Welcome to datascience’s documentation!

Release:

0.17.6

Date:

Sep 24, 2023

The datascience package was written for use in Berkeley’s DS 8 course and contains useful functionality for investigating and graphically displaying data.

Start Here: datascience Tutorial

This is a brief introduction to the functionality in datascience. For a complete reference guide, please see Tables (datascience.tables).

For other useful tutorials and examples, see:

• The textbook introduction to Tables

• Example notebooks

Getting Started

The most important functionality in the package is is the Table class, which is the structure used to represent columns of data. First, load the class:

In [1]: from datascience import Table

In the IPython notebook, type Table. followed by the TAB-key to see a list of members.

Note that for the Data Science 8 class we also import additional packages and settings for all assignments and labs. This is so that plots and other available packages mirror the ones in the textbook more closely. The exact code we use is:

# HIDDEN

import matplotlib
matplotlib.use('Agg')
from datascience import Table
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('fivethirtyeight')

In particular, the lines involving matplotlib allow for plotting within the IPython notebook.

Creating a Table

A Table is a sequence of labeled columns of data.

A Table can be constructed from scratch by extending an empty table with columns.

In [2]: t = Table().with_columns(
   ...:     'letter', ['a', 'b', 'c', 'z'],
   ...:     'count',  [  9,   3,   3,   1],
   ...:     'points', [  1,   2,   2,  10],
   ...: )
   ...: 

In [3]: print(t)
letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

---

More often, a table is read from a CSV file (or an Excel spreadsheet). Here’s the content of an example file:

In [4]: cat sample.csv
x,y,z
1,10,100
2,11,101
3,12,102

And this is how we load it in as a Table using read_table():

In [5]: Table.read_table('sample.csv')
Out[5]: 
x    | y    | z
1    | 10   | 100
2    | 11   | 101
3    | 12   | 102

CSVs from URLs are also valid inputs to read_table():

In [6]: Table.read_table('https://www.inferentialthinking.com/data/sat2014.csv')
Out[6]: 
State        | Participation Rate | Critical Reading | Math | Writing | Combined
North Dakota | 2.3                | 612              | 620  | 584     | 1816
Illinois     | 4.6                | 599              | 616  | 587     | 1802
Iowa         | 3.1                | 605              | 611  | 578     | 1794
South Dakota | 2.9                | 604              | 609  | 579     | 1792
Minnesota    | 5.9                | 598              | 610  | 578     | 1786
Michigan     | 3.8                | 593              | 610  | 581     | 1784
Wisconsin    | 3.9                | 596              | 608  | 578     | 1782
Missouri     | 4.2                | 595              | 597  | 579     | 1771
Wyoming      | 3.3                | 590              | 599  | 573     | 1762
Kansas       | 5.3                | 591              | 596  | 566     | 1753
... (41 rows omitted)

---

It’s also possible to add columns from a dictionary, but this option is discouraged because dictionaries do not preserve column order.

In [7]: t = Table().with_columns({
   ...:     'letter': ['a', 'b', 'c', 'z'],
   ...:     'count':  [  9,   3,   3,   1],
   ...:     'points': [  1,   2,   2,  10],
   ...: })
   ...: 

In [8]: print(t)
letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

Accessing Values

To access values of columns in the table, use column(), which takes a column label or index and returns an array. Alternatively, columns() returns a list of columns (arrays).

In [9]: t
Out[9]: 
letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

In [10]: t.column('letter')
Out[10]: 
array(['a', 'b', 'c', 'z'],
      dtype='<U1')

In [11]: t.column(1)
Out[11]: array([9, 3, 3, 1])

You can use bracket notation as a shorthand for this method:

In [12]: t['letter'] # This is a shorthand for t.column('letter')
Out[12]: 
array(['a', 'b', 'c', 'z'],
      dtype='<U1')

In [13]: t[1]        # This is a shorthand for t.column(1)
Out[13]: array([9, 3, 3, 1])

To access values by row, row() returns a row by index. Alternatively, rows() returns an list-like Rows object that contains tuple-like Row objects.

In [14]: t.rows
Out[14]: 
Rows(letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10)

In [15]: t.rows[0]
Out[15]: Row(letter='a', count=9, points=1)

In [16]: t.row(0)
Out[16]: Row(letter='a', count=9, points=1)

In [17]: second = t.rows[1]

In [18]: second
Out[18]: Row(letter='b', count=3, points=2)

In [19]: second[0]
Out[19]: 'b'

In [20]: second[1]
Out[20]: 3

To get the number of rows, use num_rows.

In [21]: t.num_rows
Out[21]: 4

Manipulating Data

Here are some of the most common operations on data. For the rest, see the reference (Tables (datascience.tables)).

Adding a column with with_column():

In [22]: t
Out[22]: 
letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

In [23]: t.with_column('vowel?', ['yes', 'no', 'no', 'no'])
Out[23]: 
letter | count | points | vowel?
a      | 9     | 1      | yes
b      | 3     | 2      | no
c      | 3     | 2      | no
z      | 1     | 10     | no

In [24]: t # .with_column returns a new table without modifying the original
Out[24]: 
letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

In [25]: t.with_column('2 * count', t['count'] * 2) # A simple way to operate on columns
Out[25]: 
letter | count | points | 2 * count
a      | 9     | 1      | 18
b      | 3     | 2      | 6
c      | 3     | 2      | 6
z      | 1     | 10     | 2

Selecting columns with select():

In [26]: t.select('letter')
Out[26]: 
letter
a
b
c
z

In [27]: t.select(['letter', 'points'])
Out[27]: 
letter | points
a      | 1
b      | 2
c      | 2
z      | 10

Renaming columns with relabeled():

In [28]: t
Out[28]: 
letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

In [29]: t.relabeled('points', 'other name')
Out[29]: 
letter | count | other name
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

In [30]: t
Out[30]: 
letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

In [31]: t.relabeled(['letter', 'count', 'points'], ['x', 'y', 'z'])
Out[31]: 
x    | y    | z
a    | 9    | 1
b    | 3    | 2
c    | 3    | 2
z    | 1    | 10

Selecting out rows by index with take() and conditionally with where():

