Loading a csv file

We load a tab separated data file using the load_table() function. The format is inferred from the filename suffix and you will note, in this case, it’s not actually a csv file.

Note

The known filename suffixes for reading are .csv, .tsv and .pkl or .pickle (Python’s pickle format).

Note

If you invoke the static column types argument, i.e.``load_table(…, static_column_types=True)`` and the column data are not static, those columns will be left as a string type.

Loading delimited specifying the format

Although unnecessary in this case, it’s possible to override the suffix by specifying the delimiter using the sep argument.

Loading delimited data without a header line

To create a table from the follow examples, you specify your header and use make_table().

Using load_delimited()

This is just a standard parsing function which does not do any filtering or converting elements to non-string types.

Using FilteringParser

Selectively loading parts of a big file

Loading a set number of lines from a file

The limit argument specifies the number of lines to read.

Loading only some rows

If you only want a subset of the contents of a file, use the FilteringParser. This allows skipping certain lines by using a callback function. We illustrate this with stats.tsv, skipping any rows with "Ratio" > 10.

You can also negate a condition, which is useful if the condition is complex. In this example, it means keep the rows for which Ratio > 10.

Loading only some columns

Specify the columns by their names.

Or, by their index.

Note

The negate argument does not affect the columns evaluated.

Load raw data as a list of lists of strings

We just use FilteringParser.

We just display the first two lines.

Note

The individual elements are all str.

Make a table from header and rows

Make a table from a dict

For a dict with key’s as column headers.

Specify the column order when creating from a dict.

Create the table with an index

A Table can be indexed like a dict if you designate a column as the index (and that column has a unique value for every row).

Note

The index_name argument also applies when using make_table().

Create a table from a pandas.DataFrame

Create a table from header and rows

Create a table from dict

make_table() is the utility function for creating Table objects from standard python objects.

Create a table from a 2D dict

Create a table that has complex python objects as elements

Create an empty table