利用棒棒糖圖探索Office (US)的IMDB評分

import numpy as np
import pandas as pd
import matplotlib.colors as mc
import matplotlib.image as image
import matplotlib.pyplot as pltfrom matplotlib.cm import ScalarMappable
from matplotlib.lines import Line2D
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from palettable import cartocolors # 獲得好看的顏色

數據探索

以下數據如果有需要的同學可關注公眾號HsuHeinrich，回復【數據可視化】自動獲取～

df_office = pd.read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-17/office_ratings.csv")
df_office.head()

season：季度

episode：劇集編號

imdb_rating：IMDB評級

total_votes：每個評級使用的投票數

df_office_avg = df_office.sort_values(["season", "episode"])
# 生成episode_id：自增序列
df_office_avg["episode_id"] = np.arange(len(df_office_avg)) + 1
df_office_avg["episode_mod"] =  df_office_avg["episode_id"] + (9 * df_office_avg["season"])
df_office_avg = df_office_avg.assign(avg = df_office_avg.groupby("season")["imdb_rating"].transform("mean"),mid = df_office_avg.groupby("season")["episode_mod"].transform("mean")
)df_office_avg.head()

創建df_lines數據集：以便于繪制表示每個季度評分平均值的水平線，這些線將在每個季度的第一個和最后一個劇集之間延伸，并稍微超過這些點

df_lines = df_office_avg.groupby("season").agg(start_x = ("episode_mod", min),end_x = ("episode_mod", max),y = ("avg", max) # 每個season的avg一樣，用max獲取唯一值
).reset_index()# 在每個方向上稍微延長水平線
df_lines["start_x"] -= 5
df_lines["end_x"] += 5df_lines = pd.melt(df_lines, id_vars=["season", "y"], value_vars=["start_x", "end_x"], var_name="type", value_name="x"
)
df_lines["x_group"] = np.where(df_lines["type"] == "start_x", df_lines["x"] + 0.1, df_lines["x"] - 0.1)
df_lines["x_group"] = np.where((df_lines["type"] == "start_x").values & (df_lines["x"] == np.min(df_lines["x"])).values, df_lines["x_group"] - 0.1, df_lines["x_group"]
)
df_lines["x_group"] = np.where((df_lines["type"] == "end_x").values & (df_lines["x"] == np.max(df_lines["x"])).values, df_lines["x_group"] + 0.1, df_lines["x_group"]
)
df_lines = df_lines.sort_values(["season", "x_group"])
df_lines.head()

繪制棒棒糖圖

圖片logo為the-office，可在網上自行下載，也可以關注公眾號HsuHeinrich，回復【可視化素材】自動獲取～

def adjust_lightness(color, amount=0.5):'''調整顏色亮度：低于 1 的值使其變暗，高于 1 的值使其變亮'''import matplotlib.colors as mcimport colorsystry:c = mc.cnames[color]except:c = colorc = colorsys.rgb_to_hls(*mc.to_rgb(c))return colorsys.hls_to_rgb(c[0], max(0, min(1, amount * c[1])), c[2])

# 定義一些變量# 顏色
GREY82 = "#d1d1d1"
GREY70 = "#B3B3B3"
GREY40 = "#666666"
GREY30 = "#4d4d4d"
BG_WHITE = "#fafaf5"# 季節顏色調整
COLORS = ["#486090", "#D7BFA6", "#6078A8", "#9CCCCC", "#7890A8","#C7B0C1", "#B5C9C9", "#90A8C0", "#A8A890"]
COLORS_DARK = [adjust_lightness(color, 0.8) for color in COLORS]
COLORS_LIGHT = [adjust_lightness(color, 1.2) for color in COLORS]# 三種顏色調整函數
cmap_regular = mc.LinearSegmentedColormap.from_list("regular", COLORS)
cmap_dark = mc.LinearSegmentedColormap.from_list("dark", COLORS_DARK)
cmap_light = mc.LinearSegmentedColormap.from_list("light", COLORS_LIGHT)# 對季節進行縮放
normalize = mc.Normalize(vmin=1, vmax=9)# 圖片存儲
IMAGE = image.imread("pic/the-office.jpg")# 水平線
HLINES = [6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10]

