Hackeando el Periodismo con el notebook de IPython¶

1. "Workflow" en periodismo de datos.¶

¿Qué elementos contiene una historia?¶

Técnicamente es una documento basado de JSON:

In [1]:

from IPython.nbformat import current
with open('Hackeando el periodismo.ipynb') as f:
    nb = current.read(f,'json')

In [2]:

nb.worksheets[0].cells[10:20]

Out[2]:

[{u'cell_type': u'heading',
  u'level': 2,
  u'metadata': {u'internals': {u'slide_type': u'subslide'},
   u'slideshow': {u'slide_type': u'slide'}},
  u'source': u'2. El notebook the IPython. '},
 {u'cell_type': u'markdown',
  u'metadata': {u'internals': {}, u'slideshow': {u'slide_type': u'-'}},
  u'source': u'<img src="/files/figs/logo.png" />'},
 {u'cell_type': u'markdown',
  u'metadata': {u'internals': {u'slide_helper': u'subslide_end'},
   u'slide_helper': u'subslide_end',
   u'slideshow': {u'slide_type': u'-'}},
  u'source': u'## www.ipython.org'},
 {u'cell_type': u'heading',
  u'level': 3,
  u'metadata': {u'internals': {u'slide_type': u'subslide'},
   u'slideshow': {u'slide_type': u'subslide'}},
  u'source': u'\xbfQu\xe9 es el  notebook the IPython?'},
 {u'cell_type': u'markdown',
  u'metadata': {u'internals': {u'slide_helper': u'subslide_end'},
   u'slide_helper': u'subslide_end',
   u'slideshow': {u'slide_type': u'-'}},
  u'source': u'Una herramienta para contar **historias**.'},
 {u'cell_type': u'markdown',
  u'metadata': {u'internals': {u'slide_type': u'subslide'},
   u'slideshow': {u'slide_type': u'subslide'}},
  u'source': u'T\xe9cnicamente es una documento basado de JSON:'},
 {u'cell_type': u'code',
  u'collapsed': False,
  u'input': u"from IPython.nbformat import current\nwith open('Hackeando el periodismo.ipynb') as f:\n    nb = current.read(f,'json')",
  u'language': u'python',
  u'metadata': {u'internals': {}, u'slideshow': {u'slide_type': u'-'}},
  u'outputs': [],
  u'prompt_number': 121},
 {u'cell_type': u'code',
  u'collapsed': False,
  u'input': u'nb.worksheets[0].cells[10:20]',
  u'language': u'python',
  u'metadata': {u'internals': {u'slide_helper': u'subslide_end'},
   u'slide_helper': u'subslide_end',
   u'slideshow': {u'slide_type': u'-'}},
  u'outputs': [{u'metadata': {},
    u'output_type': u'pyout',
    u'prompt_number': 122,
    u'text': u'[{u\'cell_type\': u\'heading\',\n  u\'level\': 2,\n  u\'metadata\': {u\'internals\': {u\'slide_type\': u\'subslide\'},\n   u\'slideshow\': {u\'slide_type\': u\'slide\'}},\n  u\'source\': u\'2. El notebook the IPython. \'},\n {u\'cell_type\': u\'markdown\',\n  u\'metadata\': {u\'internals\': {}, u\'slideshow\': {u\'slide_type\': u\'-\'}},\n  u\'source\': u\'<img src="/files/figs/logo.png" />\'},\n {u\'cell_type\': u\'markdown\',\n  u\'metadata\': {u\'internals\': {u\'slide_helper\': u\'subslide_end\'},\n   u\'slide_helper\': u\'subslide_end\',\n   u\'slideshow\': {u\'slide_type\': u\'-\'}},\n  u\'source\': u\'## www.ipython.org\'},\n {u\'cell_type\': u\'heading\',\n  u\'level\': 3,\n  u\'metadata\': {u\'internals\': {u\'slide_type\': u\'subslide\'},\n   u\'slideshow\': {u\'slide_type\': u\'subslide\'}},\n  u\'source\': u\'\\xbfQu\\xe9 es el  notebook the IPython?\'},\n {u\'cell_type\': u\'markdown\',\n  u\'metadata\': {u\'internals\': {u\'slide_helper\': u\'subslide_end\'},\n   u\'slide_helper\': u\'subslide_end\',\n   u\'slideshow\': {u\'slide_type\': u\'-\'}},\n  u\'source\': u\'Una herramienta para contar **historias**.\'},\n {u\'cell_type\': u\'markdown\',\n  u\'metadata\': {u\'internals\': {u\'slide_type\': u\'subslide\'},\n   u\'slideshow\': {u\'slide_type\': u\'subslide\'}},\n  u\'source\': u\'T\\xe9cnicamente es una documento basado de JSON:\'},\n {u\'cell_type\': u\'code\',\n  u\'collapsed\': False,\n  u\'input\': u"from IPython.nbformat import current\\nwith open(\'Hackeando el periodismo.ipynb\') as f:\\n    nb = current.read(f,\'json\')",\n  u\'language\': u\'python\',\n  u\'metadata\': {u\'internals\': {}, u\'slideshow\': {u\'slide_type\': u\'-\'}},\n  u\'outputs\': []},\n {u\'cell_type\': u\'code\',\n  u\'collapsed\': False,\n  u\'input\': u\'nb.worksheets[0].cells[10:20]\',\n  u\'language\': u\'python\',\n  u\'metadata\': {u\'internals\': {u\'slide_helper\': u\'subslide_end\'},\n   u\'slide_helper\': u\'subslide_end\',\n   u\'slideshow\': {u\'slide_type\': u\'-\'}},\n  u\'outputs\': []},\n {u\'cell_type\': u\'markdown\',\n  u\'metadata\': {u\'internals\': {u\'slide_helper\': u\'subslide_end\',\n    u\'slide_type\': u\'subslide\'},\n   u\'slide_helper\': u\'subslide_end\',\n   u\'slideshow\': {u\'slide_type\': u\'subslide\'}},\n  u\'source\': u\'* Genera archivos con la extensi\\xf3n `.ipynb` que se guardan en el directorio local.\\n* En el notebook podemos almacenar:\\n        * C\\xf3digo\\n        * Texto (Markdown)\\n        * Equaciones (LaTeX)\\n        * Im\\xe1genes\\n        * Links a videos\\n        * HTML\\n* Puede ser controlado por versiones.\\n* Puede verse sin tener IPython instalado usando NBViewer: http://nbviewer.ipython.org/.\\n* Puede ser exportado a otros formatos: Slides, HTML, Markdown, reStructured Text, LaTeX y PDF.\'},\n {u\'cell_type\': u\'heading\',\n  u\'level\': 3,\n  u\'metadata\': {u\'internals\': {u\'slide_type\': u\'subslide\'},\n   u\'slideshow\': {u\'slide_type\': u\'subslide\'}},\n  u\'source\': u\'\\xbfCu\\xe1les son sus caracter\\xedsticas principales?\'}]'}],
  u'prompt_number': 122},
 {u'cell_type': u'markdown',
  u'metadata': {u'internals': {u'slide_helper': u'subslide_end',
    u'slide_type': u'subslide'},
   u'slide_helper': u'subslide_end',
   u'slideshow': {u'slide_type': u'subslide'}},
  u'source': u'* Genera archivos con la extensi\xf3n `.ipynb` que se guardan en el directorio local.\n* En el notebook podemos almacenar:\n        * C\xf3digo\n        * Texto (Markdown)\n        * Equaciones (LaTeX)\n        * Im\xe1genes\n        * Links a videos\n        * HTML\n* Puede ser controlado por versiones.\n* Puede verse sin tener IPython instalado usando NBViewer: http://nbviewer.ipython.org/.\n* Puede ser exportado a otros formatos: Slides, HTML, Markdown, reStructured Text, LaTeX y PDF.'},
 {u'cell_type': u'heading',
  u'level': 3,
  u'metadata': {u'internals': {u'slide_type': u'subslide'},
   u'slideshow': {u'slide_type': u'subslide'}},
  u'source': u'\xbfCu\xe1les son sus caracter\xedsticas principales?'}]

