, 
                        box_alignment=(1.0, 1.0), xybox=(0, -5), boxcoords="offset points", zorder=4)
    ax.add_artist(ab)

# 3.3 Draw Right-side Labels
for i, row in right_df.iterrows():
    cx, cy = row["cx"], row["cy"]
    lx, ly = right_x, right_y_coords[i]
    
    rad = -0.15 if cy > ly else 0.15
    ax.annotate("", xy=(cx, cy), xytext=(lx, ly),
                arrowprops=dict(arrowstyle="-", color="#e8aeb7", connectionstyle=f"arc3,rad={rad}", lw=1.2), zorder=3)
    ax.plot(cx, cy, "o", color="#d67c8b", markersize=5, zorder=4)
    
    # 💡 CHANGED: row['name'] -> row['name_eng']
    ax.text(lx + 0.01, ly, f" {row['name_eng']} {row['pct']:.1f}%", 
            fontsize=13, ha="left", va="bottom", fontweight="bold", color="#333", zorder=5)
    
    grid = make_petal_grid(petal_img, row["n_petals"], cols=10)
    im = OffsetImage(grid, zoom=0.08)
    ab = AnnotationBbox(im, (lx + 0.01, ly), frameon=False, 
                        box_alignment=(0.0, 1.0), xybox=(0, -5), boxcoords="offset points", zorder=4)
    ax.add_artist(ab)

# --- Finalizing Axis and Legend ---
pad_x, pad_y = map_w * 0.45, map_h * 0.1
ax.set_xlim(bounds[0] - pad_x, bounds[2] + pad_x)
ax.set_ylim(bounds[1] - pad_y, bounds[3] + pad_y)
ax.set_axis_off()

# Main Title
ax.text(mcx, bounds[3] + map_h * 0.15, "Yoshino Cherry Tree Distribution by Seoul District", 
        fontsize=28, fontweight="bold", ha="center", va="center", color="#222")

# Map Legend
legend_petal = OffsetImage(petal_img, zoom=0.08)
legend_x = bounds[2] + (map_w * 0.3)
legend_y = bounds[1] - (map_h * 0.05)
ab_leg = AnnotationBbox(legend_petal, (legend_x, legend_y), frameon=False, zorder=5)
ax.add_artist(ab_leg)
ax.text(legend_x + 0.015, legend_y, "≈ 200 Trees", fontsize=12, va="center", color="#666")

plt.tight_layout()
plt.show()

import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.font_manager as fm
import os

# 폰트 설정 (영문도 깔끔하게 나오는 폰트로 유지)
fm._load_fontmanager(try_read_cache=False)
mpl.rcParams["font.family"] = "NanumBarunGothic" 
mpl.rcParams["axes.unicode_minus"] = False

# 손글씨 폰트 설정
font_path = 'NanumPenScript-Regular.ttf' 
handwriting_fp = fm.FontProperties(family='NanumBarunGothic', size=16, weight='bold')

# === 1. 데이터 준비 (영문 번역) ===
data = {
    'district': ['Gangdong-gu', 'Gangseo-gu', 'Eunpyeong-gu', 'Yeongdeungpo-gu', 'Gwanak-gu', 'Other 20 Districts'],
    'pct': [11.4, 8.2, 7.4, 7.2, 6.6, 59.2] 
}
df = pd.DataFrame(data)

# === 2. 지도 데이터 및 색상 매핑 ===
seoul_inf = gpd.read_file("seoul_gu.geojson")
bg_color = "#F8F9FA"
colors = ["#FF4D6D", "#FF758F", "#FF8FA3", "#FFB3C1", "#FFCCD5", "#E2E8F0"]

# 색상 매핑 (원본 데이터의 한글 이름과 매핑해야 하므로 하드코딩)
kr_districts = ['강동구', '강서구', '은평구', '영등포구', '관악구']
color_map = {kr_districts[i]: colors[i] for i in range(5)}
seoul_inf['color'] = seoul_inf['name'].map(color_map).fillna(colors[5])

# === 3. 캔버스 및 레이아웃 ===
fig = plt.figure(figsize=(16, 9), facecolor=bg_color)

ax_main = fig.add_axes([0.02, 0.05, 0.55, 0.75]) 
ax_map = fig.add_axes([0.62, 0.05, 0.35, 0.5])   

ax_main.set_facecolor(bg_color)
ax_map.set_facecolor(bg_color)

