matplotlib中subplotpython棒棒糖代码_25个常⽤Matplotlib图的Python代码，收藏
了！
作者：zsx_yiyiyi
编辑：python⼤本营
本⽂参考⾃：
⼤家好，今天分享给⼤家25个Matplotlib图的汇总，在数据分析和可视化中⾮常有⽤，⽂章较长，可以收藏下来慢慢练⼿。
# !pip install brewer2mpl
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import warnings; warnings.filterwarnings(action='once')
large = 22; med = 16; small = 12
params = {'axes.titlesize': large,
'legend.fontsize': med,
'figure.figsize': (16, 10),
'axes.labelsize': med,
'axes.titlesize': med,
'xtick.labelsize': med,
'ytick.labelsize': med,
'figure.titlesize': large}
plt.style.use('seaborn-whitegrid')
sns.set_style("white")
%matplotlib inline
# Version
print(mpl.__version__) #> 3.0.0
print(sns.__version__) #> 0.9.0
1、散点图
Scatteplot是⽤于研究两个变量之间关系的经典和基本图。如果数据中有多个组，则可能需要以不同颜⾊可视化每个组。在Matplotlib，你可以⽅便地使⽤。
# Import dataset
midwest = pd.read_csv("raw.githubusercontent/selva86/datasets/master/midwest_filter.csv")
# Prepare Data
# Create as many colors as there are unique midwest['category']
categories = np.unique(midwest['category'])
colors = [ab10(i/float(len(categories)-1)) for i in range(len(categories))]
# Draw Plot for Each Category
plt.figure(figsize=(16, 10), dpi= 80, facecolor='w', edgecolor='k')
for i, category in enumerate(categories):
plt.scatter('area', 'poptotal',
data=midwest.loc[midwest.category==category, :],
s=20, c=colors[i], label=str(category))
# Decorations
xlabel='Area', ylabel='Population')
plt.title("Scatterplot of Midwest Area vs Population", fontsize=22)
plt.legend(fontsize=12)
plt.show()
2、带边界的⽓泡图
有时，你希望在边界内显⽰⼀组点以强调其重要性。在此⽰例中，你将从应该被环绕的数据帧中获取记录，并将其传递给下⾯的代码中描述的记录。encircle()
from matplotlib import patches
from scipy.spatial import ConvexHull
import warnings; warnings.simplefilter('ignore')
sns.set_style("white")
# Step 1: Prepare Data
midwest = pd.read_csv("raw.githubusercontent/selva86/datasets/master/midwest_filter.csv")
# As many colors as there are unique midwest['category']
categories = np.unique(midwest['category'])
colors = [ab10(i/float(len(categories)-1)) for i in range(len(categories))]
# Step 2: Draw Scatterplot with unique color for each category
fig = plt.figure(figsize=(16, 10), dpi= 80, facecolor='w', edgecolor='k')
for i, category in enumerate(categories):
plt.scatter('area', 'poptotal', data=midwest.loc[midwest.category==category, :], s='dot_size', c=colors[i], label=str(category), edgecolors='black', linewidths=.5)
# Step 3: Encircling
def encircle(x,y, ax=None, **kw):
if not ax: a()
p = np.c_[x,y]
hull = ConvexHull(p)
poly = plt.Polygon(p[hull.vertices,:], **kw)
ax.add_patch(poly)
# Select data to be encircled
midwest_encircle_data = midwest.loc[midwest.state=='IN', :]
# Draw polygon surrounding vertices
encircle(midwest_encircle_data.area, midwest_encircle_data.poptotal, ec="k", fc="gold", alpha=0.1)
encircle(midwest_encircle_data.area, midwest_encircle_data.poptotal, ec="firebrick", fc="none", linewidth=1.5)
# Step 4: Decorations
xlabel='Area', ylabel='Population')
plt.title("Bubble Plot with Encircling", fontsize=22)
plt.legend(fontsize=12)
plt.show()
3、带线性回归最佳拟合线的散点图
