10.2 CSV & Text files

10.2.1.1 Basic

filepath_or_buffer : various

Either a path to a file (a python:str, python:pathlib.Path, or py:py._path.local.LocalPath), URL (including http, ftp, and S3 locations), or any object with a read() method (such as an open file or StringIO).

sep : str, defaults to ',' for read_csv(), \t for read_table()

Delimiter to use. If sep is None, will try to automatically determine this. Separators longer than 1 character and different from '\s+' will be interpreted as regular expressions, will force use of the python parsing engine and will ignore quotes in the data. Regex example: '\\r\\t'.

delimiter : str, default None

Alternative argument name for sep.

delim_whitespace : boolean, default False

Specifies whether or not whitespace (e.g. ' ' or '\t') will be used as the delimiter. Equivalent to setting sep='\s+'. If this option is set to True, nothing should be passed in for the delimiter parameter.

New in version 0.18.1: support for the Python parser.

10.2.1.2 Column and Index Locations and Names

header : int or list of ints, default 'infer'

Row number(s) to use as the column names, and the start of the data. Default behavior is as if header=0 if no names passed, otherwise as if header=None. Explicitly pass header=0 to be able to replace existing names. The header can be a list of ints 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 is skipped). Note that this parameter ignores commented lines and empty lines if skip_blank_lines=True, so header=0 denotes the first line of data rather than the first line of the file.

names : array-like, default None

List of column names to use. If file contains no header row, then you should explicitly pass header=None. Duplicates in this list are not allowed unless mangle_dupe_cols=True, which is the default.

index_col : int or sequence or False, default None

Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end of each line, you might consider index_col=False to force pandas to not use the first column as the index (row names).

usecols : array-like, default None

Return a subset of the columns. All elements in this array must either be positional (i.e. integer indices into the document columns) or strings that correspond to column names provided either by the user in names or inferred from the document header row(s). For example, a valid usecols parameter would be [0, 1, 2] or [‘foo’, ‘bar’, ‘baz’]. Using this parameter results in much faster parsing time and lower memory usage.

as_recarray : boolean, default False

DEPRECATED: this argument will be removed in a future version. Please call pd.read_csv(...).to_records() instead.

Return a NumPy recarray instead of a DataFrame after parsing the data. If set to True, this option takes precedence over the squeeze parameter. In addition, as row indices are not available in such a format, the index_col parameter will be ignored.

squeeze : boolean, default False

If the parsed data only contains one column then return a Series.

prefix : str, default None

Prefix to add to column numbers when no header, e.g. ‘X’ for X0, X1, ...

mangle_dupe_cols : boolean, default True

Duplicate columns will be specified as ‘X.0’...’X.N’, rather than ‘X’...’X’. Passing in False will cause data to be overwritten if there are duplicate names in the columns.

10.2.1.3 General Parsing Configuration

dtype : Type name or dict of column -> type, default None

Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32} (unsupported with engine='python'). Use str or object to preserve and not interpret dtype.

engine : {'c', 'python'}

Parser engine to use. The C engine is faster while the python engine is currently more feature-complete.

converters : dict, default None

Dict of functions for converting values in certain columns. Keys can either be integers or column labels.

true_values : list, default None

Values to consider as True.

false_values : list, default None

Values to consider as False.

skipinitialspace : boolean, default False

Skip spaces after delimiter.

skiprows : list-like or integer, default None

Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file.

skipfooter : int, default 0

Number of lines at bottom of file to skip (unsupported with engine=’c’).

skip_footer : int, default 0

DEPRECATED: use the skipfooter parameter instead, as they are identical

nrows : int, default None

Number of rows of file to read. Useful for reading pieces of large files.

low_memory : boolean, default True

Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference. To ensure no mixed types either set False, or specify the type with the dtype parameter. Note that the entire file is read into a single DataFrame regardless, use the chunksize or iterator parameter to return the data in chunks. (Only valid with C parser)

buffer_lines : int, default None

DEPRECATED: this argument will be removed in a future version because its value is not respected by the parser

compact_ints : boolean, default False

DEPRECATED: this argument will be removed in a future version

If compact_ints is True, then for any column that is of integer dtype, the parser will attempt to cast it as the smallest integer dtype possible, either signed or unsigned depending on the specification from the use_unsigned parameter.

use_unsigned : boolean, default False

DEPRECATED: this argument will be removed in a future version

If integer columns are being compacted (i.e. compact_ints=True), specify whether the column should be compacted to the smallest signed or unsigned integer dtype.

memory_map : boolean, default False

If a filepath is provided for filepath_or_buffer, map the file object directly onto memory and access the data directly from there. Using this option can improve performance because there is no longer any I/O overhead.

