python pandas plot formatting

plot formatting

code

import

      import matplotlib.pyplot as plt
      import matplotlib
      matplotlib.style.use('ggplot')
      import numpy as np
      import pandas as pd
      %matplotlib inline

      data = np.random.randn(1000)
      days = pd.date_range('2000-01-01', periods=1000)
      ts = pd.Series(data, index=days)
      plt.figure()
      ts.plot(style='k--', label='Series')

controlling the legend

     df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
     df = df.cumsum()
     df.plot(legend=False)

scales

     ts = pd.Series(np.random.randn(1000),
                    index=pd.date_range('2000-01-01', periods=1000))
     ts = np.exp(ts.cumsum())
     ts.plot(logy=True)

plotting on a secondary y-axis

basic

  df.A.plot()
  df.B.plot(secondary_y=True, style='g')

plot some columns

  # to plot some columns in a df
  # give the column names to the `secondary_y` keyword
  plt.figure()
  ax = df.plot(secondary_y=['A', 'B'])
  ax.set_ylabel('CD scale')
  ax.right_ax.set_ylabel('AB scale')

turn off (right) in legend

  # use `mark_right=False` to turn off '(right)'
  plt.figure()
  df.plot(secondary_y=['A', 'B'], mark_right=False)

suppressing tick resolution adjustment

basic
```
  plt.figure()
  df.A.plot()
```

use x_compat parameter

  # use `x_compat` parameter
  plt.figure()
  df.A.plot(x_compat=True)

use use method in pandas.plot_params

  # if you have more than one plot
  # that needs to be suppressed
  # use `use` method in `pandas.plot_params`
  plt.figure()
  with pd.plot_params.use('x_compat', True):
      df.A.plot(color='r')
      df.B.plot(color='g')
      df.C.plot(color='b')

subplots

basic

  df.plot(subplots=True, figsize=(6, 6))
  #df.plot(subplots=True, figsize=(6, 6), x_compat=True)

using layout and targetting multiple axes

     df.plot(subplots=True, layout=(2, 3), figsize=(6, 6))

     # the above example is indentical to using
     df.plot(subplots=True, layout=(2, -1), figsize=(6, 6), sharex=False)

     # when multiple axes are passed via `ax` keyword
     # `layout`, `sharex` and `sharey` keywords don't affect output
     # you should explicity pass `sharex=False` and `sharey=False`
     # otherwise you will see a warning
     fig, axes = plt.subplots(4, 4, figsize=(6, 6))
     plt.subplots_adjust(wspace=0.5, hspace=0.5)
     target1 = [axes[0][0], axes[1][1], axes[2][2], axes[3][3]]
     target2 = [axes[3][0], axes[2][1], axes[1][2], axes[0][3]]
     df.plot(subplots=True, ax=target1, legend=False, sharex=False, sharey=False)
     (-df).plot(subplots=True, ax=target2, legend=False, sharex=False, sharey=False)

     # another option is passing an `ax` argument to `Series.plot`
     # to plot on a particular axis
     fig, axes = plt.subplots(nrows=2, ncols=2)
     df.A.plot(ax=axes[0,0]); axes[0,0].set_title('A')
     df.B.plot(ax=axes[0,1]); axes[0,1].set_title('B')
     df.C.plot(ax=axes[1,0]); axes[1,0].set_title('C')
     df.D.plot(ax=axes[1,1]); axes[1,1].set_title('D')

plotting with error bars

     ix3 = pd.MultiIndex.from_arrays([['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'],
                                      ['foo', 'foo', 'bar', 'bar', 'foo', 'foo', 'bar', 'bar']],
                                     names=['letter', 'word'])
     df3 = pd.DataFrame({'data1': [3, 2, 4, 3, 2, 4, 3, 2],
                         'data2': [6, 5, 7, 5, 4, 5, 6, 5]},
                        index=ix3)
     # group by index labels
     # take means and standard deviations for each group
     gp3 = df3.groupby(level=('letter', 'word'))
     means = gp3.mean()
     errors = gp3.std()
     fig, ax = plt.subplots()
     means.plot(yerr=errors, ax=ax, kind='bar')

plotting tables

     fig, ax = plt.subplots(1, 1)
     df = pd.DataFrame(np.random.rand(5, 3), columns=list('abc'))
     ax.get_xaxis().set_visible(False)
     df.plot(table=True, ax=ax)

     fig, ax = plt.subplots(1, 1)
     ax.get_xaxis().set_visible(False)
     df.plot(table=np.round(df.T, 2), ax=ax)

     # helper function `pandas.tools.plotting.table`
     # to create a table from `Series` and `DataFrame`
     from pandas.tools.plotting import table
     fig, ax = plt.subplots(1, 1)
     table(ax, np.round(df.describe(), 2),
           loc='upper right', colWidths=[0.2, 0.2, 0.2])
     df.plot(ax=ax, ylim=(0, 2), legend=None)

colormaps

basic

  df = pd.DataFrame(np.random.randn(1000, 10), index=ts.index)
  df = df.cumsum()
  plt.figure()
  df.plot(colormap='cubehelix')

pass the colormap itself

  # pass the colormap itself
  from matplotlib import cm
  plt.figure()
  df.plot(colormap=cm.cubehelix)

used for other plot types

  # used for other plot types
  # like bar charts
  dd = pd.DataFrame(np.random.randn(10, 10)).applymap(abs)
  dd = dd.cumsum()
  plt.figure()
  dd.plot(kind='bar', colormap='Greens')

parallel coordinates charts

  # parallel coordinates charts
  from pandas import read_csv
  from pandas.tools.plotting import parallel_coordinates
  plt.figure()
  data = read_csv('iris.data')
  parallel_coordinates(data, 'Name', colormap='gist_rainbow')

andrews curves charts

  # andrews curves charts
  from pandas import read_csv
  from pandas.tools.plotting import andrews_curves
  andrews_curves(data, 'Name', colormap='winter')