厉害了，用Python绘制动态可视化图表，并保存成gif格式
安装相关的模块
首先第一步的话我们需要安装相关的模块，通过 pip 命令来安装
pip install gif
gif matplotlib plotly altair
pip install "gif[altair]"
pip install "gif[matplotlib]"
pip install
"gif[plotly]"
gif 和 matplotlib 的结合
我们先来看 gif 和 matplotlib 模块的结合，我们先来看一个简单的例子，代码如下
import random
from matplotlib import pyplot as plt
import gif
x = [random.randint(0, 100) for _ in range(100)]
y = [random.randint(0, 100) for _ in range(100)]
gif.options.matplotlib[ "dpi" ] = 300
@gif.frame
def plot(i):
xi = x[i*10:(i+1)*10]
yi = y[i*10:(i+1)*10]
plt.scatter(xi, yi)
plt.xlim((0, 100))
plt.ylim((0, 100))
frames = []
for i in range(10):
frame = plot(i)
frames.append(frame)
gif.save(frames, 'example.gif' , duration=3.5, unit= "s"
, between=
"startend"
)
output
gif for gif
gif 和 plotly 的结合
matplotlib gif plotly
import random
import plotly.graph_objects as go
import pandas as pd
import gif
df = pd.DataFrame({
't' : list(range(10)) * 10,
'x' : [random.randint(0, 100) for _ in range(100)],
'y' : [random.randint(0, 100) for _ in range(100)]
})
@gif.frame
def plot(i):
d = df[df[ 't' ] == i]
fig = go.Figure()
fig.add_trace(go.Scatter(
x=d[ "x" ],
y=d[ "y" ],
mode= "markers"
))
fig.update_layout(width=500, height=300)
return fig
frames = []
for i in range(10):
frame = plot(i)
frames.append(frame)
gif.save(frames,
'example_plotly.gif'
, duration=100)
output
整体的代码逻辑和上面的相似，这里也就不做具体的说明了
matplotlib 多子图动态可视化
上面绘制出来的图表都是在单张图表当中进行的，那当然了我们还可以在多张子图中进行动态可视化的展示，代码如下
# 读取数据
df = pd.read_csv( 'weather_hourly_darksky.csv' )
df = df.rename(columns={ "time" : "date" })
@gif.frame
def plot(df, date):
df = df.loc[df.index[0]:pd.Timestamp(date)]
fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(10, 6), dpi=100)
ax1.plot(df.temperature, marker= 'o' , linestyle= '--' , linewidth=1, markersize=3, color= 'g' )
maxi = round(df.temperature.max() + 3)
ax1.set_xlim([START, END])
ax1.set_ylim([0, maxi])
ax1.set_ylabel( 'TEMPERATURE' , color= 'green' )
ax2.plot(df.windSpeed, marker= 'o' , linestyle= '--' , linewidth=1, markersize=3, color= 'b' )
maxi = round(df.windSpeed.max() + 3)
ax2.set_xlim([START, END])
ax2.set_ylim([0, maxi])
ax2.set_ylabel( 'WIND' , color= 'blue' )
ax3.plot(df.visibility, marker= 'o' , linestyle= '--' , linewidth=1, markersize=3, color= 'r' )
maxi = round(df.visibility.max() + 3)
ax3.set_xlim([START, END])
ax3.set_ylim([0, maxi])
ax3.set_ylabel( 'VISIBILITY' , color= 'red' )
frames = []
for date in pd.date_range(start=df.index[0], end=df.index[-1], freq= '1M' ):
frame = plot(df, date)
frames.append(frame)
gif.save(frames, "文件名称.gif"
, duration=0.5, unit=
's'
)
output
动态气泡图
最后我们用 plotly 模块来绘制一个动态的气泡图，代码如下
import gif
import plotly.graph_objects as go
import numpy as np
np.random.seed(1)
N = 100
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
sz = np.random.rand(N) * 30
layout = go.Layout(
xaxis={ 'range' : [-2, 2]},
yaxis={ 'range' : [-2, 2]},
margin=dict(l=10, r=10, t=10, b=10)
)
@gif.frame
def plot(i):
fig = go.Figure(layout=layout)
fig.add_trace(go.Scatter(
x=x[:i],
y=y[:i],
mode= "markers" ,
marker=go.scatter.Marker(
size=sz[:i],
color=colors[:i],
opacity=0.6,
colorscale= "Viridis"
)
))
fig.update_layout(width=500, height=300)
return fig
frames = []
for i in range(100):
frame = plot(i)
frames.append(frame)
gif.save(frames,
"bubble.gif"
)
output