10.2.1.4 NA and Missing Data Handling

na_values : scalar, str, list-like, or dict, default None

Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: '-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A N/A', '#N/A', 'N/A', 'NA', '#NA', 'NULL', 'NaN', '-NaN', 'nan', '-nan', ''.

keep_default_na : boolean, default True

If na_values are specified and keep_default_na is False the default NaN values are overridden, otherwise they’re appended to.

na_filter : boolean, default True

Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file.

verbose : boolean, default False

Indicate number of NA values placed in non-numeric columns.

skip_blank_lines : boolean, default True

If True, skip over blank lines rather than interpreting as NaN values.

10.2.1.5 Datetime Handling

parse_dates : boolean or list of ints or names or list of lists or dict, default False.

• If True -> try parsing the index.
• If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column.
• If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column.
• If {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’. A fast-path exists for iso8601-formatted dates.

infer_datetime_format : boolean, default False

If True and parse_dates is enabled for a column, attempt to infer the datetime format to speed up the processing.

keep_date_col : boolean, default False

If True and parse_dates specifies combining multiple columns then keep the original columns.

date_parser : function, default None

Function to use for converting a sequence of string columns to an array of datetime instances. The default uses dateutil.parser.parser to do the conversion. Pandas will try to call date_parser in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the string values from the columns defined by parse_dates into a single array and pass that; and 3) call date_parser once for each row using one or more strings (corresponding to the columns defined by parse_dates) as arguments.

dayfirst : boolean, default False

DD/MM format dates, international and European format.

10.2.1.6 Iteration

iterator : boolean, default False

Return TextFileReader object for iteration or getting chunks with get_chunk().

chunksize : int, default None

Return TextFileReader object for iteration. See iterating and chunking below.

10.2.1.7 Quoting, Compression, and File Format

compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'

For on-the-fly decompression of on-disk data. If ‘infer’, then use gzip, bz2, zip, or xz if filepath_or_buffer is a string ending in ‘.gz’, ‘.bz2’, ‘.zip’, or ‘.xz’, respectively, and no decompression otherwise. If using ‘zip’, the ZIP file must contain only one data file to be read in. Set to None for no decompression.

New in version 0.18.1: support for ‘zip’ and ‘xz’ compression.

thousands : str, default None

Thousands separator.

decimal : str, default '.'

Character to recognize as decimal point. E.g. use ',' for European data.

float_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.

lineterminator : str (length 1), default None

Character to break file into lines. Only valid with C parser.

quotechar : str (length 1)

The character used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored.

quoting : int or csv.QUOTE_* instance, default 0

Control field quoting behavior per csv.QUOTE_* constants. Use one of QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).

doublequote : boolean, default True

When quotechar is specified and quoting is not QUOTE_NONE, indicate whether or not to interpret two consecutive quotechar elements inside a field as a single quotechar element.

escapechar : str (length 1), default None

One-character string used to escape delimiter when quoting is QUOTE_NONE.

comment : str, default None

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 (as long as skip_blank_lines=True), fully commented lines are ignored by the parameter header but not by skiprows. For example, if comment='#', parsing ‘#empty\na,b,c\n1,2,3’ with header=0 will result in ‘a,b,c’ being treated as the header.

encoding : str, default None

Encoding to use for UTF when reading/writing (e.g. 'utf-8'). List of Python standard encodings.

dialect : str or python:csv.Dialect instance, default None

If None defaults to Excel dialect. Ignored if sep longer than 1 char. See python:csv.Dialect documentation for more details.

tupleize_cols : boolean, default False

Leave a list of tuples on columns as is (default is to convert to a MultiIndex on the columns).

10.2.1.8 Error Handling

error_bad_lines : boolean, default True

Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these “bad lines” will dropped from the DataFrame that is returned (only valid with C parser). See bad lines below.

warn_bad_lines : boolean, default True

If error_bad_lines is False, and warn_bad_lines is True, a warning for each “bad line” will be output (only valid with C parser).

