matplotlib绘图

1. matplotlib 绘图

1.1. 案例一(折线图)

import matplotlib
import random
from matplotlib import pyplot as plt

# 设置字体
matplotlib.rc("font",family='MicroSoft YaHei',weight="bold")
# x,y的取值
x = range(120)
y = [random.randint(20, 35) for i in range(120)]
# 绘图
plt.figure(figsize=(20, 8), dpi=80)
plt.plot(x, y)
# 设置x轴的刻度
_xtick_labels = ["10点{}分".format(i) for i in range(60)]
_xtick_labels += ["11点{}分".format(i) for i in range(60)]
plt.xticks(list(x)[::3], _xtick_labels[::3], rotation=45)
# 设置x轴，y轴的名称
plt.xlabel("时间")
plt.ylabel("温度 单位(℃)")
# 设置图名称
plt.title("10点到12点每分钟的气温变化情况")
# 显示图
plt.show()

1.2. 案例二（折线图）

import matplotlib
from matplotlib import pyplot as plt

# 设置字体
matplotlib.rc("font", family='MicroSoft YaHei', weight="bold")

y_1 = [1, 0, 1, 1, 2, 4, 3, 2, 3, 4, 4, 5, 6, 5, 4, 3, 3, 1, 1, 1]
y_2 = [1, 0, 3, 1, 2, 2, 3, 3, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]

x = range(11, 31)

# 设置图形大小
plt.figure(figsize=(20, 8), dpi=80)

plt.plot(x, y_1, label="自己", color="#F08080")
plt.plot(x, y_2, label="同桌", color="#DB7093", linestyle="--")

# 设置x轴刻度
_xtick_labels = ["{}岁".format(i) for i in x]
plt.xticks(x, _xtick_labels)
# 设置y轴的刻度
plt.yticks(range(0, 9))

# 绘制网格
plt.grid(alpha=0.4, linestyle=':')

# 添加图例
plt.legend(loc="upper left")

plt.xlabel("年龄")
plt.ylabel("女朋友个数")
plt.title("11岁到30岁你和同桌交往女朋友的个数趋势图")
# 展示
plt.show()

1.3. 案例三（条形图）

import matplotlib
from matplotlib import pyplot as plt

# 设置字体
matplotlib.rc("font", family='MicroSoft YaHei', weight="bold")

a = ["猩球崛起3：终极之战", "敦刻尔克", "蜘蛛侠：英雄归来", "战狼2"]
b_16 = [15746, 312, 4497, 319]
b_15 = [12357, 156, 2045, 168]
b_14 = [2358, 399, 2358, 362]
# 条形图宽度
bar_width = 0.2
# 条形图的位置
x_14 = list(range(len(a)))
x_15 = [i + bar_width for i in x_14]
x_16 = [i + bar_width * 2 for i in x_14]

# 设置图形大小
plt.figure(figsize=(20, 8), dpi=80)
# 回执条形图
plt.bar(x_14, b_14, width=bar_width, label="9月14日")
plt.bar(x_15, b_15, width=bar_width, label="9月15日")
plt.bar(x_16, b_16, width=bar_width, label="9月16日")

# 设置图例
plt.legend(loc="upper left")

# 设置x轴的刻度
plt.xticks(x_15, a)

plt.xlabel("电影名称")
plt.ylabel("电影票房数")
plt.title("电影票房数对比")

# 保存图片到本地
plt.savefig("my.svg")

plt.show()

