IO tools (text, CSV, HDF5, …)

The pandas I/O API is a set of top level reader functions accessed like pandas.read_csv() that generally return a pandas object. The corresponding writer functions are object methods that are accessed like DataFrame.to_csv(). Below is a table containing available readers and writers.

Format Type	Data Description	Reader	Writer
text	CSV	read_csv	to_csv
text	JSON	read_json	to_json
text	HTML	read_html	to_html
text	Local clipboard	read_clipboard	to_clipboard
binary	MS Excel	read_excel	to_excel
binary	OpenDocument	read_excel
binary	HDF5 Format	read_hdf	to_hdf
binary	Feather Format	read_feather	to_feather
binary	Parquet Format	read_parquet	to_parquet
binary	Msgpack	read_msgpack	to_msgpack
binary	Stata	read_stata	to_stata
binary	SAS	read_sas
binary	Python Pickle Format	read_pickle	to_pickle
SQL	SQL	read_sql	to_sql
SQL	Google Big Query	read_gbq	to_gbq

Here 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.

CSV & text files

The workhorse function for reading text files (a.k.a. flat files) is read_csv(). See the cookbook for some advanced strategies.

Parsing options

read_csv() accepts the following common arguments:

Basic

filepath_or_buffer : various

• Either a path to a file (a str, pathlib.Path, or 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, the C engine cannot automatically detect the separator, but the Python parsing engine can, meaning the latter will be used and automatically detect the separator by Python’s builtin sniffer tool, csv.Sniffer. In addition, separators longer than 1 character and different from 's+' will be interpreted as regular expressions and will also force the use of the Python parsing engine. Note that regex delimiters are prone to ignoring quoted 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.

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 to infer the column names: if no names are passed the behavior is identical to header=0 and column names are inferred from the first line of the file, if column names are passed explicitly then the behavior is identical to 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 MultiIndex 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.

index_col : int, str, sequence of int / str, or False, default None

• Column(s) to use as the row labels of the DataFrame, either given as string name or column index. If a sequence of int / str is given, a MultiIndex is used.

• Note: index_col=False can be used to force pandas to not use the first column as the index, e.g. when you have a malformed file with delimiters at the end of each line.

usecols : list-like or callable, default None

• Return a subset of the columns. If list-like, all elements 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 list-like usecols parameter would be [0, 1, 2] or ['foo', 'bar', 'baz'].

• Element order is ignored, so usecols=[0, 1] is the same as [1, 0]. To instantiate a DataFrame from data with element order preserved use pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']] for columns in ['foo', 'bar'] order or pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']] for ['bar', 'foo'] order.

• If callable, the callable function will be evaluated against the column names, returning names where the callable function evaluates to True:

In [1]: from io import StringIO, BytesIO

In [2]: data = ('col1,col2,col3\n'
   ...:         'a,b,1\n'
   ...:         'a,b,2\n'
   ...:         'c,d,3')
   ...: 

In [3]: pd.read_csv(StringIO(data))
Out[3]: 
  col1 col2  col3
0    a    b     1
1    a    b     2
2    c    d     3

In [4]: pd.read_csv(StringIO(data), usecols=lambda x: x.upper() in ['COL1', 'COL3'])
Out[4]: 
  col1  col3
0    a     1
1    a     2
2    c     3

Using this parameter results in much faster parsing time and lower memory usage.

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’, ‘X.1’…’X.N’, rather than ‘X’…’X’. Passing in False will cause data to be overwritten if there are duplicate names in the columns.

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 together with suitable na_values settings to preserve and not interpret dtype.

• New in version 0.20.0: support for the Python parser.

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.

• If callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise:

In [5]: data = ('col1,col2,col3\n'
   ...:         'a,b,1\n'
   ...:         'a,b,2\n'
   ...:         'c,d,3')
   ...: 

In [6]: pd.read_csv(StringIO(data))
Out[6]: 
  col1 col2  col3
0    a    b     1
1    a    b     2
2    c    d     3

In [7]: pd.read_csv(StringIO(data), skiprows=lambda x: x % 2 != 0)
Out[7]: 
  col1 col2  col3
0    a    b     2

skipfooter : int, default 0

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

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)

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.

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. See na values const below for a list of the values interpreted as NaN by default.

keep_default_na : boolean, default True

• Whether or not to include the default NaN values when parsing the data. Depending on whether na_values is passed in, the behavior is as follows:

  • If keep_default_na is True, and na_values are specified, na_values is appended to the default NaN values used for parsing.
  • If keep_default_na is True, and na_values are not specified, only the default NaN values are used for parsing.
  • If keep_default_na is False, and na_values are specified, only the NaN values specified na_values are used for parsing.
  • If keep_default_na is False, and na_values are not specified, no strings will be parsed as NaN.

  Note that if na_filter is passed in as False, the keep_default_na and na_values parameters will be ignored.

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.

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.

cache_dates : boolean, default True

• If True, use a cache of unique, converted dates to apply the datetime conversion. May produce significant speed-up when parsing duplicate date strings, especially ones with timezone offsets.

New in version 0.25.0.

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.

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.

Changed in version 0.24.0: ‘infer’ option added and set to default.

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 a,b,c 1,2,3’ 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 csv.Dialect instance, default None

• If provided, this parameter will override values (default or not) for the following parameters: delimiter, doublequote, escapechar, skipinitialspace, quotechar, and quoting. If it is necessary to override values, a ParserWarning will be issued. See csv.Dialect documentation for more details.

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. 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.

Specifying column data types

You can indicate the data type for the whole DataFrame or individual columns:

In [8]: data = ('a,b,c,d\n'
   ...:         '1,2,3,4\n'
   ...:         '5,6,7,8\n'
   ...:         '9,10,11')
   ...: 

In [9]: print(data)
a,b,c,d
1,2,3,4
5,6,7,8
9,10,11

In [10]: df = pd.read_csv(StringIO(data), dtype=object)

In [11]: df
Out[11]: 
   a   b   c    d
0  1   2   3    4
1  5   6   7    8
2  9  10  11  NaN

In [12]: df['a'][0]
Out[12]: '1'

In [13]: df = pd.read_csv(StringIO(data),
   ....:                  dtype={'b': object, 'c': np.float64, 'd': 'Int64'})
   ....: 

In [14]: df.dtypes
Out[14]: 
a      int64
b     object
c    float64
d      Int64
dtype: object

Fortunately, pandas offers more than one way to ensure that your column(s) contain only one dtype. If you’re unfamiliar with these concepts, you can see here to learn more about dtypes, and here to learn more about object conversion in pandas.

For instance, you can use the converters argument of read_csv():

In [15]: data = ("col_1\n"
   ....:         "1\n"
   ....:         "2\n"
   ....:         "'A'\n"
   ....:         "4.22")
   ....: 

In [16]: df = pd.read_csv(StringIO(data), converters={'col_1': str})

In [17]: df
Out[17]: 
  col_1
0     1
1     2
2   'A'
3  4.22

In [18]: df['col_1'].apply(type).value_counts()
Out[18]: 
<class 'str'>    4
Name: col_1, dtype: int64

Or you can use the to_numeric() function to coerce the dtypes after reading in the data,

In [19]: df2 = pd.read_csv(StringIO(data))

In [20]: df2['col_1'] = pd.to_numeric(df2['col_1'], errors='coerce')

In [21]: df2
Out[21]: 
   col_1
0   1.00
1   2.00
2    NaN
3   4.22

In [22]: df2['col_1'].apply(type).value_counts()
Out[22]: 
<class 'float'>    4
Name: col_1, dtype: int64

which will convert all valid parsing to floats, leaving the invalid parsing as NaN.

Ultimately, how you deal with reading in columns containing mixed dtypes depends on your specific needs. In the case above, if you wanted to NaN out the data anomalies, then to_numeric() is probably your best option. However, if you wanted for all the data to be coerced, no matter the type, then using the converters argument of read_csv() would certainly be worth trying.

New in version 0.20.0: support for the Python parser.

The dtype option is supported by the ‘python’ engine.

Note

In some cases, reading in abnormal data with columns containing mixed dtypes will result in an inconsistent dataset. If you rely on pandas to infer the dtypes of your columns, the parsing engine will go and infer the dtypes for different chunks of the data, rather than the whole dataset at once. Consequently, you can end up with column(s) with mixed dtypes. For example,

In [23]: col_1 = list(range(500000)) + ['a', 'b'] + list(range(500000))

In [24]: df = pd.DataFrame({'col_1': col_1})

In [25]: df.to_csv('foo.csv')

In [26]: mixed_df = pd.read_csv('foo.csv')

In [27]: mixed_df['col_1'].apply(type).value_counts()
Out[27]: 
<class 'int'>    737858
<class 'str'>    262144
Name: col_1, dtype: int64

In [28]: mixed_df['col_1'].dtype
Out[28]: dtype('O')

will result with mixed_df containing an int dtype for certain chunks of the column, and str for others due to the mixed dtypes from the data that was read in. It is important to note that the overall column will be marked with a dtype of object, which is used for columns with mixed dtypes.

Specifying categorical dtype

New in version 0.19.0.

Categorical columns can be parsed directly by specifying dtype='category' or dtype=CategoricalDtype(categories, ordered).

In [29]: data = ('col1,col2,col3\n'
   ....:         'a,b,1\n'
   ....:         'a,b,2\n'
   ....:         'c,d,3')
   ....: 

In [30]: pd.read_csv(StringIO(data))
Out[30]: 
  col1 col2  col3
0    a    b     1
1    a    b     2
2    c    d     3

In [31]: pd.read_csv(StringIO(data)).dtypes
Out[31]: 
col1    object
col2    object
col3     int64
dtype: object

In [32]: pd.read_csv(StringIO(data), dtype='category').dtypes
Out[32]: 
col1    category
col2    category
col3    category
dtype: object

Individual columns can be parsed as a Categorical using a dict specification:

In [33]: pd.read_csv(StringIO(data), dtype={'col1': 'category'}).dtypes
Out[33]: 
col1    category
col2      object
col3       int64
dtype: object

New in version 0.21.0.

Specifying dtype='category' will result in an unordered Categorical whose categories are the unique values observed in the data. For more control on the categories and order, create a CategoricalDtype ahead of time, and pass that for that column’s dtype.

In [34]: from pandas.api.types import CategoricalDtype

In [35]: dtype = CategoricalDtype(['d', 'c', 'b', 'a'], ordered=True)

In [36]: pd.read_csv(StringIO(data), dtype={'col1': dtype}).dtypes
Out[36]: 
col1    category
col2      object
col3       int64
dtype: object

When using dtype=CategoricalDtype, “unexpected” values outside of dtype.categories are treated as missing values.

In [37]: dtype = CategoricalDtype(['a', 'b', 'd'])  # No 'c'

In [38]: pd.read_csv(StringIO(data), dtype={'col1': dtype}).col1
Out[38]: 
0      a
1      a
2    NaN
Name: col1, dtype: category
Categories (3, object): [a, b, d]

This matches the behavior of Categorical.set_categories().

Note

With dtype='category', the resulting categories will always be parsed as strings (object dtype). If the categories are numeric they can be converted using the to_numeric() function, or as appropriate, another converter such as to_datetime().

When dtype is a CategoricalDtype with homogeneous categories ( all numeric, all datetimes, etc.), the conversion is done automatically.

In [39]: df = pd.read_csv(StringIO(data), dtype='category')

In [40]: df.dtypes
Out[40]: 
col1    category
col2    category
col3    category
dtype: object

In [41]: df['col3']
Out[41]: 
0    1
1    2
2    3
Name: col3, dtype: category
Categories (3, object): [1, 2, 3]

In [42]: df['col3'].cat.categories = pd.to_numeric(df['col3'].cat.categories)

In [43]: df['col3']
Out[43]: 
0    1
1    2
2    3
Name: col3, dtype: category
Categories (3, int64): [1, 2, 3]

Naming and using columns

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 [44]: data = ('a,b,c\n'
   ....:         '1,2,3\n'
   ....:         '4,5,6\n'
   ....:         '7,8,9')
   ....: 

In [45]: print(data)
a,b,c
1,2,3
4,5,6
7,8,9

In [46]: pd.read_csv(StringIO(data))
Out[46]: 
   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 [47]: print(data)
a,b,c
1,2,3
4,5,6
7,8,9

In [48]: pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=0)
Out[48]: 
   foo  bar  baz
0    1    2    3
1    4    5    6
2    7    8    9

In [49]: pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=None)
Out[49]: 
  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 [50]: data = ('skip this skip it\n'
   ....:         'a,b,c\n'
   ....:         '1,2,3\n'
   ....:         '4,5,6\n'
   ....:         '7,8,9')
   ....: 

In [51]: pd.read_csv(StringIO(data), header=1)
Out[51]: 
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

Note

Default behavior is to infer the column names: if no names are passed the behavior is identical to header=0 and column names are inferred from the first non-blank line of the file, if column names are passed explicitly then the behavior is identical to header=None.

Duplicate names parsing

If the file or header contains duplicate names, pandas will by default distinguish between them so as to prevent overwriting data:

In [52]: data = ('a,b,a\n'
   ....:         '0,1,2\n'
   ....:         '3,4,5')
   ....: 

In [53]: pd.read_csv(StringIO(data))
Out[53]: 
   a  b  a.1
0  0  1    2
1  3  4    5

There is no more duplicate data because mangle_dupe_cols=True by default, which modifies a series of duplicate columns ‘X’, …, ‘X’ to become ‘X’, ‘X.1’, …, ‘X.N’. If mangle_dupe_cols=False, duplicate data can arise:

In [2]: data = 'a,b,a\n0,1,2\n3,4,5'
In [3]: pd.read_csv(StringIO(data), mangle_dupe_cols=False)
Out[3]:
   a  b  a
0  2  1  2
1  5  4  5

To prevent users from encountering this problem with duplicate data, a ValueError exception is raised if mangle_dupe_cols != True:

In [2]: data = 'a,b,a\n0,1,2\n3,4,5'
In [3]: pd.read_csv(StringIO(data), mangle_dupe_cols=False)
...
ValueError: Setting mangle_dupe_cols=False is not supported yet

Filtering columns (usecols)

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

New in version 0.20.0: support for callable usecols arguments

In [54]: data = 'a,b,c,d\n1,2,3,foo\n4,5,6,bar\n7,8,9,baz'

In [55]: pd.read_csv(StringIO(data))
Out[55]: 
   a  b  c    d
0  1  2  3  foo
1  4  5  6  bar
2  7  8  9  baz

In [56]: pd.read_csv(StringIO(data), usecols=['b', 'd'])
Out[56]: 
   b    d
0  2  foo
1  5  bar
2  8  baz

In [57]: pd.read_csv(StringIO(data), usecols=[0, 2, 3])
Out[57]: 
   a  c    d
0  1  3  foo
1  4  6  bar
2  7  9  baz

In [58]: pd.read_csv(StringIO(data), usecols=lambda x: x.upper() in ['A', 'C'])
Out[58]: 
   a  c
0  1  3
1  4  6
2  7  9

The usecols argument can also be used to specify which columns not to use in the final result:

In [59]: pd.read_csv(StringIO(data), usecols=lambda x: x not in ['a', 'c'])
Out[59]: 
   b    d
0  2  foo
1  5  bar
2  8  baz

In this case, the callable is specifying that we exclude the “a” and “c” columns from the output.