Consider a typical CSV file containing, in this case, some time series data:

In [1]: print(open('foo.csv').read())
date,A,B,C
20090101,a,1,2
20090102,b,3,4
20090103,c,4,5

The default for read_csv is to create a DataFrame with simple numbered rows:

In [2]: pd.read_csv('foo.csv')
Out[2]: 
       date  A  B  C
0  20090101  a  1  2
1  20090102  b  3  4
2  20090103  c  4  5

In the case of indexed data, you can pass the column number or column name you wish to use as the index:

In [3]: pd.read_csv('foo.csv', index_col=0)
Out[3]: 
          A  B  C
date             
20090101  a  1  2
20090102  b  3  4
20090103  c  4  5

In [4]: pd.read_csv('foo.csv', index_col='date')
Out[4]: 
          A  B  C
date             
20090101  a  1  2
20090102  b  3  4
20090103  c  4  5

You can also use a list of columns to create a hierarchical index:

In [5]: pd.read_csv('foo.csv', index_col=[0, 'A'])
Out[5]: 
            B  C
date     A      
20090101 a  1  2
20090102 b  3  4
20090103 c  4  5

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 python:csv.Dialect instance.

Suppose you had data with unenclosed quotes:

In [6]: print(data)
label1,label2,label3
index1,"a,c,e
index2,b,d,f

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

In [7]: dia = csv.excel()

In [8]: dia.quoting = csv.QUOTE_NONE

In [9]: pd.read_csv(StringIO(data), dialect=dia)
Out[9]: 
       label1 label2 label3
index1     "a      c      e
index2      b      d      f

All of the dialect options can be specified separately by keyword arguments:

In [10]: data = 'a,b,c~1,2,3~4,5,6'

In [11]: pd.read_csv(StringIO(data), lineterminator='~')
Out[11]: 
   a  b  c
0  1  2  3
1  4  5  6

Another common dialect option is skipinitialspace, to skip any whitespace after a delimiter:

In [12]: data = 'a, b, c\n1, 2, 3\n4, 5, 6'

In [13]: print(data)
a, b, c
1, 2, 3
4, 5, 6

In [14]: pd.read_csv(StringIO(data), skipinitialspace=True)
Out[14]: 
   a  b  c
0  1  2  3
1  4  5  6

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.

10.2.3.1 Handling column names

A file may or may not have a header row. pandas assumes the first row should be used as the column names:

In [35]: data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'

In [36]: print(data)
a,b,c
1,2,3
4,5,6
7,8,9

In [37]: pd.read_csv(StringIO(data))
Out[37]: 
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

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):

In [38]: print(data)
a,b,c
1,2,3
4,5,6
7,8,9

In [39]: pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=0)
Out[39]: 
   foo  bar  baz
0    1    2    3
1    4    5    6
2    7    8    9

In [40]: pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=None)
Out[40]: 
  foo bar baz
0   a   b   c
1   1   2   3
2   4   5   6
3   7   8   9

If the header is in a row other than the first, pass the row number to header. This will skip the preceding rows:

In [41]: data = 'skip this skip it\na,b,c\n1,2,3\n4,5,6\n7,8,9'

In [42]: pd.read_csv(StringIO(data), header=1)
Out[42]: 
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

10.2.4.1 Filtering columns (usecols)

The usecols argument allows you to select any subset of the columns in a file, either using the column names or position numbers:

In [45]: data = 'a,b,c,d\n1,2,3,foo\n4,5,6,bar\n7,8,9,baz'

In [46]: pd.read_csv(StringIO(data))
Out[46]: 
   a  b  c    d
0  1  2  3  foo
1  4  5  6  bar
2  7  8  9  baz

In [47]: pd.read_csv(StringIO(data), usecols=['b', 'd'])
Out[47]: 
   b    d
0  2  foo
1  5  bar
2  8  baz

In [48]: pd.read_csv(StringIO(data), usecols=[0, 2, 3])
Out[48]: 
   a  c    d
0  1  3  foo
1  4  6  bar
2  7  9  baz

10.2.5.1 Ignoring line comments and empty lines

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.