In [32]: t
Out[32]: 
letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

In [33]: t.take(2) # the third row
Out[33]: 
letter | count | points
c      | 3     | 2

In [34]: t.take[0:2] # the first and second rows
Out[34]: 
letter | count | points
a      | 9     | 1
b      | 3     | 2

In [35]: t.where('points', 2) # rows where points == 2
Out[35]: 
letter | count | points
b      | 3     | 2
c      | 3     | 2

In [36]: t.where(t['count'] < 8) # rows where count < 8
Out[36]: 
letter | count | points
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

In [37]: t['count'] < 8 # .where actually takes in an array of booleans
Out[37]: array([False,  True,  True,  True], dtype=bool)

In [38]: t.where([False, True, True, True]) # same as the last line
Out[38]: 
letter | count | points
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

Operate on table data with sort(), group(), and pivot()

In [39]: t
Out[39]: 
letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

In [40]: t.sort('count')
Out[40]: 
letter | count | points
z      | 1     | 10
b      | 3     | 2
c      | 3     | 2
a      | 9     | 1

In [41]: t.sort('letter', descending = True)
Out[41]: 
letter | count | points
z      | 1     | 10
c      | 3     | 2
b      | 3     | 2
a      | 9     | 1

# You may pass a reducing function into the collect arg
# Note the renaming of the points column because of the collect arg
In [42]: t.select(['count', 'points']).group('count', collect=sum)
Out[42]: 
count | points sum
1     | 10
3     | 4
9     | 1

In [43]: other_table = Table().with_columns(
   ....:     'mar_status',  ['married', 'married', 'partner', 'partner', 'married'],
   ....:     'empl_status', ['Working as paid', 'Working as paid', 'Not working',
   ....:                     'Not working', 'Not working'],
   ....:     'count',       [1, 1, 1, 1, 1])
   ....: 

In [44]: other_table
Out[44]: 
mar_status | empl_status     | count
married    | Working as paid | 1
married    | Working as paid | 1
partner    | Not working     | 1
partner    | Not working     | 1
married    | Not working     | 1

In [45]: other_table.pivot('mar_status', 'empl_status', 'count', collect=sum)
Out[45]: 
empl_status     | married | partner
Not working     | 1       | 2
Working as paid | 2       | 0

Visualizing Data

We’ll start with some data drawn at random from two normal distributions:

In [46]: normal_data = Table().with_columns(
   ....:     'data1', np.random.normal(loc = 1, scale = 2, size = 100),
   ....:     'data2', np.random.normal(loc = 4, scale = 3, size = 100))
   ....: 

In [47]: normal_data
Out[47]: 
data1      | data2
-1.73346   | 5.25333
2.05467    | 4.6461
2.24119    | -0.158949
-2.1273    | 3.70496
4.82042    | 4.9243
1.40334    | 0.276988
2.60555    | 9.7636
-2.49814   | -4.30364
1.57132    | 4.89357
-0.0973409 | 1.47964
... (90 rows omitted)

Draw histograms with hist():

In [48]: normal_data.hist()

In [49]: normal_data.hist(bins = range(-5, 10))

In [50]: normal_data.hist(bins = range(-5, 10), overlay = True)

If we treat the normal_data table as a set of x-y points, we can plot() and scatter():

In [51]: normal_data.sort('data1').plot('data1') # Sort first to make plot nicer

In [52]: normal_data.scatter('data1')

In [53]: normal_data.scatter('data1', fit_line = True)

Use barh() to display categorical data.

In [54]: t
Out[54]: 
letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10

In [55]: t.barh('letter')

Exporting

Exporting to CSV is the most common operation and can be done by first converting to a pandas dataframe with to_df():

In [56]: normal_data
Out[56]: 
data1      | data2
-1.73346   | 5.25333
2.05467    | 4.6461
2.24119    | -0.158949
-2.1273    | 3.70496
4.82042    | 4.9243
1.40334    | 0.276988
2.60555    | 9.7636
-2.49814   | -4.30364
1.57132    | 4.89357
-0.0973409 | 1.47964
... (90 rows omitted)

# index = False prevents row numbers from appearing in the resulting CSV
In [57]: normal_data.to_df().to_csv('normal_data.csv', index = False)

An Example

We’ll recreate the steps in Chapter 12 of the textbook to see if there is a significant difference in birth weights between smokers and non-smokers using a bootstrap test.

For more examples, check out the TableDemos repo.

From the text:

The table baby contains data on a random sample of 1,174 mothers and their newborn babies. The column Birth Weight contains the birth weight of the baby, in ounces; Gestational Days is the number of gestational days, that is, the number of days the baby was in the womb. There is also data on maternal age, maternal height, maternal pregnancy weight, and whether or not the mother was a smoker.

In [58]: baby = Table.read_table('https://www.inferentialthinking.com/data/baby.csv')

In [59]: baby # Let's take a peek at the table
Out[59]: 
Birth Weight | Gestational Days | Maternal Age | Maternal Height | Maternal Pregnancy Weight | Maternal Smoker
120          | 284              | 27           | 62              | 100                       | False
113          | 282              | 33           | 64              | 135                       | False
128          | 279              | 28           | 64              | 115                       | True
108          | 282              | 23           | 67              | 125                       | True
136          | 286              | 25           | 62              | 93                        | False
138          | 244              | 33           | 62              | 178                       | False
132          | 245              | 23           | 65              | 140                       | False
120          | 289              | 25           | 62              | 125                       | False
143          | 299              | 30           | 66              | 136                       | True
140          | 351              | 27           | 68              | 120                       | False
... (1164 rows omitted)

# Select out columns we want.
In [60]: smoker_and_wt = baby.select(['Maternal Smoker', 'Birth Weight'])

In [61]: smoker_and_wt
Out[61]: 
Maternal Smoker | Birth Weight
False           | 120
False           | 113
True            | 128
True            | 108
False           | 136
False           | 138
False           | 132
False           | 120
True            | 143
False           | 140
... (1164 rows omitted)

Let’s compare the number of smokers to non-smokers.

In [62]: smoker_and_wt.select('Maternal Smoker').group('Maternal Smoker')
Out[62]: 
Maternal Smoker | count
False           | 715
True            | 459

We can also compare the distribution of birthweights between smokers and non-smokers.

