fig = plt.figure()

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

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

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

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

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

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

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

print axes

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

data = randn(30).cumsum()

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

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

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('My first matplotlib plot')

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')

from datetime import datetime

fig = plt.figure()

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

data = pd.read_csv(SPXCSVPATH, index_col=0, parse_dates=True)

spx = data['SPX']

print 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')

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

ax.set_ylim([600, 1800])

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)

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

print s

s.plot()

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

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

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

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)

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

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

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

print df

df.plot(kind='bar')

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

tips = pd.read_csv(TIPSCSVPATH)

print tips.head()

party_counts = pd.crosstab(index=tips.day, columns=tips.sizes)

print '曜日とパーティの大きさ別に仕分け'

print party_counts

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

print 'サイズ１と６のパーティは少ないから除外'

print party_counts

print '正規化'

party_pcts = party_counts.div(party_counts.sum(1).astype(float), axis=0)

print party_pcts

tips = pd.read_csv(TIPSCSVPATH)

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

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

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

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

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

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

macro = pd.read_csv(MACRODATAPATH)

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

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

print trans_data[-5:]

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

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

data = pd.read_csv(HAICHICSVPATH)

print data

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

print 'データのカテゴリ'

print data['CATEGORY'][:6]

print 'データの詳細'

print data.describe()

print '外れたところのデータと欠損値を外す'

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()

all_cats = get_all_categories(data.CATEGORY)

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

print english_mapping['2a']

print english_mapping['6c']

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)

print dummy_frame.ix[:, :6]

print data.index

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_'))

print data.ix[:, 10:15]

from mpl_toolkits.basemap import Basemap

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

lllon=-75, urlon=-71):

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')

m.drawcoastlines()

m.drawstates()

m.drawcountries()

return m

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]

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]))