In [49]: data = '\na,b,c\n  \n# commented line\n1,2,3\n\n4,5,6'

In [50]: print(data)

a,b,c
  
# commented line
1,2,3

4,5,6

In [51]: pd.read_csv(StringIO(data), comment='#')
Out[51]: 
   a  b  c
0  1  2  3
1  4  5  6

If skip_blank_lines=False, then read_csv will not ignore blank lines:

In [52]: data = 'a,b,c\n\n1,2,3\n\n\n4,5,6'

In [53]: pd.read_csv(StringIO(data), skip_blank_lines=False)
Out[53]: 
     a    b    c
0  NaN  NaN  NaN
1  1.0  2.0  3.0
2  NaN  NaN  NaN
3  NaN  NaN  NaN
4  4.0  5.0  6.0

In [54]: data = '#comment\na,b,c\nA,B,C\n1,2,3'

In [55]: pd.read_csv(StringIO(data), comment='#', header=1)
Out[55]: 
   A  B  C
0  1  2  3

In [56]: data = 'A,B,C\n#comment\na,b,c\n1,2,3'

In [57]: pd.read_csv(StringIO(data), comment='#', skiprows=2)
Out[57]: 
   a  b  c
0  1  2  3

In [58]: data = '# empty\n# second empty line\n# third empty' \

In [58]: 'line\nX,Y,Z\n1,2,3\nA,B,C\n1,2.,4.\n5.,NaN,10.0'

In [59]: print(data)
# empty
# second empty line
# third emptyline
X,Y,Z
1,2,3
A,B,C
1,2.,4.
5.,NaN,10.0

In [60]: pd.read_csv(StringIO(data), comment='#', skiprows=4, header=1)
Out[60]: 
     A    B     C
0  1.0  2.0   4.0
1  5.0  NaN  10.0

10.2.5.2 Comments

Sometimes comments or meta data may be included in a file:

In [61]: print(open('tmp.csv').read())
ID,level,category
Patient1,123000,x # really unpleasant
Patient2,23000,y # wouldn't take his medicine
Patient3,1234018,z # awesome

By default, the parser includes the comments in the output:

In [62]: df = pd.read_csv('tmp.csv')

In [63]: df
Out[63]: 
         ID    level                        category
0  Patient1   123000           x # really unpleasant
1  Patient2    23000  y # wouldn't take his medicine
2  Patient3  1234018                     z # awesome

We can suppress the comments using the comment keyword:

In [64]: df = pd.read_csv('tmp.csv', comment='#')

In [65]: df
Out[65]: 
         ID    level category
0  Patient1   123000       x 
1  Patient2    23000       y 
2  Patient3  1234018       z 

10.2.8.1 Specifying Date Columns

To better facilitate working with datetime data, read_csv() and read_table() use 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:

# Use a column as an index, and parse it as dates.
In [78]: df = pd.read_csv('foo.csv', index_col=0, parse_dates=True)

In [79]: df
Out[79]: 
            A  B  C
date               
2009-01-01  a  1  2
2009-01-02  b  3  4
2009-01-03  c  4  5

# These are python datetime objects
In [80]: df.index
Out[80]: DatetimeIndex(['2009-01-01', '2009-01-02', '2009-01-03'], dtype='datetime64[ns]', name=u'date', freq=None)

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:

In [81]: print(open('tmp.csv').read())
KORD,19990127, 19:00:00, 18:56:00, 0.8100
KORD,19990127, 20:00:00, 19:56:00, 0.0100
KORD,19990127, 21:00:00, 20:56:00, -0.5900
KORD,19990127, 21:00:00, 21:18:00, -0.9900
KORD,19990127, 22:00:00, 21:56:00, -0.5900
KORD,19990127, 23:00:00, 22:56:00, -0.5900

In [82]: df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]])

In [83]: df
Out[83]: 
                  1_2                 1_3     0     4
0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

By default the parser removes the component date columns, but you can choose to retain them via the keep_date_col keyword:

In [84]: df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]],
   ....:                  keep_date_col=True)
   ....: 

In [85]: df
Out[85]: 
                  1_2                 1_3     0         1          2  \
0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  19990127   19:00:00   
1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  19990127   20:00:00   
2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD  19990127   21:00:00   
3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD  19990127   21:00:00   
4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD  19990127   22:00:00   
5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD  19990127   23:00:00   

           3     4  
0   18:56:00  0.81  
1   19:56:00  0.01  
2   20:56:00 -0.59  
3   21:18:00 -0.99  
4   21:56:00 -0.59  
5   22:56:00 -0.59  

