利用折线图探索伦敦不同行政区动物救助变化趋势

import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from matplotlib.lines import Line2D
# 默认字体
plt.rcParams.update({"font.family": "Fira Sans Compressed"})

# 导入数据
animal_rescues = pd.read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-06-29/animal_rescues.csv")

# animal_group_parent大写处理
animal_rescues["animal_group_parent"] = animal_rescues["animal_group_parent"].str.capitalize()

animal_rescues.head()

gb_london_boroughs_grid = pd.read_csv("https://raw.githubusercontent.com/hafen/grid-designer/master/grids/gb_london_boroughs_grid.csv")

borough_names = gb_london_boroughs_grid.rename(columns={"code_ons": "borough_code"})
borough_names.head()

# 数据预处理

# 保留2021年之前的数据
rescues_borough = animal_rescues.query("cal_year < 2021").reset_index()

# 区分猫与非猫
rescues_borough["animal_group_parent"] = np.where(
    rescues_borough["animal_group_parent"] == "Cat", "Cat", "Not_Cat"
)

# 汇总统计
rescues_borough = (
    rescues_borough.groupby(["cal_year", "borough_code", "animal_group_parent"])
    .size()
    .to_frame("n")
    .reset_index()
)

# 数据透视
rescues_borough = rescues_borough.pivot(
    index=["cal_year", "borough_code"], 
    columns="animal_group_parent", 
    values="n"
).reset_index()

# 匹配borough_names
rescues_borough = pd.merge(rescues_borough, borough_names, how="left", on="borough_code")
rescues_borough = rescues_borough.dropna(subset=["name"])

# 将行政区的row/col各减1，因为python是按照0开始索引的
rescues_borough["row"] -= 1
rescues_borough["col"] -= 1

# 创建行政区的位置信息
df_idxs = rescues_borough[["row", "col", "name"]].drop_duplicates()
NAMES = df_idxs["name"].values
ROWS = df_idxs["row"].values.astype(int)
COLS = df_idxs["col"].values.astype(int)

# 自定义基础变量
BLUE = "#3D85F7"
BLUE_LIGHT = "#5490FF"
PINK = "#C32E5A"
PINK_LIGHT = "#D34068"
GREY40 = "#666666"
GREY25 = "#404040"
GREY20 = "#333333"
BACKGROUND = "#F5F4EF"
# 绘制一张子图看看效果

# 初始布局
fig, ax = plt.subplots(figsize=(8, 5))

# 选择一个行政区 
df = rescues_borough[rescues_borough["name"] == "Enfield"]

# YEAR 为x轴
YEAR = df["cal_year"].values
# 猫的救援数量、非猫的救援数量作为y轴
CAT = df["Cat"].values
NOT_CAT = df["Not_Cat"].values

# 绘制折线图
ax.plot(YEAR, CAT, color=BLUE)
ax.plot(YEAR, NOT_CAT, color=PINK)

# 区域填充

# 当CAT > NOT_CAT时填充BLUE_LIGHT
ax.fill_between(
    YEAR, CAT, NOT_CAT, where=(CAT > NOT_CAT), 
    interpolate=True, color=BLUE_LIGHT, alpha=0.3
)

# 当CAT <= NOT_CAT时填充PINK_LIGHT
ax.fill_between(
    YEAR, CAT, NOT_CAT, where=(CAT <= NOT_CAT),
    interpolate=True, color=PINK_LIGHT, alpha=0.3
);

# 将上述的子图过程封装成函数
def single_plot(x, y1, y2, name, ax):
    '''
    x: year数组
    y1: 猫的救援数量数组
    y2: 非猫的救援数量数组
    name: 行政区名称
    ax: 子图所在位置
    '''
    ax.plot(x, y1, color=BLUE)
    ax.plot(x, y2, color=PINK)

    ax.fill_between(
        x, y1, y2, where=(y1 > y2), 
        interpolate=True, color=BLUE_LIGHT, alpha=0.3
    )

    ax.fill_between(
        x, y1, y2, where=(y1 <= y2),
        interpolate=True, color=PINK_LIGHT, alpha=0.3
    );

    ax.set_facecolor(BACKGROUND)
    fig.set_facecolor(BACKGROUND)


