Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.
matplotlib.ticker.IndexFormatter
The matplotlib.ticker.IndexFormatter class is a subclass of matplotlib.ticker class and is used to format the position x that is the nearest i-th label where i = int(x + 0.5). The positions with i len(list) have 0 tick labels.
Syntax: class matplotlib.ticker.IndexFormatter(labels)
Parameter :
- labels: It is a list of labels.
Example 1:
import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl # create dummy data x = ['str{}'.format(k) for k in range(20)] y = np.random.rand(len(x)) # create an IndexFormatter # with labels x x_fmt = mpl.ticker.IndexFormatter(x) fig,ax = plt.subplots() ax.plot(y) # set our IndexFormatter to be # responsible for major ticks ax.xaxis.set_major_formatter(x_fmt) |
Output:
Example 2:
from matplotlib.ticker import IndexFormatter, IndexLocator import pandas as pd import matplotlib.pyplot as plt years = range(2015, 2018) fields = range(4) days = range(4) bands = ['R', 'G', 'B'] index = pd.MultiIndex.from_product( [years, fields], names =['year', 'field']) columns = pd.MultiIndex.from_product( [days, bands], names =['day', 'band']) df = pd.DataFrame(0, index = index, columns = columns) df.loc[(2015, ), (0, )] = 1df.loc[(2016, ), (1, )] = 1df.loc[(2017, ), (2, )] = 1ax = plt.gca() plt.spy(df) xbase = len(bands) xoffset = xbase / 2xlabels = df.columns.get_level_values('day') ax.xaxis.set_major_locator(IndexLocator(base = xbase, offset = xoffset)) ax.xaxis.set_major_formatter(IndexFormatter(xlabels)) plt.xlabel('Day') ax.xaxis.tick_bottom() ybase = len(fields) yoffset = ybase / 2ylabels = df.index.get_level_values('year') ax.yaxis.set_major_locator(IndexLocator(base = ybase, offset = yoffset)) ax.yaxis.set_major_formatter(IndexFormatter(ylabels)) plt.ylabel('Year') plt.show() |
Output:
