关于matplotlib学习还是强烈建议常去官方http://matplotlib.org/contents.html里查一查各种用法和toturial等。
下面是jupyter notebook代码导出的md文件。

Plotting and Visualization

from __future__ import division

from numpy.random import randn

import numpy as np

import os

import matplotlib.pyplot as plt

np.random.seed(12345)

plt.rc('figure', figsize=(10, 6))

from pandas import Series, DataFrame

import pandas as pd

np.set_printoptions(precision=4)

%matplotlib inline

matplotlib API 介绍

import matplotlib.pyplot as plt

Figures and Subplots

fig = plt.figure()

ax1 = fig.add_subplot(2, 2, 1)

ax2 = fig.add_subplot(2, 2, 2)

ax3 = fig.add_subplot(2, 2, 3)

from numpy.random import randn

plt.plot(randn(50).cumsum(), 'k--')

[<matplotlib.lines.Line2D at 0x28e7668cb38>]

_ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)

ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30))

plt.close('all')

fig, axes = plt.subplots(2, 3)

axes

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000028E76BAFF98>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E76C047F0>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E76C4CB00>],

       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000028E76C89D30>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E76CD7940>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E76D0FFD0>]], dtype=object)

## 调整subplot间距

plt.subplots_adjust(left=None, bottom=None, right=None, top=None,

                wspace=None, hspace=None)

fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)

for i in range(2):

    for j in range(2):

        axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)

plt.subplots_adjust(wspace=0, hspace=0)

fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)

for i in range(2):

    for j in range(2):

        axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)

plt.subplots_adjust(wspace=0, hspace=0)

### 线条格式

plt.figure()

plt.plot(randn(30).cumsum(), 'ko--')

[<matplotlib.lines.Line2D at 0x28e7866a390>]

plt.close('all')

data = randn(30).cumsum()

plt.plot(data, 'k--', label='Default')

plt.plot(data, 'k-', drawstyle='steps-post', label='steps')

plt.legend(loc='best')

<matplotlib.legend.Legend at 0x28e781103c8>

### Ticks, labels, and legends #### Setting the title, axis labels, ticks, and ticklabels

fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)

ax.plot(randn(1000).cumsum())

ticks = ax.set_xticks([0, 250, 500, 750, 1000])

labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],

                            rotation=30, fontsize='small')

ax.set_title('some random lines')

ax.set_xlabel('Stages')

<matplotlib.text.Text at 0x28e782525c0>

#### Adding legends

fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)

ax.plot(randn(1000).cumsum(), 'k', label='one')

ax.plot(randn(1000).cumsum(), 'k--', label='two')

ax.plot(randn(1000).cumsum(), 'k.', label='three')

ax.legend(loc='best')

<matplotlib.legend.Legend at 0x28e7801e668>

### subplot 做标记

from datetime import datetime

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1)

data = pd.read_csv('julyedu/spx.csv', index_col=0, parse_dates=True)

spx = data['SPX']

spx.plot(ax=ax, style='k-')

crisis_data = [

    (datetime(2007, 10, 11), 'Peak of bull market'),

    (datetime(2008, 3, 12), 'Bear Stearns Fails'),

    (datetime(2008, 9, 15), 'Lehman Bankruptcy')

]

for date, label in crisis_data:

    ax.annotate(label, xy=(date, spx.asof(date) + 50),

                xytext=(date, spx.asof(date) + 200),

                arrowprops=dict(facecolor='black'),

                horizontalalignment='left', verticalalignment='top')

# Zoom in on 2007-2010

ax.set_xlim(['1/1/2007', '1/1/2011'])

ax.set_ylim([600, 1800])

ax.set_title('Important dates in 2008-2009 financial crisis')

<matplotlib.text.Text at 0x28e77fb7358>

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1)

rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3)

circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3)

pgon = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]],

                   color='g', alpha=0.5)

ax.add_patch(rect)

ax.add_patch(circ)

ax.add_patch(pgon)

<matplotlib.patches.Polygon at 0x28e77ed76a0>

### Saving plots to file

fig

fig.savefig('figpath.svg')

fig.savefig('figpath.png', dpi=400, bbox_inches='tight')

from io import BytesIO

buffer = BytesIO()

plt.savefig(buffer)

plot_data = buffer.getvalue()

### matplotlib configuration

plt.rc('figure', figsize=(10, 10))

## Plotting functions in pandas ### Line plots

plt.close('all')

s = Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))

s.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x28e781c0208>

df = DataFrame(np.random.randn(10, 4).cumsum(0),

               columns=['A', 'B', 'C', 'D'],

               index=np.arange(0, 100, 10))

df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x28e7809d358>

### Bar plots

fig, axes = plt.subplots(2, 1)

data = Series(np.random.rand(16), index=list('abcdefghijklmnop'))

data.plot(kind='bar', ax=axes[0], color='k', alpha=0.7)

data.plot(kind='barh', ax=axes[1], color='k', alpha=0.7)

