前言

本文的文字及图片来源于网络,仅供学习、交流使用,不具有任何商业用途,版权归原作者所有,如有问题请及时联系我们以作处理。

闲话不多说，直接上干货

1华夫饼图

waffle可以使用该pywaffle软件包创建该图表，并用于显示较大人群中各组的组成。

#! pip install pywaffle#Reference: https://stackoverflow.com/questions/41400136/how-to-do-waffle-charts-in-python-square-piechart

from pywaffle importWaffle#Import

df_raw = pd.read_csv("data/mpg_ggplot2.csv")#Prepare Data

df = df_raw.groupby('class').size().reset_index(name='counts')

n_categories=df.shape[0]

colors= [plt.cm.inferno_r(i/float(n_categories)) for i inrange(n_categories)]#Draw Plot and Decorate

fig =plt.figure(

FigureClass=Waffle,

plots={'111': {'values': df['counts'],'labels': ["{0} ({1})".format(n[0], n[1]) for n in df[['class', 'counts']].itertuples()],'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12},'title': {'label': '# Vehicles by Class', 'loc': 'center', 'fontsize':18}

},

},

rows=7,

colors=colors,

figsize=(16, 9)

)

#! pip install pywaffle

from pywaffle importWaffle#Import#df_raw = pd.read_csv("data/mpg_ggplot2.csv")

#Prepare Data#By Class Data

df_class = df_raw.groupby('class').size().reset_index(name='counts_class')

n_categories=df_class.shape[0]

colors_class= [plt.cm.Set3(i/float(n_categories)) for i inrange(n_categories)]#By Cylinders Data

df_cyl = df_raw.groupby('cyl').size().reset_index(name='counts_cyl')

n_categories=df_cyl.shape[0]

colors_cyl= [plt.cm.Spectral(i/float(n_categories)) for i inrange(n_categories)]#By Make Data

df_make = df_raw.groupby('manufacturer').size().reset_index(name='counts_make')

n_categories=df_make.shape[0]

colors_make= [plt.cm.tab20b(i/float(n_categories)) for i inrange(n_categories)]#Draw Plot and Decorate

fig =plt.figure(

FigureClass=Waffle,

plots={'311': {'values': df_class['counts_class'],'labels': ["{1}".format(n[0], n[1]) for n in df_class[['class', 'counts_class']].itertuples()],'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12, 'title':'Class'},'title': {'label': '# Vehicles by Class', 'loc': 'center', 'fontsize':18},'colors': colors_class

},'312': {'values': df_cyl['counts_cyl'],'labels': ["{1}".format(n[0], n[1]) for n in df_cyl[['cyl', 'counts_cyl']].itertuples()],'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12, 'title':'Cyl'},'title': {'label': '# Vehicles by Cyl', 'loc': 'center', 'fontsize':18},'colors': colors_cyl

},'313': {'values': df_make['counts_make'],'labels': ["{1}".format(n[0], n[1]) for n in df_make[['manufacturer', 'counts_make']].itertuples()],'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12, 'title':'Manufacturer'},'title': {'label': '# Vehicles by Make', 'loc': 'center', 'fontsize':18},'colors': colors_make

}

},

rows=9,

figsize=(16, 14)

)

2 饼图

饼图是显示组组成的经典方法。但是，如今一般不建议使用它，因为馅饼部分的面积有时可能会引起误解。因此，如果要使用饼图，强烈建议明确写下饼图各部分的百分比或数字。

#Import

df_raw = pd.read_csv("data/mpg_ggplot2.csv")#Prepare Data

df = df_raw.groupby('class').size()#Make the plot with pandas

df.plot(kind='pie', subplots=True, figsize=(8, 8), dpi= 80)

plt.title("Pie Chart of Vehicle Class - Bad")

plt.ylabel("")

plt.show()

#Import

df_raw = pd.read_csv("data/mpg_ggplot2.csv")#Prepare Data

df = df_raw.groupby('class').size().reset_index(name='counts')#Draw Plot

fig, ax = plt.subplots(figsize=(12, 7), subplot_kw=dict(aspect="equal"), dpi= 80)

data= df['counts']

categories= df['class']

explode= [0,0,0,0,0,0.1,0]deffunc(pct, allvals):

absolute= int(pct/100.*np.sum(allvals))return "{:.1f}% ({:d} )".format(pct, absolute)

wedges, texts, autotexts=ax.pie(data,

autopct=lambdapct: func(pct, data),

textprops=dict(color="w"),

colors=plt.cm.Dark2.colors,

startangle=140,

explode=explode)#Decoration

ax.legend(wedges, categories, title="Vehicle Class", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))

plt.setp(autotexts, size=10, weight=700)

ax.set_title("Class of Vehicles: Pie Chart")

plt.show()

3 树状图

树形图类似于饼形图，并且可以更好地完成工作，而不会误导每个组的贡献。

#pip install squarify

importsquarify#Import Data

df_raw = pd.read_csv("data/mpg_ggplot2.csv")#Prepare Data

df = df_raw.groupby('class').size().reset_index(name='counts')

labels= df.apply(lambda x: str(x[0]) + "n (" + str(x[1]) + ")", axis=1)

sizes= df['counts'].values.tolist()

colors= [plt.cm.Spectral(i/float(len(labels))) for i inrange(len(labels))]#Draw Plot

plt.figure(figsize=(12,8), dpi= 80)

squarify.plot(sizes=sizes, label=labels, color=colors, alpha=.8)#Decorate

plt.title('Treemap of Vechile Class')

plt.axis('off')

plt.show()

4 条形图

条形图是一种基于计数或任何给定指标可视化项目的经典方法。在下面的图表中，我为每个项目使用了不同的颜色，但是您通常可能希望为所有项目选择一种颜色，除非您按组对它们进行着色。颜色名称存储在all_colors下面的代码中。您可以通过在中设置color参数来更改条形的颜色。

importrandom#Import Data

df_raw = pd.read_csv("data/mpg_ggplot2.csv")#Prepare Data

df = df_raw.groupby('manufacturer').size().reset_index(name='counts')

n= df['manufacturer'].unique().__len__()+1all_colors=list(plt.cm.colors.cnames.keys())

random.seed(100)

c= random.choices(all_colors, k=n)#Plot Bars

plt.figure(figsize=(16,10), dpi= 80)

plt.bar(df['manufacturer'], df['counts'], color=c, width=.5)for i, val in enumerate(df['counts'].values):

plt.text(i, val, float(val), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12})#Decoration

plt.gca().set_xticklabels(df['manufacturer'], rotation=60, horizontalalignment= 'right')

plt.title("Number of Vehicles by Manaufacturers", fontsize=22)

plt.ylabel('# Vehicles')

plt.ylim(0,45)

plt.show()

不管你是零基础还是有基础都可以获取到自己相对应的学习礼包！包括Python软件工具和2020最新入门到实战教程。加群695185429即可免费获取。

内容来源于网络如有侵权请私信删除