.. currentmodule:: pandas
.. ipython:: python
:suppress:
import os
import csv
from pandas.compat import StringIO, BytesIO
import pandas as pd
ExcelWriter = pd.ExcelWriter
import numpy as np
np.random.seed(123456)
randn = np.random.randn
np.set_printoptions(precision=4, suppress=True)
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']},
index=['x','y','z'])
The pandas I/O API is a set of top level reader functions accessed like pd.read_csv() that generally return a pandas
object.
- :ref:`read_csv<io.read_csv_table>`
- :ref:`read_excel<io.excel>`
- :ref:`read_hdf<io.hdf5>`
- :ref:`read_sql<io.sql>`
- :ref:`read_json<io.json_reader>`
- :ref:`read_msgpack<io.msgpack>` (experimental)
- :ref:`read_html<io.read_html>`
- :ref:`read_gbq<io.bigquery>` (experimental)
- :ref:`read_stata<io.stata_reader>`
- :ref:`read_clipboard<io.clipboard>`
- :ref:`read_pickle<io.pickle>`
The corresponding writer functions are object methods that are accessed like df.to_csv()
- :ref:`to_csv<io.store_in_csv>`
- :ref:`to_excel<io.excel>`
- :ref:`to_hdf<io.hdf5>`
- :ref:`to_sql<io.sql>`
- :ref:`to_json<io.json_writer>`
- :ref:`to_msgpack<io.msgpack>` (experimental)
- :ref:`to_html<io.html>`
- :ref:`to_gbq<io.bigquery>` (experimental)
- :ref:`to_stata<io.stata_writer>`
- :ref:`to_clipboard<io.clipboard>`
- :ref:`to_pickle<io.pickle>`
:ref:`Here <io.perf>` is an informal performance comparison for some of these IO methods.
Note
For examples that use the StringIO class, make sure you import it
according to your Python version, i.e. from StringIO import StringIO for
Python 2 and from io import StringIO for Python 3.
The two workhorse functions for reading text files (a.k.a. flat files) are :func:`~pandas.io.parsers.read_csv` and :func:`~pandas.io.parsers.read_table`. They both use the same parsing code to intelligently convert tabular data into a DataFrame object. See the :ref:`cookbook<cookbook.csv>` for some advanced strategies
They can take a number of arguments:
filepath_or_buffer: Either a string path to a file, URL (including http, ftp, and S3 locations), or any object with areadmethod (such as an open file orStringIO).sepordelimiter: A delimiter / separator to split fields on. read_csv is capable of inferring the delimiter automatically in some cases by "sniffing." The separator may be specified as a regular expression; for instance you may use '|\s*' to indicate a pipe plus arbitrary whitespace.delim_whitespace: Parse whitespace-delimited (spaces or tabs) file (much faster than using a regular expression)compression: decompress'gzip'and'bz2'formats on the fly.dialect: string or :class:`python:csv.Dialect` instance to expose more ways to specify the file formatdtype: A data type name or a dict of column name to data type. If not specified, data types will be inferred. (Unsupported withengine='python')header: row number(s) to use as the column names, and the start of the data. Defaults to 0 if nonamespassed, otherwiseNone. Explicitly passheader=0to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns E.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example are skipped). Note that this parameter ignores commented lines and empty lines ifskip_blank_lines=True(the default), so header=0 denotes the first line of data rather than the first line of the file.skip_blank_lines: whether to skip over blank lines rather than interpreting them as NaN valuesskiprows: A collection of numbers for rows in the file to skip. Can also be an integer to skip the firstnrowsindex_col: column number, column name, or list of column numbers/names, to use as theindex(row labels) of the resulting DataFrame. By default, it will number the rows without using any column, unless there is one more data column than there are headers, in which case the first column is taken as the index.names: List of column names to use as column names. To replace header existing in file, explicitly passheader=0.na_values: optional list of strings to recognize as NaN (missing values), either in addition to or in lieu of the default set.true_values: list of strings to recognize asTruefalse_values: list of strings to recognize asFalsekeep_default_na: whether to include the default set of missing values in addition to the ones specified inna_valuesparse_dates: if True then index will be parsed as dates (False by default). You can specify more complicated options to parse a subset of columns or a combination of columns into a single date column (list of ints or names, list of lists, or dict) [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column [[1, 3]] -> combine columns 1 and 3 and parse as a single date column {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo'keep_date_col: if True, then date component columns passed intoparse_dateswill be retained in the output (False by default).date_parser: function to use to parse strings into datetime objects. Ifparse_datesis True, it defaults to the very robustdateutil.parser. Specifying this implicitly setsparse_datesas True. You can also use functions from community supported date converters from date_converters.pydayfirst: if True then uses the DD/MM international/European date format (This is False by default)thousands: specifies the thousands separator. If not None, this character will be stripped from numeric dtypes. However, if it is the first character in a field, that column will be imported as a string. In the PythonParser, if not None, then parser will try to look for it in the output and parse relevant data to numeric dtypes. Because it has to essentially scan through the data again, this causes a significant performance hit so only use if necessary.lineterminator: string (length 1), defaultNone, Character to break file into lines. Only valid with C parserquotechar: string, The character to used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored.quoting: int, Controls whether quotes should be recognized. Values are taken from csv.QUOTE_* values. Acceptable values are 0, 1, 2, and 3 for QUOTE_MINIMAL, QUOTE_ALL, QUOTE_NONE, and QUOTE_NONNUMERIC, respectively.skipinitialspace: boolean, defaultFalse, Skip spaces after delimiterescapechar: string, to specify how to escape quoted datacomment: Indicates remainder of line should not be parsed. If found at the beginning of a line, the line will be ignored altogether. This parameter must be a single character. Like empty lines, fully commented lines are ignored by the parameter header but not by skiprows. For example, if comment='#', parsing '#emptyn1,2,3na,b,c' with header=0 will result in '1,2,3' being treated as the header.nrows: Number of rows to read out of the file. Useful to only read a small portion of a large fileiterator: If True, return aTextFileReaderto enable reading a file into memory piece by piecechunksize: An number of rows to be used to "chunk" a file into pieces. Will cause anTextFileReaderobject to be returned. More on this below in the section on :ref:`iterating and chunking <io.chunking>`skip_footer: number of lines to skip at bottom of file (default 0) (Unsupported withengine='c')converters: a dictionary of functions for converting values in certain columns, where keys are either integers or column labelsencoding: a string representing the encoding to use for decoding unicode data, e.g.'utf-8`or'latin-1'. Full list of Python standard encodingsverbose: show number of NA values inserted in non-numeric columnssqueeze: if True then output with only one column is turned into Serieserror_bad_lines: if False then any lines causing an error will be skipped :ref:`bad lines <io.bad_lines>`usecols: a subset of columns to return, results in much faster parsing time and lower memory usage.mangle_dupe_cols: boolean, default True, then duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'tupleize_cols: boolean, default False, if False, convert a list of tuples to a multi-index of columns, otherwise, leave the column index as a list of tuplesfloat_precision: string, default None. Specifies which converter the C engine should use for floating-point values. The options are None for the ordinary converter, 'high' for the high-precision converter, and 'round_trip' for the round-trip converter.
.. ipython:: python
:suppress:
f = open('foo.csv','w')
f.write('date,A,B,C\n20090101,a,1,2\n20090102,b,3,4\n20090103,c,4,5')
f.close()
Consider a typical CSV file containing, in this case, some time series data:
.. ipython:: python
print(open('foo.csv').read())
The default for read_csv is to create a DataFrame with simple numbered rows:
.. ipython:: python
pd.read_csv('foo.csv')
In the case of indexed data, you can pass the column number or column name you wish to use as the index:
.. ipython:: python
pd.read_csv('foo.csv', index_col=0)
.. ipython:: python
pd.read_csv('foo.csv', index_col='date')
You can also use a list of columns to create a hierarchical index:
.. ipython:: python
pd.read_csv('foo.csv', index_col=[0, 'A'])
The dialect keyword gives greater flexibility in specifying the file format.
By default it uses the Excel dialect but you can specify either the dialect name
or a :class:`python:csv.Dialect` instance.
.. ipython:: python
:suppress:
data = ('label1,label2,label3\n'
'index1,"a,c,e\n'
'index2,b,d,f')
Suppose you had data with unenclosed quotes:
.. ipython:: python print(data)
By default, read_csv uses the Excel dialect and treats the double quote as
the quote character, which causes it to fail when it finds a newline before it
finds the closing double quote.
We can get around this using dialect
.. ipython:: python dia = csv.excel() dia.quoting = csv.QUOTE_NONE pd.read_csv(StringIO(data), dialect=dia)
All of the dialect options can be specified separately by keyword arguments:
.. ipython:: python
data = 'a,b,c~1,2,3~4,5,6'
pd.read_csv(StringIO(data), lineterminator='~')
Another common dialect option is skipinitialspace, to skip any whitespace
after a delimiter:
.. ipython:: python data = 'a, b, c\n1, 2, 3\n4, 5, 6' print(data) pd.read_csv(StringIO(data), skipinitialspace=True)
The parsers make every attempt to "do the right thing" and not be very fragile. Type inference is a pretty big deal. So if a column can be coerced to integer dtype without altering the contents, it will do so. Any non-numeric columns will come through as object dtype as with the rest of pandas objects.
Starting with v0.10, you can indicate the data type for the whole DataFrame or individual columns:
.. ipython:: python
data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'
print(data)
df = pd.read_csv(StringIO(data), dtype=object)
df
df['a'][0]
df = pd.read_csv(StringIO(data), dtype={'b': object, 'c': np.float64})
df.dtypes
Note
The dtype option is currently only supported by the C engine.
Specifying dtype with engine other than 'c' raises a
ValueError.
A file may or may not have a header row. pandas assumes the first row should be used as the column names:
.. ipython:: python
data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'
print(data)
pd.read_csv(StringIO(data))
By specifying the names argument in conjunction with header you can
indicate other names to use and whether or not to throw away the header row (if
any):
.. ipython:: python
print(data)
pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=0)
pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=None)
If the header is in a row other than the first, pass the row number to
header. This will skip the preceding rows:
.. ipython:: python
data = 'skip this skip it\na,b,c\n1,2,3\n4,5,6\n7,8,9'
pd.read_csv(StringIO(data), header=1)
The usecols argument allows you to select any subset of the columns in a
file, either using the column names or position numbers:
.. ipython:: python
data = 'a,b,c,d\n1,2,3,foo\n4,5,6,bar\n7,8,9,baz'
pd.read_csv(StringIO(data))
pd.read_csv(StringIO(data), usecols=['b', 'd'])
pd.read_csv(StringIO(data), usecols=[0, 2, 3])
If the comment parameter is specified, then completely commented lines will
be ignored. By default, completely blank lines will be ignored as well. Both of
these are API changes introduced in version 0.15.
.. ipython:: python data = '\na,b,c\n \n# commented line\n1,2,3\n\n4,5,6' print(data) pd.read_csv(StringIO(data), comment='#')
If skip_blank_lines=False, then read_csv will not ignore blank lines:
.. ipython:: python data = 'a,b,c\n\n1,2,3\n\n\n4,5,6' pd.read_csv(StringIO(data), skip_blank_lines=False)
Warning
The presence of ignored lines might create ambiguities involving line numbers;
the parameter header uses row numbers (ignoring commented/empty
lines), while skiprows uses line numbers (including commented/empty lines):
.. ipython:: python data = '#comment\na,b,c\nA,B,C\n1,2,3' pd.read_csv(StringIO(data), comment='#', header=1) data = 'A,B,C\n#comment\na,b,c\n1,2,3' pd.read_csv(StringIO(data), comment='#', skiprows=2)
If both header and skiprows are specified, header will be
relative to the end of skiprows. For example:
.. ipython:: python
data = '# empty\n# second empty line\n# third empty' \
'line\nX,Y,Z\n1,2,3\nA,B,C\n1,2.,4.\n5.,NaN,10.0'
print(data)
pd.read_csv(StringIO(data), comment='#', skiprows=4, header=1)
The encoding argument should be used for encoded unicode data, which will
result in byte strings being decoded to unicode in the result:
.. ipython:: python
data = b'word,length\nTr\xc3\xa4umen,7\nGr\xc3\xbc\xc3\x9fe,5'.decode('utf8').encode('latin-1')
df = pd.read_csv(BytesIO(data), encoding='latin-1')
df
df['word'][1]
Some formats which encode all characters as multiple bytes, like UTF-16, won't parse correctly at all without specifying the encoding. Full list of Python standard encodings
If a file has one more column of data than the number of column names, the first column will be used as the DataFrame's row names:
.. ipython:: python
data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'
pd.read_csv(StringIO(data))
.. ipython:: python
data = 'index,a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'
pd.read_csv(StringIO(data), index_col=0)
Ordinarily, you can achieve this behavior using the index_col option.