Comments and empty lines

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.

In [60]: data = ('\n'
   ....:         'a,b,c\n'
   ....:         '  \n'
   ....:         '# commented line\n'
   ....:         '1,2,3\n'
   ....:         '\n'
   ....:         '4,5,6')
   ....: 

In [61]: print(data)

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

4,5,6

In [62]: pd.read_csv(StringIO(data), comment='#')
Out[62]: 
   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 [63]: data = ('a,b,c\n'
   ....:         '\n'
   ....:         '1,2,3\n'
   ....:         '\n'
   ....:         '\n'
   ....:         '4,5,6')
   ....: 

In [64]: pd.read_csv(StringIO(data), skip_blank_lines=False)
Out[64]: 
     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

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):

In [65]: data = ('#comment\n'
   ....:         'a,b,c\n'
   ....:         'A,B,C\n'
   ....:         '1,2,3')
   ....: 

In [66]: pd.read_csv(StringIO(data), comment='#', header=1)
Out[66]: 
   A  B  C
0  1  2  3

In [67]: data = ('A,B,C\n'
   ....:         '#comment\n'
   ....:         'a,b,c\n'
   ....:         '1,2,3')
   ....: 

In [68]: pd.read_csv(StringIO(data), comment='#', skiprows=2)
Out[68]: 
   a  b  c
0  1  2  3

If both header and skiprows are specified, header will be relative to the end of skiprows. For example:

In [69]: data = ('# empty\n'
   ....:         '# second empty line\n'
   ....:         '# third emptyline\n'
   ....:         'X,Y,Z\n'
   ....:         '1,2,3\n'
   ....:         'A,B,C\n'
   ....:         '1,2.,4.\n'
   ....:         '5.,NaN,10.0\n')
   ....: 

In [70]: 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 [71]: pd.read_csv(StringIO(data), comment='#', skiprows=4, header=1)
Out[71]: 
     A    B     C
0  1.0  2.0   4.0
1  5.0  NaN  10.0

Comments

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

In [72]: 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 [73]: df = pd.read_csv('tmp.csv')

In [74]: df
Out[74]: 
         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 [75]: df = pd.read_csv('tmp.csv', comment='#')

In [76]: df
Out[76]: 
         ID    level category
0  Patient1   123000       x 
1  Patient2    23000       y 
2  Patient3  1234018       z 

Dealing with Unicode data

The encoding argument should be used for encoded unicode data, which will result in byte strings being decoded to unicode in the result:

In [77]: data = (b'word,length\n'
   ....:         b'Tr\xc3\xa4umen,7\n'
   ....:         b'Gr\xc3\xbc\xc3\x9fe,5')
   ....: 

In [78]: data = data.decode('utf8').encode('latin-1')

In [79]: df = pd.read_csv(BytesIO(data), encoding='latin-1')

In [80]: df
Out[80]: 
      word  length
0  Träumen       7
1    Grüße       5

In [81]: df['word'][1]
Out[81]: 'Grüße'

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.

Index columns and trailing delimiters

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:

In [82]: data = ('a,b,c\n'
   ....:         '4,apple,bat,5.7\n'
   ....:         '8,orange,cow,10')
   ....: 

In [83]: pd.read_csv(StringIO(data))
Out[83]: 
        a    b     c
4   apple  bat   5.7
8  orange  cow  10.0

In [84]: data = ('index,a,b,c\n'
   ....:         '4,apple,bat,5.7\n'
   ....:         '8,orange,cow,10')
   ....: 

In [85]: pd.read_csv(StringIO(data), index_col=0)
Out[85]: 
            a    b     c
index                   
4       apple  bat   5.7
8      orange  cow  10.0

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:

In [86]: data = ('a,b,c\n'
   ....:         '4,apple,bat,\n'
   ....:         '8,orange,cow,')
   ....: 

In [87]: print(data)
a,b,c
4,apple,bat,
8,orange,cow,

In [88]: pd.read_csv(StringIO(data))
Out[88]: 
        a    b   c
4   apple  bat NaN
8  orange  cow NaN

In [89]: pd.read_csv(StringIO(data), index_col=False)
Out[89]: 
   a       b    c
0  4   apple  bat
1  8  orange  cow

If a subset of data is being parsed using the usecols option, the index_col specification is based on that subset, not the original data.

In [90]: data = ('a,b,c\n'
   ....:         '4,apple,bat,\n'
   ....:         '8,orange,cow,')
   ....: 

In [91]: print(data)
a,b,c
4,apple,bat,
8,orange,cow,

In [92]: pd.read_csv(StringIO(data), usecols=['b', 'c'])
Out[92]: 
     b   c
4  bat NaN
8  cow NaN

In [93]: pd.read_csv(StringIO(data), usecols=['b', 'c'], index_col=0)
Out[93]: 
     b   c
4  bat NaN
8  cow NaN

Date Handling

Specifying date columns

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

# Use a column as an index, and parse it as dates.
In [94]: df = pd.read_csv('foo.csv', index_col=0, parse_dates=True)

In [95]: df
Out[95]: 
            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 [96]: df.index
Out[96]: DatetimeIndex(['2009-01-01', '2009-01-02', '2009-01-03'], dtype='datetime64[ns]', name='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 [97]: 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 [98]: df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]])

In [99]: df
Out[99]: 
                  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 [100]: df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]],
   .....:                  keep_date_col=True)
   .....: 

In [101]: df
Out[101]: 
                  1_2                 1_3     0         1          2          3     4
0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  19990127   19:00:00   18:56:00  0.81
1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  19990127   20:00:00   19:56:00  0.01
2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD  19990127   21:00:00   20:56:00 -0.59
3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD  19990127   21:00:00   21:18:00 -0.99
4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD  19990127   22:00:00   21:56:00 -0.59
5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD  19990127   23:00:00   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 [102]: date_spec = {'nominal': [1, 2], 'actual': [1, 3]}

In [103]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec)

In [104]: df
Out[104]: 
              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 [105]: date_spec = {'nominal': [1, 2], 'actual': [1, 3]}

In [106]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
   .....:                  index_col=0)  # index is the nominal column
   .....: 

In [107]: df
Out[107]: 
                                 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

Note

If a column or index contains an unparsable date, the entire column or index will be returned unaltered as an object data type. For non-standard datetime parsing, use to_datetime() after pd.read_csv.

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.

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 [108]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
   .....:                  date_parser=pd.io.date_converters.parse_date_time)
   .....: 

In [109]: df
Out[109]: 
              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.

Parsing a CSV with mixed timezones

Pandas cannot natively represent a column or index with mixed timezones. If your CSV file contains columns with a mixture of timezones, the default result will be an object-dtype column with strings, even with parse_dates.

In [110]: content = """\
   .....: a
   .....: 2000-01-01T00:00:00+05:00
   .....: 2000-01-01T00:00:00+06:00"""
   .....: 

In [111]: df = pd.read_csv(StringIO(content), parse_dates=['a'])

In [112]: df['a']
Out[112]: 
0    2000-01-01 00:00:00+05:00
1    2000-01-01 00:00:00+06:00
Name: a, dtype: object

To parse the mixed-timezone values as a datetime column, pass a partially-applied to_datetime() with utc=True as the date_parser.

In [113]: df = pd.read_csv(StringIO(content), parse_dates=['a'],
   .....:                  date_parser=lambda col: pd.to_datetime(col, utc=True))
   .....: 

In [114]: df['a']
Out[114]: 
0   1999-12-31 19:00:00+00:00
1   1999-12-31 18:00:00+00:00
Name: a, dtype: datetime64[ns, UTC]

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”

Note that 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 [115]: df = pd.read_csv('foo.csv', index_col=0, parse_dates=True,
   .....:                  infer_datetime_format=True)
   .....: 

In [116]: df
Out[116]: 
            A  B  C
date               
2009-01-01  a  1  2
2009-01-02  b  3  4
2009-01-03  c  4  5

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

In [118]: pd.read_csv('tmp.csv', parse_dates=[0])
Out[118]: 
        date  value cat
0 2000-01-06      5   a
1 2000-02-06     10   b
2 2000-03-06     15   c

In [119]: pd.read_csv('tmp.csv', dayfirst=True, parse_dates=[0])
Out[119]: 
        date  value cat
0 2000-06-01      5   a
1 2000-06-02     10   b
2 2000-06-03     15   c

Specifying method for floating-point conversion

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:

In [120]: val = '0.3066101993807095471566981359501369297504425048828125'

In [121]: data = 'a,b,c\n1,2,{0}'.format(val)

In [122]: abs(pd.read_csv(StringIO(data), engine='c',
   .....:                 float_precision=None)['c'][0] - float(val))
   .....: 
Out[122]: 1.1102230246251565e-16

In [123]: abs(pd.read_csv(StringIO(data), engine='c',
   .....:                 float_precision='high')['c'][0] - float(val))
   .....: 
Out[123]: 5.551115123125783e-17

In [124]: abs(pd.read_csv(StringIO(data), engine='c',
   .....:                 float_precision='round_trip')['c'][0] - float(val))
   .....: 
Out[124]: 0.0

Thousand separators

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:

By default, numbers with a thousands separator will be parsed as strings:

In [125]: print(open('tmp.csv').read())
ID|level|category
Patient1|123,000|x
Patient2|23,000|y
Patient3|1,234,018|z

In [126]: df = pd.read_csv('tmp.csv', sep='|')

In [127]: df
Out[127]: 
         ID      level category
0  Patient1    123,000        x
1  Patient2     23,000        y
2  Patient3  1,234,018        z

In [128]: df.level.dtype
Out[128]: dtype('O')

The thousands keyword allows integers to be parsed correctly:

In [129]: print(open('tmp.csv').read())
ID|level|category
Patient1|123,000|x
Patient2|23,000|y
Patient3|1,234,018|z

In [130]: df = pd.read_csv('tmp.csv', sep='|', thousands=',')

In [131]: df
Out[131]: 
         ID    level category
0  Patient1   123000        x
1  Patient2    23000        y
2  Patient3  1234018        z

In [132]: df.level.dtype
Out[132]: dtype('int64')

NA values

To control which values are parsed as missing values (which are signified by NaN), specify a string in na_values. If you specify a list of strings, then all values in it are considered to be missing 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', '#N/A', 'N/A', 'n/a', 'NA', '#NA', 'NULL', 'null', 'NaN', '-NaN', 'nan', '-nan', ''].

Let us consider some examples:

pd.read_csv('path_to_file.csv', na_values=[5])

In the example above 5 and 5.0 will be recognized as NaN, in addition to the defaults. A string will first be interpreted as a numerical 5, then as a NaN.

pd.read_csv('path_to_file.csv', keep_default_na=False, na_values=[""])

Above, only an empty field will be recognized as NaN.

pd.read_csv('path_to_file.csv', keep_default_na=False, na_values=["NA", "0"])

Above, both NA and 0 as strings are NaN.

pd.read_csv('path_to_file.csv', na_values=["Nope"])

The default values, in addition to the string "Nope" are recognized as NaN.

Infinity

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.

Returning Series

Using the squeeze keyword, the parser will return output with a single column as a Series:

In [133]: print(open('tmp.csv').read())
level
Patient1,123000
Patient2,23000
Patient3,1234018

In [134]: output = pd.read_csv('tmp.csv', squeeze=True)

In [135]: output
Out[135]: 
Patient1     123000
Patient2      23000
Patient3    1234018
Name: level, dtype: int64

In [136]: type(output)
Out[136]: pandas.core.series.Series

Boolean values

The common values True, False, TRUE, and FALSE are all recognized as boolean. Occasionally you might want to recognize other values as being boolean. To do this, use the true_values and false_values options as follows:

In [137]: data = ('a,b,c\n'
   .....:         '1,Yes,2\n'
   .....:         '3,No,4')
   .....: 

In [138]: print(data)
a,b,c
1,Yes,2
3,No,4

In [139]: pd.read_csv(StringIO(data))
Out[139]: 
   a    b  c
0  1  Yes  2
1  3   No  4

In [140]: pd.read_csv(StringIO(data), true_values=['Yes'], false_values=['No'])
Out[140]: 
   a      b  c
0  1   True  2
1  3  False  4

Handling “bad” lines

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 fields will raise an error by default:

In [141]: data = ('a,b,c\n'
   .....:         '1,2,3\n'
   .....:         '4,5,6,7\n'
   .....:         '8,9,10')
   .....: 

In [142]: pd.read_csv(StringIO(data))
---------------------------------------------------------------------------
ParserError                               Traceback (most recent call last)
<ipython-input-142-6388c394e6b8> in <module>
----> 1 pd.read_csv(StringIO(data))

/pandas/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
    683         )
    684 
--> 685         return _read(filepath_or_buffer, kwds)
    686 
    687     parser_f.__name__ = name

/pandas/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    461 
    462     try:
--> 463         data = parser.read(nrows)
    464     finally:
    465         parser.close()

/pandas/pandas/io/parsers.py in read(self, nrows)
   1152     def read(self, nrows=None):
   1153         nrows = _validate_integer("nrows", nrows)
-> 1154         ret = self._engine.read(nrows)
   1155 
   1156         # May alter columns / col_dict

/pandas/pandas/io/parsers.py in read(self, nrows)
   2046     def read(self, nrows=None):
   2047         try:
-> 2048             data = self._reader.read(nrows)
   2049         except StopIteration:
   2050             if self._first_chunk:

/pandas/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()

/pandas/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()

/pandas/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()

/pandas/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._tokenize_rows()

/pandas/pandas/_libs/parsers.pyx in pandas._libs.parsers.raise_parser_error()

ParserError: 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

You can also use the usecols parameter to eliminate extraneous column data that appear in some lines but not others:

In [30]: pd.read_csv(StringIO(data), usecols=[0, 1, 2])

 Out[30]:
    a  b   c
 0  1  2   3
 1  4  5   6
 2  8  9  10

Dialect

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 csv.Dialect instance.

Suppose you had data with unenclosed quotes:

In [143]: 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 [144]: import csv

In [145]: dia = csv.excel()

In [146]: dia.quoting = csv.QUOTE_NONE

In [147]: pd.read_csv(StringIO(data), dialect=dia)
Out[147]: 
       label1 label2 label3
index1     "a      c      e
index2      b      d      f

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

In [148]: data = 'a,b,c~1,2,3~4,5,6'

In [149]: pd.read_csv(StringIO(data), lineterminator='~')
Out[149]: 
   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 [150]: data = 'a, b, c\n1, 2, 3\n4, 5, 6'

In [151]: print(data)
a, b, c
1, 2, 3
4, 5, 6

In [152]: pd.read_csv(StringIO(data), skipinitialspace=True)
Out[152]: 
   a  b  c
0  1  2  3
1  4  5  6

The parsers make every attempt to “do the right thing” and not be fragile. Type inference is a pretty big deal. If a column can be coerced to integer dtype without altering the contents, the parser will do so. Any non-numeric columns will come through as object dtype as with the rest of pandas objects.

