def test4():
data = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")
# 画 cty 城市里程/加仑 和 hwy 公路里程/加仑 的 点图
DrawTools.init()
DrawTools.scatter(data=data, feature_x='cty', feature_y='hwy')
DrawTools.font_desc(title='关系图', xlabel='城市里程/加仑', ylabel='公路里程/加仑', legends=None)
plt.show()
def test6():
data = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")
DrawTools.init()
fig, ax = plt.subplots(figsize=(12, 8), dpi=80)
DrawTools.stripplot(data, 'cty', 'hwy', ax)
DrawTools.font_desc(ax, 'Use jittered plots to avoid overlapping of points', "气缸数量", "公路里程/加仑")
plt.show()
def test9():
df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")
name = ["英里/加仑", "气缸数量", "排量", "总马力", "驱动轴比", "重量"
, "1/4英里所用时间", "引擎", "变速器", "前进档数", "化油器数量", "用油是否高效"
, "汽车", "汽车名称"]
df.columns = name
DrawTools.init()
DrawTools.heatmap(df)
DrawTools.font_desc(ax=None, title=u'mtcars数据集的相关性矩阵', tick_rotation=45, tick_horizontalalignment='right')
plt.show()
def test2():
df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")
# ['manufacturer', 'model', 'displ', 'year', 'cyl', 'trans', 'drv', 'cty','hwy', 'fl', 'class'],
# name = ["汽车制造商","型号名称","发动机排量(L)","制造年份","气缸数量","手动/自动","驱动类型","城市里程/加仑","公路里程/加仑","汽油种类","车辆种类"]
##驱动类型：4:四轮，f:前轮，r:后轮
# 能源种类：汽油，柴油，用电等等
# 车辆种类：皮卡，SUV，小型，midsize中型等等
# 城市里程/加仑，公路里程/加仑：表示使用没加仑汽油能够跑的英里数，所以这个数值越大代表汽车越节能
# df = df.loc[df.cyl.isin([4, 8]), :]
DrawTools.init()
DrawTools.lmplot(data=df, feature_x='displ', feature_y='hwy', feature_h='cyl', xlim=(1, 7), ylim=(0, 50))
DrawTools.font_desc(title='按气缸数分组的最佳拟合线散点图', xlabel='发动机排量(L)', ylabel='公路里程/加仑', legends=['气缸数量4', '气缸数量8'])
plt.show()
def test7():
# 画 displ 发动机排量,cyl 气缸数量,hwy 公路里程/加仑 cyl是 4和8的
data = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")
DrawTools.init()
fig = plt.figure(figsize=(16, 10), dpi=80)
grid = plt.GridSpec(4, 4, hspace=0.5, wspace=0.2)
ax_main = fig.add_subplot(grid[:-1, :-1])
ax_right = fig.add_subplot(grid[:-1, -1], xticklabels=[], yticklabels=[])
ax_bottom = fig.add_subplot(grid[-1, :-1], xticklabels=[], yticklabels=[])
# displ 发动机排量(L)
# hwy 公路里程/加仑
DrawTools.scatter(data=data, feature_x='displ', feature_y='hwy', feature_c='manufacturer', feature_s='cty',
ax=ax_main)
DrawTools.font_desc(ax_main, title='边缘直方图 \n 发动机排量 vs 公路里程/加仑', xlabel='发动机排量(L)', ylabel='公里路程/加仑')
DrawTools.hist(data, feature_x='hwy', bins=40, vertical=False, color='deeppink', ax=ax_right)
DrawTools.hist(data, feature_x='displ', bins=40, vertical=True, invert_y=True, color='deeppink',
ax=ax_bottom)
xlabels = ax_main.get_xticks().tolist() # 将现有的标尺取出来，转化为带一位小数的浮点数
ax_main.set_xticklabels(xlabels) # 再将带一位小数的浮点数变成标尺
plt.show()
def test8():
data = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")
DrawTools.init()
fig = plt.figure(figsize=(16, 10), dpi=80)
grid = plt.GridSpec(4, 4, hspace=0.5, wspace=0.2)
ax_main = fig.add_subplot(grid[:-1, :-1])
ax_right = fig.add_subplot(grid[:-1, -1], xticklabels=[], yticklabels=[])
ax_bottom = fig.add_subplot(grid[-1, :-1], xticklabels=[], yticklabels=[])
# displ 发动机排量(L)
# hwy 公路里程/加仑
DrawTools.scatter(data=data, feature_x='displ', feature_y='hwy', feature_c='manufacturer', feature_s='cty',
ax=ax_main, xlim=(1, 7), ylim=(0, 50))
DrawTools.font_desc(ax_main, title='边缘直方图 \n 发动机排量 vs 公路里程/加仑', xlabel='发动机排量(L)', ylabel='公里路程/加仑')
# 对右侧和下方绘制箱线图
DrawTools.boxplot(data, 'hwy', vertical=True, color="red", ax=ax_right)
DrawTools.boxplot(data, 'displ', vertical=False, color="red", ax=ax_bottom)
ax_bottom.set(xlabel='')
ax_right.set(ylabel='')
xlabels = ax_main.get_xticks().tolist() # 将现有的标尺取出来，转化为带一位小数的浮点数
ax_main.set_xticklabels(xlabels) # 再将带一位小数的浮点数变成标尺
plt.show()
def test3():
df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")
DrawTools.init()
DrawTools.lmplot_mul(data=df, feature_x='displ', feature_y='hwy', feature_h='cyl', xlim=(1, 7), ylim=(0, 50))
DrawTools.font_desc(title='按气缸数分组的最佳拟合线散点图', xlabel='发动机排量(L)', ylabel='公路里程/加仑', legends=['气缸数量4', '气缸数量8'])
plt.show()