"pythonic生物人"的第155篇分享
往期精彩戳：NGS精进 |统计精进| py基础 |py绘图 | perl基础 | R绘图
❝本文分享最常用的「11个分布（Distribution）关系图」。
续前篇：
python可视化45|最常用10个关联（Correlation）关系图
❞
目录
四、分布（Distribution）关系图
21、连续变量堆积直方图（Stacked Histogram for Continuous Variable）
该图展示给定连续变量的频率分布。
-
# Import Data
-
df = pd.read_csv(
"./datasets/mpg_ggplot2.csv")
-
-
# Prepare data
-
x_var =
'displ'
-
groupby_var =
'class'
-
df_agg = df.loc[:, [x_var, groupby_var]].groupby(groupby_var)
-
vals = [df[x_var].values.tolist()
for i, df in df_agg]
-
-
# Draw
-
plt.figure(figsize=(
10,
6), dpi=
80)
-
colors = [plt.cm.Set1(i / float(
len(vals) -
1))
for i in
range(
len(vals))]
-
n, bins, patches = plt.hist(vals,
-
30,
-
stacked=True,
-
density=False,
-
color=colors[:
len(vals)])
-
-
# Decoration
-
plt.legend({
-
group: col
-
for group, col in zip(
-
np.unique(df[groupby_var]).tolist(), colors[:
len(vals)])
-
})
-
plt.title(f
"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$",
-
fontsize=
22)
-
plt.xlabel(x_var)
-
plt.ylabel(
"Frequency")
-
#plt.ylim(
0,
25)
-
plt.xticks(ticks=bins[::
3], labels=[round(b,
1)
for b in bins[::
3]])
-
plt.show()
22、类别变量堆积直方图（Stacked Histogram for Categorical Variable）
该图展示给定类别变量的频率分布。
-
# Import Data
-
df = pd.read_csv(
"./datasets/mpg_ggplot2.csv")
-
-
# Prepare data
-
x_var =
'manufacturer'
-
groupby_var =
'class'
-
df_agg = df.loc[:, [x_var, groupby_var]].groupby(groupby_var)
-
vals = [df[x_var].values.tolist()
for i, df in df_agg]
-
-
# Draw
-
plt.figure(figsize=(
10,
6), dpi=
80)
-
colors = [plt.cm.Set1(i / float(
len(vals) -
1))
for i in
range(
len(vals))]
-
n, bins, patches = plt.hist(vals,
-
df[x_var].unique().__len__(),
-
stacked=True,
-
density=False,
-
color=colors[:
len(vals)])
-
-
# Decoration
-
plt.legend({
-
group: col
-
for group, col in zip(
-
np.unique(df[groupby_var]).tolist(), colors[:
len(vals)])
-
})
-
plt.title(f
"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$",
-
fontsize=
22)
-
plt.xlabel(x_var)
-
plt.ylabel(
"Frequency")
-
plt.ylim(
0,
40)
-
plt.xticks(ticks=bins,
-
labels=np.unique(df[x_var]).tolist(),
-
rotation=
90,
-
horizontalalignment=
'left')
-
plt.show()
了解更多直方图：
23、密度图(Density Plot)
该图展示连续变量的分布情况。
-
# Import Data
-
df = pd.read_csv(
"./datasets/mpg_ggplot2.csv")
-
-
# Draw Plot
-
plt.figure(figsize=(
10,
8), dpi=
80)
-
sns.kdeplot(df.loc[df[
'cyl'] ==
4,
"cty"],
-
shade=True,
-
color=
"#01a2d9",
-
label=
"Cyl=4",
-
alpha=
.7)
-
sns.kdeplot(df.loc[df[
'cyl'] ==
5,
"cty"],
-
shade=True,
-
color=
"#dc2624",
-
label=
"Cyl=5",
-
alpha=
.7)
-
sns.kdeplot(df.loc[df[
'cyl'] ==
6,
"cty"],
-
shade=True,
-
color=
"#C89F91",
-
label=
"Cyl=6",
-
alpha=
.7)
-
sns.kdeplot(df.loc[df[
'cyl'] ==
8,
"cty"],
-
shade=True,
-
color=
"#649E7D",
-
label=
"Cyl=8",
-
alpha=
.7)
-
-
# Decoration
-
sns.set(style=
"whitegrid", font_scale=
1.1)
-
plt.title(
'Density Plot of City Mileage by n_Cylinders', fontsize=
18)
-
plt.legend()
-
plt.show()
24、带直方图的密度图(Density Curves with Histogram)
-
# Import Data
-
df = pd.read_csv(
"./datasets/mpg_ggplot2.csv")
-
-
# Draw Plot
-
plt.figure(figsize=(
10,
8), dpi=
80)
-
sns.distplot(df.loc[df[
'class'] ==
'compact',
"cty"],
-
color=
"#01a2d9",
-
label=
"Compact",
-
hist_kws={
'alpha':
.7},
-
kde_kws={
'linewidth':
3})
-
sns.distplot(df.loc[df[