# Non smokers
# We do this by grabbing the rows that correspond to mothers that don't
# smoke, then plotting a histogram of just the birthweights.
In [63]: smoker_and_wt.where('Maternal Smoker', 0).select('Birth Weight').hist()

# Smokers
In [64]: smoker_and_wt.where('Maternal Smoker', 1).select('Birth Weight').hist()

What’s the difference in mean birth weight of the two categories?

In [65]: nonsmoking_mean = smoker_and_wt.where('Maternal Smoker', 0).column('Birth Weight').mean()

In [66]: smoking_mean = smoker_and_wt.where('Maternal Smoker', 1).column('Birth Weight').mean()

In [67]: observed_diff = nonsmoking_mean - smoking_mean

In [68]: observed_diff
Out[68]: 9.2661425720249184

Let’s do the bootstrap test on the two categories.

In [69]: num_nonsmokers = smoker_and_wt.where('Maternal Smoker', 0).num_rows

In [70]: def bootstrap_once():
   ....:     """
   ....:     Computes one bootstrapped difference in means.
   ....:     The table.sample method lets us take random samples.
   ....:     We then split according to the number of nonsmokers in the original sample.
   ....:     """
   ....:     resample = smoker_and_wt.sample(with_replacement = True)
   ....:     bootstrap_diff = resample.column('Birth Weight')[:num_nonsmokers].mean() - \
   ....:         resample.column('Birth Weight')[num_nonsmokers:].mean()
   ....:     return bootstrap_diff
   ....: 

In [71]: repetitions = 1000

In [72]: bootstrapped_diff_means = np.array(
   ....:     [ bootstrap_once() for _ in range(repetitions) ])
   ....: 

In [73]: bootstrapped_diff_means[:10]
Out[73]: 
array([ 1.55503451, -0.48330058,  2.07701144,  1.40825754, -0.66256227,
        1.5765102 , -0.29477581,  0.14779469,  0.78852781, -0.40793455])

In [74]: num_diffs_greater = (abs(bootstrapped_diff_means) > abs(observed_diff)).sum()

In [75]: p_value = num_diffs_greater / len(bootstrapped_diff_means)

In [76]: p_value
Out[76]: 0.0

Drawing Maps

To come.

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

new_table = Table()
new_table

type(new_table)

datascience.tables.Table

Table.read_table()

Table.read_table(filename)

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

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

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.

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

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.

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

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

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

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.

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

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.

cookies.column("Cookie")

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

cookies.column(0)

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

tbl.num_rows

Computes the number of rows in a table.

trips.num_rows

100000

cookies.num_rows

5

tbl.num_columns

Computes the number of columns in a table.

trips.num_columns

11

cookies.num_columns

3

tbl.labels

Outputs the column labels in a table.

trips.labels

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

cookies.labels

('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.

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

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.

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.

cookies.drop("Quantity")

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

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

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)

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

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.

cookies

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

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

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

cookies.relabel(0, "Type")

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

cookies

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.

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.

cookies

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

cookies.sort("Price ($)")

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

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

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

# alphabetical order
cookies.sort(0)

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.

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

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

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

Type	Amount remaining	Price ($)
Chocolate chip	15	1.5

# 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)

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

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

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.

cookies

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

cookies.take(0)

Type	Amount remaining	Price ($)
Sugar cookies	10	1

cookies.take(cookies.num_rows - 1)

Type	Amount remaining	Price ($)
Peanut butter	5	1

cookies.take(0, 1, 2)

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

Table Visualizations

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

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.

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

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

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.

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)

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

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.

cookies.barh("Type")

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

cookies.barh(0, 2)

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.

actors.hist(3)

actors.hist("Gross")

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.

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

Table.static_plots()

Table.static_plots()

This function turns off plotly plots.

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

Advanced Table Functions

tbl.apply()

tbl.apply(function, column)
tbl.apply(function, col1, col2, ...)

Applies the function function to each element of the column column and returns the values returned as an array. If function takes more than one argument, you can specify multiple columns to use for each argument in order.

actors.apply(np.average, "Number of Movies")

array([41., 69., 61., 44., 53., 38., 36., 45., 58., 37., 38., 49., 60.,
       43.,  7., 31., 24., 50., 39., 79., 34., 63., 23., 34., 37., 36.,
       17., 50., 39., 42., 35., 36., 17., 46., 40., 32., 17., 29., 39.,
       26., 25., 36., 27., 43., 25., 21., 40., 35., 23., 25.])

actors

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)

The example below calculates the average gross for each movie by actor by applying a function that takes in the value of Total Gross and Number of Movies and returns their quotient.

def average_gross(total_gross, num_movies):
    return total_gross / num_movies

actors.apply(average_gross, "Total Gross", "Number of Movies")

array([118.82195122,  69.17101449,  73.25081967,  98.65454545,
        74.47735849, 100.27368421,  99.64444444,  74.85777778,
        57.78448276,  90.3027027 ,  86.68421053,  66.9244898 ,
        53.15666667,  73.8372093 , 451.84285714, 101.62580645,
       131.2125    ,  62.478     ,  79.67435897,  39.00379747,
        89.16764706,  46.70952381, 125.67826087,  84.86470588,
        76.40540541,  78.38888889, 165.63529412,  56.316     ,
        71.86153846,  65.12619048,  77.89428571,  75.425     ,
       157.75882353,  58.28913043,  67.0225    ,  83.15625   ,
       154.96470588,  89.83103448,  66.72564103,  99.25384615,
       102.308     ,  70.82777778,  94.26666667,  58.65348837,
       100.732     , 119.06190476,  61.5925    ,  70.36      ,
       106.86086957,  96.66      ])

tbl.group()

tbl.group(column_or_columns)
tbl.group(column_or_columns, func)

Groups a table by values in column_or_columns. If column_or_columns is an array, groups by each unique combination of elements in those columns. If func is specified, it should be a function that takes in an array of values and returns a single value. If unspecified, this defaults to the count of rows in the set.

trips.group("Start Station")

