#!/usr/bin/env python # coding: utf-8 # # Plotting and Visualization # In[ ]: 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) # In[ ]: get_ipython().run_line_magic('matplotlib', 'inline') # In[ ]: get_ipython().run_line_magic('pwd', '') # ## A brief matplotlib API primer # In[ ]: import matplotlib.pyplot as plt # ### Figures and Subplots # In[ ]: fig = plt.figure() # In[ ]: ax1 = fig.add_subplot(2, 2, 1) # In[ ]: ax2 = fig.add_subplot(2, 2, 2) ax3 = fig.add_subplot(2, 2, 3) # In[ ]: from numpy.random import randn plt.plot(randn(50).cumsum(), 'k--') # In[ ]: _ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3) ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30)) # In[ ]: plt.close('all') # In[ ]: fig, axes = plt.subplots(2, 3) axes # #### Adjusting the spacing around subplots # In[ ]: plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None) # In[ ]: 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) # In[ ]: 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) # ### Colors, markers, and line styles # In[ ]: plt.figure() # In[ ]: plt.plot(randn(30).cumsum(), 'ko--') # In[ ]: plt.close('all') # In[ ]: data = randn(30).cumsum() plt.plot(data, 'k--', label='Default') plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post') plt.legend(loc='best') # ### Ticks, labels, and legends # #### Setting the title, axis labels, ticks, and ticklabels # In[ ]: 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') ax.set_xlabel('Stages') # #### Adding legends # In[ ]: 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') # ### Annotations and drawing on a subplot # In[ ]: from datetime import datetime fig = plt.figure() ax = fig.add_subplot(1, 1, 1) data = pd.read_csv('ch08/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') # In[ ]: 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) # ### Saving plots to file # In[ ]: fig # In[ ]: fig.savefig('figpath.svg') # In[ ]: fig.savefig('figpath.png', dpi=400, bbox_inches='tight') # In[ ]: from io import BytesIO buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() # ### matplotlib configuration # In[ ]: plt.rc('figure', figsize=(10, 10)) # ## Plotting functions in pandas # ### Line plots # In[ ]: plt.close('all') # In[ ]: s = Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10)) s.plot() # In[ ]: df = DataFrame(np.random.randn(10, 4).cumsum(0), columns=['A', 'B', 'C', 'D'], index=np.arange(0, 100, 10)) df.plot() # ### Bar plots # In[ ]: 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) # In[ ]: 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') # In[ ]: plt.figure() # In[ ]: df.plot(kind='barh', stacked=True, alpha=0.5) # In[ ]: tips = pd.read_csv('ch08/tips.csv') party_counts = pd.crosstab(tips.day, tips.size) party_counts # Not many 1- and 6-person parties party_counts = party_counts.ix[:, 2:5] # In[ ]: # Normalize to sum to 1 party_pcts = party_counts.div(party_counts.sum(1).astype(float), axis=0) party_pcts party_pcts.plot(kind='bar', stacked=True) # ### Histograms and density plots # In[ ]: plt.figure() # In[ ]: tips['tip_pct'] = tips['tip'] / tips['total_bill'] tips['tip_pct'].hist(bins=50) # In[ ]: plt.figure() # In[ ]: tips['tip_pct'].plot(kind='kde') # In[ ]: plt.figure() # In[ ]: 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--') # ### Scatter plots # In[ ]: macro = pd.read_csv('ch08/macrodata.csv') data = macro[['cpi', 'm1', 'tbilrate', 'unemp']] trans_data = np.log(data).diff().dropna() trans_data[-5:] # In[ ]: plt.figure() # In[ ]: plt.scatter(trans_data['m1'], trans_data['unemp']) plt.title('Changes in log %s vs. log %s' % ('m1', 'unemp')) # In[ ]: pd.scatter_matrix(trans_data, diagonal='kde', color='k', alpha=0.3) # ## Plotting Maps: Visualizing Haiti Earthquake Crisis data # In[ ]: data = pd.read_csv('ch08/Haiti.csv') data.info() # In[ ]: data[['INCIDENT DATE', 'LATITUDE', 'LONGITUDE']][:10] # In[ ]: data['CATEGORY'][:6] # In[ ]: data.describe() # In[ ]: data = data[(data.LATITUDE > 18) & (data.LATITUDE < 20) & (data.LONGITUDE > -75) & (data.LONGITUDE < -70) & data.CATEGORY.notnull()] # In[ ]: 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() # In[ ]: get_english('2. Urgences logistiques | Vital Lines') # In[ ]: 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'] # In[ ]: 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) # In[ ]: dummy_frame.ix[:, :6].info() # In[ ]: 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_')) # In[ ]: data.ix[:, 10:15].info() # In[ ]: 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 # In[ ]: 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])) # In[ ]: 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])) # In[ ]: make_plot() # In[ ]: shapefile_path = 'ch08/PortAuPrince_Roads/PortAuPrince_Roads' m.readshapefile(shapefile_path, 'roads')