There are some exception cases when a file has been prepared with delimiters at
the end of each data line, confusing the parser. To explicitly disable the
index column inference and discard the last column, pass index_col=False:
.. ipython:: python
data = 'a,b,c\n4,apple,bat,\n8,orange,cow,'
print(data)
pd.read_csv(StringIO(data))
pd.read_csv(StringIO(data), index_col=False)
To better facilitate working with datetime data,
:func:`~pandas.io.parsers.read_csv` and :func:`~pandas.io.parsers.read_table`
uses the keyword arguments parse_dates and date_parser to allow users
to specify a variety of columns and date/time formats to turn the input text
data into datetime objects.
The simplest case is to just pass in parse_dates=True:
.. ipython:: python
# Use a column as an index, and parse it as dates.
df = pd.read_csv('foo.csv', index_col=0, parse_dates=True)
df
# These are python datetime objects
df.index
It is often the case that we may want to store date and time data separately,
or store various date fields separately. the parse_dates keyword can be
used to specify a combination of columns to parse the dates and/or times from.
You can specify a list of column lists to parse_dates, the resulting date
columns will be prepended to the output (so as to not affect the existing column
order) and the new column names will be the concatenation of the component
column names:
.. ipython:: python
:suppress:
data = ("KORD,19990127, 19:00:00, 18:56:00, 0.8100\n"
"KORD,19990127, 20:00:00, 19:56:00, 0.0100\n"
"KORD,19990127, 21:00:00, 20:56:00, -0.5900\n"
"KORD,19990127, 21:00:00, 21:18:00, -0.9900\n"
"KORD,19990127, 22:00:00, 21:56:00, -0.5900\n"
"KORD,19990127, 23:00:00, 22:56:00, -0.5900")
with open('tmp.csv', 'w') as fh:
fh.write(data)
.. ipython:: python
print(open('tmp.csv').read())
df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]])
df
By default the parser removes the component date columns, but you can choose
to retain them via the keep_date_col keyword:
.. ipython:: python
df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]],
keep_date_col=True)
df
Note that if you wish to combine multiple columns into a single date column, a
nested list must be used. In other words, parse_dates=[1, 2] indicates that
the second and third columns should each be parsed as separate date columns
while parse_dates=[[1, 2]] means the two columns should be parsed into a
single column.
You can also use a dict to specify custom name columns:
.. ipython:: python
date_spec = {'nominal': [1, 2], 'actual': [1, 3]}
df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec)
df
It is important to remember that if multiple text columns are to be parsed into a single date column, then a new column is prepended to the data. The index_col specification is based off of this new set of columns rather than the original data columns:
.. ipython:: python
date_spec = {'nominal': [1, 2], 'actual': [1, 3]}
df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
index_col=0) #index is the nominal column
df
Note
read_csv has a fast_path for parsing datetime strings in iso8601 format, e.g "2000-01-01T00:01:02+00:00" and similar variations. If you can arrange for your data to store datetimes in this format, load times will be significantly faster, ~20x has been observed.
Note
When passing a dict as the parse_dates argument, the order of the columns prepended is not guaranteed, because dict objects do not impose an ordering on their keys. On Python 2.7+ you may use collections.OrderedDict instead of a regular dict if this matters to you. Because of this, when using a dict for 'parse_dates' in conjunction with the index_col argument, it's best to specify index_col as a column label rather then as an index on the resulting frame.
The parameter float_precision can be specified in order to use
a specific floating-point converter during parsing with the C engine.
The options are the ordinary converter, the high-precision converter, and
the round-trip converter (which is guaranteed to round-trip values after
writing to a file). For example:
.. ipython:: python
val = '0.3066101993807095471566981359501369297504425048828125'
data = 'a,b,c\n1,2,{0}'.format(val)
abs(pd.read_csv(StringIO(data), engine='c', float_precision=None)['c'][0] - float(val))
abs(pd.read_csv(StringIO(data), engine='c', float_precision='high')['c'][0] - float(val))
abs(pd.read_csv(StringIO(data), engine='c', float_precision='round_trip')['c'][0] - float(val))
Finally, the parser allows you can specify a custom date_parser function to
take full advantage of the flexibility of the date parsing API:
.. ipython:: python
import pandas.io.date_converters as conv
df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
date_parser=conv.parse_date_time)
df
You can explore the date parsing functionality in date_converters.py and
add your own. We would love to turn this module into a community supported set
of date/time parsers. To get you started, date_converters.py contains
functions to parse dual date and time columns, year/month/day columns,
and year/month/day/hour/minute/second columns. It also contains a
generic_parser function so you can curry it with a function that deals with
a single date rather than the entire array.
.. ipython:: python
:suppress:
os.remove('tmp.csv')
If you have parse_dates enabled for some or all of your columns, and your
datetime strings are all formatted the same way, you may get a large speed
up by setting infer_datetime_format=True. If set, pandas will attempt
to guess the format of your datetime strings, and then use a faster means
of parsing the strings. 5-10x parsing speeds have been observed. pandas
will fallback to the usual parsing if either the format cannot be guessed
or the format that was guessed cannot properly parse the entire column
of strings. So in general, infer_datetime_format should not have any
negative consequences if enabled.
Here are some examples of datetime strings that can be guessed (All representing December 30th, 2011 at 00:00:00)
- "20111230"
- "2011/12/30"
- "20111230 00:00:00"
- "12/30/2011 00:00:00"
- "30/Dec/2011 00:00:00"
- "30/December/2011 00:00:00"
infer_datetime_format is sensitive to dayfirst. With
dayfirst=True, it will guess "01/12/2011" to be December 1st. With
dayfirst=False (default) it will guess "01/12/2011" to be January 12th.
.. ipython:: python
# Try to infer the format for the index column
df = pd.read_csv('foo.csv', index_col=0, parse_dates=True,
infer_datetime_format=True)
df
.. ipython:: python
:suppress:
os.remove('foo.csv')
While US date formats tend to be MM/DD/YYYY, many international formats use
DD/MM/YYYY instead. For convenience, a dayfirst keyword is provided:
.. ipython:: python
:suppress:
data = "date,value,cat\n1/6/2000,5,a\n2/6/2000,10,b\n3/6/2000,15,c"
with open('tmp.csv', 'w') as fh:
fh.write(data)
.. ipython:: python
print(open('tmp.csv').read())
pd.read_csv('tmp.csv', parse_dates=[0])
pd.read_csv('tmp.csv', dayfirst=True, parse_dates=[0])
For large numbers that have been written with a thousands separator, you can
set the thousands keyword to a string of length 1 so that integers will be parsed
correctly:
.. ipython:: python
:suppress:
data = ("ID|level|category\n"
"Patient1|123,000|x\n"
"Patient2|23,000|y\n"
"Patient3|1,234,018|z")
with open('tmp.csv', 'w') as fh:
fh.write(data)
By default, numbers with a thousands separator will be parsed as strings
.. ipython:: python
print(open('tmp.csv').read())
df = pd.read_csv('tmp.csv', sep='|')
df
df.level.dtype
The thousands keyword allows integers to be parsed correctly
.. ipython:: python
print(open('tmp.csv').read())
df = pd.read_csv('tmp.csv', sep='|', thousands=',')
df
df.level.dtype
.. ipython:: python
:suppress:
os.remove('tmp.csv')
To control which values are parsed as missing values (which are signified by NaN), specifiy a
list of strings in na_values. If you specify a number (a float, like 5.0 or an integer like 5),
the corresponding equivalent values will also imply a missing value (in this case effectively
[5.0,5] are recognized as NaN.
To completely override the default values that are recognized as missing, specify keep_default_na=False.
The default NaN recognized values are ['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A','N/A', 'NA',
'#NA', 'NULL', 'NaN', '-NaN', 'nan', '-nan'].
read_csv(path, na_values=[5])the default values, in addition to 5 , 5.0 when interpreted as numbers are recognized as NaN
read_csv(path, keep_default_na=False, na_values=[""])only an empty field will be NaN
read_csv(path, keep_default_na=False, na_values=["NA", "0"])only NA and 0 as strings are NaN
read_csv(path, na_values=["Nope"])the default values, in addition to the string "Nope" are recognized as NaN
inf like values will be parsed as np.inf (positive infinity), and -inf as -np.inf (negative infinity).
These will ignore the case of the value, meaning Inf, will also be parsed as np.inf.
Sometimes comments or meta data may be included in a file:
.. ipython:: python
:suppress:
data = ("ID,level,category\n"
"Patient1,123000,x # really unpleasant\n"
"Patient2,23000,y # wouldn't take his medicine\n"
"Patient3,1234018,z # awesome")
with open('tmp.csv', 'w') as fh:
fh.write(data)
.. ipython:: python
print(open('tmp.csv').read())
By default, the parse includes the comments in the output:
.. ipython:: python
df = pd.read_csv('tmp.csv')
df
We can suppress the comments using the comment keyword:
.. ipython:: python
df = pd.read_csv('tmp.csv', comment='#')
df
.. ipython:: python
:suppress:
os.remove('tmp.csv')
Using the squeeze keyword, the parser will return output with a single column
as a Series:
.. ipython:: python
:suppress:
data = ("level\n"
"Patient1,123000\n"
"Patient2,23000\n"
"Patient3,1234018")
with open('tmp.csv', 'w') as fh:
fh.write(data)
.. ipython:: python
print(open('tmp.csv').read())
output = pd.read_csv('tmp.csv', squeeze=True)
output
type(output)
.. ipython:: python
:suppress:
os.remove('tmp.csv')
The common values True, False, TRUE, and FALSE are all
recognized as boolean. Sometime you would want to recognize some other values
as being boolean. To do this use the true_values and false_values
options:
.. ipython:: python
data= 'a,b,c\n1,Yes,2\n3,No,4'
print(data)
pd.read_csv(StringIO(data))
pd.read_csv(StringIO(data), true_values=['Yes'], false_values=['No'])
Some files may have malformed lines with too few fields or too many. Lines with too few fields will have NA values filled in the trailing fields. Lines with too many will cause an error by default:
.. ipython:: python
:suppress:
data = 'a,b,c\n1,2,3\n4,5,6,7\n8,9,10'
In [27]: data = 'a,b,c\n1,2,3\n4,5,6,7\n8,9,10'
In [28]: pd.read_csv(StringIO(data))
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
CParserError: Error tokenizing data. C error: Expected 3 fields in line 3, saw 4
You can elect to skip bad lines:
In [29]: pd.read_csv(StringIO(data), error_bad_lines=False)
Skipping line 3: expected 3 fields, saw 4
Out[29]:
a b c
0 1 2 3
1 8 9 10
Quotes (and other escape characters) in embedded fields can be handled in any
number of ways. One way is to use backslashes; to properly parse this data, you
should pass the escapechar option:
.. ipython:: python data = 'a,b\n"hello, \\"Bob\\", nice to see you",5' print(data) pd.read_csv(StringIO(data), escapechar='\\')
While read_csv reads delimited data, the :func:`~pandas.io.parsers.read_fwf`
function works with data files that have known and fixed column widths.