Quoting and Escape Characters

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:

In [153]: data = 'a,b\n"hello, \\"Bob\\", nice to see you",5'

In [154]: print(data)
a,b
"hello, \"Bob\", nice to see you",5

In [155]: pd.read_csv(StringIO(data), escapechar='\\')
Out[155]: 
                               a  b
0  hello, "Bob", nice to see you  5

Files with fixed width columns

While read_csv() reads delimited data, the 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, and a different usage of the delimiter parameter:

• 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 behavior, if not specified, is to infer.
• widths: A list of field widths which can be used instead of ‘colspecs’ if the intervals are contiguous.
• delimiter: Characters to consider as filler characters in the fixed-width file. Can be used to specify the filler character of the fields if it is not spaces (e.g., ‘~’).

Consider a typical fixed-width data file:

In [156]: print(open('bar.csv').read())
id8141    360.242940   149.910199   11950.7
id1594    444.953632   166.985655   11788.4
id1849    364.136849   183.628767   11806.2
id1230    413.836124   184.375703   11916.8
id1948    502.953953   173.237159   12468.3

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:

# Column specifications are a list of half-intervals
In [157]: colspecs = [(0, 6), (8, 20), (21, 33), (34, 43)]

In [158]: df = pd.read_fwf('bar.csv', colspecs=colspecs, header=None, index_col=0)

In [159]: df
Out[159]: 
                 1           2        3
0                                      
id8141  360.242940  149.910199  11950.7
id1594  444.953632  166.985655  11788.4
id1849  364.136849  183.628767  11806.2
id1230  413.836124  184.375703  11916.8
id1948  502.953953  173.237159  12468.3

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:

# Widths are a list of integers
In [160]: widths = [6, 14, 13, 10]

In [161]: df = pd.read_fwf('bar.csv', widths=widths, header=None)

In [162]: df
Out[162]: 
        0           1           2        3
0  id8141  360.242940  149.910199  11950.7
1  id1594  444.953632  166.985655  11788.4
2  id1849  364.136849  183.628767  11806.2
3  id1230  413.836124  184.375703  11916.8
4  id1948  502.953953  173.237159  12468.3

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.

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).

In [163]: df = pd.read_fwf('bar.csv', header=None, index_col=0)

In [164]: df
Out[164]: 
                 1           2        3
0                                      
id8141  360.242940  149.910199  11950.7
id1594  444.953632  166.985655  11788.4
id1849  364.136849  183.628767  11806.2
id1230  413.836124  184.375703  11916.8
id1948  502.953953  173.237159  12468.3

New in version 0.20.0.

read_fwf supports the dtype parameter for specifying the types of parsed columns to be different from the inferred type.

In [165]: pd.read_fwf('bar.csv', header=None, index_col=0).dtypes
Out[165]: 
1    float64
2    float64
3    float64
dtype: object

In [166]: pd.read_fwf('bar.csv', header=None, dtype={2: 'object'}).dtypes
Out[166]: 
0     object
1    float64
2     object
3    float64
dtype: object

Indexes

Files with an “implicit” index column

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

In [167]: 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 [168]: pd.read_csv('foo.csv')
Out[168]: 
          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 [169]: df = pd.read_csv('foo.csv', parse_dates=True)

In [170]: df.index
Out[170]: DatetimeIndex(['2009-01-01', '2009-01-02', '2009-01-03'], dtype='datetime64[ns]', freq=None)

Reading an index with a MultiIndex

Suppose you have data indexed by two columns:

In [171]: print(open('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 can take a list of column numbers to turn multiple columns into a MultiIndex for the index of the returned object:

In [172]: df = pd.read_csv("data/mindex_ex.csv", index_col=[0, 1])

In [173]: df
Out[173]: 
             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 [174]: df.loc[1978]
Out[174]: 
       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

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

In [176]: df = mkdf(5, 3, r_idx_nlevels=2, c_idx_nlevels=4)

In [177]: df.to_csv('mi.csv')

In [178]: 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 [179]: pd.read_csv('mi.csv', header=[0, 1, 2, 3], index_col=[0, 1])
Out[179]: 
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

read_csv is also able to interpret a more common format of multi-columns indices.

In [180]: 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 [181]: pd.read_csv('mi2.csv', header=[0, 1], index_col=0)
Out[181]: 
     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.

Automatically “sniffing” the delimiter

read_csv is capable of inferring delimited (not necessarily comma-separated) files, as pandas uses the csv.Sniffer class of the csv module. For this, you have to specify sep=None.

In [182]: print(open('tmp2.sv').read())
:0:1:2:3
0:0.4691122999071863:-0.2828633443286633:-1.5090585031735124:-1.1356323710171934
1:1.2121120250208506:-0.17321464905330858:0.11920871129693428:-1.0442359662799567
2:-0.8618489633477999:-2.1045692188948086:-0.4949292740687813:1.071803807037338
3:0.7215551622443669:-0.7067711336300845:-1.0395749851146963:0.27185988554282986
4:-0.42497232978883753:0.567020349793672:0.27623201927771873:-1.0874006912859915
5:-0.6736897080883706:0.1136484096888855:-1.4784265524372235:0.5249876671147047
6:0.4047052186802365:0.5770459859204836:-1.7150020161146375:-1.0392684835147725
7:-0.3706468582364464:-1.1578922506419993:-1.344311812731667:0.8448851414248841
8:1.0757697837155533:-0.10904997528022223:1.6435630703622064:-1.4693879595399115
9:0.35702056413309086:-0.6746001037299882:-1.776903716971867:-0.9689138124473498


In [183]: pd.read_csv('tmp2.sv', sep=None, engine='python')
Out[183]: 
   Unnamed: 0         0         1         2         3
0           0  0.469112 -0.282863 -1.509059 -1.135632
1           1  1.212112 -0.173215  0.119209 -1.044236
2           2 -0.861849 -2.104569 -0.494929  1.071804
3           3  0.721555 -0.706771 -1.039575  0.271860
4           4 -0.424972  0.567020  0.276232 -1.087401
5           5 -0.673690  0.113648 -1.478427  0.524988
6           6  0.404705  0.577046 -1.715002 -1.039268
7           7 -0.370647 -1.157892 -1.344312  0.844885
8           8  1.075770 -0.109050  1.643563 -1.469388
9           9  0.357021 -0.674600 -1.776904 -0.968914

Reading multiple files to create a single DataFrame

It’s best to use concat() to combine multiple files. See the cookbook for an example.

Iterating through files chunk by chunk

Suppose you wish to iterate through a (potentially very large) file lazily rather than reading the entire file into memory, such as the following:

In [184]: print(open('tmp.sv').read())
|0|1|2|3
0|0.4691122999071863|-0.2828633443286633|-1.5090585031735124|-1.1356323710171934
1|1.2121120250208506|-0.17321464905330858|0.11920871129693428|-1.0442359662799567
2|-0.8618489633477999|-2.1045692188948086|-0.4949292740687813|1.071803807037338
3|0.7215551622443669|-0.7067711336300845|-1.0395749851146963|0.27185988554282986
4|-0.42497232978883753|0.567020349793672|0.27623201927771873|-1.0874006912859915
5|-0.6736897080883706|0.1136484096888855|-1.4784265524372235|0.5249876671147047
6|0.4047052186802365|0.5770459859204836|-1.7150020161146375|-1.0392684835147725
7|-0.3706468582364464|-1.1578922506419993|-1.344311812731667|0.8448851414248841
8|1.0757697837155533|-0.10904997528022223|1.6435630703622064|-1.4693879595399115
9|0.35702056413309086|-0.6746001037299882|-1.776903716971867|-0.9689138124473498


In [185]: table = pd.read_csv('tmp.sv', sep='|')

In [186]: table
Out[186]: 
   Unnamed: 0         0         1         2         3
0           0  0.469112 -0.282863 -1.509059 -1.135632
1           1  1.212112 -0.173215  0.119209 -1.044236
2           2 -0.861849 -2.104569 -0.494929  1.071804
3           3  0.721555 -0.706771 -1.039575  0.271860
4           4 -0.424972  0.567020  0.276232 -1.087401
5           5 -0.673690  0.113648 -1.478427  0.524988
6           6  0.404705  0.577046 -1.715002 -1.039268
7           7 -0.370647 -1.157892 -1.344312  0.844885
8           8  1.075770 -0.109050  1.643563 -1.469388
9           9  0.357021 -0.674600 -1.776904 -0.968914

By specifying a chunksize to read_csv, the return value will be an iterable object of type TextFileReader:

In [187]: reader = pd.read_csv('tmp.sv', sep='|', chunksize=4)

In [188]: reader
Out[188]: <pandas.io.parsers.TextFileReader at 0x7f65f17cf7f0>

In [189]: for chunk in reader:
   .....:     print(chunk)
   .....: 
   Unnamed: 0         0         1         2         3
0           0  0.469112 -0.282863 -1.509059 -1.135632
1           1  1.212112 -0.173215  0.119209 -1.044236
2           2 -0.861849 -2.104569 -0.494929  1.071804
3           3  0.721555 -0.706771 -1.039575  0.271860
   Unnamed: 0         0         1         2         3
4           4 -0.424972  0.567020  0.276232 -1.087401
5           5 -0.673690  0.113648 -1.478427  0.524988
6           6  0.404705  0.577046 -1.715002 -1.039268
7           7 -0.370647 -1.157892 -1.344312  0.844885
   Unnamed: 0         0        1         2         3
8           8  1.075770 -0.10905  1.643563 -1.469388
9           9  0.357021 -0.67460 -1.776904 -0.968914

Specifying iterator=True will also return the TextFileReader object:

In [190]: reader = pd.read_csv('tmp.sv', sep='|', iterator=True)

In [191]: reader.get_chunk(5)
Out[191]: 
   Unnamed: 0         0         1         2         3
0           0  0.469112 -0.282863 -1.509059 -1.135632
1           1  1.212112 -0.173215  0.119209 -1.044236
2           2 -0.861849 -2.104569 -0.494929  1.071804
3           3  0.721555 -0.706771 -1.039575  0.271860
4           4 -0.424972  0.567020  0.276232 -1.087401

Specifying the parser engine

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:

• sep other than a single character (e.g. regex separators)
• skipfooter
• sep=None with delim_whitespace=False

Specifying any of the above options will produce a ParserWarning unless the python engine is selected explicitly using engine='python'.

Reading remote files

You can pass in a URL to a CSV file:

df = pd.read_csv('https://download.bls.gov/pub/time.series/cu/cu.item',
                 sep='\t')

S3 URLs are handled as well but require installing the S3Fs library:

df = pd.read_csv('s3://pandas-test/tips.csv')

If your S3 bucket requires credentials you will need to set them as environment variables or in the ~/.aws/credentials config file, refer to the S3Fs documentation on credentials.

Writing out data

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 file object. If a file object it must be opened with newline=’‘
• 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
• columns: 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 os.linesep)
• quoting: Set quoting rules as in csv module (default csv.QUOTE_MINIMAL). Note that if you have set a float_format then floats are converted to strings and csv.QUOTE_NONNUMERIC will treat them as non-numeric
• 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
• date_format: Format string for datetime objects

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.

JSON

Read and write JSON format files and strings.

Writing JSON

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 be None in which case a JSON string is returned

• orient :

  Series:

  • default is index
  • allowed values are {split, records, index}

  DataFrame:

  • default is columns
  • allowed values are {split, records, index, columns, values, table}

  The format of the JSON string

  split	dict like {index -> [index], columns -> [columns], data -> [values]}
  records	list like [{column -> value}, … , {column -> value}]
  index	dict like {index -> {column -> value}}
  columns	dict like {column -> {index -> value}}
  values	just 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.

• lines : If records orient, then will write each record per line as json.

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.

In [192]: dfj = pd.DataFrame(np.random.randn(5, 2), columns=list('AB'))

In [193]: json = dfj.to_json()

In [194]: json
Out[194]: '{"A":{"0":-1.2945235903,"1":0.2766617129,"2":-0.0139597524,"3":-0.0061535699,"4":0.8957173022},"B":{"0":0.4137381054,"1":-0.472034511,"2":-0.3625429925,"3":-0.923060654,"4":0.8052440254}}'

Orient options

There are a number of different options for the format of the resulting JSON file / string. Consider the following DataFrame and Series:

In [195]: dfjo = pd.DataFrame(dict(A=range(1, 4), B=range(4, 7), C=range(7, 10)),
   .....:                     columns=list('ABC'), index=list('xyz'))
   .....: 

In [196]: dfjo
Out[196]: 
   A  B  C
x  1  4  7
y  2  5  8
z  3  6  9

In [197]: sjo = pd.Series(dict(x=15, y=16, z=17), name='D')

In [198]: sjo
Out[198]: 
x    15
y    16
z    17
Name: D, dtype: int64

Column oriented (the default for DataFrame) serializes the data as nested JSON objects with column labels acting as the primary index:

In [199]: dfjo.to_json(orient="columns")
Out[199]: '{"A":{"x":1,"y":2,"z":3},"B":{"x":4,"y":5,"z":6},"C":{"x":7,"y":8,"z":9}}'

# Not available for Series

Index oriented (the default for Series) similar to column oriented but the index labels are now primary:

In [200]: dfjo.to_json(orient="index")
Out[200]: '{"x":{"A":1,"B":4,"C":7},"y":{"A":2,"B":5,"C":8},"z":{"A":3,"B":6,"C":9}}'

In [201]: sjo.to_json(orient="index")
Out[201]: '{"x":15,"y":16,"z":17}'

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:

In [202]: dfjo.to_json(orient="records")
Out[202]: '[{"A":1,"B":4,"C":7},{"A":2,"B":5,"C":8},{"A":3,"B":6,"C":9}]'

In [203]: sjo.to_json(orient="records")
Out[203]: '[15,16,17]'

Value oriented is a bare-bones option which serializes to nested JSON arrays of values only, column and index labels are not included:

In [204]: dfjo.to_json(orient="values")
Out[204]: '[[1,4,7],[2,5,8],[3,6,9]]'

# 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:

In [205]: dfjo.to_json(orient="split")
Out[205]: '{"columns":["A","B","C"],"index":["x","y","z"],"data":[[1,4,7],[2,5,8],[3,6,9]]}'

In [206]: sjo.to_json(orient="split")
Out[206]: '{"name":"D","index":["x","y","z"],"data":[15,16,17]}'

Table oriented serializes to the JSON Table Schema, allowing for the preservation of metadata including but not limited to dtypes and index names.

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.

