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91 changes: 45 additions & 46 deletions doc/source/io.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -19,10 +19,9 @@
import matplotlib.pyplot as plt
plt.close('all')

from pandas import *
options.display.max_rows=15
import pandas.util.testing as tm
clipdf = DataFrame({'A':[1,2,3],'B':[4,5,6],'C':['p','q','r']},
clipdf = pd.DataFrame({'A':[1,2,3],'B':[4,5,6],'C':['p','q','r']},
index=['x','y','z'])

===============================
Expand Down Expand Up @@ -1195,7 +1194,7 @@ class of the csv module. For this, you have to specify ``sep=None``.
.. ipython:: python
:suppress:

df = DataFrame(np.random.randn(10, 4))
df = pd.DataFrame(np.random.randn(10, 4))
df.to_csv('tmp.sv', sep='|')
df.to_csv('tmp2.sv', sep=':')

Expand Down Expand Up @@ -1375,7 +1374,7 @@ Note ``NaN``'s, ``NaT``'s and ``None`` will be converted to ``null`` and ``datet

.. ipython:: python

dfj = DataFrame(randn(5, 2), columns=list('AB'))
dfj = pd.DataFrame(randn(5, 2), columns=list('AB'))
json = dfj.to_json()
json

Expand All @@ -1387,10 +1386,10 @@ file / string. Consider the following DataFrame and Series:

.. ipython:: python

dfjo = DataFrame(dict(A=range(1, 4), B=range(4, 7), C=range(7, 10)),
dfjo = pd.DataFrame(dict(A=range(1, 4), B=range(4, 7), C=range(7, 10)),
columns=list('ABC'), index=list('xyz'))
dfjo
sjo = Series(dict(x=15, y=16, z=17), name='D')
sjo = pd.Series(dict(x=15, y=16, z=17), name='D')
sjo

**Column oriented** (the default for ``DataFrame``) serializes the data as
Expand Down Expand Up @@ -1472,10 +1471,10 @@ Writing to a file, with a date index and a date column
.. ipython:: python

dfj2 = dfj.copy()
dfj2['date'] = Timestamp('20130101')
dfj2['date'] = pd.Timestamp('20130101')
dfj2['ints'] = list(range(5))
dfj2['bools'] = True
dfj2.index = date_range('20130101', periods=5)
dfj2.index = pd.date_range('20130101', periods=5)
dfj2.to_json('test.json')
open('test.json').read()

Expand Down Expand Up @@ -1506,7 +1505,7 @@ problems:

In [141]: from datetime import timedelta

In [142]: dftd = DataFrame([timedelta(23), timedelta(seconds=5), 42])
In [142]: dftd = pd.DataFrame([timedelta(23), timedelta(seconds=5), 42])

In [143]: dftd.to_json()

Expand Down Expand Up @@ -1633,7 +1632,7 @@ Preserve string indices:

.. ipython:: python

si = DataFrame(np.zeros((4, 4)),
si = pd.DataFrame(np.zeros((4, 4)),
columns=list(range(4)),
index=[str(i) for i in range(4)])
si
Expand Down Expand Up @@ -1681,7 +1680,7 @@ data:

randfloats = np.random.uniform(-100, 1000, 10000)
randfloats.shape = (1000, 10)
dffloats = DataFrame(randfloats, columns=list('ABCDEFGHIJ'))
dffloats = pd.DataFrame(randfloats, columns=list('ABCDEFGHIJ'))

jsonfloats = dffloats.to_json()

Expand Down Expand Up @@ -1884,7 +1883,7 @@ Read in pandas ``to_html`` output (with some loss of floating point precision)

.. code-block:: python

df = DataFrame(randn(2, 2))
df = pd.DataFrame(randn(2, 2))
s = df.to_html(float_format='{0:.40g}'.format)
dfin = read_html(s, index_col=0)

Expand Down Expand Up @@ -1937,7 +1936,7 @@ in the method ``to_string`` described above.

.. ipython:: python

df = DataFrame(randn(2, 2))
df = pd.DataFrame(randn(2, 2))
df
print(df.to_html()) # raw html

Expand Down Expand Up @@ -2013,7 +2012,7 @@ Finally, the ``escape`` argument allows you to control whether the

.. ipython:: python

df = DataFrame({'a': list('&<>'), 'b': randn(3)})
df = pd.DataFrame({'a': list('&<>'), 'b': randn(3)})


.. ipython:: python
Expand Down Expand Up @@ -2367,7 +2366,7 @@ Added support for Openpyxl >= 2.2
bio = BytesIO()

# By setting the 'engine' in the ExcelWriter constructor.
writer = ExcelWriter(bio, engine='xlsxwriter')
writer = pd.ExcelWriter(bio, engine='xlsxwriter')
df.to_excel(writer, sheet_name='Sheet1')

# Save the workbook
Expand Down Expand Up @@ -2423,7 +2422,7 @@ argument to ``to_excel`` and to ``ExcelWriter``. The built-in engines are:
df.to_excel('path_to_file.xlsx', sheet_name='Sheet1', engine='xlsxwriter')

# By setting the 'engine' in the ExcelWriter constructor.
writer = ExcelWriter('path_to_file.xlsx', engine='xlsxwriter')
writer = pd.ExcelWriter('path_to_file.xlsx', engine='xlsxwriter')

# Or via pandas configuration.
from pandas import options
Expand Down Expand Up @@ -2559,7 +2558,7 @@ both on the writing (serialization), and reading (deserialization).