The function parameters to read_fwf are largely the same as read_csv with
two extra parameters:
colspecs: A list of pairs (tuples) giving the extents of the fixed-width fields of each line as half-open intervals (i.e., [from, to[ ). String value 'infer' can be used to instruct the parser to try detecting the column specifications from the first 100 rows of the data. Default behaviour, if not specified, is to infer.widths: A list of field widths which can be used instead of 'colspecs' if the intervals are contiguous.
.. ipython:: python
:suppress:
f = open('bar.csv', 'w')
data1 = ("id8141 360.242940 149.910199 11950.7\n"
"id1594 444.953632 166.985655 11788.4\n"
"id1849 364.136849 183.628767 11806.2\n"
"id1230 413.836124 184.375703 11916.8\n"
"id1948 502.953953 173.237159 12468.3")
f.write(data1)
f.close()
Consider a typical fixed-width data file:
.. ipython:: python
print(open('bar.csv').read())
In order to parse this file into a DataFrame, we simply need to supply the column specifications to the read_fwf function along with the file name:
.. ipython:: python
#Column specifications are a list of half-intervals
colspecs = [(0, 6), (8, 20), (21, 33), (34, 43)]
df = pd.read_fwf('bar.csv', colspecs=colspecs, header=None, index_col=0)
df
Note how the parser automatically picks column names X.<column number> when
header=None argument is specified. Alternatively, you can supply just the
column widths for contiguous columns:
.. ipython:: python
#Widths are a list of integers
widths = [6, 14, 13, 10]
df = pd.read_fwf('bar.csv', widths=widths, header=None)
df
The parser will take care of extra white spaces around the columns so it's ok to have extra separation between the columns in the file.
.. versionadded:: 0.13.0
By default, read_fwf will try to infer the file's colspecs by using the
first 100 rows of the file. It can do it only in cases when the columns are
aligned and correctly separated by the provided delimiter (default delimiter
is whitespace).
.. ipython:: python
df = pd.read_fwf('bar.csv', header=None, index_col=0)
df
.. ipython:: python
:suppress:
os.remove('bar.csv')
.. ipython:: python
:suppress:
f = open('foo.csv', 'w')
f.write('A,B,C\n20090101,a,1,2\n20090102,b,3,4\n20090103,c,4,5')
f.close()
Consider a file with one less entry in the header than the number of data column:
.. ipython:: python
print(open('foo.csv').read())
In this special case, read_csv assumes that the first column is to be used
as the index of the DataFrame:
.. ipython:: python
pd.read_csv('foo.csv')
Note that the dates weren't automatically parsed. In that case you would need to do as before:
.. ipython:: python
df = pd.read_csv('foo.csv', parse_dates=True)
df.index
.. ipython:: python
:suppress:
os.remove('foo.csv')
Suppose you have data indexed by two columns:
.. ipython:: python
print(open('data/mindex_ex.csv').read())
The index_col argument to read_csv and read_table can take a list of
column numbers to turn multiple columns into a MultiIndex for the index of the
returned object:
.. ipython:: python
df = pd.read_csv("data/mindex_ex.csv", index_col=[0,1])
df
df.ix[1978]
By specifying list of row locations for the header argument, you
can read in a MultiIndex for the columns. Specifying non-consecutive
rows will skip the intervening rows. In order to have the pre-0.13 behavior
of tupleizing columns, specify tupleize_cols=True.
.. ipython:: python
from pandas.util.testing import makeCustomDataframe as mkdf
df = mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4)
df.to_csv('mi.csv')
print(open('mi.csv').read())
pd.read_csv('mi.csv',header=[0,1,2,3],index_col=[0,1])
Starting in 0.13.0, read_csv will be able to interpret a more common format
of multi-columns indices.
.. ipython:: python
:suppress:
data = ",a,a,a,b,c,c\n,q,r,s,t,u,v\none,1,2,3,4,5,6\ntwo,7,8,9,10,11,12"
fh = open('mi2.csv','w')
fh.write(data)
fh.close()
.. ipython:: python
print(open('mi2.csv').read())
pd.read_csv('mi2.csv',header=[0,1],index_col=0)
Note: If an index_col is not specified (e.g. you don't have an index, or wrote it
with df.to_csv(..., index=False), then any names on the columns index will be lost.
.. ipython:: python
:suppress:
import os
os.remove('mi.csv')
os.remove('mi2.csv')
read_csv is capable of inferring delimited (not necessarily
comma-separated) files. YMMV, as pandas uses the :class:`python:csv.Sniffer`
class of the csv module.
.. ipython:: python
:suppress:
df = DataFrame(np.random.randn(10, 4))
df.to_csv('tmp.sv', sep='|')
df.to_csv('tmp2.sv', sep=':')
.. ipython:: python
print(open('tmp2.sv').read())
pd.read_csv('tmp2.sv')
Suppose you wish to iterate through a (potentially very large) file lazily rather than reading the entire file into memory, such as the following:
.. ipython:: python
print(open('tmp.sv').read())
table = pd.read_table('tmp.sv', sep='|')
table
By specifying a chunksize to read_csv or read_table, the return
value will be an iterable object of type TextFileReader:
.. ipython:: python
reader = pd.read_table('tmp.sv', sep='|', chunksize=4)
reader
for chunk in reader:
print(chunk)
Specifying iterator=True will also return the TextFileReader object:
.. ipython:: python
reader = pd.read_table('tmp.sv', sep='|', iterator=True)
reader.get_chunk(5)
.. ipython:: python
:suppress:
os.remove('tmp.sv')
os.remove('tmp2.sv')
Under the hood pandas uses a fast and efficient parser implemented in C as well
as a python implementation which is currently more feature-complete. Where
possible pandas uses the C parser (specified as engine='c'), but may fall
back to python if C-unsupported options are specified. Currently, C-unsupported
options include:
sepother than a single character (e.g. regex separators)skip_footersep=Nonewithdelim_whitespace=False
Specifying any of the above options will produce a ParserWarning unless the
python engine is selected explicitly using engine='python'.
The Series and DataFrame objects have an instance method to_csv which
allows storing the contents of the object as a comma-separated-values file. The
function takes a number of arguments. Only the first is required.
path_or_buf: A string path to the file to write or a StringIOsep: Field delimiter for the output file (default ",")na_rep: A string representation of a missing value (default '')float_format: Format string for floating point numberscols: Columns to write (default None)header: Whether to write out the column names (default True)index: whether to write row (index) names (default True)index_label: Column label(s) for index column(s) if desired. If None (default), and header and index are True, then the index names are used. (A sequence should be given if the DataFrame uses MultiIndex).mode: Python write mode, default 'w'encoding: a string representing the encoding to use if the contents are non-ASCII, for python versions prior to 3line_terminator: Character sequence denoting line end (default '\n')quoting: Set quoting rules as in csv module (default csv.QUOTE_MINIMAL)quotechar: Character used to quote fields (default '"')doublequote: Control quoting ofquotecharin fields (default True)escapechar: Character used to escapesepandquotecharwhen appropriate (default None)chunksize: Number of rows to write at a timetupleize_cols: If False (default), write as a list of tuples, otherwise write in an expanded line format suitable forread_csvdate_format: Format string for datetime objects
The DataFrame object has an instance method to_string which allows control
over the string representation of the object. All arguments are optional:
bufdefault None, for example a StringIO objectcolumnsdefault None, which columns to writecol_spacedefault None, minimum width of each column.na_repdefaultNaN, representation of NA valueformattersdefault None, a dictionary (by column) of functions each of which takes a single argument and returns a formatted stringfloat_formatdefault None, a function which takes a single (float) argument and returns a formatted string; to be applied to floats in the DataFrame.sparsifydefault True, set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.index_namesdefault True, will print the names of the indicesindexdefault True, will print the index (ie, row labels)headerdefault True, will print the column labelsjustifydefaultleft, will print column headers left- or right-justified
The Series object also has a to_string method, but with only the buf,
na_rep, float_format arguments. There is also a length argument
which, if set to True, will additionally output the length of the Series.
Read and write JSON format files and strings.
A Series or DataFrame can be converted to a valid JSON string. Use to_json
with optional parameters:
path_or_buf: the pathname or buffer to write the output This can beNonein which case a JSON string is returnedorient:- Series :
- default is
index - allowed values are {
split,records,index}
- default is
- DataFrame
- default is
columns - allowed values are {
split,records,index,columns,values}
- default is
The format of the JSON string
splitdict like {index -> [index], columns -> [columns], data -> [values]}
recordslist like [{column -> value}, ... , {column -> value}]
indexdict like {index -> {column -> value}}
columnsdict like {column -> {index -> value}}
valuesjust the values array
date_format: string, type of date conversion, 'epoch' for timestamp, 'iso' for ISO8601.double_precision: The number of decimal places to use when encoding floating point values, default 10.force_ascii: force encoded string to be ASCII, default True.date_unit: The time unit to encode to, governs timestamp and ISO8601 precision. One of 's', 'ms', 'us' or 'ns' for seconds, milliseconds, microseconds and nanoseconds respectively. Default 'ms'.default_handler: The handler to call if an object cannot otherwise be converted to a suitable format for JSON. Takes a single argument, which is the object to convert, and returns a serializable object.
Note NaN's, NaT's and None will be converted to null and datetime objects will be converted based on the date_format and date_unit parameters.
.. ipython:: python
dfj = DataFrame(randn(5, 2), columns=list('AB'))
json = dfj.to_json()
json
There are a number of different options for the format of the resulting JSON 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)),
columns=list('ABC'), index=list('xyz'))
dfjo
sjo = Series(dict(x=15, y=16, z=17), name='D')
sjo
Column oriented (the default for DataFrame) serializes the data as
nested JSON objects with column labels acting as the primary index:
.. ipython:: python dfjo.to_json(orient="columns") # Not available for Series
Index oriented (the default for Series) similar to column oriented
but the index labels are now primary:
.. ipython:: python dfjo.to_json(orient="index") sjo.to_json(orient="index")
Record oriented serializes the data to a JSON array of column -> value records, index labels are not included. This is useful for passing DataFrame data to plotting libraries, for example the JavaScript library d3.js:
.. ipython:: python dfjo.to_json(orient="records") sjo.to_json(orient="records")
Value oriented is a bare-bones option which serializes to nested JSON arrays of values only, column and index labels are not included:
.. ipython:: python dfjo.to_json(orient="values") # Not available for Series
Split oriented serializes to a JSON object containing separate entries for
values, index and columns. Name is also included for Series:
.. ipython:: python dfjo.to_json(orient="split") sjo.to_json(orient="split")
Note
Any orient option that encodes to a JSON object will not preserve the ordering of index and column labels during round-trip serialization. If you wish to preserve label ordering use the split option as it uses ordered containers.
Writing in ISO date format
.. ipython:: python
dfd = DataFrame(randn(5, 2), columns=list('AB'))
dfd['date'] = Timestamp('20130101')
dfd = dfd.sort_index(1, ascending=False)
json = dfd.to_json(date_format='iso')
json
Writing in ISO date format, with microseconds
.. ipython:: python json = dfd.to_json(date_format='iso', date_unit='us') json
Epoch timestamps, in seconds
.. ipython:: python json = dfd.to_json(date_format='epoch', date_unit='s') json
Writing to a file, with a date index and a date column
.. ipython:: python
dfj2 = dfj.copy()
dfj2['date'] = Timestamp('20130101')
dfj2['ints'] = list(range(5))
dfj2['bools'] = True
dfj2.index = date_range('20130101', periods=5)
dfj2.to_json('test.json')
open('test.json').read()
If the JSON serializer cannot handle the container contents directly it will fallback in the following manner:
- if a
toDictmethod is defined by the unrecognised object then that will be called and its returneddictwill be JSON serialized. - if a
default_handlerhas been passed toto_jsonthat will be called to convert the object. - otherwise an attempt is made to convert the object to a
dictby parsing its contents. However if the object is complex this will often fail with anOverflowError.