Start Station	count
2nd at Folsom	2302
2nd at South Park	2610
2nd at Townsend	3904
5th at Howard	2190
Adobe on Almaden	165
Arena Green / SAP Center	176
Beale at Market	2377
Broadway St at Battery St	2157
California Ave Caltrain Station	127
Castro Street and El Camino Real	339

... (60 rows omitted)

trips.group("Start Station", np.mean).select(0,2)

Start Station	Duration mean
2nd at Folsom	512.887
2nd at South Park	654.565
2nd at Townsend	755.176
5th at Howard	819.509
Adobe on Almaden	2522.5
Arena Green / SAP Center	1999.7
Beale at Market	679.602
Broadway St at Battery St	827.753
California Ave Caltrain Station	4403.29
Castro Street and El Camino Real	1221.86

... (60 rows omitted)

trips.group("Start Station").sort("count", descending = True)

Start Station	count
San Francisco Caltrain (Townsend at 4th)	7426
San Francisco Caltrain 2 (330 Townsend)	6114
Harry Bridges Plaza (Ferry Building)	4795
Temporary Transbay Terminal (Howard at Beale)	4212
Townsend at 7th	3925
2nd at Townsend	3904
Embarcadero at Sansome	3900
Steuart at Market	3872
Market at 10th	3370
Market at Sansome	3218

... (60 rows omitted)

trips.group(['Start Station', 'End Station'])

Start Station	End Station	count
2nd at Folsom	2nd at Folsom	22
2nd at Folsom	2nd at South Park	84
2nd at Folsom	2nd at Townsend	123
2nd at Folsom	5th at Howard	28
2nd at Folsom	Beale at Market	34
2nd at Folsom	Broadway St at Battery St	18
2nd at Folsom	Civic Center BART (7th at Market)	13
2nd at Folsom	Clay at Battery	70
2nd at Folsom	Commercial at Montgomery	46
2nd at Folsom	Davis at Jackson	8

... (1616 rows omitted)

tbl.pivot()

tbl.pivot(col1, col2)
tbl.pivot(col1, col2, values, collect)

Creates a pivot table with values in col1 as columns and values in col2 as rows. If values is unspecified, the values in the cells default to counts. If values is specified, it should be the label of a column whose values to pass as an array to collect, which should return a single value.

more_cones = Table().with_columns(
    'Flavor', make_array('strawberry', 'chocolate', 'chocolate', 'strawberry', 'chocolate', 'bubblegum'),
    'Color', make_array('pink', 'light brown', 'dark brown', 'pink', 'dark brown', 'pink'),
    'Price', make_array(3.55, 4.75, 5.25, 5.25, 5.25, 4.75)
)

more_cones

Flavor	Color	Price
strawberry	pink	3.55
chocolate	light brown	4.75
chocolate	dark brown	5.25
strawberry	pink	5.25
chocolate	dark brown	5.25
bubblegum	pink	4.75

more_cones.pivot('Flavor', 'Color')

Color	bubblegum	chocolate	strawberry
dark brown	0	2	0
light brown	0	1	0
pink	1	0	2

more_cones.pivot('Flavor', 'Color', values='Price', collect=sum)

Color	bubblegum	chocolate	strawberry
dark brown	0	10.5	0
light brown	0	4.75	0
pink	4.75	0	8.8

more_cones.pivot(0, 1)

Color	bubblegum	chocolate	strawberry
dark brown	0	2	0
light brown	0	1	0
pink	1	0	2

tbl.join()

tbl1.join(col1, tbl2)
tbl1.join(col1, tbl2, col2)

Performs a join of tbl1 on tbl2 where rows are only included if the value in col1 is present in both join columns. If col2 is unspecified, it is assumed to be the same label as col1.

cones = Table().with_columns(
    'Flavor', make_array('strawberry', 'vanilla', 'chocolate', 'strawberry', 'chocolate'),
    'Price', make_array(3.55, 4.75, 6.55, 5.25, 5.75)
)
cones

Flavor	Price
strawberry	3.55
vanilla	4.75
chocolate	6.55
strawberry	5.25
chocolate	5.75

ratings = Table().with_columns(
    'Kind', make_array('strawberry', 'chocolate', 'vanilla', 'mint chip'),
    'Stars', make_array(2.5, 3.5, 4, 3)
)
ratings

Kind	Stars
strawberry	2.5
chocolate	3.5
vanilla	4
mint chip	3

# Joins cones on ratings. Note that the mint chip flavor doesn't appear since it's not in cones
rated = cones.join('Flavor', ratings, 'Kind')
rated

Flavor	Price	Stars
chocolate	6.55	3.5
chocolate	5.75	3.5
strawberry	3.55	2.5
strawberry	5.25	2.5
vanilla	4.75	4

tbl.sample()

tbl.sample(n, with_replacement=True)

Returns a new table with n rows that were randomly sampled from the original table. If with_replacement is true, sampling occurs with replacement. For sampling without replacement, set with_replacement=False.

# if you rerun this cell, you should get different results since the sample is random
rated.sample(2)

Flavor	Price	Stars
chocolate	6.55	3.5
chocolate	6.55	3.5

Notice how the table below has more rows for certain flavors than the original rated table. This is because we are sampling with replacement, so you get theoretically get 5 of the same flavors!

sampled_with_replacement = rated.sample(5)
sampled_with_replacement

Flavor	Price	Stars
strawberry	5.25	2.5
strawberry	3.55	2.5
strawberry	3.55	2.5
chocolate	6.55	3.5
vanilla	4.75	4

rated.sample(3, with_replacement = False)

Flavor	Price	Stars
vanilla	4.75	4
strawberry	3.55	2.5
chocolate	6.55	3.5

String Methods

str.split()

string.split(separator)

Splits the string string into a list on each occurence of the substring separator. The occurences of separator are removed from the resulting list.

For example, the code below splits the string Data 8hiishifun. on the substring hi.

example_string = "Data 8hiishifun."
example_string.split("hi")

['Data 8', 'is', 'fun.']