.. ipython:: python

df = DataFrame(np.random.rand(5,2),columns=list('AB'))
df = pd.DataFrame(np.random.rand(5,2),columns=list('AB'))
df.to_msgpack('foo.msg')
pd.read_msgpack('foo.msg')
s = Series(np.random.rand(5),index=date_range('20130101',periods=5))
Expand Down Expand Up @@ -2647,7 +2646,7 @@ for some advanced strategies

.. ipython:: python

store = HDFStore('store.h5')
store = pd.HDFStore('store.h5')
print(store)

Objects can be written to the file just like adding key-value pairs to a
Expand All @@ -2656,11 +2655,11 @@ dict:
.. ipython:: python

np.random.seed(1234)
index = date_range('1/1/2000', periods=8)
s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])
df = DataFrame(randn(8, 3), index=index,
index = pd.date_range('1/1/2000', periods=8)
s = pd.Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])
df = pd.DataFrame(randn(8, 3), index=index,
columns=['A', 'B', 'C'])
wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
wp = pd.Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
major_axis=date_range('1/1/2000', periods=5),
minor_axis=['A', 'B', 'C', 'D'])

Expand Down Expand Up @@ -2705,7 +2704,7 @@ Closing a Store, Context Manager

# Working with, and automatically closing the store with the context
# manager
with HDFStore('store.h5') as store:
with pd.HDFStore('store.h5') as store:
store.keys()

.. ipython:: python
Expand Down Expand Up @@ -2772,7 +2771,7 @@ This is also true for the major axis of a ``Panel``:
[[np.nan, np.nan, np.nan], [np.nan,5,6]],
[[np.nan, np.nan, np.nan],[np.nan,3,np.nan]]]

panel_with_major_axis_all_missing = Panel(matrix,
panel_with_major_axis_all_missing = pd.Panel(matrix,
items=['Item1', 'Item2','Item3'],
major_axis=[1,2],
minor_axis=['A', 'B', 'C'])
Expand Down Expand Up @@ -2816,7 +2815,7 @@ This format is specified by default when using ``put`` or ``to_hdf`` or by ``for

.. code-block:: python

DataFrame(randn(10,2)).to_hdf('test_fixed.h5','df')
pd.DataFrame(randn(10,2)).to_hdf('test_fixed.h5','df')

pd.read_hdf('test_fixed.h5','df',where='index>5')
TypeError: cannot pass a where specification when reading a fixed format.
Expand Down Expand Up @@ -2848,7 +2847,7 @@ enable ``put/append/to_hdf`` to by default store in the ``table`` format.

.. ipython:: python

store = HDFStore('store.h5')
store = pd.HDFStore('store.h5')
df1 = df[0:4]
df2 = df[4:]

Expand Down Expand Up @@ -2914,7 +2913,7 @@ defaults to `nan`.

.. ipython:: python

df_mixed = DataFrame({ 'A' : randn(8),
df_mixed = pd.DataFrame({ 'A' : randn(8),
'B' : randn(8),
'C' : np.array(randn(8),dtype='float32'),
'string' :'string',
Expand All @@ -2940,12 +2939,12 @@ storing/selecting from homogeneous index DataFrames.

.. ipython:: python

index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
index = pd.MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['foo', 'bar'])
df_mi = DataFrame(np.random.randn(10, 3), index=index,
df_mi = pd.DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
df_mi

Expand Down Expand Up @@ -3127,7 +3126,7 @@ specified in the format: ``<float>(<unit>)``, where float may be signed (and fra
.. ipython:: python

from datetime import timedelta
dftd = DataFrame(dict(A = Timestamp('20130101'), B = [ Timestamp('20130101') + timedelta(days=i,seconds=10) for i in range(10) ]))
dftd = pd.DataFrame(dict(A = Timestamp('20130101'), B = [ Timestamp('20130101') + timedelta(days=i,seconds=10) for i in range(10) ]))
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so since you eliminated the from pandas import * you need to build the docs and you will see failures, eg. pd.Timestamp

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Ah. OK, thanks for the hint.

dftd['C'] = dftd['A']-dftd['B']
dftd
store.append('dftd',dftd,data_columns=True)
Expand Down Expand Up @@ -3163,8 +3162,8 @@ Oftentimes when appending large amounts of data to a store, it is useful to turn

.. ipython:: python

df_1 = DataFrame(randn(10,2),columns=list('AB'))
df_2 = DataFrame(randn(10,2),columns=list('AB'))
df_1 = pd.DataFrame(randn(10,2),columns=list('AB'))
df_2 = pd.DataFrame(randn(10,2),columns=list('AB'))

st = pd.HDFStore('appends.h5',mode='w')
st.append('df', df_1, data_columns=['B'], index=False)
Expand Down Expand Up @@ -3261,7 +3260,7 @@ chunks.

