目录
一、绘制带趋势线的散点图
实现功能：
在散点图上添加趋势线（线性拟合线）反映两个变量是正相关、负相关或者无相关关系。
实现代码：
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings(action='once')
plt.style.use('seaborn-whitegrid')
sns.set_style("whitegrid")
print(mpl.__version__)
print(sns.__version__)
def draw_scatter(file):
# Import Data
df = pd.read_csv(file)
df_select = df.loc[df.cyl.isin([4, 8]), :]
# Plot
gridobj = sns.lmplot(
x="displ",
y="hwy",
hue="cyl",
data=df_select,
height=7,
aspect=1.6,
palette='Set1',
scatter_kws=dict(s=60, linewidths=.7, edgecolors='black'))
# Decorations
sns.set(style="whitegrid", font_scale=1.5)
gridobj.set(xlim=(0.5, 7.5), ylim=(10, 50))
gridobj.fig.set_size_inches(10, 6)
plt.tight_layout()
plt.title("Scatterplot with line of best fit grouped by number of cylinders")
plt.show()
draw_scatter("F:\数据杂坛\datasets\mpg_ggplot2.csv")
实现效果：
在散点图上添加趋势线（线性拟合线）反映两个变量是正相关、负相关或者无相关关系。红蓝两组数据分别绘制出最佳的线性拟合线。
二、绘制边缘直方图
实现功能：
python绘制边缘直方图，用于展示X和Y之间的关系、及X和Y的单变量分布情况，常用于数据探索分析。
实现代码：
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings(action='once')
plt.style.use('seaborn-whitegrid')
sns.set_style("whitegrid")
print(mpl.__version__)
print(sns.__version__)
def draw_Marginal_Histogram(file):
# Import Data
df = pd.read_csv(file)
# Create Fig and gridspec
fig = plt.figure(figsize=(10, 6), dpi=100)
grid = plt.GridSpec(4, 4, hspace=0.5, wspace=0.2)
# Define the axes
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, 0:-1], xticklabels=[], yticklabels=[])
# Scatterplot on main ax
ax_main.scatter('displ',
'hwy',
s=df.cty * 4,
c=df.manufacturer.astype('category').cat.codes,
alpha=.9,
data=df,
cmap="Set1",
edgecolors='gray',
linewidths=.5)
# histogram on the right
ax_bottom.hist(df.displ,
40,
histtype='stepfilled',
orientation='vertical',
color='#098154')
ax_bottom.invert_yaxis()
# histogram in the bottom
ax_right.hist(df.hwy,
40,
histtype='stepfilled',
orientation='horizontal',
color='#098154')
# Decorations
ax_main.set(title='Scatterplot with Histograms \n displ vs hwy',
xlabel='displ',
ylabel='hwy')
ax_main.title.set_fontsize(10)
for item in ([ax_main.xaxis.label, ax_main.yaxis.label] +
ax_main.get_xticklabels() + ax_main.get_yticklabels()):
item.set_fontsize(10)
xlabels = ax_main.get_xticks().tolist()
ax_main.set_xticklabels(xlabels)
plt.show()
draw_Marginal_Histogram("F:\数据杂坛\datasets\mpg_ggplot2.csv")
实现效果：