# split on .
another_string = "the.secret.message.is.123"
another_string.split(".")

['the', 'secret', 'message', 'is', '123']

str.join()

string.join(array)

Combines each element of array into one string with string used to connect each element.

fun_array = make_array("high", "great", "best")
"est ".join(fun_array)

'highest greatest best'

# you can join elements on the empty string to just merge the elements
some_strings = make_array("some", "list", "of", "strings")
"".join(some_strings)

'somelistofstrings'

str.replace()

string.replace(old_string, new_string)

Replaces each occurrence of old_string in string with new_string.

berkeley_string = "I saw 5 friends, 10 squirrels, and 20 people flyering on Sproul."
berkeley_string

'I saw 5 friends, 10 squirrels, and 20 people flyering on Sproul.'

berkeley_string.replace("friends", "frisbees")

'I saw 5 frisbees, 10 squirrels, and 20 people flyering on Sproul.'

# you can chain calls to .replace() since the return value is also a string
berkeley_string.replace("friends", "frisbees").replace("flyering on Sproul", "having a picnic on the Glade")

'I saw 5 frisbees, 10 squirrels, and 20 people having a picnic on the Glade.'

Array Functions and Methods

example_array = make_array(1, 3, 5, 7, 9)
example_array

array([1, 3, 5, 7, 9])

max()

max(array)

Returns the maximum value of an array.

max(example_array)

9

min()

min(array)

Returns the minimum value of an array.

min(example_array)

1

sum()

sum(array)

Returns the sum of values in an array.

sum(example_array)

25

sum(make_array(1, 2, 0, -10))

-7

abs()

abs(num)
abs(array)

Take the absolute value of number or each number in an array.

abs(-1)

1

new_arr = make_array(-3, -1, 5.2, 0.25, -4.9)
abs(new_arr)

array([3.  , 1.  , 5.2 , 0.25, 4.9 ])

round(num)

round(num)
round(num, d)
np.round(array)
np.round(array, d)

Round number or array of numbers to the nearest integer. If d is specified, rounds to d places after the decimal. Use np.round to round arrays.

round(3.14159)

3

round(3.14159, 3)

3.142

np.round(new_arr, 1)

array([-3. , -1. ,  5.2,  0.2, -4.9])

len()

len(array)

Returns the length of an array.

len(new_arr)

5

make_array()

make_array(val1, val2, ...)

Creates a new array with the values passed.

new_array = make_array(25, 16, 9, 4, 1)
new_array

array([25, 16,  9,  4,  1])

np.mean

np.mean(array)
np.average(array)

Returns the mean of the values in an array.

np.mean(new_array)

11.0

np.average(new_array)

11.0

np.std()

np.std(array)

Returns the standard deviation of the values in an array.

np.std(new_array)

8.648699324175862

np.diff()

np.diff(array)

Returns an array with the pairwise differences between elements in the input array. The output will have length len(array) - 1 and will have elements \(x_1 - x_0\), \(x_2 - x_1\), \(x_3 - x_2\), etc.

np.diff(new_array)

array([-9, -7, -5, -3])

np.diff(make_array(1, 3, 5, 7))

array([2, 2, 2])

np.sqrt()

np.sqrt(num)
np.sqrt(array)

Returns the square root of a number or an array of the square roots of each element in the input array.

np.sqrt(4)

2.0

np.sqrt(new_array)

array([5., 4., 3., 2., 1.])

np.arange()

np.arange(stop)
np.arange(start, stop)
np.arange(start, stop, step)

Returns an array of integers from start to stop incrementing by step. If start is unspecified, it is assumed be 0. If step is unspecified, it is assumed to be 1. The upper bound is exclusive, meaning that max(np.arange(10)) is 9.

np.arange(0, 11)

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

np.arange(5)

array([0, 1, 2, 3, 4])

np.arange(0, 102, 2.7)

array([ 0. ,  2.7,  5.4,  8.1, 10.8, 13.5, 16.2, 18.9, 21.6, 24.3, 27. ,
       29.7, 32.4, 35.1, 37.8, 40.5, 43.2, 45.9, 48.6, 51.3, 54. , 56.7,
       59.4, 62.1, 64.8, 67.5, 70.2, 72.9, 75.6, 78.3, 81. , 83.7, 86.4,
       89.1, 91.8, 94.5, 97.2, 99.9])

array.item()

array.item(num)

Returns the item at index num in an array (remember Python indices start at 0!).

np.arange(0, 102, 2).item(1)

2

new_array.item(2)

9

new_array.item(len(new_array) - 1)

1

np.random.choice

np.random.choice(array)
np.random.choice(array, n, replace=True)

Picks one or n of items from an array at random. By default, with replacement (set replace=False for without replacement).

np.random.choice(new_array)

25

np.random.choice(new_array, 3)

array([ 4,  4, 16])

np.random.choice(np.arange(0, 102, 2), 10, replace=False)

array([ 98,  22,  12,  56,  24,  54, 100,  52,  28,  88])

np.count_nonzero()

Returns the number of nonzero elements in an array. Because False values are considered zeros (as integers), this can also give you the number of Trues in an array of boolean valuea.

another_array = make_array(0, 1, 2, 0, 4, 0, 1, 0, 0)
np.count_nonzero(another_array)

4

bools = make_array(True, False, True, False, False, True, False)
np.count_nonzero(bools)

3

np.append()

np.append(array, item)

Returns a copy of the input array with item (must be the same type as the other entries in the array) appended to the end.

new_array

array([25, 16,  9,  4,  1])

np.append(new_array, 1000)

array([  25,   16,    9,    4,    1, 1000])

percentile()

percentile(percent, array)

Returns the value corresponding to the specified percentile of an array. percent should be in percentage form (i.e. 50 not 0.5).

long_array = make_array(1, 1, 1, 2, 2, 2, 3, 3, 3, 4)
long_array

array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])

percentile(50, long_array)

2

percentile(90, long_array)

3

Table.where Predicates

All of the predicates described below can be negated by preceding the name with not_. For example, we can find values not equal to a specific value using are.not_equal_to(value).

are.equal_to()

tbl.where(column, are.equal_to(value))

Filter leaves rows only where the value in column is equal to value.

trips.where("Duration", are.equal_to(519))

