Data 8 `datascience` Reference¶

This notebook serves as an interactive, Data 8-friendly reference for the datascience library.

`Table` Functions and Methods¶

`Table()`¶

Create an empty table, usually to extend with data

[29]:

new_table = Table()
new_table

[29]:

[30]:

type(new_table)

[30]:

datascience.tables.Table

`Table.read_table()`¶

Table.read_table(filename)

Creates a table by reading the CSV file named filename (a string).

[31]:

trips = Table.read_table('https://raw.githubusercontent.com/data-8/textbook/gh-pages/data/trip.csv')
trips

[31]:

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
876419	413	8/5/2015 8:29	Civic Center BART (7th at Market)	72	8/5/2015 8:36	Townsend at 7th	65	269	Subscriber	94518
459672	408	9/18/2014 17:11	Harry Bridges Plaza (Ferry Building)	50	9/18/2014 17:17	Embarcadero at Sansome	60	429	Subscriber	94111
903647	723	8/25/2015 7:26	San Francisco Caltrain 2 (330 Townsend)	69	8/25/2015 7:38	Market at 10th	67	631	Subscriber	94025
452829	409	9/15/2014 8:29	Steuart at Market	74	9/15/2014 8:36	Market at 4th	76	428	Subscriber	94925
491023	224	10/9/2014 16:13	Santa Clara at Almaden	4	10/9/2014 16:17	San Jose Diridon Caltrain Station	2	144	Subscriber	94117
723352	519	4/13/2015 17:04	Howard at 2nd	63	4/13/2015 17:12	San Francisco Caltrain (Townsend at 4th)	70	629	Subscriber	94061
524499	431	10/31/2014 16:36	Townsend at 7th	65	10/31/2014 16:43	Civic Center BART (7th at Market)	72	630	Subscriber	94706
518524	389	10/28/2014 8:48	Market at Sansome	77	10/28/2014 8:54	2nd at South Park	64	458	Subscriber	94610
710070	11460	4/2/2015 18:13	Powell Street BART	39	4/2/2015 21:24	Powell Street BART	39	375	Subscriber	94107
793149	616	6/4/2015 5:26	Embarcadero at Bryant	54	6/4/2015 5:36	Embarcadero at Sansome	60	289	Subscriber	94105

... (99990 rows omitted)

`tbl.with_column`¶

tbl = Table()
tbl.with_column(name, values)
tbl.with_columns(n1, v1, n2, v2,...)

Creates a new table by adding a column with name name and values values to another table. name should be a string and values should have as many entries as there are rows in the original table. If values is a single value, then every row of that column has the value values.

In the examples below, we start with adding a column to the existing table trips with values being an array we construct from existing tables.

[32]:

trips.with_column(
    "Difference in terminal", abs(trips.column("Start Terminal") - trips.column("End Terminal"))
)

[32]:

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code	Difference in terminal
876419	413	8/5/2015 8:29	Civic Center BART (7th at Market)	72	8/5/2015 8:36	Townsend at 7th	65	269	Subscriber	94518	7
459672	408	9/18/2014 17:11	Harry Bridges Plaza (Ferry Building)	50	9/18/2014 17:17	Embarcadero at Sansome	60	429	Subscriber	94111	10
903647	723	8/25/2015 7:26	San Francisco Caltrain 2 (330 Townsend)	69	8/25/2015 7:38	Market at 10th	67	631	Subscriber	94025	2
452829	409	9/15/2014 8:29	Steuart at Market	74	9/15/2014 8:36	Market at 4th	76	428	Subscriber	94925	2
491023	224	10/9/2014 16:13	Santa Clara at Almaden	4	10/9/2014 16:17	San Jose Diridon Caltrain Station	2	144	Subscriber	94117	2
723352	519	4/13/2015 17:04	Howard at 2nd	63	4/13/2015 17:12	San Francisco Caltrain (Townsend at 4th)	70	629	Subscriber	94061	7
524499	431	10/31/2014 16:36	Townsend at 7th	65	10/31/2014 16:43	Civic Center BART (7th at Market)	72	630	Subscriber	94706	7
518524	389	10/28/2014 8:48	Market at Sansome	77	10/28/2014 8:54	2nd at South Park	64	458	Subscriber	94610	13
710070	11460	4/2/2015 18:13	Powell Street BART	39	4/2/2015 21:24	Powell Street BART	39	375	Subscriber	94107	0
793149	616	6/4/2015 5:26	Embarcadero at Bryant	54	6/4/2015 5:36	Embarcadero at Sansome	60	289	Subscriber	94105	6

... (99990 rows omitted)

We can also create a new table by adding two new columns with column name followed by the array values.