'class'] ==
'suv',
"cty"],
-
color=
"#dc2624",
-
label=
"SUV",
-
hist_kws={
'alpha':
.7},
-
kde_kws={
'linewidth':
3})
-
sns.distplot(df.loc[df[
'class'] ==
'minivan',
"cty"],
-
color=
"g",
-
label=
"#C89F91",
-
hist_kws={
'alpha':
.7},
-
kde_kws={
'linewidth':
3})
-
plt.ylim(
0,
0.35)
-
-
# Decoration
-
sns.set(style=
"whitegrid", font_scale=
1.1)
-
plt.title(
'Density Plot of City Mileage by Vehicle Type', fontsize=
18)
-
plt.legend()
-
plt.show()
更多核密度图：
25、山峰叠峦图(Joy Plot)
该图展示大量分组之间的关系，比heatmap形象。
-
!pip install joypy#安装依赖包
-
#每组数据绘制核密度图，R中有ggjoy
-
import joypy
-
# Import Data
-
mpg = pd.read_csv(
"./datasets/mpg_ggplot2.csv")
-
-
# Draw Plot
-
plt.figure(figsize=(
10,
6), dpi=
80)
-
fig, axes = joypy.joyplot(mpg,
-
column=[
'hwy',
'cty'],
-
by=
"class",
-
ylim=
'own',
-
colormap=plt.cm.Set1,
-
figsize=(
10,
6))
-
-
# Decoration
-
plt.title(
'Joy Plot of City and Highway Mileage by Class', fontsize=
18)
-
plt.show()
26、分布点图(Distributed Dot Plot)
分布点图显示了按组划分的点的单变量分布。点色越浅，该区域中数据点的集中度越高。通过对中位数进行不同的着色，各组的实际位置会立即变得明显。
-
import matplotlib.patches as mpatches
-
-
# Prepare Data
-
df_raw = pd.read_csv(
"./datasets/mpg_ggplot2.csv")
-
cyl_colors = {
4:
'tab:red',
5:
'tab:green',
6:
'tab:blue',
8:
'tab:orange'}
-
df_raw[
'cyl_color'] = df_raw.cyl.
map(cyl_colors)
-
-
# Mean and Median city mileage by
make
-
df = df_raw[[
'cty',
-
'manufacturer']].groupby(
'manufacturer').apply(lambda x: x.mean())
-
df.sort_values(
'cty', ascending=False, inplace=True)
-
df.reset_index(inplace=True)
-
df_median = df_raw[[
'cty',
'manufacturer'
-
]].groupby(
'manufacturer').apply(lambda x: x.median())
-
-
# Draw horizontal lines
-
fig, ax = plt.subplots(figsize=(
11,
7), dpi=
80)
-
ax.hlines(y=df.index,
-
xmin=
0,
-
xmax=
40,
-
color=
'#01a2d9',
-
alpha=
0.5,
-
linewidth=
.5,
-
linestyles=
'dashdot')
-
-
# Draw the Dots
-
for i,
make in enumerate(df.manufacturer):
-
df_make = df_raw.loc[df_raw.manufacturer ==
make, :]
-
ax.scatter(y=np.repeat(i, df_make.shape[
0]),
-
x=
'cty',
-
data=df_make,
-
s=
75,
-
edgecolors=
'#01a2d9',
-
c=
'w',
-
alpha=
0.5)
-
ax.scatter(y=i,
-
x=
'cty',
-
data=df_median.loc[df_median.index ==
make, :],
-
s=
75,
-
c=
'#dc2624')
-
-
# Annotate
-
ax.text(
33,
-
13,
-
"$red \; dots \; are \; the \: median$",
-
fontdict={
'size':
12},
-
color=
'#dc2624')
-
-
# Decorations
-
red_patch = plt.plot([], [],
-
marker=
"o",
-
ms=
10,
-
ls=
"",
-
mec=None,
-
color=
'#dc2624',
-
label=
"Median")
-
plt.legend(handles=red_patch)
-
ax.set_title(
'Distribution of City Mileage by Make', fontdict={
'size':
18})
-
ax.set_xlabel(
'Miles Per Gallon (City)')
-
ax.set_yticks(df.index)
-
ax.set_yticklabels(df.manufacturer.str.title(),
-
fontdict={
'horizontalalignment':
'right'})
-
ax.set_xlim(
1,
40)
-
plt.gca().spines[
"top"].set_visible(False)
-
plt.gca().spines[
"bottom"].set_visible(False)
-
plt.gca().spines[
"right"].set_visible(False)
-
plt.gca().spines[
"left"].set_visible(False)
-
plt.grid(axis=
'both', alpha=
.4, linewidth=
.1)
-
plt.show()
27、箱图（boxplot）
很好的展示数据的分布情况~
-
# Import Data
-
df = pd.read_csv(
"./datasets/mpg_ggplot2.csv")
-
-
# Draw Plot
-
plt.figure(figsize=(
10,
6), dpi=
80)
-
sns.boxplot(
-
x=
'class',
-
y=
'hwy',
-
data=df,
-
notch=False,
-