    xticks = [2010, 2015, 2020]
    ax.set_xticks(xticks)
    ax.set_xticks([2012.5, 2017.5], minor=True)
    # added a 'size' argument
    ax.set_xticklabels(xticks, color=GREY40, size=10)

    yticks = [0, 10, 20]
    ax.set_yticks(yticks)
    ax.set_yticks([5, 15, 25], minor=True)
    ax.set_yticklabels(yticks, color=GREY40, size=10)
    ax.set_ylim((-1, 26))

    ax.grid(which="minor", lw=0.4, alpha=0.4)
    ax.grid(which="major", lw=0.8, alpha=0.4)
    
    ax.yaxis.set_tick_params(which="both", length=0)
    ax.xaxis.set_tick_params(which="both", length=0)
    
    ax.spines["left"].set_color("none")
    ax.spines["bottom"].set_color("none")
    ax.spines["right"].set_color("none")
    ax.spines["top"].set_color("none")
    ax.set_title(name, weight="bold", size=9, color=GREY20)

# 自定义变量
NROW = len(rescues_borough["row"].unique())
NCOL = len(rescues_borough["col"].unique())

# 绘制多子图
fig, axes = plt.subplots(NROW, NCOL, figsize=(12, 10), sharex=True, sharey=True)

for i, name in enumerate(NAMES):
    # 选择指定name
    df = rescues_borough[rescues_borough["name"] == name]
    
    # 获取坐标轴
    ax = axes[ROWS[i], COLS[i]]
    
    # 获取对应的x、y1、y2
    YEAR = df["cal_year"].values
    CAT = df["Cat"].values
    NOT_CAT = df["Not_Cat"].values
    
    # 调用函数绘制
    single_plot(YEAR, CAT, NOT_CAT, name, ax)

# 移除空面板的子图
for i in range(7):
    for j in range(8):
        # 子图包含线条（即含有数据）
        if axes[i, j].lines:
            continue
        # 否则删除该子图
        else:
            axes[i, j].remove()
fig

# 自定义刻度标签

# 每一行的第一个非空子图显示y轴标签
for i in range(7):
    first_in_row = True
    for j in range(8):
        if first_in_row and axes[i, j].lines:
            axes[i, j].yaxis.set_tick_params(labelleft=True)
            first_in_row = False


# 每一列的最后一个非空子图显示x轴标签
for j in range(8):
    first_in_col = True
    for i in reversed(range(7)): # note the 'reversed()'
        if first_in_col and axes[i, j].lines:
            axes[i, j].xaxis.set_tick_params(labelbottom=True)
            first_in_col = False

fig

# 创建图例样式
handles = [
    Line2D([], [], c=color, lw=1.2, label=label)
    for label, color in zip(["cats", "other"], [BLUE, PINK])
]

# 将图例添加至fig中
fig.legend(
    handles=handles,
    loc=(0.75, 0.94),
    ncol=2,           # 图例为两列，即1行*2列展示
    columnspacing=1,  # 图例的列间距
    handlelength=1.2, # 险段长度
    frameon=False     # 无框
)


# 创建填充式图例样式
cats = patches.Patch(facecolor=BLUE_LIGHT, alpha=0.3, label="more cats")
other = patches.Patch(facecolor=PINK_LIGHT, alpha=0.3, label="more other")

fig.legend(
    handles=[cats, other],
    loc=(0.75, 0.9),
    ncol=2,         
    columnspacing=1, 
    handlelength=2,
    handleheight=2, 
    frameon=False,   
)

# 标题
fig.text(
    x=0.05, y=0.975, s="Rescues of\ncats vs other animals by\nthe London fire brigade\n2009-2020",
    color=GREY25, fontsize=26, fontfamily="KyivType Sans", fontweight="bold",
    ha="left", 
    va="top", 
    ma="left"  # 多行左对齐
)

# 著作信息
fig.text(
    x=0.95, y=0.025, s="Source: London.gov · Graphic: Georgios Karamanis", 
    fontsize=11, 
    ha="right",  
    va="baseline" 
)

# 自定义子图空间边距
fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.3, wspace=0.08)
fig