[33]:

cookies = Table()
cookies = cookies.with_columns(
    "Cookie", make_array("Sugar cookies", "Chocolate chip", "Red velvet", "Oatmeal raisin", "Peanut butter"),
    "Quantity", make_array(10, 15, 15, 10, 5)
)
cookies

[33]:

Cookie	Quantity
Sugar cookies	10
Chocolate chip	15
Red velvet	15
Oatmeal raisin	10
Peanut butter	5

[34]:

prices = make_array(1.00, 1.50, 1.75, 1.25, 1.00)
cookies = cookies.with_column("Price ($)", prices)
cookies

[34]:

Cookie	Quantity	Price ($)
Sugar cookies	10	1
Chocolate chip	15	1.5
Red velvet	15	1.75
Oatmeal raisin	10	1.25
Peanut butter	5	1

In the last examples, we add a new column Delicious with one value “yes,” and we see every column has the same value.

[35]:

cookies.with_column("Delicious", "yes")

[35]:

Cookie	Quantity	Price ($)	Delicious
Sugar cookies	10	1	yes
Chocolate chip	15	1.5	yes
Red velvet	15	1.75	yes
Oatmeal raisin	10	1.25	yes
Peanut butter	5	1	yes

`tbl.column()`¶

tbl.column(column_name_or_index)

Outputs an array of values of the column column_name_or_index. column_name_or_index is a string of the column name or number which is the index of the column.

In the examples below, we start with an array of the Cookie column from the table cookies first by the column name then by using the index of the column.

[36]:

cookies.column("Cookie")

[36]:

array(['Sugar cookies', 'Chocolate chip', 'Red velvet', 'Oatmeal raisin',
       'Peanut butter'], dtype='<U14')

[37]:

cookies.column(0)

[37]:

array(['Sugar cookies', 'Chocolate chip', 'Red velvet', 'Oatmeal raisin',
       'Peanut butter'], dtype='<U14')

`tbl.num_rows`¶

Computes the number of rows in a table.

[38]:

trips.num_rows

[38]:

[39]:

cookies.num_rows

[39]:

`tbl.num_columns`¶

Computes the number of columns in a table.

[40]:

trips.num_columns

[40]:

[41]:

cookies.num_columns

[41]:

`tbl.labels`¶

Outputs the column labels in a table.

[42]:

trips.labels

[42]:

('Trip ID',
 'Duration',
 'Start Date',
 'Start Station',
 'Start Terminal',
 'End Date',
 'End Station',
 'End Terminal',
 'Bike #',
 'Subscriber Type',
 'Zip Code')

[43]:

cookies.labels

[43]:

('Cookie', 'Quantity', 'Price ($)')

`tbl.select()`¶

tbl.select(col1, col2, ...)

Creates a copy of a table with only the selected columns. Each column is the column name as a string or the integer index of the column.

Suppose we want to select the Trip ID, Duration, Bike #, and Zip Code columns from the trips table.

[44]:

trips.select("Trip ID", "Duration", "Bike #", "Zip Code")

[44]:

Trip ID	Duration	Bike #	Zip Code
876419	413	269	94518
459672	408	429	94111
903647	723	631	94025
452829	409	428	94925
491023	224	144	94117
723352	519	629	94061
524499	431	630	94706
518524	389	458	94610
710070	11460	375	94107
793149	616	289	94105

... (99990 rows omitted)

Similarly, we can use indexes to select columns. Remember to start indexing at 0.

[45]:

trips.select(0, 1, 8, 10).show(5)

Trip ID	Duration	Bike #	Zip Code
876419	413	269	94518
459672	408	429	94111
903647	723	631	94025
452829	409	428	94925
491023	224	144	94117

... (99995 rows omitted)

`tbl.drop()`¶

tbl.drop(col1, col2, ...)

Creates a copy of a table without the specified columns. Each column is the column name as a string or integer index.