Your best bet when encountering OverflowError during serialization
is to specify a default_handler. For example timedelta can cause
problems:
.. ipython:: python :suppress: from datetime import timedelta dftd = DataFrame([timedelta(23), timedelta(seconds=5), 42])
In [141]: from datetime import timedelta
In [142]: dftd = DataFrame([timedelta(23), timedelta(seconds=5), 42])
In [143]: dftd.to_json()
---------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
OverflowError: Maximum recursion level reached
which can be dealt with by specifying a simple default_handler:
.. ipython:: python
dftd.to_json(default_handler=str)
def my_handler(obj):
return obj.total_seconds()
dftd.to_json(default_handler=my_handler)
Reading a JSON string to pandas object can take a number of parameters.
The parser will try to parse a DataFrame if typ is not supplied or
is None. To explicitly force Series parsing, pass typ=series
filepath_or_buffer: a VALID JSON string or file handle / StringIO. The string could be a URL. Valid URL schemes include http, ftp, S3, and file. For file URLs, a host is expected. For instance, a local file could be file ://localhost/path/to/table.jsontyp: type of object to recover (series or frame), default 'frame'orient:- Series :
- default is
index - allowed values are {
split,records,index}
- default is
- DataFrame
- default is
columns - allowed values are {
split,records,index,columns,values}
- default is
The format of the JSON string
splitdict like {index -> [index], columns -> [columns], data -> [values]}
recordslist like [{column -> value}, ... , {column -> value}]
indexdict like {index -> {column -> value}}
columnsdict like {column -> {index -> value}}
valuesjust the values array
dtype: if True, infer dtypes, if a dict of column to dtype, then use those, if False, then don't infer dtypes at all, default is True, apply only to the dataconvert_axes: boolean, try to convert the axes to the proper dtypes, default is Trueconvert_dates: a list of columns to parse for dates; If True, then try to parse date-like columns, default is Truekeep_default_dates: boolean, default True. If parsing dates, then parse the default date-like columnsnumpy: direct decoding to numpy arrays. default is False; Supports numeric data only, although labels may be non-numeric. Also note that the JSON ordering MUST be the same for each term ifnumpy=Trueprecise_float: boolean, defaultFalse. Set to enable usage of higher precision (strtod) function when decoding string to double values. Default (False) is to use fast but less precise builtin functionalitydate_unit: string, the timestamp unit to detect if converting dates. Default None. By default the timestamp precision will be detected, if this is not desired then pass one of 's', 'ms', 'us' or 'ns' to force timestamp precision to seconds, milliseconds, microseconds or nanoseconds respectively.
The parser will raise one of ValueError/TypeError/AssertionError if the JSON is not parseable.
If a non-default orient was used when encoding to JSON be sure to pass the same
option here so that decoding produces sensible results, see Orient Options for an
overview.
The default of convert_axes=True, dtype=True, and convert_dates=True will try to parse the axes, and all of the data
into appropriate types, including dates. If you need to override specific dtypes, pass a dict to dtype. convert_axes should only
be set to False if you need to preserve string-like numbers (e.g. '1', '2') in an axes.
Note
Large integer values may be converted to dates if convert_dates=True and the data and / or column labels appear 'date-like'. The exact threshold depends on the date_unit specified.
Warning
When reading JSON data, automatic coercing into dtypes has some quirks:
- an index can be reconstructed in a different order from serialization, that is, the returned order is not guaranteed to be the same as before serialization
- a column that was
floatdata will be converted tointegerif it can be done safely, e.g. a column of1.- bool columns will be converted to
integeron reconstruction
Thus there are times where you may want to specify specific dtypes via the dtype keyword argument.
Reading from a JSON string:
.. ipython:: python pd.read_json(json)
Reading from a file:
.. ipython:: python
pd.read_json('test.json')
Don't convert any data (but still convert axes and dates):
.. ipython:: python
pd.read_json('test.json', dtype=object).dtypes
Specify dtypes for conversion:
.. ipython:: python
pd.read_json('test.json', dtype={'A' : 'float32', 'bools' : 'int8'}).dtypes
Preserve string indices:
.. ipython:: python
si = DataFrame(np.zeros((4, 4)),
columns=list(range(4)),
index=[str(i) for i in range(4)])
si
si.index
si.columns
json = si.to_json()
sij = pd.read_json(json, convert_axes=False)
sij
sij.index
sij.columns
Dates written in nanoseconds need to be read back in nanoseconds:
.. ipython:: python json = dfj2.to_json(date_unit='ns') # Try to parse timestamps as millseconds -> Won't Work dfju = pd.read_json(json, date_unit='ms') dfju # Let pandas detect the correct precision dfju = pd.read_json(json) dfju # Or specify that all timestamps are in nanoseconds dfju = pd.read_json(json, date_unit='ns') dfju
Note
This supports numeric data only. Index and columns labels may be non-numeric, e.g. strings, dates etc.
If numpy=True is passed to read_json an attempt will be made to sniff
an appropriate dtype during deserialization and to subsequently decode directly
to numpy arrays, bypassing the need for intermediate Python objects.
This can provide speedups if you are deserialising a large amount of numeric data:
.. ipython:: python
randfloats = np.random.uniform(-100, 1000, 10000)
randfloats.shape = (1000, 10)
dffloats = DataFrame(randfloats, columns=list('ABCDEFGHIJ'))
jsonfloats = dffloats.to_json()
.. ipython:: python timeit read_json(jsonfloats)
.. ipython:: python timeit read_json(jsonfloats, numpy=True)
The speedup is less noticeable for smaller datasets:
.. ipython:: python jsonfloats = dffloats.head(100).to_json()
.. ipython:: python timeit read_json(jsonfloats)
.. ipython:: python timeit read_json(jsonfloats, numpy=True)
Warning
Direct numpy decoding makes a number of assumptions and may fail or produce unexpected output if these assumptions are not satisfied:
- data is numeric.
- data is uniform. The dtype is sniffed from the first value decoded. A
ValueErrormay be raised, or incorrect output may be produced if this condition is not satisfied.- labels are ordered. Labels are only read from the first container, it is assumed that each subsequent row / column has been encoded in the same order. This should be satisfied if the data was encoded using
to_jsonbut may not be the case if the JSON is from another source.
.. ipython:: python
:suppress:
import os
os.remove('test.json')
.. versionadded:: 0.13.0
pandas provides a utility function to take a dict or list of dicts and normalize this semi-structured data into a flat table.
.. ipython:: python
from pandas.io.json import json_normalize
data = [{'state': 'Florida',
'shortname': 'FL',
'info': {
'governor': 'Rick Scott'
},
'counties': [{'name': 'Dade', 'population': 12345},
{'name': 'Broward', 'population': 40000},
{'name': 'Palm Beach', 'population': 60000}]},
{'state': 'Ohio',
'shortname': 'OH',
'info': {
'governor': 'John Kasich'
},
'counties': [{'name': 'Summit', 'population': 1234},
{'name': 'Cuyahoga', 'population': 1337}]}]
json_normalize(data, 'counties', ['state', 'shortname', ['info', 'governor']])
Warning
We highly encourage you to read the :ref:`HTML parsing gotchas <html-gotchas>` regarding the issues surrounding the BeautifulSoup4/html5lib/lxml parsers.
.. versionadded:: 0.12.0
The top-level :func:`~pandas.io.html.read_html` function can accept an HTML string/file/URL and will parse HTML tables into list of pandas DataFrames. Let's look at a few examples.
Note
read_html returns a list of DataFrame objects, even if there is
only a single table contained in the HTML content
Read a URL with no options
.. ipython:: python url = 'http://www.fdic.gov/bank/individual/failed/banklist.html' dfs = read_html(url) dfs
Note
The data from the above URL changes every Monday so the resulting data above and the data below may be slightly different.
Read in the content of the file from the above URL and pass it to read_html
as a string
.. ipython:: python
:suppress:
import os
file_path = os.path.abspath(os.path.join('source', '_static', 'banklist.html'))
.. ipython:: python
with open(file_path, 'r') as f:
dfs = read_html(f.read())
dfs
You can even pass in an instance of StringIO if you so desire
.. ipython:: python
with open(file_path, 'r') as f:
sio = StringIO(f.read())
dfs = read_html(sio)
dfs
Note
The following examples are not run by the IPython evaluator due to the fact that having so many network-accessing functions slows down the documentation build. If you spot an error or an example that doesn't run, please do not hesitate to report it over on pandas GitHub issues page.
Read a URL and match a table that contains specific text
match = 'Metcalf Bank'
df_list = read_html(url, match=match)Specify a header row (by default <th> elements are used to form the column
index); if specified, the header row is taken from the data minus the parsed
header elements (<th> elements).
dfs = read_html(url, header=0)Specify an index column
dfs = read_html(url, index_col=0)Specify a number of rows to skip
dfs = read_html(url, skiprows=0)Specify a number of rows to skip using a list (xrange (Python 2 only) works
as well)
dfs = read_html(url, skiprows=range(2))Don't infer numeric and date types
dfs = read_html(url, infer_types=False)Specify an HTML attribute
dfs1 = read_html(url, attrs={'id': 'table'})
dfs2 = read_html(url, attrs={'class': 'sortable'})
print(np.array_equal(dfs1[0], dfs2[0])) # Should be TrueUse some combination of the above
dfs = read_html(url, match='Metcalf Bank', index_col=0)Read in pandas to_html output (with some loss of floating point precision)
df = DataFrame(randn(2, 2))
s = df.to_html(float_format='{0:.40g}'.format)
dfin = read_html(s, index_col=0)The lxml backend will raise an error on a failed parse if that is the only
parser you provide (if you only have a single parser you can provide just a
string, but it is considered good practice to pass a list with one string if,
for example, the function expects a sequence of strings)
dfs = read_html(url, 'Metcalf Bank', index_col=0, flavor=['lxml'])or
dfs = read_html(url, 'Metcalf Bank', index_col=0, flavor='lxml')However, if you have bs4 and html5lib installed and pass None or ['lxml',
'bs4'] then the parse will most likely succeed. Note that as soon as a parse
succeeds, the function will return.
dfs = read_html(url, 'Metcalf Bank', index_col=0, flavor=['lxml', 'bs4'])DataFrame objects have an instance method to_html which renders the
contents of the DataFrame as an HTML table. The function arguments are as
in the method to_string described above.
Note
Not all of the possible options for DataFrame.to_html are shown here for
brevity's sake. See :func:`~pandas.core.frame.DataFrame.to_html` for the
full set of options.
.. ipython:: python
:suppress:
def write_html(df, filename, *args, **kwargs):
static = os.path.abspath(os.path.join('source', '_static'))
with open(os.path.join(static, filename + '.html'), 'w') as f:
df.to_html(f, *args, **kwargs)
.. ipython:: python df = DataFrame(randn(2, 2)) df print(df.to_html()) # raw html
.. ipython:: python :suppress: write_html(df, 'basic')
HTML:
The columns argument will limit the columns shown
.. ipython:: python print(df.to_html(columns=[0]))
.. ipython:: python :suppress: write_html(df, 'columns', columns=[0])
HTML:
float_format takes a Python callable to control the precision of floating
point values
.. ipython:: python
print(df.to_html(float_format='{0:.10f}'.format))
.. ipython:: python
:suppress:
write_html(df, 'float_format', float_format='{0:.10f}'.format)
HTML:
bold_rows will make the row labels bold by default, but you can turn that
off
.. ipython:: python print(df.to_html(bold_rows=False))
.. ipython:: python :suppress: write_html(df, 'nobold', bold_rows=False)
The classes argument provides the ability to give the resulting HTML
table CSS classes. Note that these classes are appended to the existing
'dataframe' class.