VOTES_MAX = df_office_avg["total_votes"].max()
VOTES_MIN = df_office_avg["total_votes"].min()# 縮放票數，用于棒棒糖的大小
def scale_to_interval(x, low=15, high=150):return ((x - VOTES_MIN) / (VOTES_MAX - VOTES_MIN)) * (high - low) + low

# 初始化布局
fig, ax = plt.subplots(figsize = (15, 10))# 背景顏色
fig.patch.set_facecolor(BG_WHITE)
ax.set_facecolor(BG_WHITE)# 繪制水平參考線，zorder=0位于圖層最下方
for h in HLINES:plt.axhline(h, color=GREY82, zorder=0)# 垂直線段：代表劇集評分與平均評分的偏差
plt.vlines(x="episode_mod", ymin="imdb_rating", ymax="avg",color=cmap_light(normalize(df_office_avg["season"])),data = df_office_avg
)# 水平線：連接平均值的灰線
plt.plot("x", "y", "-", color=GREY40, data=df_lines)# 水平線：每個賽季的平均分 
for season in df_lines["season"].unique():d = df_lines[df_lines["season"] == season]plt.plot("x_group", "y", "", color=cmap_dark(normalize(season)), lw=5, data=d, solid_capstyle="butt")# 點：每集的評分，大小由投票數決定
plt.scatter("episode_mod", "imdb_rating",s = scale_to_interval(df_office_avg["total_votes"]),color=cmap_regular(normalize(df_office_avg["season"])), data=df_office_avg,zorder=3
)# Season標簽
midpoints = df_office_avg["mid"].unique()
for season, mid in enumerate(midpoints):color = cmap_dark(normalize(season + 1))plt.text(mid, 10.12, f" Season {season + 1} ", color=color,weight="bold",ha="center",va="center",fontname="Special Elite",fontsize=11,bbox=dict(facecolor="none", edgecolor=color, linewidth=1,boxstyle="round", pad=0.2))# ------------------------自定義布局------------------------# 隱藏邊框
ax.spines["right"].set_color("none")
ax.spines["top"].set_color("none")
ax.spines["bottom"].set_color("none")
ax.spines["left"].set_color("none")# 自定義y軸
plt.tick_params(axis="y", labelright=True, length=0)
plt.yticks(HLINES, fontname="Roboto", fontsize=11, color=GREY30)
plt.ylim(0.98 * 6.5, 10.2 * 1.02)# 移除x刻度
plt.xticks([], "")# 設置y標簽
plt.ylabel("IMDb Rating", fontname="Roboto", fontsize=14)# 添加著作信息
plt.text(0.5, -0.03, "Visualization by Cédric Scherer  ?  Data by IMDb via data.world  ?  Fanart Logo by ArieS", fontname="Special Elite", fontsize=11, color=GREY70,ha="center", va="center", transform=ax.transAxes # so coordinates are in terms of the axis.
)# 放置圖片logo
ab = AnnotationBbox(OffsetImage(IMAGE, zoom=0.2), (1, 6.75), xycoords="data", box_alignment=(0, 0.5),pad=0, frameon=False
)
ax.add_artist(ab)# ------------------------自定義圖例------------------------# 水平圖例點及其標簽的位置
x_pos = [0.44, 0.48, 0.52, 0.56]
votes = [2000, 4000, 6000, 8000]# 繪制圖例點
plt.scatter(x_pos, [0.065] * 4, s=scale_to_interval(np.array(votes)), color="black",transform=ax.transAxes
)# 添加圖例標簽名
plt.text(0.5, 0.0875, "Votes per Episode", fontname="Roboto", fontsize=10, ha="center", transform=ax.transAxes)# 將圖例放在圖例標記下方
for (xpos, vote) in zip(x_pos, votes):plt.text(xpos, 0.035,  f"{vote}", fontname="Roboto", fontsize=9, ha="center", transform=ax.transAxes)plt.show()

參考：The Office Ratings with Python and Matplotlib

共勉～