[46]:

cookies.drop("Quantity")

[46]:

Cookie	Price ($)
Sugar cookies	1
Chocolate chip	1.5
Red velvet	1.75
Oatmeal raisin	1.25
Peanut butter	1

[47]:

trips.drop("End Date", "Subscriber Type")

[47]:

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Station	End Terminal	Bike #	Zip Code
876419	413	8/5/2015 8:29	Civic Center BART (7th at Market)	72	Townsend at 7th	65	269	94518
459672	408	9/18/2014 17:11	Harry Bridges Plaza (Ferry Building)	50	Embarcadero at Sansome	60	429	94111
903647	723	8/25/2015 7:26	San Francisco Caltrain 2 (330 Townsend)	69	Market at 10th	67	631	94025
452829	409	9/15/2014 8:29	Steuart at Market	74	Market at 4th	76	428	94925
491023	224	10/9/2014 16:13	Santa Clara at Almaden	4	San Jose Diridon Caltrain Station	2	144	94117
723352	519	4/13/2015 17:04	Howard at 2nd	63	San Francisco Caltrain (Townsend at 4th)	70	629	94061
524499	431	10/31/2014 16:36	Townsend at 7th	65	Civic Center BART (7th at Market)	72	630	94706
518524	389	10/28/2014 8:48	Market at Sansome	77	2nd at South Park	64	458	94610
710070	11460	4/2/2015 18:13	Powell Street BART	39	Powell Street BART	39	375	94107
793149	616	6/4/2015 5:26	Embarcadero at Bryant	54	Embarcadero at Sansome	60	289	94105

... (99990 rows omitted)

[48]:

trips.drop(3, 6, 8, 9, 10)

[48]:

Trip ID	Duration	Start Date	Start Terminal	End Date	End Terminal
876419	413	8/5/2015 8:29	72	8/5/2015 8:36	65
459672	408	9/18/2014 17:11	50	9/18/2014 17:17	60
903647	723	8/25/2015 7:26	69	8/25/2015 7:38	67
452829	409	9/15/2014 8:29	74	9/15/2014 8:36	76
491023	224	10/9/2014 16:13	4	10/9/2014 16:17	2
723352	519	4/13/2015 17:04	63	4/13/2015 17:12	70
524499	431	10/31/2014 16:36	65	10/31/2014 16:43	72
518524	389	10/28/2014 8:48	77	10/28/2014 8:54	64
710070	11460	4/2/2015 18:13	39	4/2/2015 21:24	39
793149	616	6/4/2015 5:26	54	6/4/2015 5:36	60

... (99990 rows omitted)

`tbl.relabel()`¶

tbl.relabel(old_label, new_label)

Modifies the table by changing the label of the column named old_label to new_label. old_label can be a string column name or an integer index.

[49]:

cookies

[49]:

Cookie	Quantity	Price ($)
Sugar cookies	10	1
Chocolate chip	15	1.5
Red velvet	15	1.75
Oatmeal raisin	10	1.25
Peanut butter	5	1

[50]:

cookies.relabel("Quantity", "Amount remaining")

[50]:

Cookie	Amount remaining	Price ($)
Sugar cookies	10	1
Chocolate chip	15	1.5
Red velvet	15	1.75
Oatmeal raisin	10	1.25
Peanut butter	5	1

[51]:

cookies.relabel(0, "Type")

[51]:

Type	Amount remaining	Price ($)
Sugar cookies	10	1
Chocolate chip	15	1.5
Red velvet	15	1.75
Oatmeal raisin	10	1.25
Peanut butter	5	1

[52]:

cookies

[52]:

Type	Amount remaining	Price ($)
Sugar cookies	10	1
Chocolate chip	15	1.5
Red velvet	15	1.75
Oatmeal raisin	10	1.25
Peanut butter	5	1

`tbl.show()`¶

tbl.show(n)

Displays the first n rows of a table. If no n is provided, displays all rows.

[53]:

trips.show(5)

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
876419	413	8/5/2015 8:29	Civic Center BART (7th at Market)	72	8/5/2015 8:36	Townsend at 7th	65	269	Subscriber	94518
459672	408	9/18/2014 17:11	Harry Bridges Plaza (Ferry Building)	50	9/18/2014 17:17	Embarcadero at Sansome	60	429	Subscriber	94111
903647	723	8/25/2015 7:26	San Francisco Caltrain 2 (330 Townsend)	69	8/25/2015 7:38	Market at 10th	67	631	Subscriber	94025
452829	409	9/15/2014 8:29	Steuart at Market	74	9/15/2014 8:36	Market at 4th	76	428	Subscriber	94925
491023	224	10/9/2014 16:13	Santa Clara at Almaden	4	10/9/2014 16:17	San Jose Diridon Caltrain Station	2	144	Subscriber	94117

... (99995 rows omitted)

`tbl.sort()`¶

tbl.sort(column_name_or_index, descending=False)

Sorts the rows in the table by the values in the column column_name_or_index in ascending order by default. Set descending=True to sort in descending order. column_name_or_index can be a string column label or an integer index.