# === 3-1. 타이틀 벚꽃잎 아이콘 추가 ===
petal_img_path = "/home/jonghwan/git/jonghwanyoon.github.io/src/content/blog/30daychartchallenge/day1-part-to-whole/cherry-blossom.png"

try:
    petal_img = plt.imread(petal_img_path)
    ax_icon = fig.add_axes([0.03, 0.88, 0.035, 0.05]) 
    ax_icon.imshow(petal_img)
    ax_icon.axis('off')
    title_start_x = 0.075 # 영문 길이에 맞춰 아이콘 옆 여백 조정
except FileNotFoundError:
    print("벚꽃잎 이미지를 찾을 수 없어 아이콘 없이 타이틀을 출력합니다.")
    title_start_x = 0.05

# === 4. 메인 타이틀 (영문 번역) ===
fig.text(title_start_x, 0.90, "Where are the Most Cherry Trees in Seoul?", fontsize=28, fontweight='bold', color='#1A202C')
fig.text(0.04, 0.84, "Approximately 40% of Yoshino cherry trees are concentrated in the top 5 districts.", fontsize=15, color='#4A5568')

# === 5. 도넛 차트 ===
wedges, texts, autotexts = ax_main.pie(
    df['pct'], colors=colors, autopct='%1.1f%%', startangle=160, pctdistance=0.82,
    wedgeprops=dict(width=0.35, edgecolor=bg_color, linewidth=4),
    textprops=dict(fontsize=13, fontweight='bold')
)

for i, autotext in enumerate(autotexts):
    autotext.set_color('white' if i < 3 else '#4A5568')

ax_main.text(0, 0.12, "2024", ha='center', va='center', fontsize=18, fontweight='bold', color='#A0AEC0')
ax_main.text(0, -0.1, "Seoul Cherry Tree\nDistribution", ha='center', va='center', fontsize=22, fontweight='bold', color='#2D3748', linespacing=1.4)

# === 6. 범례 (영문 번역) ===
legend_labels = [f"{row['district']} ({row['pct']:.1f}%)" for _, row in df.iterrows()]
ax_main.legend(wedges, legend_labels, title="Share by District", title_fontproperties={'weight':'bold', 'size':15},
          loc="upper left", bbox_to_anchor=(1.05, 0.95), frameon=False, fontsize=14, labelspacing=1.3)

# === 7. 미니맵 그리기 ===
seoul_inf.plot(ax=ax_map, color=seoul_inf['color'], edgecolor="white", linewidth=1.2, zorder=1)
ax_map.set_axis_off()

# 💡 실제 벚나무 데이터 오버레이 
gdf_real = gpd.GeoDataFrame(cb_df, geometry=gpd.points_from_xy(cb_df.LOT, cb_df.LAT), crs="EPSG:4326")
gdf_real.plot(ax=ax_map, color='#FF4D6D', markersize=1, alpha=0.6, zorder=2)

# === 8. 주요 벚꽃 명소 손글씨 라벨링 (영문 번역 및 좌표 미세 조정) ===
# 영문 텍스트가 더 길기 때문에 xytext(라벨 여백)를 조금씩 더 넓혔습니다.
spots = [
    {'name': 'Gomdallae-gil', 'coords': (126.845, 37.532), 'xytext': (-50, 20), 'rad': -0.2},
    {'name': 'Anyangcheon', 'coords': (126.885, 37.480), 'xytext': (-50, -30), 'rad': 0.2},
    {'name': 'Yeouido Yunjung-ro', 'coords': (126.918, 37.528), 'xytext': (-10, 40), 'rad': 0.3},
    {'name': 'Seokchon Lake', 'coords': (127.103, 37.508), 'xytext': (40, -40), 'rad': -0.3},
    {'name': 'Yangjaecheon', 'coords': (127.045, 37.475), 'xytext': (20, -40), 'rad': -0.2}
]


for spot in spots:
    cx, cy = spot['coords']
    ax_map.annotate(
        spot['name'],
        xy=(cx, cy),
        xytext=spot['xytext'],
        textcoords='offset points',
        ha='center', va='center',
        fontproperties=handwriting_fp, 
        color='#333333',
        zorder=5, 
        arrowprops=dict(
            arrowstyle="->",
            color="#555555",
            lw=1.2,
            connectionstyle=f"arc3,rad={spot['rad']}"
        )
    )

# 미니맵 타이틀 (영문 번역)
ax_map.text(0.5, 1.05, "Spots", transform=ax_map.transAxes, ha='center', va='bottom', fontsize=14, fontweight='bold', color='#718096')

plt.show()