.. ipython:: python print(df.to_html(classes=['awesome_table_class', 'even_more_awesome_class']))
Finally, the escape argument allows you to control whether the
"<", ">" and "&" characters escaped in the resulting HTML (by default it is
True). So to get the HTML without escaped characters pass escape=False
.. ipython:: python
df = DataFrame({'a': list('&<>'), 'b': randn(3)})
.. ipython:: python :suppress: write_html(df, 'escape') write_html(df, 'noescape', escape=False)
Escaped:
.. ipython:: python print(df.to_html())
Not escaped:
.. ipython:: python print(df.to_html(escape=False))
Note
Some browsers may not show a difference in the rendering of the previous two HTML tables.
The :func:`~pandas.read_excel` method can read Excel 2003 (.xls) and
Excel 2007 (.xlsx) files using the xlrd Python
module and use the same parsing code as the above to convert tabular data into
a DataFrame. See the :ref:`cookbook<cookbook.excel>` for some
advanced strategies
Besides read_excel you can also read Excel files using the ExcelFile
class. The following two commands are equivalent:
# using the ExcelFile class
xls = pd.ExcelFile('path_to_file.xls')
xls.parse('Sheet1', index_col=None, na_values=['NA'])
# using the read_excel function
read_excel('path_to_file.xls', 'Sheet1', index_col=None, na_values=['NA'])The class based approach can be used to read multiple sheets or to introspect
the sheet names using the sheet_names attribute.
Note
The prior method of accessing ExcelFile has been moved from
pandas.io.parsers to the top level namespace starting from pandas
0.12.0.
.. versionadded:: 0.13
There are now two ways to read in sheets from an Excel file. You can provide
either the index of a sheet or its name to by passing different values for
sheet_name.
- Pass a string to refer to the name of a particular sheet in the workbook.
- Pass an integer to refer to the index of a sheet. Indices follow Python convention, beginning at 0.
- The default value is
sheet_name=0. This reads the first sheet.
Using the sheet name:
read_excel('path_to_file.xls', 'Sheet1', index_col=None, na_values=['NA'])Using the sheet index:
read_excel('path_to_file.xls', 0, index_col=None, na_values=['NA'])Using all default values:
read_excel('path_to_file.xls')It is often the case that users will insert columns to do temporary computations in Excel and you may not want to read in those columns. read_excel takes a parse_cols keyword to allow you to specify a subset of columns to parse.
If parse_cols is an integer, then it is assumed to indicate the last column to be parsed.
read_excel('path_to_file.xls', 'Sheet1', parse_cols=2)If parse_cols is a list of integers, then it is assumed to be the file column indices to be parsed.
read_excel('path_to_file.xls', 'Sheet1', parse_cols=[0, 2, 3])To write a DataFrame object to a sheet of an Excel file, you can use the
to_excel instance method. The arguments are largely the same as to_csv
described above, the first argument being the name of the excel file, and the
optional second argument the name of the sheet to which the DataFrame should be
written. For example:
df.to_excel('path_to_file.xlsx', sheet_name='Sheet1')Files with a .xls extension will be written using xlwt and those with a
.xlsx extension will be written using xlsxwriter (if available) or
openpyxl.
The DataFrame will be written in a way that tries to mimic the REPL output. One
difference from 0.12.0 is that the index_label will be placed in the second
row instead of the first. You can get the previous behaviour by setting the
merge_cells option in to_excel() to False:
df.to_excel('path_to_file.xlsx', index_label='label', merge_cells=False)The Panel class also has a to_excel instance method,
which writes each DataFrame in the Panel to a separate sheet.
In order to write separate DataFrames to separate sheets in a single Excel file, one can pass an :class:`~pandas.io.excel.ExcelWriter`.
with ExcelWriter('path_to_file.xlsx') as writer:
df1.to_excel(writer, sheet_name='Sheet1')
df2.to_excel(writer, sheet_name='Sheet2')Note
Wringing a little more performance out of read_excel
Internally, Excel stores all numeric data as floats. Because this can
produce unexpected behavior when reading in data, pandas defaults to trying
to convert integers to floats if it doesn't lose information (1.0 -->
1). You can pass convert_float=False to disable this behavior, which
may give a slight performance improvement.
.. versionadded:: 0.13
pandas chooses an Excel writer via two methods:
- the
enginekeyword argument - the filename extension (via the default specified in config options)
By default, pandas uses the XlsxWriter for .xlsx and openpyxl
for .xlsm files and xlwt for .xls files. If you have multiple
engines installed, you can set the default engine through :ref:`setting the
config options <options>` io.excel.xlsx.writer and
io.excel.xls.writer. pandas will fall back on openpyxl for .xlsx
files if Xlsxwriter is not available.
To specify which writer you want to use, you can pass an engine keyword
argument to to_excel and to ExcelWriter. The built-in engines are:
openpyxl: This includes stable support for OpenPyxl 1.6.1 up to but not including 2.0.0, and experimental support for OpenPyxl 2.0.0 and later.xlsxwriterxlwt
# By setting the 'engine' in the DataFrame and Panel 'to_excel()' methods.
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')
# Or via pandas configuration.
from pandas import options
options.io.excel.xlsx.writer = 'xlsxwriter'
df.to_excel('path_to_file.xlsx', sheet_name='Sheet1')A handy way to grab data is to use the read_clipboard method, which takes
the contents of the clipboard buffer and passes them to the read_table
method. For instance, you can copy the following
text to the clipboard (CTRL-C on many operating systems):
A B C
x 1 4 p
y 2 5 q
z 3 6 rAnd then import the data directly to a DataFrame by calling:
clipdf = pd.read_clipboard().. ipython:: python clipdf
The to_clipboard method can be used to write the contents of a DataFrame to
the clipboard. Following which you can paste the clipboard contents into other
applications (CTRL-V on many operating systems). Here we illustrate writing a
DataFrame into clipboard and reading it back.
.. ipython:: python
df=pd.DataFrame(randn(5,3))
df
df.to_clipboard()
pd.read_clipboard()
We can see that we got the same content back, which we had earlier written to the clipboard.
Note
You may need to install xclip or xsel (with gtk or PyQt4 modules) on Linux to use these methods.
All pandas objects are equipped with to_pickle methods which use Python's
cPickle module to save data structures to disk using the pickle format.
.. ipython:: python
df
df.to_pickle('foo.pkl')
The read_pickle function in the pandas namespace can be used to load
any pickled pandas object (or any other pickled object) from file:
.. ipython:: python
read_pickle('foo.pkl')
.. ipython:: python
:suppress:
import os
os.remove('foo.pkl')
Warning
Loading pickled data received from untrusted sources can be unsafe.
See: http://docs.python.org/2.7/library/pickle.html
Warning
Several internal refactorings, 0.13 (:ref:`Series Refactoring <whatsnew_0130.refactoring>`), and 0.15 (:ref:`Index Refactoring <whatsnew_0150.refactoring>`),
preserve compatibility with pickles created prior to these versions. However, these must
be read with pd.read_pickle, rather than the default python pickle.load.
See this question
for a detailed explanation.
Note
These methods were previously pd.save and pd.load, prior to 0.12.0, and are now deprecated.
.. versionadded:: 0.13.0
Starting in 0.13.0, pandas is supporting the msgpack format for
object serialization. This is a lightweight portable binary format, similar
to binary JSON, that is highly space efficient, and provides good performance
both on the writing (serialization), and reading (deserialization).
Warning
This is a very new feature of pandas. We intend to provide certain
optimizations in the io of the msgpack data. Since this is marked
as an EXPERIMENTAL LIBRARY, the storage format may not be stable until a future release.
.. ipython:: python
df = 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))
You can pass a list of objects and you will receive them back on deserialization.
.. ipython:: python
pd.to_msgpack('foo.msg', df, 'foo', np.array([1,2,3]), s)
pd.read_msgpack('foo.msg')
You can pass iterator=True to iterate over the unpacked results
.. ipython:: python
for o in pd.read_msgpack('foo.msg',iterator=True):
print o
You can pass append=True to the writer to append to an existing pack
.. ipython:: python
df.to_msgpack('foo.msg',append=True)
pd.read_msgpack('foo.msg')
Unlike other io methods, to_msgpack is available on both a per-object basis,
df.to_msgpack() and using the top-level pd.to_msgpack(...) where you
can pack arbitrary collections of python lists, dicts, scalars, while intermixing
pandas objects.
.. ipython:: python
pd.to_msgpack('foo2.msg', { 'dict' : [ { 'df' : df }, { 'string' : 'foo' }, { 'scalar' : 1. }, { 's' : s } ] })
pd.read_msgpack('foo2.msg')
.. ipython:: python
:suppress:
:okexcept:
os.remove('foo.msg')
os.remove('foo2.msg')
Msgpacks can also be read from and written to strings.
.. ipython:: python df.to_msgpack()
Furthermore you can concatenate the strings to produce a list of the original objects.
.. ipython:: python pd.read_msgpack(df.to_msgpack() + s.to_msgpack())
HDFStore is a dict-like object which reads and writes pandas using
the high performance HDF5 format using the excellent PyTables library. See the :ref:`cookbook <cookbook.hdf>`
for some advanced strategies
Warning
As of version 0.15.0, pandas requires PyTables >= 3.0.0. Stores written with prior versions of pandas / PyTables >= 2.3 are fully compatible (this was the previous minimum PyTables required version).
.. ipython:: python
:suppress:
:okexcept:
os.remove('store.h5')
.. ipython:: python
store = HDFStore('store.h5')
print(store)
Objects can be written to the file just like adding key-value pairs to a 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,
columns=['A', 'B', 'C'])
wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
major_axis=date_range('1/1/2000', periods=5),
minor_axis=['A', 'B', 'C', 'D'])
# store.put('s', s) is an equivalent method
store['s'] = s
store['df'] = df
store['wp'] = wp
# the type of stored data
store.root.wp._v_attrs.pandas_type
store
In a current or later Python session, you can retrieve stored objects:
.. ipython:: python
# store.get('df') is an equivalent method
store['df']
# dotted (attribute) access provides get as well
store.df
Deletion of the object specified by the key
.. ipython:: python
# store.remove('wp') is an equivalent method
del store['wp']
store
Closing a Store, Context Manager
.. ipython:: python
store.close()
store
store.is_open
# Working with, and automatically closing the store with the context
# manager
with get_store('store.h5') as store:
store.keys()
.. ipython:: python
:suppress:
store.close()
import os
os.remove('store.h5')
HDFStore supports an top-level API using read_hdf for reading and to_hdf for writing,
similar to how read_csv and to_csv work. (new in 0.11.0)
.. ipython:: python
df_tl = DataFrame(dict(A=list(range(5)), B=list(range(5))))
df_tl.to_hdf('store_tl.h5','table',append=True)
read_hdf('store_tl.h5', 'table', where = ['index>2'])
.. ipython:: python
:suppress:
:okexcept:
os.remove('store_tl.h5')
Note
This was prior to 0.13.0 the Storer format.
The examples above show storing using put, which write the HDF5 to PyTables in a fixed array format, called
the fixed format. These types of stores are are not appendable once written (though you can simply
remove them and rewrite). Nor are they queryable; they must be
retrieved in their entirety. They also do not support dataframes with non-unique column names.
The fixed format stores offer very fast writing and slightly faster reading than table stores.
This format is specified by default when using put or to_hdf or by format='fixed' or format='f'
Warning
A fixed format will raise a TypeError if you try to retrieve using a where .
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.
this store must be selected in its entiretyHDFStore supports another PyTables format on disk, the table
format. Conceptually a table is shaped very much like a DataFrame,
with rows and columns. A table may be appended to in the same or
other sessions. In addition, delete & query type operations are
supported. This format is specified by format='table' or format='t'
to append or put or to_hdf
.. versionadded:: 0.13
This format can be set as an option as well pd.set_option('io.hdf.default_format','table') to
enable put/append/to_hdf to by default store in the table format.
.. ipython:: python
:suppress:
:okexcept:
os.remove('store.h5')
.. ipython:: python
store = HDFStore('store.h5')
df1 = df[0:4]
df2 = df[4:]
# append data (creates a table automatically)
store.append('df', df1)
store.append('df', df2)
store
# select the entire object
store.select('df')
# the type of stored data
store.root.df._v_attrs.pandas_type
Note
You can also create a table by passing format='table' or format='t' to a put operation.