如果你想了解两个变量如何相互改变，那么最合适的线就是要⾛的路。下图显⽰了数据中各组之间最佳拟合线的差异。要禁⽤分组并仅为整个数据集绘制⼀条最佳拟合线，请从下⾯的调⽤中删除该参数。
# Import Data
df = pd.read_csv("raw.githubusercontent/selva86/datasets/master/mpg_ggplot2.csv")
df_select = df.l.isin([4,8]), :]
# Plot
sns.set_style("white")
gridobj = sns.lmplot(x="displ", y="hwy", hue="cyl", data=df_select,
height=7, aspect=1.6, robust=True, palette='tab10',
scatter_kws=dict(s=60, linewidths=.7, edgecolors='black'))
# Decorations
gridobj.set(xlim=(0.5, 7.5), ylim=(0, 50))
plt.title("Scatterplot with line of best fit grouped by number of cylinders", fontsize=20)
每个回归线都在⾃⼰的列中
或者，你可以在其⾃⼰的列中显⽰每个组的最佳拟合线。你可以通过在⾥⾯设置参数来实现这⼀点。
# Import Data
df = pd.read_csv("raw.githubusercontent/selva86/datasets/master/mpg_ggplot2.csv")
df_select = df.l.isin([4,8]), :]
# Each line in its own column
sns.set_style("white")
gridobj = sns.lmplot(x="displ", y="hwy",
data=df_select,
height=7,
robust=True,
palette='Set1',
col="cyl",
scatter_kws=dict(s=60, linewidths=.7, edgecolors='black'))
# Decorations
gridobj.set(xlim=(0.5, 7.5), ylim=(0, 50))
plt.show()
4、抖动图
通常，多个数据点具有完全相同的X和Y值。结果，多个点相互绘制并隐藏。为避免这种情况，请稍微抖动点，以便你可以直观地看到它们。这很⽅便使⽤
# Import Data
df = pd.read_csv("raw.githubusercontent/selva86/datasets/master/mpg_ggplot2.csv")
# Draw Stripplot
fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
sns., df.hwy, jitter=0.25, size=8, ax=ax, linewidth=.5)
# Decorations
plt.title('Use jittered plots to avoid overlapping of points', fontsize=22)
plt.show()
5、计数图
避免点重叠问题的另⼀个选择是增加点的⼤⼩，这取决于该点中有多少点。因此，点的⼤⼩越⼤，周围的点的集中度就越⼤。
# Import Data
df = pd.read_csv("raw.githubusercontent/selva86/datasets/master/mpg_ggplot2.csv")
df_counts = df.groupby(['hwy', 'cty']).size().reset_index(name='counts')
# Draw Stripplot
fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
sns.stripplot(, df_counts.hwy, size=unts*2, ax=ax)
# Decorations
plt.title('Counts Plot - Size of circle is bigger as more points overlap', fontsize=22)
plt.show()
6、边缘直⽅图
边缘直⽅图具有沿X和Y轴变量的直⽅图。这⽤于可视化X和Y之间的关系以及单独的X和Y的单变量分布。该图如果经常⽤于探索性数据分析(EDA)。
# Import Data
df = pd.read_csv("raw.githubusercontent/selva86/datasets/master/mpg_ggplot2.csv")
# Create Fig and gridspec
fig = plt.figure(figsize=(16, 10), dpi= 80)
grid = plt.GridSpec(4, 4, hspace=.5, wspace=.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', *4, c=df.manufacturer.astype('category').des, alpha=.9, data=df, cmap="tab10", edgecolors='gray', linewidths=.5)
# histogram on the right
ax_bottom.hist(df.displ, 40, histtype='stepfilled', orientation='vertical', color='deeppink')
ax_bottom.invert_yaxis()
# histogram in the bottom
ax_right.hist(df.hwy, 40, histtype='stepfilled', orientation='horizontal', color='deeppink')
# Decorations
ax_main.set(title='Scatterplot with Histograms
displ vs hwy', xlabel='displ', ylabel='hwy')
ax_main.title.set_fontsize(20)
for item in ([ax_main.xaxis.label, ax_main.yaxis.label] + _xticklabels() + _yticklabels()):
item.set_fontsize(14)
xlabels = _xticks().tolist()
ax_main.set_xticklabels(xlabels)
plt.show()
7、边缘箱形图
版权声明：本站内容均来自互联网，仅供演示用，请勿用于商业和其他非法用途。如果侵犯了您的权益请与我们联系QQ:729038198，我们将在24小时内删除。
发表评论