python pandas plotting other plot

other plots

code

import and basic

import

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

basic

  # 
  data = np.random.randn(1000)
  days = pd.date_range('2000-01-01', periods=1000)
  ts = pd.Series(data, index=days)
  ts = ts.cumsum()
  # the plot method on Series and DataFrame
  # is just a simple wrapper around plt.plot()
  ts.plot()

  # 
  data = np.random.randn(1000, 4)
  df = pd.DataFrame(data, index=ts.index, columns=list('ABCD'))
  df = df.cumsum()
  plt.figure(); df.plot();

bar plots

basic

  plt.figure()
  # calling a df's plot() method
  # with `kind='bar'` produces a multiple bar plot
  df.ix[5].plot(kind='bar'); plt.axhline(0, color='k')

  df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
  df2.plot(kind='bar');

stasked bar

  # to produce a stacked bar plot
  # pass `stacked=True`
  df2.plot(kind='bar', stacked=True)

horizontal bar

  # to get horizontal bar plots
  # pass `kind='barh'`
  df2.plot(kind='barh', stacked=True)

histograms

basic

  df4 = pd.DataFrame({'a': np.random.randn(1000) + 1,
                     'b': np.random.randn(1000),
                     'c': np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
  plt.figure();
  df4.plot(kind='hist', alpha=0.5)

stacked histogram

  # histogram can be stacked by `stacked=True`
  # bin size can be changed by `bins` keyword
  plt.figure();
  df4.plot(kind='hist', stacked=True, bins=20)

horizontal and cumulative

  # horizontal by orientation='horizontal'
  # cumulative by cumulative=True
  plt.figure();
  df4['a'].plot(kind='hist', orientation='horizontal', cumulative=True)

  plt.figure();
  df['A'].diff().hist()

color alpha

  plt.figure()
  df.diff().hist(color='k', alpha=0.5, bins=50)

  data = pd.Series(np.random.randn(1000))
  data.hist(by=np.random.randint(0, 4, 1000), figsize=(6, 4))

box plots

basic

  # five trials of 10 observations of
  # a uniform random variable on [0, 1)
  df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])
  df.plot(kind='box')

  color = dict(boxes='DarkGreen',
              whiskers='DarkOrange',
              medians='DarkBlue',
              caps='Gray')
  df.plot(kind='box', color=color, sym='r+')

vert and positions

  # vert=False and positions
  df.plot(kind='box', vert=False, positions=[1, 2, 5, 6, 8])

  df = pd.DataFrame(np.random.rand(10, 5))
  plt.figure();
  bp = df.boxplot()

  # using `by` keyword
  df = pd.DataFrame(np.random.rand(10, 2), columns=['col1', 'col2'])
  df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])
  plt.figure()
  bp = df.boxplot(by='X')

  # pass a subset of colomns to plot
  # as well as group by multiple columns
  df = pd.DataFrame(np.random.rand(10, 3), columns=['col1', 'col2', 'col3'])
  df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])
  df['Y'] = pd.Series(['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B'])
  plt.figure();
  bp = df.boxplot(column=['col1', 'col2'], by=['X', 'Y'])

groupby

  # when calling boxplot on a `groupby` object
  # a dict of `return_type` is returned
  np.random.seed(1234)
  df_box = pd.DataFrame(np.random.randn(50, 2))
  df_box['g'] = np.random.choice(['A', 'B'], size=50)
  df_box.loc[df_box['g'] == 'B', 1] += 3
  bp = df_box.boxplot(by='g')

  bp = df_box.groupby('g').boxplot()

area plot

basic

  df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
  df.plot(kind='area')

stacked area

  # pass `stacked=False`
  # `alpha` value is set to 0.5
  df.plot(kind='area', stacked=False)

scatter plot

basic

  df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
  df.plot(kind='scatter', x='a', y='b')

multiple column groups

  # to plot multiple column groups in a single axes
  # repeat `plot` method specifying target `ax`
  # recommended to specify `color` and `label` keywords
  # to distinguish each groups
  ax = df.plot(kind='scatter', x='a', y='b',
               color='DarkBlue', label='Group 1');
  df.plot(kind='scatter', x='c', y='d',
          color='DarkGreen', label='Group 2', ax=ax);

c and s keyword

  # keyword `c` may be given as the name of a column
  # to provide colors for each point
  df.plot(kind='scatter', x='a', y='b', c='c', s=50)

bubble size

  # show bubble chart using a df's column as bubble size
  df.plot(kind='scatter', x='a', y='b', s=df.c*200)

hexagonal bin plot

basic

  df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])
  df.b = df.b + np.arange(1000)
  # `gridsize` controls the number of hexagons in the `x-direction`
  # default value is `100`
  # a larger `gridsize` means more, smaller bins
  df.plot(kind='hexbin', x='a', y='b', gridsize=25)

C and reduce_C_function

  df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])
  df.b = df.b + np.arange(1000)
  df['z'] = np.random.uniform(0, 3, 1000)
  df.plot(kind='hexbin', x='a', y='b', 
          C='z', reduce_C_function=np.max, gridsize=25)

pie plot

basic

  series = pd.Series(3 * np.random.rand(4),
                     index=['a', 'b', 'c', 'd'],
                     name='series')
  series.plot(kind='pie', figsize=(6, 6))

legend and labels

  df = pd.DataFrame(3 * np.random.rand(4, 2),
                    index=['a', 'b', 'c', 'd'],
                    columns=['x', 'y'])
  # `legend=False` to hide legend
  # `labels=None` to hide labels
  df.plot(kind='pie', subplots=True, figsize=(8,4),
          legend=False, labels=None)

percentage and fontsize

  series.plot(kind='pie',
              labels=['AA', 'BB', 'CC', 'DD'],
              colors=['r', 'g', 'b', 'c'],
              autopct='%.2f', fontsize=20, figsize=(6, 6))

semicircle

  # pass values whose sum total is less than 1.0
  # matplotlib draws a semicircle
  series = pd.Series([0.1] * 4, name='series2',
                     index=['a', 'b', 'c', 'd'])
  series.plot(kind='pie', figsize=(6, 6))