Keys to a store can be specified as a string. These can be in a
hierarchical path-name like format (e.g. foo/bar/bah), which will
generate a hierarchy of sub-stores (or Groups in PyTables
parlance). Keys can be specified with out the leading '/' and are ALWAYS
absolute (e.g. 'foo' refers to '/foo'). Removal operations can remove
everything in the sub-store and BELOW, so be careful.
.. ipython:: python
store.put('foo/bar/bah', df)
store.append('food/orange', df)
store.append('food/apple', df)
store
# a list of keys are returned
store.keys()
# remove all nodes under this level
store.remove('food')
store
Storing mixed-dtype data is supported. Strings are stored as a fixed-width using the maximum size of the appended column. Subsequent appends will truncate strings at this length.
Passing min_itemsize={`values`: size} as a parameter to append
will set a larger minimum for the string columns. Storing floats,
strings, ints, bools, datetime64 are currently supported. For string
columns, passing nan_rep = 'nan' to append will change the default
nan representation on disk (which converts to/from np.nan), this
defaults to nan.
.. ipython:: python
df_mixed = DataFrame({ 'A' : randn(8),
'B' : randn(8),
'C' : np.array(randn(8),dtype='float32'),
'string' :'string',
'int' : 1,
'bool' : True,
'datetime64' : Timestamp('20010102')},
index=list(range(8)))
df_mixed.ix[3:5,['A', 'B', 'string', 'datetime64']] = np.nan
store.append('df_mixed', df_mixed, min_itemsize = {'values': 50})
df_mixed1 = store.select('df_mixed')
df_mixed1
df_mixed1.get_dtype_counts()
# we have provided a minimum string column size
store.root.df_mixed.table
Storing multi-index dataframes as tables is very similar to storing/selecting from homogeneous index DataFrames.
.. ipython:: python
index = 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,
columns=['A', 'B', 'C'])
df_mi
store.append('df_mi',df_mi)
store.select('df_mi')
# the levels are automatically included as data columns
store.select('df_mi', 'foo=bar')
Warning
This query capabilities have changed substantially starting in 0.13.0.
Queries from prior version are accepted (with a DeprecationWarning) printed
if its not string-like.
select and delete operations have an optional criterion that can
be specified to select/delete only a subset of the data. This allows one
to have a very large on-disk table and retrieve only a portion of the
data.
A query is specified using the Term class under the hood, as a boolean expression.
indexandcolumnsare supported indexers of a DataFramemajor_axis,minor_axis, anditemsare supported indexers of the Panel- if
data_columnsare specified, these can be used as additional indexers
Valid comparison operators are:
=, ==, !=, >, >=, <, <=
Valid boolean expressions are combined with:
|: or&: and(and): for grouping
These rules are similar to how boolean expressions are used in pandas for indexing.
Note
=will be automatically expanded to the comparison operator==~is the not operator, but can only be used in very limited circumstances- If a list/tuple of expressions is passed they will be combined via
&
The following are valid expressions:
'index>=date'"columns=['A', 'D']""columns in ['A', 'D']"'columns=A''columns==A'"~(columns=['A','B'])"'index>df.index[3] & string="bar"''(index>df.index[3] & index<=df.index[6]) | string="bar"'"ts>=Timestamp('2012-02-01')""major_axis>=20130101"
The indexers are on the left-hand side of the sub-expression:
columns,major_axis,ts
The right-hand side of the sub-expression (after a comparison operator) can be:
- functions that will be evaluated, e.g.
Timestamp('2012-02-01')- strings, e.g.
"bar"- date-like, e.g.
20130101, or"20130101"- lists, e.g.
"['A','B']"- variables that are defined in the local names space, e.g.
date
Note
Passing a string to a query by interpolating it into the query expression is not recommended. Simply assign the string of interest to a variable and use that variable in an expression. For example, do this
string = "HolyMoly'"
store.select('df', 'index == string')instead of this
string = "HolyMoly'"
store.select('df', 'index == %s' % string)The latter will not work and will raise a SyntaxError.Note that
there's a single quote followed by a double quote in the string
variable.
If you must interpolate, use the '%r' format specifier
store.select('df', 'index == %r' % string)which will quote string.
Here are some examples:
.. ipython:: python
dfq = DataFrame(randn(10,4),columns=list('ABCD'),index=date_range('20130101',periods=10))
store.append('dfq',dfq,format='table',data_columns=True)
Use boolean expressions, with in-line function evaluation.
.. ipython:: python
store.select('dfq',"index>Timestamp('20130104') & columns=['A', 'B']")
Use and inline column reference
.. ipython:: python
store.select('dfq',where="A>0 or C>0")
Works with a Panel as well.
.. ipython:: python
store.append('wp',wp)
store
store.select('wp', "major_axis>Timestamp('20000102') & minor_axis=['A', 'B']")
The columns keyword can be supplied to select a list of columns to be
returned, this is equivalent to passing a
'columns=list_of_columns_to_filter':
.. ipython:: python
store.select('df', "columns=['A', 'B']")
start and stop parameters can be specified to limit the total search
space. These are in terms of the total number of rows in a table.
.. ipython:: python
# this is effectively what the storage of a Panel looks like
wp.to_frame()
# limiting the search
store.select('wp',"major_axis>20000102 & minor_axis=['A','B']",
start=0, stop=10)
Note
select will raise a ValueError if the query expression has an unknown
variable reference. Usually this means that you are trying to select on a column
that is not a data_column.
select will raise a SyntaxError if the query expression is not valid.
Using timedelta64[ns]
.. versionadded:: 0.13
Beginning in 0.13.0, you can store and query using the timedelta64[ns] type. Terms can be
specified in the format: <float>(<unit>), where float may be signed (and fractional), and unit can be
D,s,ms,us,ns for the timedelta. Here's an example:
Warning
This requires numpy >= 1.7
.. 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['C'] = dftd['A']-dftd['B']
dftd
store.append('dftd',dftd,data_columns=True)
store.select('dftd',"C<'-3.5D'")
You can create/modify an index for a table with create_table_index
after data is already in the table (after and append/put
operation). Creating a table index is highly encouraged. This will
speed your queries a great deal when you use a select with the
indexed dimension as the where.
Note
Indexes are automagically created (starting 0.10.1) on the indexables
and any data columns you specify. This behavior can be turned off by passing
index=False to append.
.. ipython:: python
# we have automagically already created an index (in the first section)
i = store.root.df.table.cols.index.index
i.optlevel, i.kind
# change an index by passing new parameters
store.create_table_index('df', optlevel=9, kind='full')
i = store.root.df.table.cols.index.index
i.optlevel, i.kind
See here for how to create a completely-sorted-index (CSI) on an existing store.
You can designate (and index) certain columns that you want to be able
to perform queries (other than the indexable columns, which you can
always query). For instance say you want to perform this common
operation, on-disk, and return just the frame that matches this
query. You can specify data_columns = True to force all columns to
be data_columns
.. ipython:: python
df_dc = df.copy()
df_dc['string'] = 'foo'
df_dc.ix[4:6,'string'] = np.nan
df_dc.ix[7:9,'string'] = 'bar'
df_dc['string2'] = 'cool'
df_dc.ix[1:3,['B','C']] = 1.0
df_dc
# on-disk operations
store.append('df_dc', df_dc, data_columns = ['B', 'C', 'string', 'string2'])
store.select('df_dc', [ Term('B>0') ])
# getting creative
store.select('df_dc', 'B > 0 & C > 0 & string == foo')
# this is in-memory version of this type of selection
df_dc[(df_dc.B > 0) & (df_dc.C > 0) & (df_dc.string == 'foo')]
# we have automagically created this index and the B/C/string/string2
# columns are stored separately as ``PyTables`` columns
store.root.df_dc.table
There is some performance degradation by making lots of columns into data columns, so it is up to the user to designate these. In addition, you cannot change data columns (nor indexables) after the first append/put operation (Of course you can simply read in the data and create a new table!)
Starting in 0.11.0, you can pass, iterator=True or chunksize=number_in_a_chunk
to select and select_as_multiple to return an iterator on the results.
The default is 50,000 rows returned in a chunk.
.. ipython:: python
for df in store.select('df', chunksize=3):
print(df)
Note
.. versionadded:: 0.12.0
You can also use the iterator with read_hdf which will open, then
automatically close the store when finished iterating.
for df in read_hdf('store.h5','df', chunksize=3):
print(df)Note, that the chunksize keyword applies to the source rows. So if you are doing a query, then the chunksize will subdivide the total rows in the table and the query applied, returning an iterator on potentially unequal sized chunks.
Here is a recipe for generating a query and using it to create equal sized return chunks.
.. ipython:: python
dfeq = DataFrame({'number': np.arange(1,11)})
dfeq
store.append('dfeq', dfeq, data_columns=['number'])
def chunks(l, n):
return [l[i:i+n] for i in range(0, len(l), n)]
evens = [2,4,6,8,10]
coordinates = store.select_as_coordinates('dfeq','number=evens')
for c in chunks(coordinates, 2):
print store.select('dfeq',where=c)
Select a Single Column
To retrieve a single indexable or data column, use the
method select_column. This will, for example, enable you to get the index
very quickly. These return a Series of the result, indexed by the row number.
These do not currently accept the where selector.
.. ipython:: python
store.select_column('df_dc', 'index')
store.select_column('df_dc', 'string')
Selecting coordinates
Sometimes you want to get the coordinates (a.k.a the index locations) of your query. This returns an
Int64Index of the resulting locations. These coordinates can also be passed to subsequent
where operations.
.. ipython:: python
df_coord = 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()
store.select('df_coord',where=c)
Selecting using a where mask
Sometime your query can involve creating a list of rows to select. Usually this mask would
be a resulting index from an indexing operation. This example selects the months of
a datetimeindex which are 5.
.. ipython:: python
df_mask = 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
store.select('df_mask',where=where)
Storer Object
If you want to inspect the stored object, retrieve via
get_storer. You could use this programmatically to say get the number
of rows in an object.
.. ipython:: python
store.get_storer('df_dc').nrows
New in 0.10.1 are the methods append_to_multiple and
select_as_multiple, that can perform appending/selecting from
multiple tables at once. The idea is to have one table (call it the
selector table) that you index most/all of the columns, and perform your
queries. The other table(s) are data tables with an index matching the
selector table's index. You can then perform a very fast query
on the selector table, yet get lots of data back. This method is similar to
having a very wide table, but enables more efficient queries.
The append_to_multiple method splits a given single DataFrame
into multiple tables according to d, a dictionary that maps the
table names to a list of 'columns' you want in that table. If None
is used in place of a list, that table will have the remaining
unspecified columns of the given DataFrame. The argument selector
defines which table is the selector table (which you can make queries from).
The argument dropna will drop rows from the input DataFrame to ensure
tables are synchronized. This means that if a row for one of the tables
being written to is entirely np.NaN, that row will be dropped from all tables.
If dropna is False, THE USER IS RESPONSIBLE FOR SYNCHRONIZING THE TABLES.
Remember that entirely np.Nan rows are not written to the HDFStore, so if
you choose to call dropna=False, some tables may have more rows than others,
and therefore select_as_multiple may not work or it may return unexpected
results.
.. ipython:: python
df_mt = 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
# you can also create the tables individually
store.append_to_multiple({'df1_mt': ['A', 'B'], 'df2_mt': None },
df_mt, selector='df1_mt')
store
# individual tables were created
store.select('df1_mt')
store.select('df2_mt')
# as a multiple
store.select_as_multiple(['df1_mt', 'df2_mt'], where=['A>0', 'B>0'],
selector = 'df1_mt')
You can delete from a table selectively by specifying a where. In
deleting rows, it is important to understand the PyTables deletes
rows by erasing the rows, then moving the following data. Thus
deleting can potentially be a very expensive operation depending on the
orientation of your data. This is especially true in higher dimensional
objects (Panel and Panel4D). To get optimal performance, it's
worthwhile to have the dimension you are deleting be the first of the
indexables.
