50个常用统计图表代码总结(7）
Composition31. Waffle Chart
The waffle chart can be created using the pywaffle package and is used to show the compositions of groups in a larger population.
#! pip install pywaffle
# Reference: <a href="https://stackoverflow.com/questions/41400136/how-to-do-waffle-charts-in-python-square-piechart" target="_blank">https://stackoverflow.com/questions/41400136/how-to-do-waffle-charts-in-python-square-piechart</a>
from pywaffle importWaffle
# Import
df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")
# Prepare Data
df = df_raw.groupby('class').size().reset_index(name='counts')
n_categories = df.shape
colors =
# Draw Plot and Decorate
fig = plt.figure(
FigureClass=Waffle,
plots={
'111':{
'values': df['counts'],
'labels':["{0} ({1})".format(n, n)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("https://github.com/selva86/datasets/raw/master/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
colors_class =
# By Cylinders Data
df_cyl = df_raw.groupby('cyl').size().reset_index(name='counts_cyl')
n_categories = df_cyl.shape
colors_cyl =
# By Make Data
df_make = df_raw.groupby('manufacturer').size().reset_index(name='counts_make')
n_categories = df_make.shape
colors_make =
# Draw Plot and Decorate
fig = plt.figure(
FigureClass=Waffle,
plots={
'311':{
'values': df_class['counts_class'],
'labels':["{1}".format(n, n)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, n)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, n)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)
)
32. Pie Chart
Pie chart is a classic way to show the composition of groups. However, its not generally advisable to use nowadays because the area of the pie portions can sometimes become misleading. So, if you are to use pie chart, its highly recommended to explicitly write down the percentage or numbers for each portion of the pie.
# Import
df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/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("https://github.com/selva86/datasets/raw/master/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 =
def func(pct, allvals):
absolute = int(pct/100.*np.sum(allvals))
return"{:.1f}% ({:d} )".format(pct, absolute)
wedges, texts, autotexts = ax.pie(data,
autopct=lambda pct: 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()
33. Treemap
Tree map is similar to a pie chart and it does a better work without misleading the contributions by each group.
# pip install squarify
import squarify
# Import Data
df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")
# Prepare Data
df = df_raw.groupby('class').size().reset_index(name='counts')
labels = df.apply(lambda x: str(x)+"\n ("+ str(x)+")", axis=1)
sizes = df['counts'].values.tolist()
colors =
# 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()
34. Bar Chart
Bar chart is a classic way of visualizing items based on counts or any given metric. In below chart, I have used a different color for each item, but you might typically want to pick one color for all items unless you to color them by groups. The color names get stored inside all_colors in the code below. You can change the color of the bars by setting the color parameter in plt.plot().
import random
# Import Data
df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")
# Prepare Data
df = df_raw.groupby('manufacturer').size().reset_index(name='counts')
n = df['manufacturer'].unique().__len__()+1
all_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()
Change
35. Time Series Plot
Time series plot is used to visualise how a given metric changes over time. Here you can see how the Air Passenger traffic changed between 1949 and 1969. Check this free video tutorial on how to implement line plots for analyzing time series.
# Import Data
df = pd.read_csv('https://github.com/selva86/datasets/raw/master/AirPassengers.csv')
# Draw Plot
plt.figure(figsize=(16,10), dpi=80)
plt.plot('date','traffic', data=df, color='tab:red')
# Decoration
plt.ylim(50,750)
xtick_location = df.index.tolist()[::12]
xtick_labels =for x in df.date.tolist()[::12]]
plt.xticks(ticks=xtick_location, labels=xtick_labels, rotation=0, fontsize=12, horizontalalignment='center', alpha=.7)
plt.yticks(fontsize=12, alpha=.7)
plt.title("Air Passengers Traffic (1949 - 1969)", fontsize=22)
plt.grid(axis='both', alpha=.3)
# Remove borders
plt.gca().spines["top"].set_alpha(0.0)
plt.gca().spines["bottom"].set_alpha(0.3)
plt.gca().spines["right"].set_alpha(0.0)
plt.gca().spines["left"].set_alpha(0.3)
plt.show()
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