.. ipython:: python

dfeq = DataFrame({'number': np.arange(1,11)})
dfeq = pd.DataFrame({'number': np.arange(1,11)})
dfeq

store.append('dfeq', dfeq, data_columns=['number'])
Expand Down Expand Up @@ -3301,7 +3300,7 @@ Sometimes you want to get the coordinates (a.k.a the index locations) of your qu

.. ipython:: python

df_coord = DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000))
df_coord = pd.DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000))
store.append('df_coord',df_coord)
c = store.select_as_coordinates('df_coord','index>20020101')
c.summary()
Expand All @@ -3318,7 +3317,7 @@ a datetimeindex which are 5.

.. ipython:: python

df_mask = DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000))
df_mask = pd.DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000))
store.append('df_mask',df_mask)
c = store.select_column('df_mask','index')
where = c[DatetimeIndex(c).month==5].index
Expand Down Expand Up @@ -3366,7 +3365,7 @@ results.

.. ipython:: python

df_mt = DataFrame(randn(8, 6), index=date_range('1/1/2000', periods=8),
df_mt = pd.DataFrame(randn(8, 6), index=date_range('1/1/2000', periods=8),
columns=['A', 'B', 'C', 'D', 'E', 'F'])
df_mt['foo'] = 'bar'
df_mt.ix[1, ('A', 'B')] = np.nan
Expand Down Expand Up @@ -3458,7 +3457,7 @@ Compression for all objects within the file

.. code-block:: python

store_compressed = HDFStore('store_compressed.h5', complevel=9, complib='blosc')
store_compressed = pd.HDFStore('store_compressed.h5', complevel=9, complib='blosc')

Or on-the-fly compression (this only applies to tables). You can turn
off file compression for a specific table by passing ``complevel=0``
Expand Down Expand Up @@ -3556,7 +3555,7 @@ stored in a more efficient manner.

.. ipython:: python

dfcat = DataFrame({ 'A' : Series(list('aabbcdba')).astype('category'),
dfcat = pd.DataFrame({ 'A' : Series(list('aabbcdba')).astype('category'),
'B' : np.random.randn(8) })
dfcat
dfcat.dtypes
Expand Down Expand Up @@ -3614,7 +3613,7 @@ Starting in 0.11.0, passing a ``min_itemsize`` dict will cause all passed column

.. ipython:: python

dfs = DataFrame(dict(A = 'foo', B = 'bar'),index=list(range(5)))
dfs = pd.DataFrame(dict(A = 'foo', B = 'bar'),index=list(range(5)))
dfs

# A and B have a size of 30
Expand All @@ -3633,7 +3632,7 @@ You could inadvertently turn an actual ``nan`` value into a missing value.

.. ipython:: python

dfss = DataFrame(dict(A = ['foo','bar','nan']))
dfss = pd.DataFrame(dict(A = ['foo','bar','nan']))
dfss

store.append('dfss', dfss)
Expand Down Expand Up @@ -3667,7 +3666,7 @@ It is possible to write an ``HDFStore`` object that can easily be imported into
index=range(100))
df_for_r.head()

store_export = HDFStore('export.h5')
store_export = pd.HDFStore('export.h5')
store_export.append('df_for_r', df_for_r, data_columns=df_dc.columns)
store_export

Expand Down Expand Up @@ -3756,7 +3755,7 @@ number of options, please see the docstring.
.. ipython:: python

# a legacy store
legacy_store = HDFStore(legacy_file_path,'r')
legacy_store = pd.HDFStore(legacy_file_path,'r')
legacy_store

# copy (and return the new handle)
Expand Down Expand Up @@ -3920,7 +3919,7 @@ the database using :func:`~pandas.DataFrame.to_sql`.
(42, datetime.datetime(2010,10,19), 'Y', -12.5, False),
(63, datetime.datetime(2010,10,20), 'Z', 5.73, True)]

data = DataFrame(d, columns=c)
data = pd.DataFrame(d, columns=c)

.. ipython:: python

Expand Down Expand Up @@ -4400,7 +4399,7 @@ into a .dta file. The format version of this file is always 115 (Stata 12).

.. ipython:: python

df = DataFrame(randn(10, 2), columns=list('AB'))
df = pd.DataFrame(randn(10, 2), columns=list('AB'))
df.to_stata('stata.dta')

*Stata* data files have limited data type support; only strings with
Expand Down Expand Up @@ -4625,7 +4624,7 @@ This is an informal comparison of various IO methods, using pandas 0.13.1.

.. code-block:: python

In [1]: df = DataFrame(randn(1000000,2),columns=list('AB'))
In [1]: df = pd.DataFrame(randn(1000000,2),columns=list('AB'))

In [2]: df.info()
<class 'pandas.core.frame.DataFrame'>
Expand Down Expand Up @@ -4699,7 +4698,7 @@ And here's the code
import os
from pandas.io import sql

df = DataFrame(randn(1000000,2),columns=list('AB'))
df = pd.DataFrame(randn(1000000,2),columns=list('AB'))

def test_sql_write(df):
if os.path.exists('test.sql'):
Expand Down