Data is ordered (on the disk) in terms of the indexables. Here's a
simple use case. You store panel-type data, with dates in the
major_axis and ids in the minor_axis. The data is then
interleaved like this:
- date_1
- id_1
- id_2
- .
- id_n
- date_2
- id_1
- .
- id_n
It should be clear that a delete operation on the major_axis will be
fairly quick, as one chunk is removed, then the following data moved. On
the other hand a delete operation on the minor_axis will be very
expensive. In this case it would almost certainly be faster to rewrite
the table using a where that selects all but the missing data.
.. ipython:: python
# returns the number of rows deleted
store.remove('wp', 'major_axis>20000102' )
store.select('wp')
Please note that HDF5 DOES NOT RECLAIM SPACE in the h5 files
automatically. Thus, repeatedly deleting (or removing nodes) and adding
again WILL TEND TO INCREASE THE FILE SIZE. To clean the file, use
ptrepack (see below).
PyTables allows the stored data to be compressed. This applies to
all kinds of stores, not just tables.
- Pass
complevel=intfor a compression level (1-9, with 0 being no compression, and the default)- Pass
complib=libwhere lib is any ofzlib, bzip2, lzo, bloscfor whichever compression library you prefer.
HDFStore will use the file based compression scheme if no overriding
complib or complevel options are provided. blosc offers very
fast compression, and is my most used. Note that lzo and bzip2
may not be installed (by Python) by default.
Compression for all objects within the file
store_compressed = 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
store.append('df', df, complib='zlib', complevel=5)
ptrepack
PyTables offers better write performance when tables are compressed after
they are written, as opposed to turning on compression at the very
beginning. You can use the supplied PyTables utility
ptrepack. In addition, ptrepack can change compression levels
after the fact.
ptrepack --chunkshape=auto --propindexes --complevel=9 --complib=blosc in.h5 out.h5
Furthermore ptrepack in.h5 out.h5 will repack the file to allow
you to reuse previously deleted space. Alternatively, one can simply
remove the file and write again, or use the copy method.
- Once a
tableis created its items (Panel) / columns (DataFrame) are fixed; only exactly the same columns can be appended- If a row has
np.nanfor EVERY COLUMN (having ananin a string, or aNaTin a datetime-like column counts as having a value), then those rows WILL BE DROPPED IMPLICITLY. This limitation may be addressed in the future.HDFStoreis not-threadsafe for writing. The underlyingPyTablesonly supports concurrent reads (via threading or processes). If you need reading and writing at the same time, you need to serialize these operations in a single thread in a single process. You will corrupt your data otherwise. See the issue (:2397) for more information.- If you use locks to manage write access between multiple processes, you may want to use :py:func:`~os.fsync` before releasing write locks. For convenience you can use
store.flush(fsync=True)to do this for you.PyTablesonly supports fixed-width string columns intables. The sizes of a string based indexing column (e.g. columns or minor_axis) are determined as the maximum size of the elements in that axis or by passing the parameter- Be aware that timezones (e.g.,
pytz.timezone('US/Eastern')) are not necessarily equal across timezone versions. So if data is localized to a specific timezone in the HDFStore using one version of a timezone library and that data is updated with another version, the data will be converted to UTC since these timezones are not considered equal. Either use the same version of timezone library or usetz_convertwith the updated timezone definition.
Warning
PyTables will show a NaturalNameWarning if a column name
cannot be used as an attribute selector. Generally identifiers that
have spaces, start with numbers, or _, or have - embedded are not considered
natural. These types of identifiers cannot be used in a where clause
and are generally a bad idea.
HDFStore will map an object dtype to the PyTables underlying
dtype. This means the following types are known to work:
- floating :
float64, float32, float16(usingnp.nanto represent invalid values)- integer :
int64, int32, int8, uint64, uint32, uint8- bool
- datetime64[ns] (using
NaTto represent invalid values)- object :
strings(usingnp.nanto represent invalid values)
Currently, unicode and datetime columns (represented with a
dtype of object), WILL FAIL. In addition, even though a column
may look like a datetime64[ns], if it contains np.nan, this
WILL FAIL. You can try to convert datetimelike columns to proper
datetime64[ns] columns, that possibly contain NaT to represent
invalid values. (Some of these issues have been addressed and these
conversion may not be necessary in future versions of pandas)
.. ipython:: python
import datetime
df = DataFrame(dict(datelike=Series([datetime.datetime(2001, 1, 1),
datetime.datetime(2001, 1, 2), np.nan])))
df
df.dtypes
# to convert
df['datelike'] = Series(df['datelike'].values, dtype='M8[ns]')
df
df.dtypes
min_itemsize
The underlying implementation of HDFStore uses a fixed column width (itemsize) for string columns.
A string column itemsize is calculated as the maximum of the
length of data (for that column) that is passed to the HDFStore, in the first append. Subsequent appends,
may introduce a string for a column larger than the column can hold, an Exception will be raised (otherwise you
could have a silent truncation of these columns, leading to loss of information). In the future we may relax this and
allow a user-specified truncation to occur.
Pass min_itemsize on the first table creation to a-priori specify the minimum length of a particular string column.
min_itemsize can be an integer, or a dict mapping a column name to an integer. You can pass values as a key to
allow all indexables or data_columns to have this min_itemsize.
Starting in 0.11.0, passing a min_itemsize dict will cause all passed columns to be created as data_columns automatically.
Note
If you are not passing any data_columns, then the min_itemsize will be the maximum of the length of any string passed
.. ipython:: python
dfs = DataFrame(dict(A = 'foo', B = 'bar'),index=list(range(5)))
dfs
# A and B have a size of 30
store.append('dfs', dfs, min_itemsize = 30)
store.get_storer('dfs').table
# A is created as a data_column with a size of 30
# B is size is calculated
store.append('dfs2', dfs, min_itemsize = { 'A' : 30 })
store.get_storer('dfs2').table
nan_rep
String columns will serialize a np.nan (a missing value) with the nan_rep string representation. This defaults to the string value nan.
You could inadvertently turn an actual nan value into a missing value.
.. ipython:: python
dfss = DataFrame(dict(A = ['foo','bar','nan']))
dfss
store.append('dfss', dfss)
store.select('dfss')
# here you need to specify a different nan rep
store.append('dfss2', dfss, nan_rep='_nan_')
store.select('dfss2')
HDFStore write table format objects in specific formats suitable for
producing loss-less round trips to pandas objects. For external
compatibility, HDFStore can read native PyTables format
tables. It is possible to write an HDFStore object that can easily
be imported into R using the rhdf5 library. Create a table
format store like this:
.. ipython:: python store_export = HDFStore('export.h5') store_export.append('df_dc', df_dc, data_columns=df_dc.columns) store_export.. ipython:: python :suppress: store_export.close() import os os.remove('export.h5')
0.10.1 of HDFStore can read tables created in a prior version of pandas,
however query terms using the
prior (undocumented) methodology are unsupported. HDFStore will
issue a warning if you try to use a legacy-format file. You must
read in the entire file and write it out using the new format, using the
method copy to take advantage of the updates. The group attribute
pandas_version contains the version information. copy takes a
number of options, please see the docstring.
.. ipython:: python :suppress: import os legacy_file_path = os.path.abspath('source/_static/legacy_0.10.h5').. ipython:: python # a legacy store legacy_store = HDFStore(legacy_file_path,'r') legacy_store # copy (and return the new handle) new_store = legacy_store.copy('store_new.h5') new_store new_store.close().. ipython:: python :suppress: legacy_store.close() import os os.remove('store_new.h5')
Tablescome with a writing performance penalty as compared to regular stores. The benefit is the ability to append/delete and query (potentially very large amounts of data). Write times are generally longer as compared with regular stores. Query times can be quite fast, especially on an indexed axis.- You can pass
chunksize=<int>toappend, specifying the write chunksize (default is 50000). This will significantly lower your memory usage on writing.- You can pass
expectedrows=<int>to the firstappend, to set the TOTAL number of expected rows thatPyTableswill expected. This will optimize read/write performance.- Duplicate rows can be written to tables, but are filtered out in selection (with the last items being selected; thus a table is unique on major, minor pairs)
- A
PerformanceWarningwill be raised if you are attempting to store types that will be pickled by PyTables (rather than stored as endemic types). See Here for more information and some solutions.
HDFStore supports Panel4D storage.
.. ipython:: python
p4d = Panel4D({ 'l1' : wp })
p4d
store.append('p4d', p4d)
store
These, by default, index the three axes items, major_axis,
minor_axis. On an AppendableTable it is possible to setup with the
first append a different indexing scheme, depending on how you want to
store your data. Pass the axes keyword with a list of dimensions
(currently must by exactly 1 less than the total dimensions of the
object). This cannot be changed after table creation.
.. ipython:: python
store.append('p4d2', p4d, axes=['labels', 'major_axis', 'minor_axis'])
store
store.select('p4d2', [ Term('labels=l1'), Term('items=Item1'), Term('minor_axis=A_big_strings') ])
.. ipython:: python
:suppress:
store.close()
import os
os.remove('store.h5')
The :mod:`pandas.io.sql` module provides a collection of query wrappers to both facilitate data retrieval and to reduce dependency on DB-specific API. Database abstraction is provided by SQLAlchemy if installed, in addition you will need a driver library for your database.
.. versionadded:: 0.14.0
If SQLAlchemy is not installed, a fallback is only provided for sqlite (and for mysql for backwards compatibility, but this is deprecated and will be removed in a future version). This mode requires a Python database adapter which respect the Python DB-API.
See also some :ref:`cookbook examples <cookbook.sql>` for some advanced strategies.
The key functions are:
.. autosummary::
:toctree: generated/
read_sql_table
read_sql_query
read_sql
DataFrame.to_sql
Note
The function :func:`~pandas.read_sql` is a convenience wrapper around :func:`~pandas.read_sql_table` and :func:`~pandas.read_sql_query` (and for backward compatibility) and will delegate to specific function depending on the provided input (database table name or sql query).
In the following example, we use the SQlite SQL database engine. You can use a temporary SQLite database where data are stored in "memory".
To connect with SQLAlchemy you use the :func:`create_engine` function to create an engine object from database URI. You only need to create the engine once per database you are connecting to. For more information on :func:`create_engine` and the URI formatting, see the examples below and the SQLAlchemy documentation
.. ipython:: python
from sqlalchemy import create_engine
# Create your connection.
engine = create_engine('sqlite:///:memory:')
Assuming the following data is in a DataFrame data, we can insert it into
the database using :func:`~pandas.DataFrame.to_sql`.
| id | Date | Col_1 | Col_2 | Col_3 |
|---|---|---|---|---|
| 26 | 2012-10-18 | X | 25.7 | True |
| 42 | 2012-10-19 | Y | -12.4 | False |
| 63 | 2012-10-20 | Z | 5.73 | True |
.. ipython:: python :suppress: import datetime c = ['id', 'Date', 'Col_1', 'Col_2', 'Col_3'] d = [(26, datetime.datetime(2010,10,18), 'X', 27.5, True), (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)
.. ipython:: python
data.to_sql('data', engine)
With some databases, writing large DataFrames can result in errors due to packet size limitations being exceeded. This can be avoided by setting the chunksize parameter when calling to_sql. For example, the following writes data to the database in batches of 1000 rows at a time:
.. ipython:: python
data.to_sql('data_chunked', engine, chunksize=1000)
Note
Due to the limited support for timedelta's in the different database
flavors, columns with type timedelta64 will be written as integer
values as nanoseconds to the database and a warning will be raised.
:func:`~pandas.read_sql_table` will read a database table given the table name and optionally a subset of columns to read.
Note
In order to use :func:`~pandas.read_sql_table`, you must have the SQLAlchemy optional dependency installed.