<matplotlib.axes._subplots.AxesSubplot at 0x11fd02b50>

df = DataFrame(np.random.rand(6, 4),

               index=['one', 'two', 'three', 'four', 'five', 'six'],

               columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))

df

df.plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x28e77f482e8>

plt.figure()

df.plot(kind='barh', stacked=True, alpha=0.5)

<matplotlib.axes._subplots.AxesSubplot at 0x28e77e05be0>

png

tips = pd.read_csv('julyedu/tips.csv')

party_counts = pd.crosstab(tips.day, tips.size)

print(party_counts)

# Not many 1- and 6-person parties

party_counts = party_counts.ix[:, 2:5]

print(party_counts)

col_0 1708 day Fri 19 Sat 87 Sun 76 Thur 62 Empty DataFrame Columns: [] Index: [Fri, Sat, Sun, Thur] ### Histograms and density plots

plt.figure()

tips['tip_pct'] = tips['tip'] / tips['total_bill']

print(tips.head())

tips['tip_pct'].hist(bins=50)

   total_bill   tip     sex smoker  day    time  size   tip_pct

0       16.99  1.01  Female     No  Sun  Dinner     2  0.059447

1       10.34  1.66    Male     No  Sun  Dinner     3  0.160542

2       21.01  3.50    Male     No  Sun  Dinner     3  0.166587

3       23.68  3.31    Male     No  Sun  Dinner     2  0.139780

4       24.59  3.61  Female     No  Sun  Dinner     4  0.146808

<matplotlib.axes._subplots.AxesSubplot at 0x28e7997b390>

png

plt.figure()

tips['tip_pct'].plot(kind='kde')

plt.figure()

comp1 = np.random.normal(0, 1, size=200)  # N(0, 1)

comp2 = np.random.normal(10, 2, size=200)  # N(10, 4)

values = Series(np.concatenate([comp1, comp2]))

values.hist(bins=100, alpha=0.3, color='k', normed=True)

values.plot(kind='kde', style='k--')

<matplotlib.axes._subplots.AxesSubplot at 0x28e79b24358>

### Scatter plots

macro = pd.read_csv('julyedu/macrodata.csv')

data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]

trans_data = np.log(data).diff().dropna()

trans_data[-5:]

	cpi	m1	tbilrate	unemp
198	-0.007904	0.045361	-0.396881	0.105361
199	-0.021979	0.066753	-2.277267	0.139762
200	0.002340	0.010286	0.606136	0.160343
201	0.008419	0.037461	-0.200671	0.127339
202	0.008894	0.012202	-0.405465	0.042560

plt.figure()

plt.scatter(trans_data['m1'], trans_data['unemp'])

plt.title('Changes in log %s vs. log %s' % ('m1', 'unemp'))

<matplotlib.text.Text at 0x28e7bfebcc0>

pd.scatter_matrix(trans_data, diagonal='kde', alpha=0.3)

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7CA07EF0>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7C6E9128>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7DFEEBA8>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7C3DB3C8>],

       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7C9E5EB8>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7C9D0E10>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7BFE87B8>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7C732FD0>],

       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7C9704E0>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7CF63320>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7C8BB748>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7C820978>],

       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7C6BBB00>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7C3405F8>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7C874DA0>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x0000028E7E036550>]], dtype=object)

## Plotting Maps: Visualizing Haiti Earthquake Crisis data

data = pd.read_csv('julyedu/Haiti.csv')

data.info()

data[['INCIDENT DATE', 'LATITUDE', 'LONGITUDE']][:10]

	INCIDENT DATE	LATITUDE	LONGITUDE
0	05/07/2010 17:26	18.233333	-72.533333
1	28/06/2010 23:06	50.226029	5.729886
2	24/06/2010 16:21	22.278381	114.174287
3	20/06/2010 21:59	44.407062	8.933989
4	18/05/2010 16:26	18.571084	-72.334671
5	26/04/2010 13:14	18.593707	-72.310079
6	26/04/2010 14:19	18.482800	-73.638800
7	26/04/2010 14:27	18.415000	-73.195000
8	15/03/2010 10:58	18.517443	-72.236841
9	15/03/2010 11:00	18.547790	-72.410010

data['CATEGORY'][:6]

data.describe()

	Serial	LATITUDE	LONGITUDE
count	3593.000000	3593.000000	3593.000000
mean	2080.277484	18.611495	-72.322680
std	1171.100360	0.738572	3.650776
min	4.000000	18.041313	-74.452757
25%	1074.000000	18.524070	-72.417500
50%	2163.000000	18.539269	-72.335000
75%	3088.000000	18.561820	-72.293570
max	4052.000000	50.226029	114.174287

data = data[(data.LATITUDE > 18) & (data.LATITUDE < 20) &

            (data.LONGITUDE > -75) & (data.LONGITUDE < -70)