Date handling

Writing in ISO date format:

In [207]: dfd = pd.DataFrame(np.random.randn(5, 2), columns=list('AB'))

In [208]: dfd['date'] = pd.Timestamp('20130101')

In [209]: dfd = dfd.sort_index(1, ascending=False)

In [210]: json = dfd.to_json(date_format='iso')

In [211]: json
Out[211]: '{"date":{"0":"2013-01-01T00:00:00.000Z","1":"2013-01-01T00:00:00.000Z","2":"2013-01-01T00:00:00.000Z","3":"2013-01-01T00:00:00.000Z","4":"2013-01-01T00:00:00.000Z"},"B":{"0":2.5656459463,"1":1.3403088498,"2":-0.2261692849,"3":0.8138502857,"4":-0.8273169356},"A":{"0":-1.2064117817,"1":1.4312559863,"2":-1.1702987971,"3":0.4108345112,"4":0.1320031703}}'

Writing in ISO date format, with microseconds:

In [212]: json = dfd.to_json(date_format='iso', date_unit='us')

In [213]: json
Out[213]: '{"date":{"0":"2013-01-01T00:00:00.000000Z","1":"2013-01-01T00:00:00.000000Z","2":"2013-01-01T00:00:00.000000Z","3":"2013-01-01T00:00:00.000000Z","4":"2013-01-01T00:00:00.000000Z"},"B":{"0":2.5656459463,"1":1.3403088498,"2":-0.2261692849,"3":0.8138502857,"4":-0.8273169356},"A":{"0":-1.2064117817,"1":1.4312559863,"2":-1.1702987971,"3":0.4108345112,"4":0.1320031703}}'

Epoch timestamps, in seconds:

In [214]: json = dfd.to_json(date_format='epoch', date_unit='s')

In [215]: json
Out[215]: '{"date":{"0":1356998400,"1":1356998400,"2":1356998400,"3":1356998400,"4":1356998400},"B":{"0":2.5656459463,"1":1.3403088498,"2":-0.2261692849,"3":0.8138502857,"4":-0.8273169356},"A":{"0":-1.2064117817,"1":1.4312559863,"2":-1.1702987971,"3":0.4108345112,"4":0.1320031703}}'

Writing to a file, with a date index and a date column:

In [216]: dfj2 = dfj.copy()

In [217]: dfj2['date'] = pd.Timestamp('20130101')

In [218]: dfj2['ints'] = list(range(5))

In [219]: dfj2['bools'] = True

In [220]: dfj2.index = pd.date_range('20130101', periods=5)

In [221]: dfj2.to_json('test.json')

In [222]: with open('test.json') as fh:
   .....:     print(fh.read())
   .....: 
{"A":{"1356998400000":-1.2945235903,"1357084800000":0.2766617129,"1357171200000":-0.0139597524,"1357257600000":-0.0061535699,"1357344000000":0.8957173022},"B":{"1356998400000":0.4137381054,"1357084800000":-0.472034511,"1357171200000":-0.3625429925,"1357257600000":-0.923060654,"1357344000000":0.8052440254},"date":{"1356998400000":1356998400000,"1357084800000":1356998400000,"1357171200000":1356998400000,"1357257600000":1356998400000,"1357344000000":1356998400000},"ints":{"1356998400000":0,"1357084800000":1,"1357171200000":2,"1357257600000":3,"1357344000000":4},"bools":{"1356998400000":true,"1357084800000":true,"1357171200000":true,"1357257600000":true,"1357344000000":true}}

Fallback behavior

If the JSON serializer cannot handle the container contents directly it will fall back in the following manner:

• if the dtype is unsupported (e.g. np.complex) then the default_handler, if provided, will be called for each value, otherwise an exception is raised.

• if an object is unsupported it will attempt the following:

  • check if the object has defined a toDict method and call it. A toDict method should return a dict which will then be JSON serialized.
  • invoke the default_handler if one was provided.
  • convert the object to a dict by traversing its contents. However this will often fail with an OverflowError or give unexpected results.

In general the best approach for unsupported objects or dtypes is to provide a default_handler. For example:

>>> DataFrame([1.0, 2.0, complex(1.0, 2.0)]).to_json()  # raises
RuntimeError: Unhandled numpy dtype 15

can be dealt with by specifying a simple default_handler:

In [223]: pd.DataFrame([1.0, 2.0, complex(1.0, 2.0)]).to_json(default_handler=str)
Out[223]: '{"0":{"0":"(1+0j)","1":"(2+0j)","2":"(1+2j)"}}'

Reading JSON

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.json

• typ : type of object to recover (series or frame), default ‘frame’

• orient :

  Series :

  • default is index
  • allowed values are {split, records, index}

  DataFrame

  • default is columns
  • allowed values are {split, records, index, columns, values, table}

  The format of the JSON string

  split	dict like {index -> [index], columns -> [columns], data -> [values]}
  records	list like [{column -> value}, … , {column -> value}]
  index	dict like {index -> {column -> value}}
  columns	dict like {column -> {index -> value}}
  values	just the values array
  table	adhering to the JSON Table Schema

• 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 data.

• convert_axes : boolean, try to convert the axes to the proper dtypes, default is True

• convert_dates : a list of columns to parse for dates; If True, then try to parse date-like columns, default is True.

• keep_default_dates : boolean, default True. If parsing dates, then parse the default date-like columns.

• numpy : 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 if numpy=True.

• precise_float : boolean, default False. 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 functionality.

• date_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.

• lines : reads file as one json object per line.

• encoding : The encoding to use to decode py3 bytes.

• chunksize : when used in combination with lines=True, return a JsonReader which reads in chunksize lines per iteration.

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.

Data conversion

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. ‘date-like’ means that the column label meets one of the following criteria:

• it ends with '_at'
• it ends with '_time'
• it begins with 'timestamp'
• it is 'modified'
• it is 'date'

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 float data will be converted to integer if it can be done safely, e.g. a column of 1.
• bool columns will be converted to integer on reconstruction

Thus there are times where you may want to specify specific dtypes via the dtype keyword argument.

Reading from a JSON string:

In [224]: pd.read_json(json)
Out[224]: 
        date         B         A
0 2013-01-01  2.565646 -1.206412
1 2013-01-01  1.340309  1.431256
2 2013-01-01 -0.226169 -1.170299
3 2013-01-01  0.813850  0.410835
4 2013-01-01 -0.827317  0.132003

Reading from a file:

In [225]: pd.read_json('test.json')
Out[225]: 
                   A         B       date  ints  bools
2013-01-01 -1.294524  0.413738 2013-01-01     0   True
2013-01-02  0.276662 -0.472035 2013-01-01     1   True
2013-01-03 -0.013960 -0.362543 2013-01-01     2   True
2013-01-04 -0.006154 -0.923061 2013-01-01     3   True
2013-01-05  0.895717  0.805244 2013-01-01     4   True

Don’t convert any data (but still convert axes and dates):

In [226]: pd.read_json('test.json', dtype=object).dtypes
Out[226]: 
A        object
B        object
date     object
ints     object
bools    object
dtype: object

Specify dtypes for conversion:

In [227]: pd.read_json('test.json', dtype={'A': 'float32', 'bools': 'int8'}).dtypes
Out[227]: 
A               float32
B               float64
date     datetime64[ns]
ints              int64
bools              int8
dtype: object

Preserve string indices:

In [228]: si = pd.DataFrame(np.zeros((4, 4)), columns=list(range(4)),
   .....:                   index=[str(i) for i in range(4)])
   .....: 

In [229]: si
Out[229]: 
     0    1    2    3
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
3  0.0  0.0  0.0  0.0

In [230]: si.index
Out[230]: Index(['0', '1', '2', '3'], dtype='object')

In [231]: si.columns
Out[231]: Int64Index([0, 1, 2, 3], dtype='int64')

In [232]: json = si.to_json()

In [233]: sij = pd.read_json(json, convert_axes=False)

In [234]: sij
Out[234]: 
   0  1  2  3
0  0  0  0  0
1  0  0  0  0
2  0  0  0  0
3  0  0  0  0

In [235]: sij.index
Out[235]: Index(['0', '1', '2', '3'], dtype='object')

In [236]: sij.columns
Out[236]: Index(['0', '1', '2', '3'], dtype='object')

Dates written in nanoseconds need to be read back in nanoseconds:

In [237]: json = dfj2.to_json(date_unit='ns')

# Try to parse timestamps as milliseconds -> Won't Work
In [238]: dfju = pd.read_json(json, date_unit='ms')

In [239]: dfju
Out[239]: 
                            A         B                 date  ints  bools
1356998400000000000 -1.294524  0.413738  1356998400000000000     0   True
1357084800000000000  0.276662 -0.472035  1356998400000000000     1   True
1357171200000000000 -0.013960 -0.362543  1356998400000000000     2   True
1357257600000000000 -0.006154 -0.923061  1356998400000000000     3   True
1357344000000000000  0.895717  0.805244  1356998400000000000     4   True

# Let pandas detect the correct precision
In [240]: dfju = pd.read_json(json)

In [241]: dfju
Out[241]: 
                   A         B       date  ints  bools
2013-01-01 -1.294524  0.413738 2013-01-01     0   True
2013-01-02  0.276662 -0.472035 2013-01-01     1   True
2013-01-03 -0.013960 -0.362543 2013-01-01     2   True
2013-01-04 -0.006154 -0.923061 2013-01-01     3   True
2013-01-05  0.895717  0.805244 2013-01-01     4   True

# Or specify that all timestamps are in nanoseconds
In [242]: dfju = pd.read_json(json, date_unit='ns')

In [243]: dfju
Out[243]: 
                   A         B       date  ints  bools
2013-01-01 -1.294524  0.413738 2013-01-01     0   True
2013-01-02  0.276662 -0.472035 2013-01-01     1   True
2013-01-03 -0.013960 -0.362543 2013-01-01     2   True
2013-01-04 -0.006154 -0.923061 2013-01-01     3   True
2013-01-05  0.895717  0.805244 2013-01-01     4   True

The Numpy parameter

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:

In [244]: randfloats = np.random.uniform(-100, 1000, 10000)

In [245]: randfloats.shape = (1000, 10)

In [246]: dffloats = pd.DataFrame(randfloats, columns=list('ABCDEFGHIJ'))

In [247]: jsonfloats = dffloats.to_json()

In [248]: %timeit pd.read_json(jsonfloats)
12.4 ms +- 116 us per loop (mean +- std. dev. of 7 runs, 100 loops each)

In [249]: %timeit pd.read_json(jsonfloats, numpy=True)
9.56 ms +- 82.8 us per loop (mean +- std. dev. of 7 runs, 100 loops each)

The speedup is less noticeable for smaller datasets:

In [250]: jsonfloats = dffloats.head(100).to_json()

In [251]: %timeit pd.read_json(jsonfloats)
8.05 ms +- 120 us per loop (mean +- std. dev. of 7 runs, 100 loops each)

In [252]: %timeit pd.read_json(jsonfloats, numpy=True)
7 ms +- 162 us per loop (mean +- std. dev. of 7 runs, 100 loops each)

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 ValueError may 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_json but may not be the case if the JSON is from another source.

Normalization

pandas provides a utility function to take a dict or list of dicts and normalize this semi-structured data into a flat table.

In [253]: from pandas.io.json import json_normalize

In [254]: data = [{'id': 1, 'name': {'first': 'Coleen', 'last': 'Volk'}},
   .....:         {'name': {'given': 'Mose', 'family': 'Regner'}},
   .....:         {'id': 2, 'name': 'Faye Raker'}]
   .....: 

In [255]: json_normalize(data)
Out[255]: 
    id name.first name.last name.given name.family        name
0  1.0     Coleen      Volk        NaN         NaN         NaN
1  NaN        NaN       NaN       Mose      Regner         NaN
2  2.0        NaN       NaN        NaN         NaN  Faye Raker

In [256]: 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}]}]
   .....: 

In [257]: json_normalize(data, 'counties', ['state', 'shortname', ['info', 'governor']])
Out[257]: 
         name  population    state shortname info.governor
0        Dade       12345  Florida        FL    Rick Scott
1     Broward       40000  Florida        FL    Rick Scott
2  Palm Beach       60000  Florida        FL    Rick Scott
3      Summit        1234     Ohio        OH   John Kasich
4    Cuyahoga        1337     Ohio        OH   John Kasich

The max_level parameter provides more control over which level to end normalization. With max_level=1 the following snippet normalizes until 1st nesting level of the provided dict.

In [258]: data = [{'CreatedBy': {'Name': 'User001'},
   .....:          'Lookup': {'TextField': 'Some text',
   .....:                     'UserField': {'Id': 'ID001',
   .....:                                   'Name': 'Name001'}},
   .....:          'Image': {'a': 'b'}
   .....:          }]
   .....: 

In [259]: json_normalize(data, max_level=1)
Out[259]: 
  CreatedBy.Name Lookup.TextField                    Lookup.UserField Image.a
0        User001        Some text  {'Id': 'ID001', 'Name': 'Name001'}       b

Line delimited json

New in version 0.19.0.

pandas is able to read and write line-delimited json files that are common in data processing pipelines using Hadoop or Spark.

New in version 0.21.0.

For line-delimited json files, pandas can also return an iterator which reads in chunksize lines at a time. This can be useful for large files or to read from a stream.

In [260]: jsonl = '''
   .....:     {"a": 1, "b": 2}
   .....:     {"a": 3, "b": 4}
   .....: '''
   .....: 

In [261]: df = pd.read_json(jsonl, lines=True)

In [262]: df
Out[262]: 
   a  b
0  1  2
1  3  4

In [263]: df.to_json(orient='records', lines=True)
Out[263]: '{"a":1,"b":2}\n{"a":3,"b":4}'

# reader is an iterator that returns `chunksize` lines each iteration
In [264]: reader = pd.read_json(StringIO(jsonl), lines=True, chunksize=1)

In [265]: reader
Out[265]: <pandas.io.json._json.JsonReader at 0x7f65f15bac18>

In [266]: for chunk in reader:
   .....:     print(chunk)
   .....: 
Empty DataFrame
Columns: []
Index: []
   a  b
0  1  2
   a  b
1  3  4

Table schema

New in version 0.20.0.

Table Schema is a spec for describing tabular datasets as a JSON object. The JSON includes information on the field names, types, and other attributes. You can use the orient table to build a JSON string with two fields, schema and data.

In [267]: df = pd.DataFrame({'A': [1, 2, 3],
   .....:                    'B': ['a', 'b', 'c'],
   .....:                    'C': pd.date_range('2016-01-01', freq='d', periods=3)},
   .....:                   index=pd.Index(range(3), name='idx'))
   .....: 

In [268]: df
Out[268]: 
     A  B          C
idx                 
0    1  a 2016-01-01
1    2  b 2016-01-02
2    3  c 2016-01-03

In [269]: df.to_json(orient='table', date_format="iso")
Out[269]: '{"schema": {"fields":[{"name":"idx","type":"integer"},{"name":"A","type":"integer"},{"name":"B","type":"string"},{"name":"C","type":"datetime"}],"primaryKey":["idx"],"pandas_version":"0.20.0"}, "data": [{"idx":0,"A":1,"B":"a","C":"2016-01-01T00:00:00.000Z"},{"idx":1,"A":2,"B":"b","C":"2016-01-02T00:00:00.000Z"},{"idx":2,"A":3,"B":"c","C":"2016-01-03T00:00:00.000Z"}]}'

The schema field contains the fields key, which itself contains a list of column name to type pairs, including the Index or MultiIndex (see below for a list of types). The schema field also contains a primaryKey field if the (Multi)index is unique.

The second field, data, contains the serialized data with the records orient. The index is included, and any datetimes are ISO 8601 formatted, as required by the Table Schema spec.