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
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
824979	519	6/27/2015 15:02	Japantown	9	6/27/2015 15:11	San Jose City Hall	10	660	Customer	nil
439946	519	9/5/2014 12:38	Yerba Buena Center of the Arts (3rd @ Howard)	68	9/5/2014 12:47	Civic Center BART (7th at Market)	72	452	Subscriber	94105
788261	519	6/1/2015 9:21	Powell at Post (Union Square)	71	6/1/2015 9:30	Steuart at Market	74	575	Subscriber	94108
560479	519	11/28/2014 14:20	South Van Ness at Market	66	11/28/2014 14:29	Powell at Post (Union Square)	71	609	Subscriber	94108
653797	519	2/23/2015 8:47	Market at 10th	67	2/23/2015 8:55	Yerba Buena Center of the Arts (3rd @ Howard)	68	546	Subscriber	94102
887134	519	8/12/2015 17:29	Civic Center BART (7th at Market)	72	8/12/2015 17:38	Mechanics Plaza (Market at Battery)	75	313	Subscriber	94103
482225	519	10/3/2014 16:41	Spear at Folsom	49	10/3/2014 16:50	Broadway St at Battery St	82	209	Subscriber	94133
681697	519	3/14/2015 10:19	Embarcadero at Sansome	60	3/14/2015 10:28	Harry Bridges Plaza (Ferry Building)	50	368	Customer	95120
912821	519	8/31/2015 17:00	Embarcadero at Folsom	51	8/31/2015 17:09	San Francisco Caltrain (Townsend at 4th)	70	354	Subscriber	94085

... (115 rows omitted)

are.above()

tbl.where(column, are.above(value))

Filter leaves rows only where the value in column is strictly greater than value.

trips.where("Duration", are.above(1000))

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
710070	11460	4/2/2015 18:13	Powell Street BART	39	4/2/2015 21:24	Powell Street BART	39	375	Subscriber	94107
589964	15097	1/3/2015 15:22	Embarcadero at Sansome	60	1/3/2015 19:33	Golden Gate at Polk	59	599	Customer	29225
831509	1057	7/2/2015 10:14	2nd at Folsom	62	7/2/2015 10:31	South Van Ness at Market	66	631	Subscriber	94114
442750	6084	9/8/2014 13:32	Embarcadero at Sansome	60	9/8/2014 15:14	Embarcadero at Sansome	60	368	Customer	474454
608714	19799	1/18/2015 10:07	University and Emerson	35	1/18/2015 15:37	San Francisco Caltrain (Townsend at 4th)	70	686	Customer	nil
711961	1026	4/4/2015 7:07	Davis at Jackson	42	4/4/2015 7:24	Harry Bridges Plaza (Ferry Building)	50	189	Subscriber	94111
833071	2314	7/4/2015 11:13	Market at 4th	76	7/4/2015 11:52	Washington at Kearny	46	602	Customer	94806
570731	1218	12/8/2014 23:51	MLK Library	11	12/9/2014 0:12	St James Park	13	299	Customer	95033
853698	1048	7/20/2015 10:53	Broadway St at Battery St	82	7/20/2015 11:10	Embarcadero at Sansome	60	636	Customer	91436
787510	3670	5/31/2015 10:47	Mountain View City Hall	27	5/31/2015 11:48	Castro Street and El Camino Real	32	713	Customer	94041

... (11576 rows omitted)

are.above_or_equal_to()

tbl.where(column, are.above_or_equal_to(value))

Filter leaves rows only where the value in column is greater than or equal to value.

trips.where("Duration", are.above_or_equal_to(1000))

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
710070	11460	4/2/2015 18:13	Powell Street BART	39	4/2/2015 21:24	Powell Street BART	39	375	Subscriber	94107
589964	15097	1/3/2015 15:22	Embarcadero at Sansome	60	1/3/2015 19:33	Golden Gate at Polk	59	599	Customer	29225
831509	1057	7/2/2015 10:14	2nd at Folsom	62	7/2/2015 10:31	South Van Ness at Market	66	631	Subscriber	94114
442750	6084	9/8/2014 13:32	Embarcadero at Sansome	60	9/8/2014 15:14	Embarcadero at Sansome	60	368	Customer	474454
608714	19799	1/18/2015 10:07	University and Emerson	35	1/18/2015 15:37	San Francisco Caltrain (Townsend at 4th)	70	686	Customer	nil
711961	1026	4/4/2015 7:07	Davis at Jackson	42	4/4/2015 7:24	Harry Bridges Plaza (Ferry Building)	50	189	Subscriber	94111
833071	2314	7/4/2015 11:13	Market at 4th	76	7/4/2015 11:52	Washington at Kearny	46	602	Customer	94806
570731	1218	12/8/2014 23:51	MLK Library	11	12/9/2014 0:12	St James Park	13	299	Customer	95033
853698	1048	7/20/2015 10:53	Broadway St at Battery St	82	7/20/2015 11:10	Embarcadero at Sansome	60	636	Customer	91436
787510	3670	5/31/2015 10:47	Mountain View City Hall	27	5/31/2015 11:48	Castro Street and El Camino Real	32	713	Customer	94041

... (11597 rows omitted)

are.below()

tbl.where(column, are.below(value))

Filter leaves rows only where the value in column is strictly less than value.

trips.where("Duration", are.below(100))

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
482797	65	10/4/2014 7:50	San Francisco Caltrain (Townsend at 4th)	70	10/4/2014 7:52	San Francisco Caltrain (Townsend at 4th)	70	430	Subscriber	95112
483052	81	10/4/2014 13:52	Harry Bridges Plaza (Ferry Building)	50	10/4/2014 13:53	Harry Bridges Plaza (Ferry Building)	50	306	Customer	nan
569620	84	12/8/2014 10:09	Civic Center BART (7th at Market)	72	12/8/2014 10:10	Civic Center BART (7th at Market)	72	326	Subscriber	94111
502332	79	10/16/2014 17:26	Beale at Market	56	10/16/2014 17:27	Temporary Transbay Terminal (Howard at Beale)	55	613	Subscriber	94602
604012	76	1/14/2015 15:18	Davis at Jackson	42	1/14/2015 15:19	Broadway St at Battery St	82	601	Subscriber	94107
704918	70	3/30/2015 22:51	Broadway St at Battery St	82	3/30/2015 22:52	Broadway St at Battery St	82	394	Subscriber	94107
513458	83	10/24/2014 8:50	2nd at Folsom	62	10/24/2014 8:51	Howard at 2nd	63	569	Subscriber	94107
696725	94	3/25/2015 8:47	Post at Kearny	47	3/25/2015 8:49	Washington at Kearny	46	516	Subscriber	94109
829817	86	7/1/2015 9:27	Market at Sansome	77	7/1/2015 9:28	2nd at South Park	64	292	Subscriber	94538
745895	73	4/29/2015 13:05	Yerba Buena Center of the Arts (3rd @ Howard)	68	4/29/2015 13:06	Yerba Buena Center of the Arts (3rd @ Howard)	68	380	Subscriber	94947