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:

In [86]: date_spec = {'nominal': [1, 2], 'actual': [1, 3]}

In [87]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec)

In [88]: df
Out[88]: 
              nominal              actual     0     4
0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

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:

In [89]: date_spec = {'nominal': [1, 2], 'actual': [1, 3]}

In [90]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
   ....:                  index_col=0) #index is the nominal column
   ....: 

In [91]: df
Out[91]: 
                                 actual     0     4
nominal                                            
1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

10.2.8.2 Date Parsing Functions

Finally, the parser allows you to specify a custom date_parser function to take full advantage of the flexibility of the date parsing API:

In [92]: import pandas.io.date_converters as conv

In [93]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
   ....:                  date_parser=conv.parse_date_time)
   ....: 

In [94]: df
Out[94]: 
              nominal              actual     0     4
0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

Pandas will try to call the date_parser function in three different ways. If an exception is raised, the next one is tried:

1. date_parser is first called with one or more arrays as arguments, as defined using parse_dates (e.g., date_parser(['2013', '2013'], ['1', '2']))
2. If #1 fails, date_parser is called with all the columns concatenated row-wise into a single array (e.g., date_parser(['2013 1', '2013 2']))
3. If #2 fails, date_parser is called once for every row with one or more string arguments from the columns indicated with parse_dates (e.g., date_parser('2013', '1') for the first row, date_parser('2013', '2') for the second, etc.)

Note that performance-wise, you should try these methods of parsing dates in order:

1. Try to infer the format using infer_datetime_format=True (see section below)
2. If you know the format, use pd.to_datetime(): date_parser=lambda x: pd.to_datetime(x, format=...)
3. If you have a really non-standard format, use a custom date_parser function. For optimal performance, this should be vectorized, i.e., it should accept arrays as arguments.

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.

10.2.8.3 Inferring Datetime Format

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.

# Try to infer the format for the index column
In [95]: df = pd.read_csv('foo.csv', index_col=0, parse_dates=True,
   ....:                  infer_datetime_format=True)
   ....: 

In [96]: df
Out[96]: 
            A  B  C
date               
2009-01-01  a  1  2
2009-01-02  b  3  4
2009-01-03  c  4  5

10.2.8.4 International Date Formats

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:

In [97]: print(open('tmp.csv').read())
date,value,cat
1/6/2000,5,a
2/6/2000,10,b
3/6/2000,15,c

In [98]: pd.read_csv('tmp.csv', parse_dates=[0])
Out[98]: 
        date  value cat
0 2000-01-06      5   a
1 2000-02-06     10   b
2 2000-03-06     15   c

In [99]: pd.read_csv('tmp.csv', dayfirst=True, parse_dates=[0])
Out[99]: 
        date  value cat
0 2000-06-01      5   a
1 2000-06-02     10   b
2 2000-06-03     15   c

10.2.18.1 Files with an “implicit” index column

Consider a file with one less entry in the header than the number of data column:

In [133]: print(open('foo.csv').read())
A,B,C
20090101,a,1,2
20090102,b,3,4
20090103,c,4,5

In this special case, read_csv assumes that the first column is to be used as the index of the DataFrame:

In [134]: pd.read_csv('foo.csv')
Out[134]: 
          A  B  C
20090101  a  1  2
20090102  b  3  4
20090103  c  4  5

Note that the dates weren’t automatically parsed. In that case you would need to do as before:

In [135]: df = pd.read_csv('foo.csv', parse_dates=True)

In [136]: df.index
Out[136]: DatetimeIndex(['2009-01-01', '2009-01-02', '2009-01-03'], dtype='datetime64[ns]', freq=None)

10.2.18.2 Reading an index with a MultiIndex

Suppose you have data indexed by two columns:

#print os.getcwd()
In [137]: print(open("./source/data/mindex_ex.csv").read())
year,indiv,zit,xit
1977,"A",1.2,.6
1977,"B",1.5,.5
1977,"C",1.7,.8
1978,"A",.2,.06
1978,"B",.7,.2
1978,"C",.8,.3
1978,"D",.9,.5
1978,"E",1.4,.9
1979,"C",.2,.15
1979,"D",.14,.05
1979,"E",.5,.15
1979,"F",1.2,.5
1979,"G",3.4,1.9
1979,"H",5.4,2.7
1979,"I",6.4,1.2