.. ipython:: python
pd.read_sql_table('data', engine)
You can also specify the name of the column as the DataFrame index, and specify a subset of columns to be read.
.. ipython:: python
pd.read_sql_table('data', engine, index_col='id')
pd.read_sql_table('data', engine, columns=['Col_1', 'Col_2'])
And you can explicitly force columns to be parsed as dates:
.. ipython:: python
pd.read_sql_table('data', engine, parse_dates=['Date'])
If needed you can explicitly specify a format string, or a dict of arguments to pass to :func:`pandas.to_datetime`:
pd.read_sql_table('data', engine, parse_dates={'Date': '%Y-%m-%d'})
pd.read_sql_table('data', engine, parse_dates={'Date': {'format': '%Y-%m-%d %H:%M:%S'}})You can check if a table exists using :func:`~pandas.io.sql.has_table`
.. versionadded:: 0.15.0
Reading from and writing to different schema's is supported through the schema
keyword in the :func:`~pandas.read_sql_table` and :func:`~pandas.DataFrame.to_sql`
functions. Note however that this depends on the database flavor (sqlite does not
have schema's). For example:
df.to_sql('table', engine, schema='other_schema')
pd.read_sql_table('table', engine, schema='other_schema')You can query using raw SQL in the :func:`~pandas.read_sql_query` function. In this case you must use the SQL variant appropriate for your database. When using SQLAlchemy, you can also pass SQLAlchemy Expression language constructs, which are database-agnostic.
.. ipython:: python
pd.read_sql_query('SELECT * FROM data', engine)
Of course, you can specify a more "complex" query.
.. ipython:: python
pd.read_sql_query("SELECT id, Col_1, Col_2 FROM data WHERE id = 42;", engine)
You can also run a plain query without creating a dataframe with
:func:`~pandas.io.sql.execute`. This is useful for queries that don't return values,
such as INSERT. This is functionally equivalent to calling execute on the
SQLAlchemy engine or db connection object. Again, you must use the SQL syntax
variant appropriate for your database.
from pandas.io import sql
sql.execute('SELECT * FROM table_name', engine)
sql.execute('INSERT INTO table_name VALUES(?, ?, ?)', engine, params=[('id', 1, 12.2, True)])To connect with SQLAlchemy you use the :func:`create_engine` function to create an engine object from database URI. You only need to create the engine once per database you are connecting to.
from sqlalchemy import create_engine
engine = create_engine('postgresql://scott:tiger@localhost:5432/mydatabase')
engine = create_engine('mysql+mysqldb://scott:tiger@localhost/foo')
engine = create_engine('oracle://scott:[email protected]:1521/sidname')
engine = create_engine('mssql+pyodbc://mydsn')
# sqlite://<nohostname>/<path>
# where <path> is relative:
engine = create_engine('sqlite:///foo.db')
# or absolute, starting with a slash:
engine = create_engine('sqlite:////absolute/path/to/foo.db')For more information see the examples the SQLAlchemy documentation
The use of sqlite is supported without using SQLAlchemy. This mode requires a Python database adapter which respect the Python DB-API.
You can create connections like so:
import sqlite3
con = sqlite3.connect(':memory:')And then issue the following queries:
data.to_sql('data', cnx)
pd.read_sql_query("SELECT * FROM data", con).. versionadded:: 0.13.0
The :mod:`pandas.io.gbq` module provides a wrapper for Google's BigQuery analytics web service to simplify retrieving results from BigQuery tables using SQL-like queries. Result sets are parsed into a pandas DataFrame with a shape and data types derived from the source table. Additionally, DataFrames can be appended to existing BigQuery tables if the destination table is the same shape as the DataFrame.
For specifics on the service itself, see here
As an example, suppose you want to load all data from an existing BigQuery table : test_dataset.test_table into a DataFrame using the :func:`~pandas.io.read_gbq` function.
# Insert your BigQuery Project ID Here
# Can be found in the Google web console
projectid = "xxxxxxxx"
data_frame = pd.read_gbq('SELECT * FROM test_dataset.test_table', project_id = projectid)You will then be authenticated to the specified BigQuery account via Google's Oauth2 mechanism. In general, this is as simple as following the prompts in a browser window which will be opened for you. Should the browser not be available, or fail to launch, a code will be provided to complete the process manually. Additional information on the authentication mechanism can be found here
You can define which column from BigQuery to use as an index in the destination DataFrame as well as a preferred column order as follows:
data_frame = pd.read_gbq('SELECT * FROM test_dataset.test_table',
index_col='index_column_name',
col_order=['col1', 'col2', 'col3'], project_id = projectid)Finally, you can append data to a BigQuery table from a pandas DataFrame
using the :func:`~pandas.io.to_gbq` function. This function uses the
Google streaming API which requires that your destination table exists in
BigQuery. Given the BigQuery table already exists, your DataFrame should
match the destination table in column order, structure, and data types.
DataFrame indexes are not supported. By default, rows are streamed to
BigQuery in chunks of 10,000 rows, but you can pass other chuck values
via the chunksize argument. You can also see the progess of your
post via the verbose flag which defaults to True. The http
response code of Google BigQuery can be successful (200) even if the
append failed. For this reason, if there is a failure to append to the
table, the complete error response from BigQuery is returned which
can be quite long given it provides a status for each row. You may want
to start with smaller chunks to test that the size and types of your
dataframe match your destination table to make debugging simpler.
df = pandas.DataFrame({'string_col_name' : ['hello'],
'integer_col_name' : [1],
'boolean_col_name' : [True]})
df.to_gbq('my_dataset.my_table', project_id = projectid)The BigQuery SQL query language has some oddities, see here
While BigQuery uses SQL-like syntax, it has some important differences from traditional databases both in functionality, API limitations (size and quantity of queries or uploads), and how Google charges for use of the service. You should refer to Google documentation often as the service seems to be changing and evolving. BiqQuery is best for analyzing large sets of data quickly, but it is not a direct replacement for a transactional database.
You can access the management console to determine project id's by: <https://code.google.com/apis/console/b/0/?noredirect>
Warning
To use this module, you will need a valid BigQuery account. See <https://cloud.google.com/products/big-query> for details on the service.
.. versionadded:: 0.12.0
The method :func:`~pandas.core.frame.DataFrame.to_stata` will write a DataFrame 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.to_stata('stata.dta')
Stata data files have limited data type support; only strings with 244 or
fewer characters, int8, int16, int32 and float64 can be stored
in .dta files. Stata reserves certain values to represent
missing data. Furthermore, when a value is encountered outside of the
permitted range, the data type is upcast to the next larger size. For
example, int8 values are restricted to lie between -127 and 100, and so
variables with values above 100 will trigger a conversion to int16. nan
values in floating points data types are stored as the basic missing data type
(. in Stata). It is not possible to indicate missing data values for
integer data types.
The Stata writer gracefully handles other data types including int64,
bool, uint8, uint16, uint32 and float32 by upcasting to
the smallest supported type that can represent the data. For example, data
with a type of uint8 will be cast to int8 if all values are less than
100 (the upper bound for non-missing int8 data in Stata), or, if values are
outside of this range, the data is cast to int16.
Warning
Conversion from int64 to float64 may result in a loss of precision
if int64 values are larger than 2**53.
Warning
:class:`~pandas.io.stata.StataWriter`` and
:func:`~pandas.core.frame.DataFrame.to_stata` only support fixed width
strings containing up to 244 characters, a limitation imposed by the version
115 dta file format. Attempting to write Stata dta files with strings
longer than 244 characters raises a ValueError.
The top-level function read_stata will read a dta format file
and return a DataFrame:
The class :class:`~pandas.io.stata.StataReader` will read the header of the
given dta file at initialization. Its method
:func:`~pandas.io.stata.StataReader.data` will read the observations,
converting them to a DataFrame which is returned:
.. ipython:: python
pd.read_stata('stata.dta')
Currently the index is retrieved as a column on read back.
The parameter convert_categoricals indicates whether value labels should be
read and used to create a Categorical variable from them. Value labels can
also be retrieved by the function variable_labels, which requires data to be
called before (see pandas.io.stata.StataReader).
The parameter convert_missing indicates whether missing value
representations in Stata should be preserved. If False (the default),
missing values are represented as np.nan. If True, missing values are
represented using StataMissingValue objects, and columns containing missing
values will have dtype set to object.
The StataReader supports .dta Formats 104, 105, 108, 113-115 and 117. Alternatively, the function :func:`~pandas.io.stata.read_stata` can be used
.. ipython:: python
:suppress:
import os
os.remove('stata.dta')
This is an informal comparison of various IO methods, using pandas 0.13.1.
In [3]: df = DataFrame(randn(1000000,2),columns=list('AB'))
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1000000 entries, 0 to 999999
Data columns (total 2 columns):
A 1000000 non-null values
B 1000000 non-null values
dtypes: float64(2)Writing
In [14]: %timeit test_sql_write(df)
1 loops, best of 3: 6.24 s per loop
In [15]: %timeit test_hdf_fixed_write(df)
1 loops, best of 3: 237 ms per loop
In [26]: %timeit test_hdf_fixed_write_compress(df)
1 loops, best of 3: 245 ms per loop
In [16]: %timeit test_hdf_table_write(df)
1 loops, best of 3: 901 ms per loop
In [27]: %timeit test_hdf_table_write_compress(df)
1 loops, best of 3: 952 ms per loop
In [17]: %timeit test_csv_write(df)
1 loops, best of 3: 3.44 s per loopReading
In [18]: %timeit test_sql_read()
1 loops, best of 3: 766 ms per loop
In [19]: %timeit test_hdf_fixed_read()
10 loops, best of 3: 19.1 ms per loop
In [28]: %timeit test_hdf_fixed_read_compress()
10 loops, best of 3: 36.3 ms per loop
In [20]: %timeit test_hdf_table_read()
10 loops, best of 3: 39 ms per loop
In [29]: %timeit test_hdf_table_read_compress()
10 loops, best of 3: 60.6 ms per loop
In [22]: %timeit test_csv_read()
1 loops, best of 3: 620 ms per loopSpace on disk (in bytes)
25843712 Apr 8 14:11 test.sql
24007368 Apr 8 14:11 test_fixed.hdf
15580682 Apr 8 14:11 test_fixed_compress.hdf
24458444 Apr 8 14:11 test_table.hdf
16797283 Apr 8 14:11 test_table_compress.hdf
46152810 Apr 8 14:11 test.csvAnd here's the code
import sqlite3
import os
from pandas.io import sql
df = DataFrame(randn(1000000,2),columns=list('AB'))
def test_sql_write(df):
if os.path.exists('test.sql'):
os.remove('test.sql')
sql_db = sqlite3.connect('test.sql')
sql.write_frame(df, name='test_table', con=sql_db)
sql_db.close()
def test_sql_read():
sql_db = sqlite3.connect('test.sql')
sql.read_frame("select * from test_table", sql_db)
sql_db.close()
def test_hdf_fixed_write(df):
df.to_hdf('test_fixed.hdf','test',mode='w')
def test_hdf_fixed_read():
pd.read_hdf('test_fixed.hdf','test')
def test_hdf_fixed_write_compress(df):
df.to_hdf('test_fixed_compress.hdf','test',mode='w',complib='blosc')
def test_hdf_fixed_read_compress():
pd.read_hdf('test_fixed_compress.hdf','test')
def test_hdf_table_write(df):
df.to_hdf('test_table.hdf','test',mode='w',format='table')
def test_hdf_table_read():
pd.read_hdf('test_table.hdf','test')
def test_hdf_table_write_compress(df):
df.to_hdf('test_table_compress.hdf','test',mode='w',complib='blosc',format='table')
def test_hdf_table_read_compress():
pd.read_hdf('test_table_compress.hdf','test')
def test_csv_write(df):
df.to_csv('test.csv',mode='w')
def test_csv_read():
pd.read_csv('test.csv',index_col=0)