1.4. 案例四（直方图）

import matplotlib
from matplotlib import pyplot as plt

# 设置字体
matplotlib.rc("font", family='MicroSoft YaHei', weight="bold")

a = [131, 98, 125, 131, 124, 139, 131, 117, 128, 108, 135, 138, 131, 102, 107, 114, 119, 128, 121, 142, 127, 130, 124,
     101, 110, 116, 117, 110, 128, 128, 115, 99, 136, 126, 134, 95, 138, 117, 111, 78, 132, 124, 113, 150, 110, 117, 86,
     95, 144, 105, 126, 130, 126, 130, 126, 116, 123, 106, 112, 138, 123, 86, 101, 99, 136, 123, 117, 119, 105, 137,
     123, 128, 125, 104, 109, 134, 125, 127, 105, 120, 107, 129, 116, 108, 132, 103, 136, 118, 102, 120, 114, 105, 115,
     132, 145, 119, 121, 112, 139, 125, 138, 109, 132, 134, 156, 106, 117, 127, 144, 139, 139, 119, 140, 83, 110, 102,
     123, 107, 143, 115, 136, 118, 139, 123, 112, 118, 125, 109, 119, 133, 112, 114, 122, 109, 106, 123, 116, 131, 127,
     115, 118, 112, 135, 115, 146, 137, 116, 103, 144, 83, 123, 111, 110, 111, 100, 154, 136, 100, 118, 119, 133, 134,
     106, 129, 126, 110, 111, 109, 141, 120, 117, 106, 149, 122, 122, 110, 118, 127, 121, 114, 125, 126, 114, 140, 103,
     130, 141, 117, 106, 114, 121, 114, 133, 137, 92, 121, 112, 146, 97, 137, 105, 98, 117, 112, 81, 97, 139, 113, 134,
     106, 144, 110, 137, 137, 111, 104, 117, 100, 111, 101, 110, 105, 129, 137, 112, 120, 113, 133, 112, 83, 94, 146,
     133, 101, 131, 116, 111, 84, 137, 115, 122, 106, 144, 109, 123, 116, 111, 111, 133, 150]

# 计算组数
d = 3  # 组距

num_bins = (max(a) - min(a)) // d

print(max(a), min(a), max(a) - min(a))
print(num_bins)

# 设置图形的大小
plt.figure(figsize=(20, 8), dpi=80)
plt.hist(a, num_bins, density=True)

# 设置x轴的刻度
plt.xticks(range(min(a), max(a) + d, d))

plt.title("直方图")
plt.xlabel("组距")
plt.ylabel("所占百分比")

plt.grid()

plt.show()

1.5. 案例五（散点图）

import matplotlib
from matplotlib import pyplot as plt

# 设置字体
matplotlib.rc("font", family='MicroSoft YaHei', weight="bold")

y_3 = [11, 17, 16, 11, 12, 11, 12, 6, 6, 7, 8, 9, 12, 15, 14, 17, 18, 21, 16, 17, 20, 14, 15, 15, 15, 19, 21, 22, 22,
       22, 23]
y_10 = [26, 26, 28, 19, 21, 17, 16, 19, 18, 20, 20, 19, 22, 23, 17, 20, 21, 20, 22, 15, 11, 15, 5, 13, 17, 10, 11, 13,
        12, 13, 6]

x_3 = range(1, 32)
x_10 = range(51, 82)

# 设置图形大小
plt.figure(figsize=(20, 8), dpi=80)

# 使用scatter方法绘制散点图,和之前绘制折线图的唯一区别
plt.scatter(x_3, y_3, label="3月份")
plt.scatter(x_10, y_10, label="10月份")

# 调整x轴的刻度
_x = list(x_3) + list(x_10)
_xtick_labels = ["3月{}日".format(i) for i in x_3]
_xtick_labels += ["10月{}日".format(i - 50) for i in x_10]
plt.xticks(_x[::3], _xtick_labels[::3], rotation=45)

# 添加图例
plt.legend(loc="upper left")

# 添加描述信息
plt.xlabel("时间")
plt.ylabel("温度")
plt.title("标题")
# 展示
plt.show()

1.6. 案例六（条形图）

import matplotlib
from matplotlib import pyplot as plt

# 设置字体
matplotlib.rc("font", family='MicroSoft YaHei', weight="bold")

a = ["战狼2", "速度与激情8", "功夫瑜伽", "西游伏妖篇", "变形金刚5：最后的骑士", "摔跤吧！爸爸", "加勒比海盗5：死无对证", "金刚：骷髅岛", "极限特工：终极回归", "生化危机6：终章",
     "乘风破浪", "神偷奶爸3", "智取威虎山", "大闹天竺", "金刚狼3：殊死一战", "蜘蛛侠：英雄归来", "悟空传", "银河护卫队2", "情圣", "新木乃伊", ]

b = [56.01, 26.94, 17.53, 16.49, 15.45, 12.96, 11.8, 11.61, 11.28, 11.12, 10.49, 10.3, 8.75, 7.55, 7.32, 6.99, 6.88,
     6.86, 6.58, 6.23]