            & data.CATEGORY.notnull()]

def to_cat_list(catstr):

    stripped = (x.strip() for x in catstr.split(','))

    return [x for x in stripped if x]

def get_all_categories(cat_series):

    cat_sets = (set(to_cat_list(x)) for x in cat_series)

    return sorted(set.union(*cat_sets))

def get_english(cat):

    code, names = cat.split('.')

    if '|' in names:

        names = names.split(' | ')[1]

    return code, names.strip()

get_english('2. Urgences logistiques | Vital Lines')

('2', 'Vital Lines')

all_cats = get_all_categories(data.CATEGORY)

# Generator expression

english_mapping = dict(get_english(x) for x in all_cats)

english_mapping['2a']

english_mapping['6c']

'Earthquake and aftershocks'

def get_code(seq):

    return [x.split('.')[0] for x in seq if x]

all_codes = get_code(all_cats)

code_index = pd.Index(np.unique(all_codes))

dummy_frame = DataFrame(np.zeros((len(data), len(code_index))),

                        index=data.index, columns=code_index)

dummy_frame.ix[:, :6].info()

<class 'pandas.core.frame.DataFrame'>

Int64Index: 3569 entries, 0 to 3592

Data columns (total 6 columns):

1     3569 non-null float64

1a    3569 non-null float64

1b    3569 non-null float64

1c    3569 non-null float64

1d    3569 non-null float64

2     3569 non-null float64

dtypes: float64(6)

memory usage: 195.2 KB

for row, cat in zip(data.index, data.CATEGORY):

    codes = get_code(to_cat_list(cat))

    dummy_frame.ix[row, codes] = 1

data = data.join(dummy_frame.add_prefix('category_'))

data.ix[:, 10:15].info()

<class 'pandas.core.frame.DataFrame'>

Int64Index: 3569 entries, 0 to 3592

Data columns (total 5 columns):

category_1     3569 non-null float64

category_1a    3569 non-null float64

category_1b    3569 non-null float64

category_1c    3569 non-null float64

category_1d    3569 non-null float64

dtypes: float64(5)

memory usage: 167.3 KB

from mpl_toolkits.basemap import Basemap

import matplotlib.pyplot as plt

def basic_haiti_map(ax=None, lllat=17.25, urlat=20.25,

                    lllon=-75, urlon=-71):

    # create polar stereographic Basemap instance.

    m = Basemap(ax=ax, projection='stere',

                lon_0=(urlon + lllon) / 2,

                lat_0=(urlat + lllat) / 2,

                llcrnrlat=lllat, urcrnrlat=urlat,

                llcrnrlon=lllon, urcrnrlon=urlon,

                resolution='f')

    # draw coastlines, state and country boundaries, edge of map.

    m.drawcoastlines()

    m.drawstates()

    m.drawcountries()

    return m

---------------------------------------------------------------------------

ImportError                               Traceback (most recent call last)

<ipython-input-66-ec31ba3e955e> in <module>()

----> 1 from mpl_toolkits.basemap import Basemap

      2 import matplotlib.pyplot as plt

      3

      4 def basic_haiti_map(ax=None, lllat=17.25, urlat=20.25,

      5                     lllon=-75, urlon=-71):

ImportError: No module named 'mpl_toolkits.basemap'

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))

fig.subplots_adjust(hspace=0.05, wspace=0.05)

to_plot = ['2a', '1', '3c', '7a']

lllat=17.25; urlat=20.25; lllon=-75; urlon=-71

for code, ax in zip(to_plot, axes.flat):

    m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,

                        lllon=lllon, urlon=urlon)

    cat_data = data[data['category_%s' % code] == 1]

    # compute map proj coordinates.

    x, y = m(cat_data.LONGITUDE.values, cat_data.LATITUDE.values)

    m.plot(x, y, 'k.', alpha=0.5)

    ax.set_title('%s: %s' % (code, english_mapping[code]))

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))

fig.subplots_adjust(hspace=0.05, wspace=0.05)

to_plot = ['2a', '1', '3c', '7a']

lllat=17.25; urlat=20.25; lllon=-75; urlon=-71

def make_plot():

    for i, code in enumerate(to_plot):

        cat_data = data[data['category_%s' % code] == 1]

        lons, lats = cat_data.LONGITUDE, cat_data.LATITUDE

        ax = axes.flat[i]

        m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,

                            lllon=lllon, urlon=urlon)

        # compute map proj coordinates.

        x, y = m(lons.values, lats.values)

        m.plot(x, y, 'k.', alpha=0.5)

        ax.set_title('%s: %s' % (code, english_mapping[code]))

make_plot()

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