... (403 rows omitted)

are.below_or_equal_to()

tbl.where(column, are.below_or_equal_to(value))

Filter leaves rows only where the value in column is less than or equal to value.

trips.where("Duration", are.below_or_equal_to(100))

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
482797	65	10/4/2014 7:50	San Francisco Caltrain (Townsend at 4th)	70	10/4/2014 7:52	San Francisco Caltrain (Townsend at 4th)	70	430	Subscriber	95112
483052	81	10/4/2014 13:52	Harry Bridges Plaza (Ferry Building)	50	10/4/2014 13:53	Harry Bridges Plaza (Ferry Building)	50	306	Customer	nan
569620	84	12/8/2014 10:09	Civic Center BART (7th at Market)	72	12/8/2014 10:10	Civic Center BART (7th at Market)	72	326	Subscriber	94111
502332	79	10/16/2014 17:26	Beale at Market	56	10/16/2014 17:27	Temporary Transbay Terminal (Howard at Beale)	55	613	Subscriber	94602
604012	76	1/14/2015 15:18	Davis at Jackson	42	1/14/2015 15:19	Broadway St at Battery St	82	601	Subscriber	94107
704918	70	3/30/2015 22:51	Broadway St at Battery St	82	3/30/2015 22:52	Broadway St at Battery St	82	394	Subscriber	94107
513458	83	10/24/2014 8:50	2nd at Folsom	62	10/24/2014 8:51	Howard at 2nd	63	569	Subscriber	94107
696725	94	3/25/2015 8:47	Post at Kearny	47	3/25/2015 8:49	Washington at Kearny	46	516	Subscriber	94109
808199	100	6/15/2015 20:57	Post at Kearny	47	6/15/2015 20:58	2nd at South Park	64	537	Subscriber	94107
829817	86	7/1/2015 9:27	Market at Sansome	77	7/1/2015 9:28	2nd at South Park	64	292	Subscriber	94538

... (430 rows omitted)

are.between()

tbl.where(column, are.between(x, y))

Filter leaves rows only where the value in column is greather than or equal to x and less than y (i.e. in the interval \([x, y)\)).

trips.where("Duration", are.between(100, 200))

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
437830	151	9/4/2014 9:13	Grant Avenue at Columbus Avenue	73	9/4/2014 9:15	Commercial at Montgomery	45	306	Subscriber	94104
436255	195	9/3/2014 11:53	2nd at Folsom	62	9/3/2014 11:57	Spear at Folsom	49	403	Subscriber	94107
585884	151	12/26/2014 13:34	Broadway St at Battery St	82	12/26/2014 13:37	Harry Bridges Plaza (Ferry Building)	50	576	Subscriber	94107
548322	191	11/17/2014 20:10	Yerba Buena Center of the Arts (3rd @ Howard)	68	11/17/2014 20:13	Market at Sansome	77	29	Subscriber	94705
594999	185	1/7/2015 17:53	San Antonio Caltrain Station	29	1/7/2015 17:56	San Antonio Shopping Center	31	176	Subscriber	94040
468534	194	9/24/2014 19:08	Mechanics Plaza (Market at Battery)	75	9/24/2014 19:11	Harry Bridges Plaza (Ferry Building)	50	443	Subscriber	94107
873710	169	8/3/2015 17:20	Broadway St at Battery St	82	8/3/2015 17:23	Embarcadero at Sansome	60	532	Subscriber	94114
853087	168	7/20/2015 7:27	Temporary Transbay Terminal (Howard at Beale)	55	7/20/2015 7:30	2nd at Folsom	62	418	Subscriber	94602
863019	162	7/27/2015 8:31	Temporary Transbay Terminal (Howard at Beale)	55	7/27/2015 8:34	Mechanics Plaza (Market at Battery)	75	504	Subscriber	94111
883134	173	8/10/2015 15:11	Embarcadero at Folsom	51	8/10/2015 15:14	Beale at Market	56	363	Subscriber	94117

... (5083 rows omitted)

are.between_or_equal_to()

tbl.where(column, are.between_or_equal_to(x, y))

Filter leaves rows only where the value in column is between or equal to x and y (i.e. in the interval \([x, y]\)).

trips.where("Duration", are.between_or_equal_to(100, 200))

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
437830	151	9/4/2014 9:13	Grant Avenue at Columbus Avenue	73	9/4/2014 9:15	Commercial at Montgomery	45	306	Subscriber	94104
436255	195	9/3/2014 11:53	2nd at Folsom	62	9/3/2014 11:57	Spear at Folsom	49	403	Subscriber	94107
585884	151	12/26/2014 13:34	Broadway St at Battery St	82	12/26/2014 13:37	Harry Bridges Plaza (Ferry Building)	50	576	Subscriber	94107
548322	191	11/17/2014 20:10	Yerba Buena Center of the Arts (3rd @ Howard)	68	11/17/2014 20:13	Market at Sansome	77	29	Subscriber	94705
903735	200	8/25/2015 7:59	Temporary Transbay Terminal (Howard at Beale)	55	8/25/2015 8:02	Steuart at Market	74	453	Subscriber	94501
594999	185	1/7/2015 17:53	San Antonio Caltrain Station	29	1/7/2015 17:56	San Antonio Shopping Center	31	176	Subscriber	94040
468534	194	9/24/2014 19:08	Mechanics Plaza (Market at Battery)	75	9/24/2014 19:11	Harry Bridges Plaza (Ferry Building)	50	443	Subscriber	94107
873710	169	8/3/2015 17:20	Broadway St at Battery St	82	8/3/2015 17:23	Embarcadero at Sansome	60	532	Subscriber	94114
853087	168	7/20/2015 7:27	Temporary Transbay Terminal (Howard at Beale)	55	7/20/2015 7:30	2nd at Folsom	62	418	Subscriber	94602
863019	162	7/27/2015 8:31	Temporary Transbay Terminal (Howard at Beale)	55	7/27/2015 8:34	Mechanics Plaza (Market at Battery)	75	504	Subscriber	94111