Cargar código remoto¶

In [13]:

%load http://matplotlib.sourceforge.net/mpl_examples/api/collections_demo.py

In [14]:

#!/usr/bin/env python
'''Demonstration of LineCollection, PolyCollection, and
RegularPolyCollection with autoscaling.

For the first two subplots, we will use spirals.  Their
size will be set in plot units, not data units.  Their positions
will be set in data units by using the "offsets" and "transOffset"
kwargs of the LineCollection and PolyCollection.

The third subplot will make regular polygons, with the same
type of scaling and positioning as in the first two.

The last subplot illustrates the use of "offsets=(xo,yo)",
that is, a single tuple instead of a list of tuples, to generate
successively offset curves, with the offset given in data
units.  This behavior is available only for the LineCollection.

'''

import matplotlib.pyplot as plt
from matplotlib import collections, transforms
from matplotlib.colors import colorConverter
import numpy as np

nverts = 50
npts = 100

# Make some spirals
r = np.array(range(nverts))
theta = np.array(range(nverts)) * (2*np.pi)/(nverts-1)
xx = r * np.sin(theta)
yy = r * np.cos(theta)
spiral = list(zip(xx,yy))

# Make some offsets
rs = np.random.RandomState([12345678])
xo = rs.randn(npts)
yo = rs.randn(npts)
xyo = list(zip(xo, yo))

# Make a list of colors cycling through the rgbcmyk series.
colors = [colorConverter.to_rgba(c) for c in ('r','g','b','c','y','m','k')]

fig, axes = plt.subplots(2,2)
((ax1, ax2), (ax3, ax4)) = axes # unpack the axes


col = collections.LineCollection([spiral], offsets=xyo,
                                transOffset=ax1.transData)
trans = fig.dpi_scale_trans + transforms.Affine2D().scale(1.0/72.0)
col.set_transform(trans)  # the points to pixels transform
    # Note: the first argument to the collection initializer
    # must be a list of sequences of x,y tuples; we have only
    # one sequence, but we still have to put it in a list.
ax1.add_collection(col, autolim=True)
    # autolim=True enables autoscaling.  For collections with
    # offsets like this, it is neither efficient nor accurate,
    # but it is good enough to generate a plot that you can use
    # as a starting point.  If you know beforehand the range of
    # x and y that you want to show, it is better to set them
    # explicitly, leave out the autolim kwarg (or set it to False),
    # and omit the 'ax1.autoscale_view()' call below.