# 设置图形大小
plt.figure(figsize=(20, 15), dpi=80)
# 绘制条形图
plt.bar(range(len(a)), b, width=0.2)
# 设置字符串到x轴
plt.xticks(range(len(a)), a, rotation=45)

plt.savefig("./movie.png")

plt.title("电影票房数")
plt.xlabel("电影名称")
plt.ylabel("好评数")

plt.show()

1.7. 案例七（水平条形图）

import matplotlib
from matplotlib import pyplot as plt

# 设置字体
matplotlib.rc("font", family='MicroSoft YaHei', weight="bold")

a = ["战狼2", "速度与激情8", "功夫瑜伽", "西游伏妖篇", "变形金刚5：最后的骑士", "摔跤吧！爸爸", "加勒比海盗5：死无对证", "金刚：骷髅岛", "极限特工：终极回归", "生化危机6：终章",
     "乘风破浪", "神偷奶爸3", "智取威虎山", "大闹天竺", "金刚狼3：殊死一战", "蜘蛛侠：英雄归来", "悟空传", "银河护卫队2", "情圣", "新木乃伊", ]

b = [56.01, 26.94, 17.53, 16.49, 15.45, 12.96, 11.8, 11.61, 11.28, 11.12, 10.49, 10.3, 8.75, 7.55, 7.32, 6.99, 6.88,
     6.86, 6.58, 6.23]

# 设置图形大小
plt.figure(figsize=(20, 8), dpi=80)
# 绘制条形图
plt.barh(range(len(a)), b, height=0.3, color="orange")
# 设置字符串到x轴
plt.yticks(range(len(a)), a)

plt.grid(alpha=0.3)
# plt.savefig("./movie.png")

plt.title("电影票房数")
plt.xlabel("票房数：（单位：亿）")
plt.ylabel("电影名称")

plt.show()

2. pandas，numpy， matplotlib绘图案例

2.1. 案例一（条形图）

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib

# 设置字体
matplotlib.rc("font", family='MicroSoft YaHei', weight="bold")

file_path = "./IMDB-Movie-Data.csv"
df = pd.read_csv(file_path)
df.info()

df['Genre'].head(3)
# 统计分类列表
temp_list = df['Genre'].str.split(",").tolist()
genre_list = list(set([i for j in temp_list for i in j]))
# 构造全零数组
zero_df = pd.DataFrame(np.zeros((df.shape[0], len(genre_list))), columns=genre_list)
# 给每个电影出现分类的位置赋值为1
for i in range(df.shape[0]):
    zero_df.loc[i, temp_list[i]] = 1
# 统计每个分类的电影的数量和
genre_count = zero_df.sum(axis=0)
# 排序
genre_count = genre_count.sort_values(ascending=False)
print(genre_count)
_x = genre_count.index
_y = genre_count.values
# 画图
plt.figure(figsize=(20,8), dpi=80)
plt.bar(range(len(_x)), _y, width=0.4, color='orange')
plt.xticks(range(len(_x)), _x, rotation=45)

plt.title("不同电影类别对应的电影数量")
plt.xlabel("类别")
plt.ylabel("电影数量")
plt.grid(alpha=0.4, linestyle=':')

plt.show()z`

2.2. 案例二（直方图）

import matplotlib
import pandas as pd
from matplotlib import pyplot as plt

# 设置字体
matplotlib.rc("font", family='MicroSoft YaHei', weight="bold")

file_path = "./IMDB-Movie-Data.csv"
df = pd.read_csv(file_path)

runtime_data = df['Rating'].values
max_runtime = runtime_data.max()
min_runtime = runtime_data.min()

# 计算数组
num_bin = (max_runtime - min_runtime) // 0.5
plt.figure(figsize=(20, 8), dpi=80)
plt.hist(runtime_data, int(num_bin))
print(min_runtime, max_runtime)
_x = [min_runtime]
i = min_runtime
while i <= max_runtime + 0.5:
    i = i + 0.5
    _x.append(i)

plt.xticks(_x)
plt.show()