[54]:

cookies

[54]:

Type	Amount remaining	Price ($)
Sugar cookies	10	1
Chocolate chip	15	1.5
Red velvet	15	1.75
Oatmeal raisin	10	1.25
Peanut butter	5	1

[55]:

cookies.sort("Price ($)")

[55]:

Type	Amount remaining	Price ($)
Sugar cookies	10	1
Peanut butter	5	1
Oatmeal raisin	10	1.25
Chocolate chip	15	1.5
Red velvet	15	1.75

[56]:

# sort in descending order
cookies.sort("Amount remaining", descending = True)

[56]:

Type	Amount remaining	Price ($)
Red velvet	15	1.75
Chocolate chip	15	1.5
Oatmeal raisin	10	1.25
Sugar cookies	10	1
Peanut butter	5	1

[57]:

# alphabetical order
cookies.sort(0)

[57]:

Type	Amount remaining	Price ($)
Chocolate chip	15	1.5
Oatmeal raisin	10	1.25
Peanut butter	5	1
Red velvet	15	1.75
Sugar cookies	10	1

`tbl.where()`¶

tbl.where(column, predicate)

Filters the table for rows where the predicate is true. predicate should be one of the provided are.<something> functions. column can be a string column label or an integer index. A list of available predicates can be found below.

[58]:

cookies.where("Amount remaining", are.above(10))

[58]:

Type	Amount remaining	Price ($)
Chocolate chip	15	1.5
Red velvet	15	1.75

[59]:

cookies.where(0, are.equal_to("Chocolate chip"))

[59]:

Type	Amount remaining	Price ($)
Chocolate chip	15	1.5

[62]:

# if predicate is a value, look for rows where the column == the value
# equivalent to cookies.where(1, are.eual_to(15))
cookies.where(1, 15)

[62]:

Type	Amount remaining	Price ($)
Chocolate chip	15	1.5
Red velvet	15	1.75

[63]:

cookies.where("Price ($)", are.below(1.25))

[63]:

Type	Amount remaining	Price ($)
Sugar cookies	10	1
Peanut butter	5	1

`tbl.take()`¶

tbl.take(row_index, ...)

Returns a copy of the table with only the specified rows included. Rows are specified by their integer index, so 0 for the first, 1 for the second, etc.

[64]:

cookies

[64]:

Type	Amount remaining	Price ($)
Sugar cookies	10	1
Chocolate chip	15	1.5
Red velvet	15	1.75
Oatmeal raisin	10	1.25
Peanut butter	5	1

[65]:

cookies.take(0)

[65]:

Type	Amount remaining	Price ($)
Sugar cookies	10	1

[66]:

cookies.take(cookies.num_rows - 1)

[66]:

Type	Amount remaining	Price ($)
Peanut butter	5	1

[67]:

cookies.take(0, 1, 2)

[67]:

Type	Amount remaining	Price ($)
Sugar cookies	10	1
Chocolate chip	15	1.5
Red velvet	15	1.75

`Table` Visualizations¶

[68]:

actors = Table().read_table("https://github.com/data-8/textbook/raw/gh-pages/data/actors.csv")
actors

[68]:

Actor	Total Gross	Number of Movies	Average per Movie	#1 Movie	Gross
Harrison Ford	4871.7	41	118.8	Star Wars: The Force Awakens	936.7
Samuel L. Jackson	4772.8	69	69.2	The Avengers	623.4
Morgan Freeman	4468.3	61	73.3	The Dark Knight	534.9
Tom Hanks	4340.8	44	98.7	Toy Story 3	415
Robert Downey, Jr.	3947.3	53	74.5	The Avengers	623.4
Eddie Murphy	3810.4	38	100.3	Shrek 2	441.2
Tom Cruise	3587.2	36	99.6	War of the Worlds	234.3
Johnny Depp	3368.6	45	74.9	Dead Man's Chest	423.3
Michael Caine	3351.5	58	57.8	The Dark Knight	534.9
Scarlett Johansson	3341.2	37	90.3	The Avengers	623.4

... (40 rows omitted)

`tbl.scatter()`¶

tbl.scatter(x_column, y_column, fit_line=False)

Creates a scatter plot with x_column on the horizontal axis and y_column on the vertical axis. These labels can be column names as strings or integer indices. Set fit_line=True to include a line of best fit for the data. You can find more examples in the textbook.