The full list of types supported are described in the Table Schema spec. This table shows the mapping from pandas types:

Pandas type	Table Schema type
int64	integer
float64	number
bool	boolean
datetime64[ns]	datetime
timedelta64[ns]	duration
categorical	any
object	str

A few notes on the generated table schema:

• The schema object contains a pandas_version field. This contains the version of pandas’ dialect of the schema, and will be incremented with each revision.
• All dates are converted to UTC when serializing. Even timezone naive values, which are treated as UTC with an offset of 0.

In [270]: from pandas.io.json import build_table_schema

In [271]: s = pd.Series(pd.date_range('2016', periods=4))

In [272]: build_table_schema(s)
Out[272]: 
{'fields': [{'name': 'index', 'type': 'integer'},
  {'name': 'values', 'type': 'datetime'}],
 'primaryKey': ['index'],
 'pandas_version': '0.20.0'}

• datetimes with a timezone (before serializing), include an additional field tz with the time zone name (e.g. 'US/Central').

In [273]: s_tz = pd.Series(pd.date_range('2016', periods=12,
   .....:                                tz='US/Central'))
   .....: 

In [274]: build_table_schema(s_tz)
Out[274]: 
{'fields': [{'name': 'index', 'type': 'integer'},
  {'name': 'values', 'type': 'datetime', 'tz': 'US/Central'}],
 'primaryKey': ['index'],
 'pandas_version': '0.20.0'}

• Periods are converted to timestamps before serialization, and so have the same behavior of being converted to UTC. In addition, periods will contain and additional field freq with the period’s frequency, e.g. 'A-DEC'.

In [275]: s_per = pd.Series(1, index=pd.period_range('2016', freq='A-DEC',
   .....:                                            periods=4))
   .....: 

In [276]: build_table_schema(s_per)
Out[276]: 
{'fields': [{'name': 'index', 'type': 'datetime', 'freq': 'A-DEC'},
  {'name': 'values', 'type': 'integer'}],
 'primaryKey': ['index'],
 'pandas_version': '0.20.0'}

• Categoricals use the any type and an enum constraint listing the set of possible values. Additionally, an ordered field is included:

In [277]: s_cat = pd.Series(pd.Categorical(['a', 'b', 'a']))

In [278]: build_table_schema(s_cat)
Out[278]: 
{'fields': [{'name': 'index', 'type': 'integer'},
  {'name': 'values',
   'type': 'any',
   'constraints': {'enum': ['a', 'b']},
   'ordered': False}],
 'primaryKey': ['index'],
 'pandas_version': '0.20.0'}

• A primaryKey field, containing an array of labels, is included if the index is unique:

In [279]: s_dupe = pd.Series([1, 2], index=[1, 1])

In [280]: build_table_schema(s_dupe)
Out[280]: 
{'fields': [{'name': 'index', 'type': 'integer'},
  {'name': 'values', 'type': 'integer'}],
 'pandas_version': '0.20.0'}

• The primaryKey behavior is the same with MultiIndexes, but in this case the primaryKey is an array:

In [281]: s_multi = pd.Series(1, index=pd.MultiIndex.from_product([('a', 'b'),
   .....:                                                          (0, 1)]))
   .....: 

In [282]: build_table_schema(s_multi)
Out[282]: 
{'fields': [{'name': 'level_0', 'type': 'string'},
  {'name': 'level_1', 'type': 'integer'},
  {'name': 'values', 'type': 'integer'}],
 'primaryKey': FrozenList(['level_0', 'level_1']),
 'pandas_version': '0.20.0'}

• The default naming roughly follows these rules:

  • For series, the object.name is used. If that’s none, then the name is values
  • For DataFrames, the stringified version of the column name is used
  • For Index (not MultiIndex), index.name is used, with a fallback to index if that is None.
  • For MultiIndex, mi.names is used. If any level has no name, then level_ is used.

New in version 0.23.0.

read_json also accepts orient='table' as an argument. This allows for the preservation of metadata such as dtypes and index names in a round-trippable manner.

In [283]: df = pd.DataFrame({'foo': [1, 2, 3, 4],
   .....:        'bar': ['a', 'b', 'c', 'd'],
   .....:        'baz': pd.date_range('2018-01-01', freq='d', periods=4),
   .....:        'qux': pd.Categorical(['a', 'b', 'c', 'c'])
   .....:        }, index=pd.Index(range(4), name='idx'))
   .....: 

In [284]: df
Out[284]: 
     foo bar        baz qux
idx                        
0      1   a 2018-01-01   a
1      2   b 2018-01-02   b
2      3   c 2018-01-03   c
3      4   d 2018-01-04   c

In [285]: df.dtypes
Out[285]: 
foo             int64
bar            object
baz    datetime64[ns]
qux          category
dtype: object

In [286]: df.to_json('test.json', orient='table')

In [287]: new_df = pd.read_json('test.json', orient='table')

In [288]: new_df
Out[288]: 
     foo bar        baz qux
idx                        
0      1   a 2018-01-01   a
1      2   b 2018-01-02   b
2      3   c 2018-01-03   c
3      4   d 2018-01-04   c

In [289]: new_df.dtypes
Out[289]: 
foo             int64
bar            object
baz    datetime64[ns]
qux          category
dtype: object

Please note that the literal string ‘index’ as the name of an Index is not round-trippable, nor are any names beginning with 'level_' within a MultiIndex. These are used by default in DataFrame.to_json() to indicate missing values and the subsequent read cannot distinguish the intent.

In [290]: df.index.name = 'index'

In [291]: df.to_json('test.json', orient='table')

In [292]: new_df = pd.read_json('test.json', orient='table')

In [293]: print(new_df.index.name)
None

HTML

Reading HTML content

Warning

We highly encourage you to read the HTML Table Parsing gotchas below regarding the issues surrounding the BeautifulSoup4/html5lib/lxml parsers.

The top-level 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:

In [294]: url = 'https://www.fdic.gov/bank/individual/failed/banklist.html'

In [295]: dfs = pd.read_html(url)

In [296]: dfs
Out[296]: 
[                                             Bank Name        City  ST   CERT                Acquiring Institution       Closing Date       Updated Date
 0                                 The Enloe State Bank      Cooper  TX  10716                   Legend Bank, N. A.       May 31, 2019      June 18, 2019
 1                  Washington Federal Bank for Savings     Chicago  IL  30570                   Royal Savings Bank  December 15, 2017   February 1, 2019
 2      The Farmers and Merchants State Bank of Argonia     Argonia  KS  17719                          Conway Bank   October 13, 2017  February 21, 2018
 3                                  Fayette County Bank  Saint Elmo  IL   1802            United Fidelity Bank, fsb       May 26, 2017   January 29, 2019
 4    Guaranty Bank, (d/b/a BestBank in Georgia & Mi...   Milwaukee  WI  30003  First-Citizens Bank & Trust Company        May 5, 2017     March 22, 2018
 ..                                                 ...         ...  ..    ...                                  ...                ...                ...
 551                                 Superior Bank, FSB    Hinsdale  IL  32646                Superior Federal, FSB      July 27, 2001    August 19, 2014
 552                                Malta National Bank       Malta  OH   6629                    North Valley Bank        May 3, 2001  November 18, 2002
 553                    First Alliance Bank & Trust Co.  Manchester  NH  34264  Southern New Hampshire Bank & Trust   February 2, 2001  February 18, 2003
 554                  National State Bank of Metropolis  Metropolis  IL   3815              Banterra Bank of Marion  December 14, 2000     March 17, 2005
 555                                   Bank of Honolulu    Honolulu  HI  21029                   Bank of the Orient   October 13, 2000     March 17, 2005
 
 [556 rows x 7 columns]]

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:

In [297]: with open(file_path, 'r') as f:
   .....:     dfs = pd.read_html(f.read())
   .....: 

In [298]: dfs
Out[298]: 
[                                    Bank Name          City  ST   CERT                Acquiring Institution       Closing Date       Updated Date
 0    Banks of Wisconsin d/b/a Bank of Kenosha       Kenosha  WI  35386                North Shore Bank, FSB       May 31, 2013       May 31, 2013
 1                        Central Arizona Bank    Scottsdale  AZ  34527                   Western State Bank       May 14, 2013       May 20, 2013
 2                                Sunrise Bank      Valdosta  GA  58185                         Synovus Bank       May 10, 2013       May 21, 2013
 3                       Pisgah Community Bank     Asheville  NC  58701                   Capital Bank, N.A.       May 10, 2013       May 14, 2013
 4                         Douglas County Bank  Douglasville  GA  21649                  Hamilton State Bank     April 26, 2013       May 16, 2013
 ..                                        ...           ...  ..    ...                                  ...                ...                ...
 500                        Superior Bank, FSB      Hinsdale  IL  32646                Superior Federal, FSB      July 27, 2001       June 5, 2012
 501                       Malta National Bank         Malta  OH   6629                    North Valley Bank        May 3, 2001  November 18, 2002
 502           First Alliance Bank & Trust Co.    Manchester  NH  34264  Southern New Hampshire Bank & Trust   February 2, 2001  February 18, 2003
 503         National State Bank of Metropolis    Metropolis  IL   3815              Banterra Bank of Marion  December 14, 2000     March 17, 2005
 504                          Bank of Honolulu      Honolulu  HI  21029                   Bank of the Orient   October 13, 2000     March 17, 2005
 
 [505 rows x 7 columns]]

You can even pass in an instance of StringIO if you so desire:

In [299]: with open(file_path, 'r') as f:
   .....:     sio = StringIO(f.read())
   .....: 

In [300]: dfs = pd.read_html(sio)

In [301]: dfs
Out[301]: 
[                                    Bank Name          City  ST   CERT                Acquiring Institution       Closing Date       Updated Date
 0    Banks of Wisconsin d/b/a Bank of Kenosha       Kenosha  WI  35386                North Shore Bank, FSB       May 31, 2013       May 31, 2013
 1                        Central Arizona Bank    Scottsdale  AZ  34527                   Western State Bank       May 14, 2013       May 20, 2013
 2                                Sunrise Bank      Valdosta  GA  58185                         Synovus Bank       May 10, 2013       May 21, 2013
 3                       Pisgah Community Bank     Asheville  NC  58701                   Capital Bank, N.A.       May 10, 2013       May 14, 2013
 4                         Douglas County Bank  Douglasville  GA  21649                  Hamilton State Bank     April 26, 2013       May 16, 2013
 ..                                        ...           ...  ..    ...                                  ...                ...                ...
 500                        Superior Bank, FSB      Hinsdale  IL  32646                Superior Federal, FSB      July 27, 2001       June 5, 2012
 501                       Malta National Bank         Malta  OH   6629                    North Valley Bank        May 3, 2001  November 18, 2002
 502           First Alliance Bank & Trust Co.    Manchester  NH  34264  Southern New Hampshire Bank & Trust   February 2, 2001  February 18, 2003
 503         National State Bank of Metropolis    Metropolis  IL   3815              Banterra Bank of Marion  December 14, 2000     March 17, 2005
 504                          Bank of Honolulu      Honolulu  HI  21029                   Bank of the Orient   October 13, 2000     March 17, 2005
 
 [505 rows x 7 columns]]

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 = pd.read_html(url, match=match)

`` elements).

dfs = pd.read_html(url, header=0)

Specify an index column:

dfs = pd.read_html(url, index_col=0)

Specify a number of rows to skip:

dfs = pd.read_html(url, skiprows=0)

Specify a number of rows to skip using a list (xrange (Python 2 only) works as well):

dfs = pd.read_html(url, skiprows=range(2))

Specify an HTML attribute:

dfs1 = pd.read_html(url, attrs={'id': 'table'})
dfs2 = pd.read_html(url, attrs={'class': 'sortable'})
print(np.array_equal(dfs1[0], dfs2[0]))  # Should be True

Specify values that should be converted to NaN:

dfs = pd.read_html(url, na_values=['No Acquirer'])

New in version 0.19.

Specify whether to keep the default set of NaN values:

dfs = pd.read_html(url, keep_default_na=False)

New in version 0.19.

Specify converters for columns. This is useful for numerical text data that has leading zeros. By default columns that are numerical are cast to numeric types and the leading zeros are lost. To avoid this, we can convert these columns to strings.

url_mcc = 'https://en.wikipedia.org/wiki/Mobile_country_code'
dfs = pd.read_html(url_mcc, match='Telekom Albania', header=0,
                   converters={'MNC': str})

New in version 0.19.

Use some combination of the above:

dfs = pd.read_html(url, match='Metcalf Bank', index_col=0)

Read in pandas to_html output (with some loss of floating point precision):

df = pd.DataFrame(np.random.randn(2, 2))
s = df.to_html(float_format='{0:.40g}'.format)
dfin = pd.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. You may use:

dfs = pd.read_html(url, 'Metcalf Bank', index_col=0, flavor=['lxml'])

Or you could pass flavor='lxml' without a list:

dfs = pd.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 = pd.read_html(url, 'Metcalf Bank', index_col=0, flavor=['lxml', 'bs4'])

Writing to HTML files

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 to_html() for the full set of options.

In [302]: df = pd.DataFrame(np.random.randn(2, 2))

In [303]: df
Out[303]: 
          0         1
0 -0.184744  0.496971
1 -0.856240  1.857977

In [304]: print(df.to_html())  # raw html
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-0.184744</td>
      <td>0.496971</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-0.856240</td>
      <td>1.857977</td>
    </tr>
  </tbody>
</table>

HTML:

| 0 | 1 ---|---|--- 0 | -0.184744 | 0.496971 1 | -0.856240 | 1.857977

The columns argument will limit the columns shown:

In [305]: print(df.to_html(columns=[0]))
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-0.184744</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-0.856240</td>
    </tr>
  </tbody>
</table>

HTML:

-	0
0	-0.184744
1	-0.856240

float_format takes a Python callable to control the precision of floating point values:

In [306]: print(df.to_html(float_format='{0:.10f}'.format))
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-0.1847438576</td>
      <td>0.4969711327</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-0.8562396763</td>
      <td>1.8579766508</td>
    </tr>
  </tbody>
</table>

HTML:

| 0 | 1 ---|---|--- 0 | -0.1847438576 | 0.4969711327 1 | -0.8562396763 | 1.8579766508

bold_rows will make the row labels bold by default, but you can turn that off:

In [307]: print(df.to_html(bold_rows=False))
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>0</td>
      <td>-0.184744</td>
      <td>0.496971</td>
    </tr>
    <tr>
      <td>1</td>
      <td>-0.856240</td>
      <td>1.857977</td>
    </tr>
  </tbody>
</table>

| 0 | 1 ---|---|--- 0 | -0.184744 | 0.496971 1 | -0.856240 | 1.857977

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.

In [308]: print(df.to_html(classes=['awesome_table_class', 'even_more_awesome_class']))
<table border="1" class="dataframe awesome_table_class even_more_awesome_class">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-0.184744</td>
      <td>0.496971</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-0.856240</td>
      <td>1.857977</td>
    </tr>
  </tbody>
</table>

The render_links argument provides the ability to add hyperlinks to cells that contain URLs.

New in version 0.24.

