def get_inspect(self,
n=10,
offset=0,
columns=None,
wrap=inspect_settings._unspecified,
truncate=inspect_settings._unspecified,
round=inspect_settings._unspecified,
width=inspect_settings._unspecified,
margin=inspect_settings._unspecified,
with_types=inspect_settings._unspecified):
"""Returns an ATable object representing the table inspect --see frame.inspect()"""
from sparktk.frame.ops.take import take_rich
format_settings = inspect_settings.copy(wrap, truncate, round, width,
margin, with_types)
result = take_rich(self, n, offset, columns)
return ATable(result.data,
result.schema,
offset=offset,
format_settings=format_settings)
def inspect(self,
n=10,
offset=0,
columns=None,
wrap=inspect_settings._unspecified,
truncate=inspect_settings._unspecified,
round=inspect_settings._unspecified,
width=inspect_settings._unspecified,
margin=inspect_settings._unspecified,
with_types=inspect_settings._unspecified):
"""
Pretty-print of the frame data
Essentially returns a string, but technically returns a RowInspection object which renders a string.
The RowInspection object naturally converts to a str when needed, like when printed or when displayed
by python REPL (i.e. using the object's __repr__). If running in a script and want the inspect output
to be printed, then it must be explicitly printed, then `print frame.inspect()`
Parameters
----------
:param n: (Optional[int]) The number of rows to print
:param offset: (Optional[int]) The number of rows to skip before printing.
:param columns: (Optional[List[str]]) Filter columns to be included. By default, all columns are included.
:param wrap: (Optional[int or 'stripes']) If set to 'stripes' then inspect prints rows in stripes; if set to an
integer N, rows will be printed in clumps of N columns, where the columns are wrapped.
:param truncate: (Optional[int]) If set to integer N, all strings will be truncated to length N, including all
tagged ellipses.
:param round: (Optional[int]) If set to integer N, all floating point numbers will be rounded and truncated to
N digits.
:param width: (Optional[int]) If set to integer N, the print out will try to honor a max line width of N.
:param margin: (Optional[int]) Applies to 'stripes' mode only. If set to integer N, the margin for printing names
in a stripe will be limited to N characters.
:param with_types: (Optinoal[bool]) If set to True, header will include the data_type of each column.
:return: (RowsInspection) An object which naturally converts to a pretty-print string.
Examples
--------
To look at the first 4 rows of data in a frame:
<skip>
>>> frame.inspect(4)
[#] animal name age weight
==================================
[0] human George 8 542.5
[1] human Ursula 6 495.0
[2] ape Ape 41 400.0
[3] elephant Shep 5 8630.0
</skip>
# For other examples, see :ref:`example_frame.inspect`.
Note: if the frame data contains unicode characters, this method may raise a Unicode exception when
running in an interactive REPL or otherwise which triggers the standard python repr(). To get around
this problem, explicitly print the unicode of the returned object:
<skip>
>>> print unicode(frame.inspect())
</skip>
**Global Settings**
If not specified, the arguments that control formatting receive default values from
'sparktk.inspect_settings'. Make changes there to affect all calls to inspect.
>>> import sparktk
>>> sparktk.inspect_settings
wrap 20
truncate None
round None
width 80
margin None
with_types False
>>> sparktk.inspect_settings.width = 120 # changes inspect to use 120 width globally
>>> sparktk.inspect_settings.truncate = 16 # changes inspect to always truncate strings to 16 chars
>>> sparktk.inspect_settings
wrap 20
truncate 16
round None
width 120
margin None
with_types False
>>> sparktk.inspect_settings.width = None # return value back to default
>>> sparktk.inspect_settings
wrap 20
truncate 16
round None
width 80
margin None
with_types False
>>> sparktk.inspect_settings.reset() # set everything back to default
>>> sparktk.inspect_settings
wrap 20
truncate None
round None
width 80
margin None
with_types False
"""
from sparktk.frame.ops.take import take_rich
format_settings = inspect_settings.copy(wrap, truncate, round, width, margin, with_types)
result = take_rich(self, n, offset, columns)
return RowsInspection(result.data, result.schema, offset=offset, format_settings=format_settings)
def to_pandas(self, n=None, offset=0, columns=None):
"""
Brings data into a local pandas dataframe.
Similar to the 'take' function, but puts the data into a pandas dataframe.
Parameters
----------
:param n: (Optional(int)) The number of rows to get from the frame (warning: do not overwhelm the python session
by taking too much)
:param offset: (Optional(int)) The number of rows to skip before copying. Defaults to 0.
:param columns: (Optional(List[str])) Column filter. The list of names to be included. Default is all columns.
:return: (pandas.DataFrame) A new pandas dataframe object containing the taken frame data.
Examples
--------
<hide>
>>> data = [["Fred", "555-1234"],["Susan", "555-0202"],["Thurston","555-4510"],["Judy","555-2183"]]
>>> column_names = ["name", "phone"]
>>> frame = tc.frame.create(data, column_names)
</hide>
Consider the following spark-tk frame, where we have columns for name and phone number:
>>> frame.inspect()
[#] name phone
=======================
[0] Fred 555-1234
[1] Susan 555-0202
[2] Thurston 555-4510
[3] Judy 555-2183
>>> frame.schema
[('name', <type 'str'>), ('phone', <type 'str'>)]
The frame to_pandas() method is used to get a pandas DataFrame that contains the data from the spark-tk frame. Note
that since no parameters are provided when to_pandas() is called, the default values are used for the number of
rows, the row offset, and the columns.