[71]:

actors.scatter('Number of Movies', 'Total Gross')

../_images/reference-nb_datascience-reference_60_0.png

[73]:

actors.scatter(2, 3, fit_line=True)

../_images/reference-nb_datascience-reference_61_0.png

`tbl.plot()`¶

tbl.plot(x_column, y_column)

Plot a line graph with x_column on the horizontal axis and y_column on the vertical axis. Sorts the table in ascending order by values in x_column first. x_column and y_column can be column names as strings or integer indices.

[74]:

movies_by_year = Table.read_table('https://github.com/data-8/textbook/raw/gh-pages/data/movies_by_year.csv')
movies_by_year.show(3)

Year	Total Gross	Number of Movies	#1 Movie
2015	11128.5	702	Star Wars: The Force Awakens
2014	10360.8	702	American Sniper
2013	10923.6	688	Catching Fire

... (33 rows omitted)

[75]:

movies_by_year.plot('Year', 'Number of Movies')

../_images/reference-nb_datascience-reference_64_0.png

`tbl.barh()`¶

tbl.barh(categories)
tbl.barh(categories, values)

Plots a horizontal bar chart broken down by categories as the bars. If values is unspecified, one bar for each column of the table (except categories) is plotted. categories and values can be column names as strings or integer indices.

[76]:

cookies.barh("Type")

../_images/reference-nb_datascience-reference_66_0.png

[77]:

cookies.barh("Type", "Amount remaining")

../_images/reference-nb_datascience-reference_67_0.png

[78]:

cookies.barh(0, 2)

../_images/reference-nb_datascience-reference_68_0.png

`tbl.hist()`¶

tbl.hist(column)
tbl.hist(column, bins=...)

Plot a histogram of the values in column. Defaults to 10 bins of equal width. If bins is specified, it can be a number of bins to use (e.g. bins=25 will produce a histogram with 25 bins) or an array of values to use as bins (e.g. bins=make_array(1, 3, 4) will produce 2 bins: $[1, 3)$ and $[3, 4)$). column can be column names as strings or integer indices.

[79]:

actors.hist(3)

../_images/reference-nb_datascience-reference_70_0.png

[80]:

actors.hist("Gross")

../_images/reference-nb_datascience-reference_71_0.png

`Table.interactive_plots()`¶

Table.interactive_plots()

This function will change from static plots like the ones above to interactive plots made with plotly. If a plotting method has a plotly version, that method will be used instead.

[193]:

Table.interactive_plots()
actors.scatter("Total Gross", "Gross")

Data 8 datascience Reference¶

Table Functions and Methods¶

Table()¶

Table.read_table()¶

tbl.with_column¶

tbl.column()¶

tbl.num_rows¶

tbl.num_columns¶

tbl.labels¶

tbl.select()¶

tbl.drop()¶

tbl.relabel()¶

tbl.show()¶

tbl.sort()¶

tbl.where()¶

tbl.take()¶

Table Visualizations¶

tbl.scatter()¶

tbl.plot()¶

tbl.barh()¶

tbl.hist()¶

Table.interactive_plots()¶

Data 8 `datascience` Reference¶

`Table` Functions and Methods¶

`Table()`¶

`Table.read_table()`¶

`tbl.with_column`¶

`tbl.column()`¶

`tbl.num_rows`¶

`tbl.num_columns`¶

`tbl.labels`¶

`tbl.select()`¶

`tbl.drop()`¶

`tbl.relabel()`¶

`tbl.show()`¶

`tbl.sort()`¶

`tbl.where()`¶

`tbl.take()`¶

`Table` Visualizations¶

`tbl.scatter()`¶

`tbl.plot()`¶

`tbl.barh()`¶

`tbl.hist()`¶

`Table.interactive_plots()`¶