# Make a transform for the line segments such that their size is
# given in points:
col.set_color(colors)

ax1.autoscale_view()  # See comment above, after ax1.add_collection.
ax1.set_title('LineCollection using offsets')


# The same data as above, but fill the curves.
col = collections.PolyCollection([spiral], offsets=xyo,
                                transOffset=ax2.transData)
trans = transforms.Affine2D().scale(fig.dpi/72.0)
col.set_transform(trans)  # the points to pixels transform
ax2.add_collection(col, autolim=True)
col.set_color(colors)


ax2.autoscale_view()
ax2.set_title('PolyCollection using offsets')

# 7-sided regular polygons

col = collections.RegularPolyCollection(7,
                                        sizes = np.fabs(xx)*10.0, offsets=xyo,
                                        transOffset=ax3.transData)
trans = transforms.Affine2D().scale(fig.dpi/72.0)
col.set_transform(trans)  # the points to pixels transform
ax3.add_collection(col, autolim=True)
col.set_color(colors)
ax3.autoscale_view()
ax3.set_title('RegularPolyCollection using offsets')


# Simulate a series of ocean current profiles, successively
# offset by 0.1 m/s so that they form what is sometimes called
# a "waterfall" plot or a "stagger" plot.

nverts = 60
ncurves = 20
offs = (0.1, 0.0)

yy = np.linspace(0, 2*np.pi, nverts)
ym = np.amax(yy)
xx = (0.2 + (ym-yy)/ym)**2 * np.cos(yy-0.4) * 0.5
segs = []
for i in range(ncurves):
    xxx = xx + 0.02*rs.randn(nverts)
    curve = list(zip(xxx, yy*100))
    segs.append(curve)

col = collections.LineCollection(segs, offsets=offs)
ax4.add_collection(col, autolim=True)
col.set_color(colors)
ax4.autoscale_view()
ax4.set_title('Successive data offsets')
ax4.set_xlabel('Zonal velocity component (m/s)')
ax4.set_ylabel('Depth (m)')
# Reverse the y-axis so depth increases downward
ax4.set_ylim(ax4.get_ylim()[::-1])


plt.show()

Reendeo de LaTeX:¶

In [15]:

from IPython.display import Latex
Latex(r"""\begin{eqnarray}
\nabla \times \vec{\mathbf{B}} -\, \frac1c\, 
\frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
\nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
\nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, 
\frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
\nabla \cdot \vec{\mathbf{B}} & = 0 
\end{eqnarray}""")

Out[15]:

$\begin{eqnarray} \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\ \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\ \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\ \nabla \cdot \vec{\mathbf{B}} & = 0 \end{eqnarray}$

	Date	Open	High	Low	Close	Volume	Adj Close
0	2012-06-01	569.16	590.00	548.50	584.00	14077000	581.50
1	2012-05-01	584.90	596.76	522.18	577.73	18827900	575.26
2	2012-04-02	601.83	644.00	555.00	583.98	28759100	581.48
3	2012-03-01	548.17	621.45	516.22	599.55	26486000	596.99
4	2012-02-01	458.41	547.61	453.98	542.44	22001000	540.12
5	2012-01-03	409.40	458.24	409.00	456.48	12949100	454.53

Hackeando el Periodismo con el notebook de IPython¶

Damián Avila ¶

@damian_avila ¶

Github ¶

Email: info@oquanta.info¶

http://damianavila.github.io/hackperiodismo ¶

1. "Workflow" en periodismo de datos.¶

¿Qué hace un periodista de datos?¶

¿Qué elementos contiene una historia?¶

2. El notebook the IPython.¶

www.ipython.org¶

¿Qué es el notebook the IPython?¶

¿Cuáles son sus características principales?¶

2. Instalando el notebook de IPython.¶

Interfase de usuario.¶

Tipo de celdas¶

"Shortcuts" del teclado¶

3. Usando el notebook de IPython.¶

Trabajar con datos, códido y correrlo...¶

Documentar el flujo de trabajo.¶

Si el navegador puede mostrarlo...¶

Cargar código remoto¶

Reendeo de LaTeX:¶

Pandas¶

4. Discusión.¶

5. El fututo del notebook the IPython¶

GRACIAS!!¶

Damián Avila ¶

@damian_avila ¶

Github ¶

Email: info@oquanta.info¶