>>> pandas_frame = frame.to_pandas()
>>> pandas_frame
name phone
0 Fred 555-1234
1 Susan 555-0202
2 Thurston 555-4510
3 Judy 555-2183
"""
try:
import pandas
except:
raise RuntimeError(
"pandas module not found, unable to download. Install pandas or try the take command."
)
from sparktk.frame.ops.take import take_rich
result = take_rich(self, n, offset, columns)
headers, data_types = zip(*result.schema)
frame_data = result.data
from sparktk import dtypes
import datetime
date_time_columns = [
i for i, x in enumerate(self.schema)
if x[1] in (dtypes.datetime, datetime.datetime)
]
has_date_time = len(date_time_columns) > 0
# translate our datetime long to datetime, so that it gets into the pandas df as a datetime column
def long_to_date_time(row):
for i in date_time_columns:
if isinstance(row[i], long):
row[i] = datetime.datetime.fromtimestamp(
row[i] // 1000).replace(microsecond=row[i] % 1000 * 1000)
return row
if (has_date_time):
frame_data = map(long_to_date_time, frame_data)
# create pandas df
pandas_df = pandas.DataFrame(frame_data, columns=headers)
for i, dtype in enumerate(data_types):
dtype_str = _sparktk_dtype_to_pandas_str(dtype)
try:
pandas_df[[headers[i]]] = pandas_df[[headers[i]]].astype(dtype_str)
except (TypeError, ValueError):
if dtype_str.startswith("int"):
# DataFrame does not handle missing values in int columns. If we get this error, use the 'object' datatype instead.
print "WARNING - Encountered problem casting column %s to %s, possibly due to missing values (i.e. presence of None). Continued by casting column %s as 'object'" % (
headers[i], dtype_str, headers[i])
pandas_df[[headers[i]]] = pandas_df[[headers[i]
]].astype("object")
else:
raise
return pandas_df
def to_pandas(self, n=None, offset=0, columns=None):
"""
Brings data into a local pandas dataframe.
Similar to the 'take' function, but puts the data into a pandas dataframe.
Parameters
----------
:param n: (Optional(int)) The number of rows to get from the frame (warning: do not overwhelm the python session
by taking too much)
:param offset: (Optional(int)) The number of rows to skip before copying. Defaults to 0.
:param columns: (Optional(List[str])) Column filter. The list of names to be included. Default is all columns.
:return: (pandas.DataFrame) A new pandas dataframe object containing the taken frame data.
Examples
--------
<hide>
>>> data = [["Fred", "555-1234"],["Susan", "555-0202"],["Thurston","555-4510"],["Judy","555-2183"]]
>>> column_names = ["name", "phone"]
>>> frame = tc.frame.create(data, column_names)
</hide>
Consider the following spark-tk frame, where we have columns for name and phone number:
>>> frame.inspect()
[#] name phone
=======================
[0] Fred 555-1234
[1] Susan 555-0202
[2] Thurston 555-4510
[3] Judy 555-2183
>>> frame.schema
[('name', <type 'str'>), ('phone', <type 'str'>)]
The frame to_pandas() method is used to get a pandas DataFrame that contains the data from the spark-tk frame. Note
that since no parameters are provided when to_pandas() is called, the default values are used for the number of
rows, the row offset, and the columns.
>>> pandas_frame = frame.to_pandas()
>>> pandas_frame
name phone
0 Fred 555-1234
1 Susan 555-0202
2 Thurston 555-4510
3 Judy 555-2183
"""
try:
import pandas
except:
raise RuntimeError("pandas module not found, unable to download. Install pandas or try the take command.")
from sparktk.frame.ops.take import take_rich
result = take_rich(self, n, offset, columns)
headers, data_types = zip(*result.schema)
frame_data = result.data
from sparktk import dtypes
import datetime
date_time_columns = [i for i, x in enumerate(self.schema) if x[1] in (dtypes.datetime, datetime.datetime)]
has_date_time = len(date_time_columns) > 0
# translate our datetime long to datetime, so that it gets into the pandas df as a datetime column
def long_to_date_time(row):
for i in date_time_columns:
if isinstance(row[i], long):
row[i] = datetime.datetime.fromtimestamp(row[i]//1000).replace(microsecond=row[i]%1000*1000)
return row
if (has_date_time):
frame_data = map(long_to_date_time, frame_data)
# create pandas df
pandas_df = pandas.DataFrame(frame_data, columns=headers)
for i, dtype in enumerate(data_types):
dtype_str = _sparktk_dtype_to_pandas_str(dtype)
try:
pandas_df[[headers[i]]] = pandas_df[[headers[i]]].astype(dtype_str)
except (TypeError, ValueError):
if dtype_str.startswith("int"):
# DataFrame does not handle missing values in int columns. If we get this error, use the 'object' datatype instead.
print "WARNING - Encountered problem casting column %s to %s, possibly due to missing values (i.e. presence of None). Continued by casting column %s as 'object'" % (headers[i], dtype_str, headers[i])
pandas_df[[headers[i]]] = pandas_df[[headers[i]]].astype("object")
else:
raise
return pandas_df
