Tables

Tables are a fundamental object type for representing data sets. A table can be viewed in two ways:

• a sequence of named columns that each describe a single aspect of all entries in a data set, or
• a sequence of rows that each contain all information about a single entry in a data set.

In order to use tables, import all of the module called datascience, a module created for this text.

from datascience import *

Empty tables can be created using the Table function. An empty table is usefuly because it can be extended to contain new rows and columns.

Table()

The with_columns method on a table constructs a new table with additional labeled columns. Each column of a table is an array. To add one new column to a table, call with_columns with a label and an array. (The with_column method can be used with the same effect.)

Below, we begin each example with an empty table that has no columns.

Table().with_columns('Number of petals', make_array(8, 34, 5))

To add two (or more) new columns, provide the label and array for each column. All columns must have the same length, or an error will occur.

Table().with_columns(
    'Number of petals', make_array(8, 34, 5),
    'Name', make_array('lotus', 'sunflower', 'rose')
)

We can give this table a name, and then extend the table with another column.

flowers = Table().with_columns(
    'Number of petals', make_array(8, 34, 5),
    'Name', make_array('lotus', 'sunflower', 'rose')
)

flowers.with_columns(
    'Color', make_array('pink', 'yellow', 'red')
)

The with_columns method creates a new table each time it is called, so the original table is not affected. For example, the table flowers still has only the two columns that it had when it was created.

flowers

Creating tables in this way involves a lot of typing. If the data have already been entered somewhere, it is usually possible to use Python to read it into a table, instead of typing it all in cell by cell.

Often, tables are created from files that contain comma-separated values. Such files are called CSV files.

Below, we use the Table method read_table to read a CSV file that contains some of the data used by Minard in his graphic about Napoleon’s Russian campaign. The data are placed in a table named minard.

minard = Table.read_table(path_data + 'minard.csv')
minard

We will use this small table to demonstrate some useful Table methods. We will then use those same methods, and develop other methods, on much larger tables of data.

The Size of the Table

The method num_columns gives the number of columns in the table, and num_rows the number of rows.

minard.num_columns

5

minard.num_rows

8

Column Labels

The method labels can be used to list the labels of all the columns. With minard we don’t gain much by this, but it can be very useful for tables that are so large that not all columns are visible on the screen.

minard.labels

('Longitude', 'Latitude', 'City', 'Direction', 'Survivors')

We can change column labels using the relabeled method. This creates a new table and leaves minard unchanged.

minard.relabeled('City', 'City Name')

However, this method does not change the original table.

minard

A common pattern is to assign the original name minard to the new table, so that all future uses of minard will refer to the relabeled table.

minard = minard.relabeled('City', 'City Name')
minard

Accessing the Data in a Column

We can use a column’s label to access the array of data in the column.

minard.column('Survivors')

array([145000, 140000, 127100, 100000,  55000,  24000,  20000,  12000])

The 5 columns are indexed 0, 1, 2, 3, and 4. The column Survivors can also be accessed by using its column index.

minard.column(4)

array([145000, 140000, 127100, 100000,  55000,  24000,  20000,  12000])

The 8 items in the array are indexed 0, 1, 2, and so on, up to 7. The items in the column can be accessed using item, as with any array.

minard.column(4).item(0)

145000

minard.column(4).item(5)

24000

Working with the Data in a Column

Because columns are arrays, we can use array operations on them to discover new information. For example, we can create a new column that contains the percent of all survivors at each city after Smolensk.

initial = minard.column('Survivors').item(0)
minard = minard.with_columns(
    'Percent Surviving', minard.column('Survivors')/initial
)
minard

To make the proportions in the new columns appear as percents, we can use the method set_format with the option PercentFormatter. The set_format method takes Formatter objects, which exist for dates (DateFormatter), currencies (CurrencyFormatter), numbers, and percentages.

minard.set_format('Percent Surviving', PercentFormatter)

Choosing Sets of Columns

The method select creates a new table that contains only the specified columns.

minard.select('Longitude', 'Latitude')

The same selection can be made using column indices instead of labels.

minard.select(0, 1)

The result of using select is a new table, even when you select just one column.

minard.select('Survivors')

Notice that the result is a table, unlike the result of column, which is an array.

minard.column('Survivors')

array([145000, 140000, 127100, 100000,  55000,  24000,  20000,  12000])

Another way to create a new table consisting of a set of columns is to drop the columns you don’t want.

minard.drop('Longitude', 'Latitude', 'Direction')

Neither select nor drop change the original table. Instead, they create new smaller tables that share the same data. The fact that the original table is preserved is useful! You can generate multiple different tables that only consider certain columns without worrying that one analysis will affect the other.

minard

All of the methods that we have used above can be applied to any table.