Plotting data from a file

Plotting data from a file is actually a two-step process.

  1. Interpreting the file and loading the data.
  2. Creating the actual plot.

pyplot.plotfile tried to do both at once. But each of the steps has so many possible variations and parameters that it does not make sense to squeeze both into a single function. Therefore, pyplot.plotfile has been deprecated.

The recommended way of plotting data from a file is therefore to use dedicated functions such as numpy.loadtxt or pandas.read_csv to read the data. These are more powerful and faster. Then plot the obtained data using matplotlib.

Note that pandas.DataFrame.plot is a convenient wrapper around Matplotlib to create simple plots.

import matplotlib.pyplot as plt
import matplotlib.cbook as cbook

import numpy as np
import pandas as pd

Using pandas

Subsequent are a few examples of how to replace plotfile with pandas. All examples need the the pandas.read_csv call first. Note that you can use the filename directly as a parameter:

msft = pd.read_csv('msft.csv')

The following slightly more involved pandas.read_csv call is only to make automatic rendering of the example work:

fname = cbook.get_sample_data('msft.csv', asfileobj=False)
with cbook.get_sample_data('msft.csv') as file:
    msft = pd.read_csv(file)

When working with dates, additionally call pandas.plotting.register_matplotlib_converters and use the parse_dates argument of pandas.read_csv:

.. code-block:: default


with cbook.get_sample_data('msft.csv') as file:
msft = pd.read_csv(file, parse_dates=['Date'])

Use indices

# Deprecated:
plt.plotfile(fname, (0, 5, 6))

# Use instead:
msft.plot(0, [5, 6], subplots=True)

Use names

# Deprecated:
plt.plotfile(fname, ('date', 'volume', 'adj_close'))

# Use instead:
msft.plot("Date", ["Volume", "Adj. Close*"], subplots=True)

Use semilogy for volume

# Deprecated:
plt.plotfile(fname, ('date', 'volume', 'adj_close'),
             plotfuncs={'volume': 'semilogy'})

# Use instead:
fig, axs = plt.subplots(2, sharex=True)
msft.plot("Date", "Volume", ax=axs[0], logy=True)
msft.plot("Date", "Adj. Close*", ax=axs[1])

Use semilogy for volume (by index)

# Deprecated:
plt.plotfile(fname, (0, 5, 6), plotfuncs={5: 'semilogy'})

# Use instead:
fig, axs = plt.subplots(2, sharex=True)
msft.plot(0, 5, ax=axs[0], logy=True)
msft.plot(0, 6, ax=axs[1])

Single subplot

# Deprecated:
plt.plotfile(fname, ('date', 'open', 'high', 'low', 'close'), subplots=False)

# Use instead:
msft.plot("Date", ["Open", "High", "Low", "Close"])

Use bar for volume

# Deprecated:
plt.plotfile(fname, (0, 5, 6), plotfuncs={5: "bar"})

# Use instead:
fig, axs = plt.subplots(2, sharex=True)
axs[0].bar(msft.iloc[:, 0], msft.iloc[:, 5])
axs[1].plot(msft.iloc[:, 0], msft.iloc[:, 6])

Using numpy

fname2 = cbook.get_sample_data('data_x_x2_x3.csv', asfileobj=False)
with cbook.get_sample_data('data_x_x2_x3.csv') as file:
    array = np.loadtxt(file)

Labeling, if no names in csv-file

# Deprecated:
plt.plotfile(fname2, cols=(0, 1, 2), delimiter=' ',
             names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$'])

# Use instead:
fig, axs = plt.subplots(2, sharex=True)
axs[0].plot(array[:, 0], array[:, 1])
axs[1].plot(array[:, 0], array[:, 2])
axs[1].set(xlabel='$x$', ylabel='$f(x)=x^3$')

More than one file per figure

# For simplicity of the example we reuse the same file.
# In general they will be different.
fname3 = fname2

# Depreacted:
plt.plotfile(fname2, cols=(0, 1), delimiter=' ')
plt.plotfile(fname3, cols=(0, 2), delimiter=' ',
             newfig=False)  # use current figure
plt.ylabel(r'$f(x) = x^2, x^3$')

# Use instead:
fig, ax = plt.subplots()
ax.plot(array[:, 0], array[:, 1])
ax.plot(array[:, 0], array[:, 2])
ax.set(xlabel='$x$', ylabel='$f(x)=x^3$')

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