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:

In [138]: df = pd.read_csv("./source/data/mindex_ex.csv", index_col=[0,1])

In [139]: df
Out[139]: 
             zit   xit
year indiv            
1977 A      1.20  0.60
     B      1.50  0.50
     C      1.70  0.80
1978 A      0.20  0.06
     B      0.70  0.20
     C      0.80  0.30
     D      0.90  0.50
     E      1.40  0.90
1979 C      0.20  0.15
     D      0.14  0.05
     E      0.50  0.15
     F      1.20  0.50
     G      3.40  1.90
     H      5.40  2.70
     I      6.40  1.20

In [140]: df.ix[1978]
Out[140]: 
       zit   xit
indiv           
A      0.2  0.06
B      0.7  0.20
C      0.8  0.30
D      0.9  0.50
E      1.4  0.90

10.2.18.3 Reading columns with a MultiIndex

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.

In [141]: from pandas.util.testing import makeCustomDataframe as mkdf

In [142]: df = mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4)

In [143]: df.to_csv('mi.csv')

In [144]: print(open('mi.csv').read())
C0,,C_l0_g0,C_l0_g1,C_l0_g2
C1,,C_l1_g0,C_l1_g1,C_l1_g2
C2,,C_l2_g0,C_l2_g1,C_l2_g2
C3,,C_l3_g0,C_l3_g1,C_l3_g2
R0,R1,,,
R_l0_g0,R_l1_g0,R0C0,R0C1,R0C2
R_l0_g1,R_l1_g1,R1C0,R1C1,R1C2
R_l0_g2,R_l1_g2,R2C0,R2C1,R2C2
R_l0_g3,R_l1_g3,R3C0,R3C1,R3C2
R_l0_g4,R_l1_g4,R4C0,R4C1,R4C2


In [145]: pd.read_csv('mi.csv',header=[0,1,2,3],index_col=[0,1])
Out[145]: 
C0              C_l0_g0 C_l0_g1 C_l0_g2
C1              C_l1_g0 C_l1_g1 C_l1_g2
C2              C_l2_g0 C_l2_g1 C_l2_g2
C3              C_l3_g0 C_l3_g1 C_l3_g2
R0      R1                             
R_l0_g0 R_l1_g0    R0C0    R0C1    R0C2
R_l0_g1 R_l1_g1    R1C0    R1C1    R1C2
R_l0_g2 R_l1_g2    R2C0    R2C1    R2C2
R_l0_g3 R_l1_g3    R3C0    R3C1    R3C2
R_l0_g4 R_l1_g4    R4C0    R4C1    R4C2

Starting in 0.13.0, read_csv will be able to interpret a more common format of multi-columns indices.

In [146]: print(open('mi2.csv').read())
,a,a,a,b,c,c
,q,r,s,t,u,v
one,1,2,3,4,5,6
two,7,8,9,10,11,12

In [147]: pd.read_csv('mi2.csv',header=[0,1],index_col=0)
Out[147]: 
     a         b   c    
     q  r  s   t   u   v
one  1  2  3   4   5   6
two  7  8  9  10  11  12

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.

10.2.22.1 Writing to CSV format

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 StringIO
• sep : Field delimiter for the output file (default ”,”)
• na_rep: A string representation of a missing value (default ‘’)
• float_format: Format string for floating point numbers
• cols: 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 3
• line_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 of quotechar in fields (default True)
• escapechar: Character used to escape sep and quotechar when appropriate (default None)
• chunksize: Number of rows to write at a time
• tupleize_cols: If False (default), write as a list of tuples, otherwise write in an expanded line format suitable for read_csv
• date_format: Format string for datetime objects

10.2.22.2 Writing a formatted string

The DataFrame object has an instance method to_string which allows control over the string representation of the object. All arguments are optional:

• buf default None, for example a StringIO object
• columns default None, which columns to write
• col_space default None, minimum width of each column.
• na_rep default NaN, representation of NA value
• formatters default None, a dictionary (by column) of functions each of which takes a single argument and returns a formatted string
• float_format default None, a function which takes a single (float) argument and returns a formatted string; to be applied to floats in the DataFrame.
• sparsify default True, set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.
• index_names default True, will print the names of the indices
• index default True, will print the index (ie, row labels)
• header default True, will print the column labels
• justify default left, 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.