palette=
"Set1",
-
)
-
-
-
# Add N Obs inside boxplot (optional)
-
def add_n_obs(df, group_col, y):
-
medians_dict = {
-
grp[
0]: grp[
1][y].median()
-
for grp in df.groupby(group_col)
-
}
-
xticklabels = [x.get_text()
for x in plt.gca().get_xticklabels()]
-
n_obs = df.groupby(group_col)[y].size().values
-
for (x, xticklabel), n_ob in zip(enumerate(xticklabels), n_obs):
-
plt.text(x,
-
medians_dict[xticklabel] *
1.01,
-
"#obs : " + str(n_ob),
-
horizontalalignment=
'center',
-
fontdict={
'size':
12},
-
color=
'black')
-
-
-
add_n_obs(df, group_col=
'class', y=
'hwy')
-
-
# Decoration
-
sns.set(style=
"whitegrid", font_scale=
1.1)
-
plt.title(
'Box Plot of Highway Mileage by Vehicle Class', fontsize=
16)
-
plt.ylim(
10,
40)
-
plt.show()
28、箱图结合点图（Dot + Box Plot）
该图展示箱图及箱图绘制所用的详细点。
-
# Import Data
-
df = pd.read_csv(
"./datasets/mpg_ggplot2.csv")
-
-
# Draw Plot
-
plt.figure(figsize=(
13,
10), dpi=
80)
-
sns.boxplot(
-
x=
'class',
-
y=
'hwy',
-
data=df,
-
hue=
'cyl',
-
palette=
"Set1",
-
)
-
plt.legend(loc=
9)
-
sns.stripplot(x=
'class',
-
y=
'hwy',
-
data=df,
-
color=
'#dc2624',
-
size=
5,
-
jitter=
1)
-
-
for i in
range(
len(df[
'class'].unique()) -
1):
-
plt.vlines(i +
.5,
10,
45, linestyles=
'solid', colors=
'gray', alpha=
0.2)
-
-
# Decoration
-
plt.title(
'Box Plot of Highway Mileage by Vehicle Class', fontsize=
18)
-
-
plt.show()
更多关于箱图：
29、小提琴图（Violin Plot）
比箱图更好看，但不常用，小提琴的形状或面积由该位置数据次数决定。
-
# Import Data
-
df = pd.read_csv(
"./datasets/mpg_ggplot2.csv")
-
-
# Draw Plot
-
plt.figure(figsize=(
13,
10), dpi=
80)
-
sns.violinplot(x=
'class',
-
y=
'hwy',
-
data=df,
-
scale=
'width',
-
palette=
'Set1',
-
inner=
'quartile')
-
-
# Decoration
-
plt.title(
'Violin Plot of Highway Mileage by Vehicle Class', fontsize=
18)
-
plt.show()
30、金字塔图(Population Pyramid)
可以理解为一种排过序的分组水平柱状图barplot，可很好展示不同分组之间的差异，可可视化逐级过滤或者漏斗的每个阶段。
-
# Read data
-
df = pd.read_csv(
"./datasets/email_campaign_funnel.csv")
-
-
# Draw Plot
-
plt.figure(figsize=(
12,
8), dpi=
80)
-
group_col =
'Gender'
-
order_of_bars = df.Stage.unique()[::
-1]
-
colors = [
-
plt.cm.Set1(i / float(
len(df[group_col].unique()) -
1))
-
for i in
range(
len(df[group_col].unique()))
-
]
-
-
-
for c, group in zip(colors, df[group_col].unique()):
-
sns.barplot(x=
'Users',
-
y=
'Stage',
-
data=df.loc[df[group_col] == group, :],
-
order=order_of_bars,
-
color=c,
-
label=group)
-
-
# Decorations
-
plt.xlabel(
"$Users$")
-
plt.ylabel(
"Stage of Purchase")
-
plt.yticks(fontsize=
12)
-
plt.title(
"Population Pyramid of the Marketing Funnel", fontsize=
18)
-
plt.legend()
-
plt.show()
31、分类图（Categorical Plots）
展示彼此相关多个（>=2个）分类变量的计数分布，其实就是seaborn的分面图。
-
# Load Dataset
-
titanic = pd.read_csv(
'./datasets/titanic.csv')
-
# Plot
-
g = sns.catplot(
"alive",
-
col=
"deck",
-
col_wrap=
4,
-
data=titanic[titanic.deck.notnull()],
-
kind=
"count",
-
height=
3.5,
-
aspect=
.8,
-
palette=
'Set1')
-
-
plt.show()
-
# Plot
-
sns.catplot(x=
"age",
-
y=
"embark_town",
-
hue=
"sex",
-
col=
"class",
-
data=titanic[titanic.embark_town.notnull()],
-
orient=
"h",
-
height=
5,
-
aspect=
1,
-
palette=
"Set1",
-
kind=
"violin",
-
dodge=True,
-
cut=
0,
-
bw=
.2)
更多关于分面图：
有用请“点赞”“在看”“分享”
转载：https://blog.csdn.net/qq_21478261/article/details/113750415
查看评论