In [309]: url_df = pd.DataFrame({
   .....:     'name': ['Python', 'Pandas'],
   .....:     'url': ['https://www.python.org/', 'http://pandas.pydata.org']})
   .....: 

In [310]: print(url_df.to_html(render_links=True))
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>url</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Python</td>
      <td><a href="https://www.python.org/" target="_blank">https://www.python.org/</a></td>
    </tr>
    <tr>
      <th>1</th>
      <td>Pandas</td>
      <td><a href="http://pandas.pydata.org" target="_blank">http://pandas.pydata.org</a></td>
    </tr>
  </tbody>
</table>

HTML:

-	name	url
0	Python	https://www.python.org/
1	Pandas	http://pandas.pydata.org

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

In [311]: df = pd.DataFrame({'a': list('&<>'), 'b': np.random.randn(3)})

Escaped:

In [312]: print(df.to_html())
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>a</th>
      <th>b</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>&amp;</td>
      <td>-0.474063</td>
    </tr>
    <tr>
      <th>1</th>
      <td>&lt;</td>
      <td>-0.230305</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&gt;</td>
      <td>-0.400654</td>
    </tr>
  </tbody>
</table>

-	a	b
0	&	-0.474063
1	<	-0.230305
2	>	-0.400654

Not escaped:

In [313]: print(df.to_html(escape=False))
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>a</th>
      <th>b</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>&</td>
      <td>-0.474063</td>
    </tr>
    <tr>
      <th>1</th>
      <td><</td>
      <td>-0.230305</td>
    </tr>
    <tr>
      <th>2</th>
      <td>></td>
      <td>-0.400654</td>
    </tr>
  </tbody>
</table>

-	a	b
0	&	-0.474063
1	<	-0.230305
2	>	-0.400654

Note

Some browsers may not show a difference in the rendering of the previous two HTML tables.

HTML Table Parsing Gotchas

There are some versioning issues surrounding the libraries that are used to parse HTML tables in the top-level pandas io function read_html.

Issues with lxml

• Benefits
  • lxml is very fast.
  • lxml requires Cython to install correctly.
• Drawbacks
  • lxml does not make any guarantees about the results of its parse unless it is given strictly valid markup.
  • In light of the above, we have chosen to allow you, the user, to use the lxml backend, but this backend will use html5lib if lxml fails to parse
  • It is therefore highly recommended that you install both BeautifulSoup4 and html5lib, so that you will still get a valid result (provided everything else is valid) even if lxml fails.

Issues with BeautifulSoup4 using lxml as a backend

• The above issues hold here as well since BeautifulSoup4 is essentially just a wrapper around a parser backend.

Issues with BeautifulSoup4 using html5lib as a backend

• Benefits
  • html5lib is far more lenient than lxml and consequently deals with real-life markup in a much saner way rather than just, e.g., dropping an element without notifying you.
  • html5lib generates valid HTML5 markup from invalid markup automatically. This is extremely important for parsing HTML tables, since it guarantees a valid document. However, that does NOT mean that it is “correct”, since the process of fixing markup does not have a single definition.
  • html5lib is pure Python and requires no additional build steps beyond its own installation.
• Drawbacks
  • The biggest drawback to using html5lib is that it is slow as molasses. However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from the URL over the web, i.e., IO (input-output). For very large tables, this might not be true.

Excel files

The read_excel() method can read Excel 2003 (.xls) files using the xlrd Python module. Excel 2007+ (.xlsx) files can be read using either xlrd or openpyxl. The to_excel() instance method is used for saving a DataFrame to Excel. Generally the semantics are similar to working with csv data. See the cookbook for some advanced strategies.

Reading Excel files

In the most basic use-case, read_excel takes a path to an Excel file, and the sheet_name indicating which sheet to parse.

# Returns a DataFrame
pd.read_excel('path_to_file.xls', sheet_name='Sheet1')

ExcelFile class

To facilitate working with multiple sheets from the same file, the ExcelFile class can be used to wrap the file and can be passed into read_excel There will be a performance benefit for reading multiple sheets as the file is read into memory only once.

xlsx = pd.ExcelFile('path_to_file.xls')
df = pd.read_excel(xlsx, 'Sheet1')

The ExcelFile class can also be used as a context manager.

with pd.ExcelFile('path_to_file.xls') as xls:
    df1 = pd.read_excel(xls, 'Sheet1')
    df2 = pd.read_excel(xls, 'Sheet2')

The sheet_names property will generate a list of the sheet names in the file.

The primary use-case for an ExcelFile is parsing multiple sheets with different parameters:

data = {}
# For when Sheet1's format differs from Sheet2
with pd.ExcelFile('path_to_file.xls') as xls:
    data['Sheet1'] = pd.read_excel(xls, 'Sheet1', index_col=None,
                                   na_values=['NA'])
    data['Sheet2'] = pd.read_excel(xls, 'Sheet2', index_col=1)

Note that if the same parsing parameters are used for all sheets, a list of sheet names can simply be passed to read_excel with no loss in performance.

# using the ExcelFile class
data = {}
with pd.ExcelFile('path_to_file.xls') as xls:
    data['Sheet1'] = pd.read_excel(xls, 'Sheet1', index_col=None,
                                   na_values=['NA'])
    data['Sheet2'] = pd.read_excel(xls, 'Sheet2', index_col=None,
                                   na_values=['NA'])

# equivalent using the read_excel function
data = pd.read_excel('path_to_file.xls', ['Sheet1', 'Sheet2'],
                     index_col=None, na_values=['NA'])

ExcelFile can also be called with a xlrd.book.Book object as a parameter. This allows the user to control how the excel file is read. For example, sheets can be loaded on demand by calling xlrd.open_workbook() with on_demand=True.

import xlrd
xlrd_book = xlrd.open_workbook('path_to_file.xls', on_demand=True)
with pd.ExcelFile(xlrd_book) as xls:
    df1 = pd.read_excel(xls, 'Sheet1')
    df2 = pd.read_excel(xls, 'Sheet2')

Specifying sheets

Note

The second argument is sheet_name, not to be confused with ExcelFile.sheet_names.

Note

An ExcelFile’s attribute sheet_names provides access to a list of sheets.

• The arguments sheet_name allows specifying the sheet or sheets to read.
• The default value for sheet_name is 0, indicating to read the first sheet
• 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.
• Pass a list of either strings or integers, to return a dictionary of specified sheets.
• Pass a None to return a dictionary of all available sheets.

# Returns a DataFrame
pd.read_excel('path_to_file.xls', 'Sheet1', index_col=None, na_values=['NA'])

Using the sheet index:

# Returns a DataFrame
pd.read_excel('path_to_file.xls', 0, index_col=None, na_values=['NA'])

Using all default values:

# Returns a DataFrame
pd.read_excel('path_to_file.xls')

Using None to get all sheets:

# Returns a dictionary of DataFrames
pd.read_excel('path_to_file.xls', sheet_name=None)

Using a list to get multiple sheets:

# Returns the 1st and 4th sheet, as a dictionary of DataFrames.
pd.read_excel('path_to_file.xls', sheet_name=['Sheet1', 3])

read_excel can read more than one sheet, by setting sheet_name to either a list of sheet names, a list of sheet positions, or None to read all sheets. Sheets can be specified by sheet index or sheet name, using an integer or string, respectively.

Reading a MultiIndex

read_excel can read a MultiIndex index, by passing a list of columns to index_col and a MultiIndex column by passing a list of rows to header. If either the index or columns have serialized level names those will be read in as well by specifying the rows/columns that make up the levels.

For example, to read in a MultiIndex index without names:

In [314]: df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [5, 6, 7, 8]},
   .....:                   index=pd.MultiIndex.from_product([['a', 'b'], ['c', 'd']]))
   .....: 

In [315]: df.to_excel('path_to_file.xlsx')

In [316]: df = pd.read_excel('path_to_file.xlsx', index_col=[0, 1])

In [317]: df
Out[317]: 
     a  b
a c  1  5
  d  2  6
b c  3  7
  d  4  8

If the index has level names, they will parsed as well, using the same parameters.

In [318]: df.index = df.index.set_names(['lvl1', 'lvl2'])

In [319]: df.to_excel('path_to_file.xlsx')

In [320]: df = pd.read_excel('path_to_file.xlsx', index_col=[0, 1])

In [321]: df
Out[321]: 
           a  b
lvl1 lvl2      
a    c     1  5
     d     2  6
b    c     3  7
     d     4  8

If the source file has both MultiIndex index and columns, lists specifying each should be passed to index_col and header:

In [322]: df.columns = pd.MultiIndex.from_product([['a'], ['b', 'd']],
   .....:                                         names=['c1', 'c2'])
   .....: 

In [323]: df.to_excel('path_to_file.xlsx')

In [324]: df = pd.read_excel('path_to_file.xlsx', index_col=[0, 1], header=[0, 1])

In [325]: df
Out[325]: 
c1         a   
c2         b  d
lvl1 lvl2      
a    c     1  5
     d     2  6
b    c     3  7
     d     4  8

Parsing specific columns

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 usecols keyword to allow you to specify a subset of columns to parse.

Deprecated since version 0.24.0.

Passing in an integer for usecols has been deprecated. Please pass in a list of ints from 0 to usecols inclusive instead.

If usecols is an integer, then it is assumed to indicate the last column to be parsed.

pd.read_excel('path_to_file.xls', 'Sheet1', usecols=2)

You can also specify a comma-delimited set of Excel columns and ranges as a string:

pd.read_excel('path_to_file.xls', 'Sheet1', usecols='A,C:E')

If usecols is a list of integers, then it is assumed to be the file column indices to be parsed.

pd.read_excel('path_to_file.xls', 'Sheet1', usecols=[0, 2, 3])

Element order is ignored, so usecols=[0, 1] is the same as [1, 0].

New in version 0.24.

If usecols is a list of strings, it is assumed that each string corresponds to a column name provided either by the user in names or inferred from the document header row(s). Those strings define which columns will be parsed:

pd.read_excel('path_to_file.xls', 'Sheet1', usecols=['foo', 'bar'])

Element order is ignored, so usecols=['baz', 'joe'] is the same as ['joe', 'baz'].

New in version 0.24.

If usecols is callable, the callable function will be evaluated against the column names, returning names where the callable function evaluates to True.

pd.read_excel('path_to_file.xls', 'Sheet1', usecols=lambda x: x.isalpha())

Parsing dates

Datetime-like values are normally automatically converted to the appropriate dtype when reading the excel file. But if you have a column of strings that look like dates (but are not actually formatted as dates in excel), you can use the parse_dates keyword to parse those strings to datetimes:

pd.read_excel('path_to_file.xls', 'Sheet1', parse_dates=['date_strings'])

Cell converters

It is possible to transform the contents of Excel cells via the converters option. For instance, to convert a column to boolean:

pd.read_excel('path_to_file.xls', 'Sheet1', converters={'MyBools': bool})

This options handles missing values and treats exceptions in the converters as missing data. Transformations are applied cell by cell rather than to the column as a whole, so the array dtype is not guaranteed. For instance, a column of integers with missing values cannot be transformed to an array with integer dtype, because NaN is strictly a float. You can manually mask missing data to recover integer dtype:

def cfun(x):
    return int(x) if x else -1


pd.read_excel('path_to_file.xls', 'Sheet1', converters={'MyInts': cfun})

Dtype specifications

New in version 0.20.

As an alternative to converters, the type for an entire column can be specified using the dtype keyword, which takes a dictionary mapping column names to types. To interpret data with no type inference, use the type str or object.

pd.read_excel('path_to_file.xls', dtype={'MyInts': 'int64', 'MyText': str})

Writing Excel files

Writing Excel files to disk

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. The index_label will be placed in the second row instead of the first. You can place it in the first row by setting the merge_cells option in to_excel() to False:

df.to_excel('path_to_file.xlsx', index_label='label', merge_cells=False)

In order to write separate DataFrames to separate sheets in a single Excel file, one can pass an ExcelWriter.

with pd.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.

Writing Excel files to memory

Pandas supports writing Excel files to buffer-like objects such as StringIO or BytesIO using ExcelWriter.

# Safe import for either Python 2.x or 3.x
try:
    from io import BytesIO
except ImportError:
    from cStringIO import StringIO as BytesIO

bio = BytesIO()

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

# Save the workbook
writer.save()

# Seek to the beginning and read to copy the workbook to a variable in memory
bio.seek(0)
workbook = bio.read()

Note

engine is optional but recommended. Setting the engine determines the version of workbook produced. Setting engine='xlrd' will produce an Excel 2003-format workbook (xls). Using either 'openpyxl' or 'xlsxwriter' will produce an Excel 2007-format workbook (xlsx). If omitted, an Excel 2007-formatted workbook is produced.

Excel writer engines

Pandas chooses an Excel writer via two methods:

1. the engine keyword argument
2. the filename extension (via the default specified in config options)

By default, pandas uses the XlsxWriter for .xlsx, openpyxl for .xlsm, and xlwt for .xls files. If you have multiple engines installed, you can set the default engine through setting the config 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: version 2.4 or higher is required
• xlsxwriter
• xlwt

# By setting the 'engine' in the DataFrame 'to_excel()' methods.
df.to_excel('path_to_file.xlsx', sheet_name='Sheet1', engine='xlsxwriter')

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

# Or via pandas configuration.
from pandas import options                                     # noqa: E402
options.io.excel.xlsx.writer = 'xlsxwriter'

df.to_excel('path_to_file.xlsx', sheet_name='Sheet1')

Style and formatting

The look and feel of Excel worksheets created from pandas can be modified using the following parameters on the DataFrame’s to_excel method.

• float_format : Format string for floating point numbers (default None).
• freeze_panes : A tuple of two integers representing the bottommost row and rightmost column to freeze. Each of these parameters is one-based, so (1, 1) will freeze the first row and first column (default None).

Using the Xlsxwriter engine provides many options for controlling the format of an Excel worksheet created with the to_excel method. Excellent examples can be found in the Xlsxwriter documentation here: https://xlsxwriter.readthedocs.io/working_with_pandas.html

OpenDocument Spreadsheets

New in version 0.25.

The read_excel() method can also read OpenDocument spreadsheets using the odfpy module. The semantics and features for reading OpenDocument spreadsheets match what can be done for Excel files using engine='odf'.

# Returns a DataFrame
pd.read_excel('path_to_file.ods', engine='odf')

Note

Currently pandas only supports reading OpenDocument spreadsheets. Writing is not implemented.

Clipboard

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_csv 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 r

And then import the data directly to a DataFrame by calling:

>>> clipdf = pd.read_clipboard()
>>> clipdf
  A B C
x 1 4 p
y 2 5 q
z 3 6 r

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.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [4, 5, 6],
...                    'C': ['p', 'q', 'r']},
...                   index=['x', 'y', 'z'])
>>> df
  A B C
x 1 4 p
y 2 5 q
z 3 6 r
>>> df.to_clipboard()
>>> pd.read_clipboard()
  A B C
x 1 4 p
y 2 5 q
z 3 6 r

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 PyQt5, PyQt4 or qtpy) on Linux to use these methods.

Pickling

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.

In [326]: df
Out[326]: 
c1         a   
c2         b  d
lvl1 lvl2      
a    c     1  5
     d     2  6
b    c     3  7
     d     4  8

In [327]: 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:

In [328]: pd.read_pickle('foo.pkl')
Out[328]: 
c1         a   
c2         b  d
lvl1 lvl2      
a    c     1  5
     d     2  6
b    c     3  7
     d     4  8

Warning

Loading pickled data received from untrusted sources can be unsafe.

