Notebook Example

Links: notebook, html ., PDF, python, slides ., presentation .

from pyquickhelper.ipythonhelper import add_notebook_menu
add_notebook_menu(menu_id="main_menu")

This is an example of a section with a formula : 1 + ... + n = \frac{n(n+1)}{2}. The conversion to HTML relies on MathJax. It sometimes fails if the link to this library is missing (nbconvert: Math is not displayed in the html output #6440).

import pyquickhelper

from IPython.core.display import HTML

from pyquickhelper.helpgen import docstring2html
from pyquickhelper.pandashelper import df2rst
docstring2html(df2rst)

builds a string in RST format from a dataframe

param df:dataframe
param add_line:
 (bool) add a line separator between each row
param align:
 a string in [l,r,c,p{5cm}] or a list of the same, or something like ['1x','2x','5x'] to specify a ratio between column (alignment is left)
return:string

None values are replaced by empty string (4 spaces). It produces the following results::

+------------------------+------------+----------+----------+
| Header row, column 1   | Header 2   | Header 3 | Header 4 |
| (header rows optional) |            |          |          |
+========================+============+==========+==========+
| body row 1, column 1   | column 2   | column 3 | column 4 |
+------------------------+------------+----------+----------+
| body row 2             | ...        | ...      |          |
+------------------------+------------+----------+----------+

import pandas
from pyquickhelper.helpgen import docstring2html
docstring2html(pandas.read_csv)

Read CSV (comma-separated) file into DataFrame

Also supports optionally iterating or breaking of the file into chunks.

Parameters

filepath_or_buffer : 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.csv
sep : string, default ','
Delimiter to use. If sep is None, will try to automatically determine this. Regular expressions are accepted.
engine : {'c', 'python'}
Parser engine to use. The C engine is faster while the python engine is currently more feature-complete.
lineterminator : string (length 1), default None
Character to break file into lines. Only valid with C parser
quotechar : string (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 None
Control field quoting behavior per csv.QUOTE_* constants. Use one of QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). Default (None) results in QUOTE_MINIMAL behavior.
skipinitialspace : boolean, default False
Skip spaces after delimiter
escapechar : string (length 1), default None
One-character string used to escape delimiter when quoting is QUOTE_NONE.
dtype : Type name or dict of column -> type
Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32} (Unsupported with engine='python')
compression : {'gzip', 'bz2', 'infer', None}, default 'infer'
For on-the-fly decompression of on-disk data. If 'infer', then use gzip or bz2 if filepath_or_buffer is a string ending in '.gz' or '.bz2', respectively, and no decompression otherwise. Set to None for no decompression.
dialect : string or csv.Dialect instance, default None
If None defaults to Excel dialect. Ignored if sep longer than 1 char See csv.Dialect documentation for more details
header : int, list of ints
Row number(s) to use as the column names, and the start of the data. Defaults to 0 if no names passed, otherwise None. Explicitly pass header=0 to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns E.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example are skipped). Note that this parameter ignores commented lines and empty lines if skip_blank_lines=True, so header=0 denotes the first line of data rather than the first line of the file.
skiprows : list-like or integer
Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file
index_col : int or sequence or False, default None
Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end of each line, you might consider index_col=False to force pandas to _not_ use the first column as the index (row names)
names : array-like
List of column names to use. If file contains no header row, then you should explicitly pass header=None
prefix : string, default None
Prefix to add to column numbers when no header, e.g 'X' for X0, X1, ...
na_values : list-like or dict, default None
Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values
true_values : list
Values to consider as True
false_values : list
Values to consider as False
keep_default_na : bool, default True
If na_values are specified and keep_default_na is False the default NaN values are overridden, otherwise they're appended to
parse_dates : boolean, list of ints or names, list of lists, or dict
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. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo' A fast-path exists for iso8601-formatted dates.
keep_date_col : boolean, default False
If True and parse_dates specifies combining multiple columns then keep the original columns.
date_parser : function
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
thousands : str, default None
Thousands separator
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 '#emptyna,b,cn1,2,3' with header=0 will result in 'a,b,c' being treated as the header.
decimal : str, default '.'
Character to recognize as decimal point. E.g. use ',' for European data
nrows : int, default None
Number of rows of file to read. Useful for reading pieces of large files
iterator : boolean, default False
Return TextFileReader object
chunksize : int, default None
Return TextFileReader object for iteration
skipfooter : int, default 0
Number of lines at bottom of file to skip (Unsupported with engine='c')
converters : dict, default None
Dict of functions for converting values in certain columns. Keys can either be integers or column labels
verbose : boolean, default False
Indicate number of NA values placed in non-numeric columns
delimiter : string, default None
Alternative argument name for sep. Regular expressions are accepted.
encoding : string, default None
Encoding to use for UTF when reading/writing (ex. 'utf-8'). List of Python standard encodings
squeeze : boolean, default False
If the parsed data only contains one column then return a Series
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
usecols : array-like
Return a subset of the columns. Results in much faster parsing time and lower memory usage.
mangle_dupe_cols : boolean, default True
Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'
tupleize_cols : boolean, default False
Leave a list of tuples on columns as is (default is to convert to a Multi Index on the columns)
error_bad_lines : boolean, default True
Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these "bad lines" will dropped from the DataFrame that is returned. (Only valid with C parser)
warn_bad_lines : boolean, default True
If error_bad_lines is False, and warn_bad_lines is True, a warning for each "bad line" will be output. (Only valid with C parser).
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
skip_blank_lines : boolean, default True
If True, skip over blank lines rather than interpreting as NaN values

Returns

result : DataFrame or TextParser