%pip install -q bqplot

import numpy as np
from bqplot import *

np.random.seed(0)

n = 100

x = list(range(n))
y = np.cumsum(np.random.randn(n)) + 100.0

sc_x = LinearScale()
sc_y = LinearScale()

lines = Lines(x=x, y=y, scales={"x": sc_x, "y": sc_y})
ax_x = Axis(scale=sc_x, label="Index")
ax_y = Axis(scale=sc_y, orientation="vertical", label="lines")

Figure(marks=[lines], axes=[ax_x, ax_y], title="Lines")

lines.colors = ["green"]

lines.fill = "bottom"

lines.marker = "circle"

n = 100

x = list(range(n))
y = np.cumsum(np.random.randn(n))

sc_x = LinearScale()
sc_y = LinearScale()

bars = Bars(x=x, y=y, scales={"x": sc_x, "y": sc_y})
ax_x = Axis(scale=sc_x, label="Index")
ax_y = Axis(scale=sc_y, orientation="vertical", label="bars")

Figure(marks=[bars], axes=[ax_x, ax_y], title="Bars", animation_duration=1000)

bars.y = np.cumsum(np.random.randn(n))

import numpy as np
from bqplot import *

np.random.seed(0)
y1 = np.cumsum(np.random.randn(150)) + 100.0
y2 = np.cumsum(np.random.randn(150)) + 100.0
y3 = np.cumsum(np.random.randn(150)) + 100.0
y4 = np.cumsum(np.random.randn(150)) + 100.0

sc_x = LinearScale()
sc_y = LinearScale()

lines = Lines(x=np.arange(len(y1)), y=[y1, y2, y3, y4], scales={"x": sc_x, "y": sc_y})
ax_x = Axis(scale=sc_x, label="Index")
ax_y = Axis(scale=sc_y, orientation="vertical", label="lines")

Figure(marks=[lines], axes=[ax_x, ax_y], title="Lines")