See: https://docs.python.org/3/library/pickle.html

Warning

read_pickle() is only guaranteed backwards compatible back to pandas version 0.20.3

Compressed pickle files

New in version 0.20.0.

read_pickle(), DataFrame.to_pickle() and Series.to_pickle() can read and write compressed pickle files. The compression types of gzip, bz2, xz are supported for reading and writing. The zip file format only supports reading and must contain only one data file to be read.

The compression type can be an explicit parameter or be inferred from the file extension. If ‘infer’, then use gzip, bz2, zip, or xz if filename ends in '.gz', '.bz2', '.zip', or '.xz', respectively.

In [329]: df = pd.DataFrame({
   .....:     'A': np.random.randn(1000),
   .....:     'B': 'foo',
   .....:     'C': pd.date_range('20130101', periods=1000, freq='s')})
   .....: 

In [330]: df
Out[330]: 
            A    B                   C
0   -0.288267  foo 2013-01-01 00:00:00
1   -0.084905  foo 2013-01-01 00:00:01
2    0.004772  foo 2013-01-01 00:00:02
3    1.382989  foo 2013-01-01 00:00:03
4    0.343635  foo 2013-01-01 00:00:04
..        ...  ...                 ...
995 -0.220893  foo 2013-01-01 00:16:35
996  0.492996  foo 2013-01-01 00:16:36
997 -0.461625  foo 2013-01-01 00:16:37
998  1.361779  foo 2013-01-01 00:16:38
999 -1.197988  foo 2013-01-01 00:16:39

[1000 rows x 3 columns]

Using an explicit compression type:

In [331]: df.to_pickle("data.pkl.compress", compression="gzip")

In [332]: rt = pd.read_pickle("data.pkl.compress", compression="gzip")

In [333]: rt
Out[333]: 
            A    B                   C
0   -0.288267  foo 2013-01-01 00:00:00
1   -0.084905  foo 2013-01-01 00:00:01
2    0.004772  foo 2013-01-01 00:00:02
3    1.382989  foo 2013-01-01 00:00:03
4    0.343635  foo 2013-01-01 00:00:04
..        ...  ...                 ...
995 -0.220893  foo 2013-01-01 00:16:35
996  0.492996  foo 2013-01-01 00:16:36
997 -0.461625  foo 2013-01-01 00:16:37
998  1.361779  foo 2013-01-01 00:16:38
999 -1.197988  foo 2013-01-01 00:16:39

[1000 rows x 3 columns]

Inferring compression type from the extension:

In [334]: df.to_pickle("data.pkl.xz", compression="infer")

In [335]: rt = pd.read_pickle("data.pkl.xz", compression="infer")

In [336]: rt
Out[336]: 
            A    B                   C
0   -0.288267  foo 2013-01-01 00:00:00
1   -0.084905  foo 2013-01-01 00:00:01
2    0.004772  foo 2013-01-01 00:00:02
3    1.382989  foo 2013-01-01 00:00:03
4    0.343635  foo 2013-01-01 00:00:04
..        ...  ...                 ...
995 -0.220893  foo 2013-01-01 00:16:35
996  0.492996  foo 2013-01-01 00:16:36
997 -0.461625  foo 2013-01-01 00:16:37
998  1.361779  foo 2013-01-01 00:16:38
999 -1.197988  foo 2013-01-01 00:16:39

[1000 rows x 3 columns]

The default is to ‘infer’:

In [337]: df.to_pickle("data.pkl.gz")

In [338]: rt = pd.read_pickle("data.pkl.gz")

In [339]: rt
Out[339]: 
            A    B                   C
0   -0.288267  foo 2013-01-01 00:00:00
1   -0.084905  foo 2013-01-01 00:00:01
2    0.004772  foo 2013-01-01 00:00:02
3    1.382989  foo 2013-01-01 00:00:03
4    0.343635  foo 2013-01-01 00:00:04
..        ...  ...                 ...
995 -0.220893  foo 2013-01-01 00:16:35
996  0.492996  foo 2013-01-01 00:16:36
997 -0.461625  foo 2013-01-01 00:16:37
998  1.361779  foo 2013-01-01 00:16:38
999 -1.197988  foo 2013-01-01 00:16:39

[1000 rows x 3 columns]

In [340]: df["A"].to_pickle("s1.pkl.bz2")

In [341]: rt = pd.read_pickle("s1.pkl.bz2")

In [342]: rt
Out[342]: 
0     -0.288267
1     -0.084905
2      0.004772
3      1.382989
4      0.343635
         ...   
995   -0.220893
996    0.492996
997   -0.461625
998    1.361779
999   -1.197988
Name: A, Length: 1000, dtype: float64

msgpack

pandas supports 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

The msgpack format is deprecated as of 0.25 and will be removed in a future version. It is recommended to use pyarrow for on-the-wire transmission of pandas objects.

Warning

read_msgpack() is only guaranteed backwards compatible back to pandas version 0.20.3

In [343]: df = pd.DataFrame(np.random.rand(5, 2), columns=list('AB'))

In [344]: df.to_msgpack('foo.msg')

In [345]: pd.read_msgpack('foo.msg')
Out[345]: 
          A         B
0  0.275432  0.293583
1  0.842639  0.165381
2  0.608925  0.778891
3  0.136543  0.029703
4  0.318083  0.604870

In [346]: s = pd.Series(np.random.rand(5), index=pd.date_range('20130101', periods=5))

You can pass a list of objects and you will receive them back on deserialization.

In [347]: pd.to_msgpack('foo.msg', df, 'foo', np.array([1, 2, 3]), s)

In [348]: pd.read_msgpack('foo.msg')
Out[348]: 
[          A         B
 0  0.275432  0.293583
 1  0.842639  0.165381
 2  0.608925  0.778891
 3  0.136543  0.029703
 4  0.318083  0.604870, 'foo', array([1, 2, 3]), 2013-01-01    0.330824
 2013-01-02    0.790825
 2013-01-03    0.308468
 2013-01-04    0.092397
 2013-01-05    0.703091
 Freq: D, dtype: float64]

You can pass iterator=True to iterate over the unpacked results:

In [349]: for o in pd.read_msgpack('foo.msg', iterator=True):
   .....:     print(o)
   .....: 
          A         B
0  0.275432  0.293583
1  0.842639  0.165381
2  0.608925  0.778891
3  0.136543  0.029703
4  0.318083  0.604870
foo
[1 2 3]
2013-01-01    0.330824
2013-01-02    0.790825
2013-01-03    0.308468
2013-01-04    0.092397
2013-01-05    0.703091
Freq: D, dtype: float64

You can pass append=True to the writer to append to an existing pack:

In [350]: df.to_msgpack('foo.msg', append=True)

In [351]: pd.read_msgpack('foo.msg')
Out[351]: 
[          A         B
 0  0.275432  0.293583
 1  0.842639  0.165381
 2  0.608925  0.778891
 3  0.136543  0.029703
 4  0.318083  0.604870, 'foo', array([1, 2, 3]), 2013-01-01    0.330824
 2013-01-02    0.790825
 2013-01-03    0.308468
 2013-01-04    0.092397
 2013-01-05    0.703091
 Freq: D, dtype: float64,           A         B
 0  0.275432  0.293583
 1  0.842639  0.165381
 2  0.608925  0.778891
 3  0.136543  0.029703
 4  0.318083  0.604870]

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.

In [352]: pd.to_msgpack('foo2.msg', {'dict': [{'df': df}, {'string': 'foo'},
   .....:                                     {'scalar': 1.}, {'s': s}]})
   .....: 

In [353]: pd.read_msgpack('foo2.msg')
Out[353]: 
{'dict': ({'df':           A         B
   0  0.275432  0.293583
   1  0.842639  0.165381
   2  0.608925  0.778891
   3  0.136543  0.029703
   4  0.318083  0.604870},
  {'string': 'foo'},
  {'scalar': 1.0},
  {'s': 2013-01-01    0.330824
   2013-01-02    0.790825
   2013-01-03    0.308468
   2013-01-04    0.092397
   2013-01-05    0.703091
   Freq: D, dtype: float64})}

Read/write API

Msgpacks can also be read from and written to strings.

In [354]: df.to_msgpack()
Out[354]: b'\x84\xa3typ\xadblock_manager\xa5klass\xa9DataFrame\xa4axes\x92\x86\xa3typ\xa5index\xa5klass\xa5Index\xa4name\xc0\xa5dtype\xa6object\xa4data\x92\xa1A\xa1B\xa8compress\xc0\x86\xa3typ\xabrange_index\xa5klass\xaaRangeIndex\xa4name\xc0\xa5start\x00\xa4stop\x05\xa4step\x01\xa6blocks\x91\x86\xa4locs\x86\xa3typ\xa7ndarray\xa5shape\x91\x02\xa4ndim\x01\xa5dtype\xa5int64\xa4data\xd8\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\xa8compress\xc0\xa6values\xc7P\x00\xc84 \x84\xac\xa0\xd1?\x0f\xa4.\xb5\xe6\xf6\xea?\xb9\x85\x9aLO|\xe3?\xac\xf0\xd7\x81>z\xc1?\\\xca\x97\ty[\xd4?\x9c\x9b\x8a:\x11\xca\xd2?\x14zX\xd01+\xc5?4=\x19b\xad\xec\xe8?\xc0!\xe9\xf4\x8ej\x9e?\xa7>_\xac\x17[\xe3?\xa5shape\x92\x02\x05\xa5dtype\xa7float64\xa5klass\xaaFloatBlock\xa8compress\xc0'

Furthermore you can concatenate the strings to produce a list of the original objects.

In [355]: pd.read_msgpack(df.to_msgpack() + s.to_msgpack())
Out[355]: 
[          A         B
 0  0.275432  0.293583
 1  0.842639  0.165381
 2  0.608925  0.778891
 3  0.136543  0.029703
 4  0.318083  0.604870, 2013-01-01    0.330824
 2013-01-02    0.790825
 2013-01-03    0.308468
 2013-01-04    0.092397
 2013-01-05    0.703091
 Freq: D, dtype: float64]

HDF5 (PyTables)

HDFStore is a dict-like object which reads and writes pandas using the high performance HDF5 format using the excellent PyTables library. See the cookbook for some advanced strategies

Warning

pandas requires PyTables >= 3.0.0. There is a indexing bug in PyTables < 3.2 which may appear when querying stores using an index. If you see a subset of results being returned, upgrade to PyTables >= 3.2. Stores created previously will need to be rewritten using the updated version.

In [356]: store = pd.HDFStore('store.h5')

In [357]: print(store)
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

Objects can be written to the file just like adding key-value pairs to a dict:

In [358]: index = pd.date_range('1/1/2000', periods=8)

In [359]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

In [360]: df = pd.DataFrame(np.random.randn(8, 3), index=index,
   .....:                   columns=['A', 'B', 'C'])
   .....: 

# store.put('s', s) is an equivalent method
In [361]: store['s'] = s

In [362]: store['df'] = df

In [363]: store
Out[363]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

In a current or later Python session, you can retrieve stored objects:

# store.get('df') is an equivalent method
In [364]: store['df']
Out[364]: 
                   A         B         C
2000-01-01 -0.426936 -1.780784  0.322691
2000-01-02  1.638174 -2.184251  0.049673
2000-01-03 -1.022803  0.889445  2.827717
2000-01-04  1.767446 -1.305266 -0.378355
2000-01-05  0.486743  0.954551  0.859671
2000-01-06 -1.170458 -1.211386 -0.852728
2000-01-07 -0.450781  1.064650  1.014927
2000-01-08 -0.810399  0.254343 -0.875526

# dotted (attribute) access provides get as well
In [365]: store.df
Out[365]: 
                   A         B         C
2000-01-01 -0.426936 -1.780784  0.322691
2000-01-02  1.638174 -2.184251  0.049673
2000-01-03 -1.022803  0.889445  2.827717
2000-01-04  1.767446 -1.305266 -0.378355
2000-01-05  0.486743  0.954551  0.859671
2000-01-06 -1.170458 -1.211386 -0.852728
2000-01-07 -0.450781  1.064650  1.014927
2000-01-08 -0.810399  0.254343 -0.875526

Deletion of the object specified by the key:

# store.remove('df') is an equivalent method
In [366]: del store['df']

In [367]: store
Out[367]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

Closing a Store and using a context manager:

In [368]: store.close()

In [369]: store
Out[369]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

In [370]: store.is_open
Out[370]: False

# Working with, and automatically closing the store using a context manager
In [371]: with pd.HDFStore('store.h5') as store:
   .....:     store.keys()
   .....: 

Read/write API

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.

In [372]: df_tl = pd.DataFrame({'A': list(range(5)), 'B': list(range(5))})

In [373]: df_tl.to_hdf('store_tl.h5', 'table', append=True)

In [374]: pd.read_hdf('store_tl.h5', 'table', where=['index>2'])
Out[374]: 
   A  B
3  3  3
4  4  4

HDFStore will by default not drop rows that are all missing. This behavior can be changed by setting dropna=True.

In [375]: df_with_missing = pd.DataFrame({'col1': [0, np.nan, 2],
   .....:                                 'col2': [1, np.nan, np.nan]})
   .....: 

In [376]: df_with_missing
Out[376]: 
   col1  col2
0   0.0   1.0
1   NaN   NaN
2   2.0   NaN

In [377]: df_with_missing.to_hdf('file.h5', 'df_with_missing',
   .....:                        format='table', mode='w')
   .....: 

In [378]: pd.read_hdf('file.h5', 'df_with_missing')
Out[378]: 
   col1  col2
0   0.0   1.0
1   NaN   NaN
2   2.0   NaN

In [379]: df_with_missing.to_hdf('file.h5', 'df_with_missing',
   .....:                        format='table', mode='w', dropna=True)
   .....: 

In [380]: pd.read_hdf('file.h5', 'df_with_missing')
Out[380]: 
   col1  col2
0   0.0   1.0
2   2.0   NaN

Fixed 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 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:

>>> pd.DataFrame(np.random.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 entirety

Table format

HDFStore 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 and query type operations are supported. This format is specified by format='table' or format='t' to append or put or to_hdf.

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.

In [381]: store = pd.HDFStore('store.h5')

In [382]: df1 = df[0:4]

In [383]: df2 = df[4:]

# append data (creates a table automatically)
In [384]: store.append('df', df1)

In [385]: store.append('df', df2)

In [386]: store
Out[386]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

# select the entire object
In [387]: store.select('df')
Out[387]: 
                   A         B         C
2000-01-01 -0.426936 -1.780784  0.322691
2000-01-02  1.638174 -2.184251  0.049673
2000-01-03 -1.022803  0.889445  2.827717
2000-01-04  1.767446 -1.305266 -0.378355
2000-01-05  0.486743  0.954551  0.859671
2000-01-06 -1.170458 -1.211386 -0.852728
2000-01-07 -0.450781  1.064650  1.014927
2000-01-08 -0.810399  0.254343 -0.875526

# the type of stored data
In [388]: store.root.df._v_attrs.pandas_type
Out[388]: 'frame_table'

Note

You can also create a table by passing format='table' or format='t' to a put operation.

Hierarchical keys

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.

In [389]: store.put('foo/bar/bah', df)

In [390]: store.append('food/orange', df)

In [391]: store.append('food/apple', df)

In [392]: store
Out[392]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

# a list of keys are returned
In [393]: store.keys()
Out[393]: ['/df', '/food/apple', '/food/orange', '/foo/bar/bah']

# remove all nodes under this level
In [394]: store.remove('food')

In [395]: store
Out[395]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

You can walk through the group hierarchy using the walk method which will yield a tuple for each group key along with the relative keys of its contents.

New in version 0.24.0.

In [396]: for (path, subgroups, subkeys) in store.walk():
   .....:     for subgroup in subgroups:
   .....:         print('GROUP: {}/{}'.format(path, subgroup))
   .....:     for subkey in subkeys:
   .....:         key = '/'.join([path, subkey])
   .....:         print('KEY: {}'.format(key))
   .....:         print(store.get(key))
   .....: 
GROUP: /foo
KEY: /df
                   A         B         C
2000-01-01 -0.426936 -1.780784  0.322691
2000-01-02  1.638174 -2.184251  0.049673
2000-01-03 -1.022803  0.889445  2.827717
2000-01-04  1.767446 -1.305266 -0.378355
2000-01-05  0.486743  0.954551  0.859671
2000-01-06 -1.170458 -1.211386 -0.852728
2000-01-07 -0.450781  1.064650  1.014927
2000-01-08 -0.810399  0.254343 -0.875526
GROUP: /foo/bar
KEY: /foo/bar/bah
                   A         B         C
2000-01-01 -0.426936 -1.780784  0.322691
2000-01-02  1.638174 -2.184251  0.049673
2000-01-03 -1.022803  0.889445  2.827717
2000-01-04  1.767446 -1.305266 -0.378355
2000-01-05  0.486743  0.954551  0.859671
2000-01-06 -1.170458 -1.211386 -0.852728
2000-01-07 -0.450781  1.064650  1.014927
2000-01-08 -0.810399  0.254343 -0.875526

Warning

Hierarchical keys cannot be retrieved as dotted (attribute) access as described above for items stored under the root node.

In [8]: store.foo.bar.bah
AttributeError: 'HDFStore' object has no attribute 'foo'

# you can directly access the actual PyTables node but using the root node
In [9]: store.root.foo.bar.bah
Out[9]:
/foo/bar/bah (Group) ''
  children := ['block0_items' (Array), 'block0_values' (Array), 'axis0' (Array), 'axis1' (Array)]

Instead, use explicit string based keys:

In [397]: store['foo/bar/bah']
Out[397]: 
                   A         B         C
2000-01-01 -0.426936 -1.780784  0.322691
2000-01-02  1.638174 -2.184251  0.049673
2000-01-03 -1.022803  0.889445  2.827717
2000-01-04  1.767446 -1.305266 -0.378355
2000-01-05  0.486743  0.954551  0.859671
2000-01-06 -1.170458 -1.211386 -0.852728
2000-01-07 -0.450781  1.064650  1.014927
2000-01-08 -0.810399  0.254343 -0.875526

Storing types

Storing mixed types in a table

Storing mixed-dtype data is supported. Strings are stored as a fixed-width using the maximum size of the appended column. Subsequent attempts at appending longer strings will raise a ValueError.

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.

In [398]: df_mixed = pd.DataFrame({'A': np.random.randn(8),
   .....:                          'B': np.random.randn(8),
   .....:                          'C': np.array(np.random.randn(8), dtype='float32'),
   .....:                          'string': 'string',
   .....:                          'int': 1,
   .....:                          'bool': True,
   .....:                          'datetime64': pd.Timestamp('20010102')},
   .....:                         index=list(range(8)))
   .....: 

In [399]: df_mixed.loc[df_mixed.index[3:5],
   .....:              ['A', 'B', 'string', 'datetime64']] = np.nan
   .....: 

In [400]: store.append('df_mixed', df_mixed, min_itemsize={'values': 50})

In [401]: df_mixed1 = store.select('df_mixed')

In [402]: df_mixed1
Out[402]: 
          A         B         C  string  int  bool datetime64
0 -0.980856  0.298656  0.151508  string    1  True 2001-01-02
1 -0.906920 -1.294022  0.587939  string    1  True 2001-01-02
2  0.988185 -0.618845  0.043096  string    1  True 2001-01-02
3       NaN       NaN  0.362451     NaN    1  True        NaT
4       NaN       NaN  1.356269     NaN    1  True        NaT
5 -0.772889 -0.340872  1.798994  string    1  True 2001-01-02
6 -0.043509 -0.303900  0.567265  string    1  True 2001-01-02
7  0.768606 -0.871948 -0.044348  string    1  True 2001-01-02

In [403]: df_mixed1.dtypes.value_counts()
Out[403]: 
float64           2
float32           1
datetime64[ns]    1
int64             1
bool              1
object            1
dtype: int64

# we have provided a minimum string column size
In [404]: store.root.df_mixed.table
Out[404]: 
/df_mixed/table (Table(8,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": Float64Col(shape=(2,), dflt=0.0, pos=1),
  "values_block_1": Float32Col(shape=(1,), dflt=0.0, pos=2),
  "values_block_2": Int64Col(shape=(1,), dflt=0, pos=3),
  "values_block_3": Int64Col(shape=(1,), dflt=0, pos=4),
  "values_block_4": BoolCol(shape=(1,), dflt=False, pos=5),
  "values_block_5": StringCol(itemsize=50, shape=(1,), dflt=b'', pos=6)}
  byteorder := 'little'
  chunkshape := (689,)
  autoindex := True
  colindexes := {
    "index": Index(6, medium, shuffle, zlib(1)).is_csi=False}

Storing MultiIndex DataFrames

Storing MultiIndex DataFrames as tables is very similar to storing/selecting from homogeneous index DataFrames.

In [405]: index = pd.MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
   .....:                               ['one', 'two', 'three']],
   .....:                       codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
   .....:                              [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
   .....:                       names=['foo', 'bar'])
   .....: 

In [406]: df_mi = pd.DataFrame(np.random.randn(10, 3), index=index,
   .....:                      columns=['A', 'B', 'C'])
   .....: 

In [407]: df_mi
Out[407]: 
                  A         B         C
foo bar                                
foo one    0.031885  0.641045  0.479460
    two   -0.630652 -0.182400 -0.789979
    three -0.282700 -0.813404  1.252998
bar one    0.758552  0.384775 -1.133177
    two   -1.002973 -1.644393 -0.311536
baz two   -0.615506 -0.084551 -1.318575
    three  0.923929 -0.105981  0.429424
qux one   -1.034590  0.542245 -0.384429
    two    0.170697 -0.200289  1.220322
    three -1.001273  0.162172  0.376816

In [408]: store.append('df_mi', df_mi)

In [409]: store.select('df_mi')
Out[409]: 
                  A         B         C
foo bar                                
foo one    0.031885  0.641045  0.479460
    two   -0.630652 -0.182400 -0.789979
    three -0.282700 -0.813404  1.252998
bar one    0.758552  0.384775 -1.133177
    two   -1.002973 -1.644393 -0.311536
baz two   -0.615506 -0.084551 -1.318575
    three  0.923929 -0.105981  0.429424
qux one   -1.034590  0.542245 -0.384429
    two    0.170697 -0.200289  1.220322
    three -1.001273  0.162172  0.376816

# the levels are automatically included as data columns
In [410]: store.select('df_mi', 'foo=bar')
Out[410]: 
                A         B         C
foo bar                              
bar one  0.758552  0.384775 -1.133177
    two -1.002973 -1.644393 -0.311536

Querying

Querying a table

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.

• index and columns are supported indexers of a DataFrames.
• if data_columns are 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:

In [411]: dfq = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'),
   .....:                    index=pd.date_range('20130101', periods=10))
   .....: 

In [412]: store.append('dfq', dfq, format='table', data_columns=True)

Use boolean expressions, with in-line function evaluation.

In [413]: store.select('dfq', "index>pd.Timestamp('20130104') & columns=['A', 'B']")
Out[413]: 
                   A         B
2013-01-05  0.450263  0.755221
2013-01-06  0.019915  0.300003
2013-01-07  1.878479 -0.026513
2013-01-08  3.272320  0.077044
2013-01-09 -0.398346  0.507286
2013-01-10  0.516017 -0.501550

Use and inline column reference

In [414]: store.select('dfq', where="A>0 or C>0")
Out[414]: 
                   A         B         C         D
2013-01-01 -0.161614 -1.636805  0.835417  0.864817
2013-01-02  0.843452 -0.122918 -0.026122 -1.507533
2013-01-03  0.335303 -1.340566 -1.024989  1.125351
2013-01-05  0.450263  0.755221 -1.506656  0.808794
2013-01-06  0.019915  0.300003 -0.727093 -1.119363
2013-01-07  1.878479 -0.026513  0.573793  0.154237
2013-01-08  3.272320  0.077044  0.397034 -0.613983
2013-01-10  0.516017 -0.501550  0.138212  0.218366

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':

In [415]: store.select('df', "columns=['A', 'B']")
Out[415]: 
                   A         B
2000-01-01 -0.426936 -1.780784
2000-01-02  1.638174 -2.184251
2000-01-03 -1.022803  0.889445
2000-01-04  1.767446 -1.305266
2000-01-05  0.486743  0.954551
2000-01-06 -1.170458 -1.211386
2000-01-07 -0.450781  1.064650
2000-01-08 -0.810399  0.254343

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.

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]

You can store and query using the timedelta64[ns] type. Terms can be specified in the format: (), where float may be signed (and fractional), and unit can be D,s,ms,us,ns for the timedelta. Here’s an example:

In [416]: from datetime import timedelta

In [417]: dftd = pd.DataFrame({'A': pd.Timestamp('20130101'),
   .....:                      'B': [pd.Timestamp('20130101') + timedelta(days=i,
   .....:                                                                 seconds=10)
   .....:                            for i in range(10)]})
   .....: 

In [418]: dftd['C'] = dftd['A'] - dftd['B']

In [419]: dftd
Out[419]: 
           A                   B                  C
0 2013-01-01 2013-01-01 00:00:10  -1 days +23:59:50
1 2013-01-01 2013-01-02 00:00:10  -2 days +23:59:50
2 2013-01-01 2013-01-03 00:00:10  -3 days +23:59:50
3 2013-01-01 2013-01-04 00:00:10  -4 days +23:59:50
4 2013-01-01 2013-01-05 00:00:10  -5 days +23:59:50
5 2013-01-01 2013-01-06 00:00:10  -6 days +23:59:50
6 2013-01-01 2013-01-07 00:00:10  -7 days +23:59:50
7 2013-01-01 2013-01-08 00:00:10  -8 days +23:59:50
8 2013-01-01 2013-01-09 00:00:10  -9 days +23:59:50
9 2013-01-01 2013-01-10 00:00:10 -10 days +23:59:50

In [420]: store.append('dftd', dftd, data_columns=True)

In [421]: store.select('dftd', "C<'-3.5D'")
Out[421]: 
           A                   B                  C
4 2013-01-01 2013-01-05 00:00:10  -5 days +23:59:50
5 2013-01-01 2013-01-06 00:00:10  -6 days +23:59:50
6 2013-01-01 2013-01-07 00:00:10  -7 days +23:59:50
7 2013-01-01 2013-01-08 00:00:10  -8 days +23:59:50
8 2013-01-01 2013-01-09 00:00:10  -9 days +23:59:50
9 2013-01-01 2013-01-10 00:00:10 -10 days +23:59:50

Indexing

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 on the indexables and any data columns you specify. This behavior can be turned off by passing index=False to append.

# we have automagically already created an index (in the first section)
In [422]: i = store.root.df.table.cols.index.index

In [423]: i.optlevel, i.kind
Out[423]: (6, 'medium')

# change an index by passing new parameters
In [424]: store.create_table_index('df', optlevel=9, kind='full')

In [425]: i = store.root.df.table.cols.index.index

In [426]: i.optlevel, i.kind
Out[426]: (9, 'full')

Oftentimes when appending large amounts of data to a store, it is useful to turn off index creation for each append, then recreate at the end.

In [427]: df_1 = pd.DataFrame(np.random.randn(10, 2), columns=list('AB'))

In [428]: df_2 = pd.DataFrame(np.random.randn(10, 2), columns=list('AB'))

In [429]: st = pd.HDFStore('appends.h5', mode='w')

In [430]: st.append('df', df_1, data_columns=['B'], index=False)

In [431]: st.append('df', df_2, data_columns=['B'], index=False)

In [432]: st.get_storer('df').table
Out[432]: 
/df/table (Table(20,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
  "B": Float64Col(shape=(), dflt=0.0, pos=2)}
  byteorder := 'little'
  chunkshape := (2730,)

Then create the index when finished appending.

In [433]: st.create_table_index('df', columns=['B'], optlevel=9, kind='full')

In [434]: st.get_storer('df').table
Out[434]: 
/df/table (Table(20,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
  "B": Float64Col(shape=(), dflt=0.0, pos=2)}
  byteorder := 'little'
  chunkshape := (2730,)
  autoindex := True
  colindexes := {
    "B": Index(9, full, shuffle, zlib(1)).is_csi=True}

In [435]: st.close()

See here for how to create a completely-sorted-index (CSI) on an existing store.

Query via data columns

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.

In [436]: df_dc = df.copy()

In [437]: df_dc['string'] = 'foo'

In [438]: df_dc.loc[df_dc.index[4:6], 'string'] = np.nan

In [439]: df_dc.loc[df_dc.index[7:9], 'string'] = 'bar'

In [440]: df_dc['string2'] = 'cool'

In [441]: df_dc.loc[df_dc.index[1:3], ['B', 'C']] = 1.0

In [442]: df_dc
Out[442]: 
                   A         B         C string string2
2000-01-01 -0.426936 -1.780784  0.322691    foo    cool
2000-01-02  1.638174  1.000000  1.000000    foo    cool
2000-01-03 -1.022803  1.000000  1.000000    foo    cool
2000-01-04  1.767446 -1.305266 -0.378355    foo    cool
2000-01-05  0.486743  0.954551  0.859671    NaN    cool
2000-01-06 -1.170458 -1.211386 -0.852728    NaN    cool
2000-01-07 -0.450781  1.064650  1.014927    foo    cool
2000-01-08 -0.810399  0.254343 -0.875526    bar    cool

# on-disk operations
In [443]: store.append('df_dc', df_dc, data_columns=['B', 'C', 'string', 'string2'])

In [444]: store.select('df_dc', where='B > 0')
Out[444]: 
                   A         B         C string string2
2000-01-02  1.638174  1.000000  1.000000    foo    cool
2000-01-03 -1.022803  1.000000  1.000000    foo    cool
2000-01-05  0.486743  0.954551  0.859671    NaN    cool
2000-01-07 -0.450781  1.064650  1.014927    foo    cool
2000-01-08 -0.810399  0.254343 -0.875526    bar    cool

# getting creative
In [445]: store.select('df_dc', 'B > 0 & C > 0 & string == foo')
Out[445]: 
                   A        B         C string string2
2000-01-02  1.638174  1.00000  1.000000    foo    cool