... (5180 rows omitted)

are.contained_in()

tbl.where(column, are.contained_in(string_or_array))

Filter leaves rows only where the value in column is a substring of string_or_array if it is a string or an element of string_or_array if it is an array

trips.where("Start Station", are.contained_in("2nd at Folsom San Antonio Caltrain Station"))

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
436255	195	9/3/2014 11:53	2nd at Folsom	62	9/3/2014 11:57	Spear at Folsom	49	403	Subscriber	94107
831509	1057	7/2/2015 10:14	2nd at Folsom	62	7/2/2015 10:31	South Van Ness at Market	66	631	Subscriber	94114
877160	306	8/5/2015 16:33	2nd at Folsom	62	8/5/2015 16:39	Beale at Market	56	527	Subscriber	94602
768619	840	5/15/2015 11:35	2nd at Folsom	62	5/15/2015 11:49	Market at 10th	67	604	Subscriber	94903
594999	185	1/7/2015 17:53	San Antonio Caltrain Station	29	1/7/2015 17:56	San Antonio Shopping Center	31	176	Subscriber	94040
701211	252	3/27/2015 16:26	2nd at Folsom	62	3/27/2015 16:30	Spear at Folsom	49	405	Subscriber	94105
487432	561	10/7/2014 17:48	2nd at Folsom	62	10/7/2014 17:58	Commercial at Montgomery	45	342	Subscriber	94107
610970	808	1/20/2015 13:28	2nd at Folsom	62	1/20/2015 13:42	Harry Bridges Plaza (Ferry Building)	50	310	Subscriber	94025
753668	196	5/5/2015 11:48	2nd at Folsom	62	5/5/2015 11:52	Temporary Transbay Terminal (Howard at Beale)	55	533	Subscriber	94973
466551	222	9/23/2014 18:12	2nd at Folsom	62	9/23/2014 18:16	2nd at Townsend	61	620	Subscriber	94107

... (2578 rows omitted)

trips.where("Start Terminal", are.contained_in(make_array(62, 29)))

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
436255	195	9/3/2014 11:53	2nd at Folsom	62	9/3/2014 11:57	Spear at Folsom	49	403	Subscriber	94107
831509	1057	7/2/2015 10:14	2nd at Folsom	62	7/2/2015 10:31	South Van Ness at Market	66	631	Subscriber	94114
877160	306	8/5/2015 16:33	2nd at Folsom	62	8/5/2015 16:39	Beale at Market	56	527	Subscriber	94602
768619	840	5/15/2015 11:35	2nd at Folsom	62	5/15/2015 11:49	Market at 10th	67	604	Subscriber	94903
594999	185	1/7/2015 17:53	San Antonio Caltrain Station	29	1/7/2015 17:56	San Antonio Shopping Center	31	176	Subscriber	94040
701211	252	3/27/2015 16:26	2nd at Folsom	62	3/27/2015 16:30	Spear at Folsom	49	405	Subscriber	94105
487432	561	10/7/2014 17:48	2nd at Folsom	62	10/7/2014 17:58	Commercial at Montgomery	45	342	Subscriber	94107
610970	808	1/20/2015 13:28	2nd at Folsom	62	1/20/2015 13:42	Harry Bridges Plaza (Ferry Building)	50	310	Subscriber	94025
753668	196	5/5/2015 11:48	2nd at Folsom	62	5/5/2015 11:52	Temporary Transbay Terminal (Howard at Beale)	55	533	Subscriber	94973
466551	222	9/23/2014 18:12	2nd at Folsom	62	9/23/2014 18:16	2nd at Townsend	61	620	Subscriber	94107

... (2578 rows omitted)

are.containing()

tbl.where(column, are.containing(value))

Filter leaves rows only where the value in column contains the substring value.

trips.where("End Station", are.containing("at"))

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
793149	616	6/4/2015 5:26	Embarcadero at Bryant	54	6/4/2015 5:36	Embarcadero at Sansome	60	289	Subscriber	94105
681771	895	3/14/2015 11:46	Market at 10th	67	3/14/2015 12:01	Market at 4th	76	416	Subscriber	94107

... (78805 rows omitted)

are.strictly_between()

tbl.where(column, are.strictly_between(x, y))

Filter leaves rows only where the value in column is strictly greater than x and less than y (i.e. in the interval \((x, y)\)).

trips.where("Duration", are.strictly_between(100, 200))

Trip ID	Duration	Start Date	Start Station	Start Terminal	End Date	End Station	End Terminal	Bike #	Subscriber Type	Zip Code
437830	151	9/4/2014 9:13	Grant Avenue at Columbus Avenue	73	9/4/2014 9:15	Commercial at Montgomery	45	306	Subscriber	94104
436255	195	9/3/2014 11:53	2nd at Folsom	62	9/3/2014 11:57	Spear at Folsom	49	403	Subscriber	94107
585884	151	12/26/2014 13:34	Broadway St at Battery St	82	12/26/2014 13:37	Harry Bridges Plaza (Ferry Building)	50	576	Subscriber	94107
548322	191	11/17/2014 20:10	Yerba Buena Center of the Arts (3rd @ Howard)	68	11/17/2014 20:13	Market at Sansome	77	29	Subscriber	94705
594999	185	1/7/2015 17:53	San Antonio Caltrain Station	29	1/7/2015 17:56	San Antonio Shopping Center	31	176	Subscriber	94040
468534	194	9/24/2014 19:08	Mechanics Plaza (Market at Battery)	75	9/24/2014 19:11	Harry Bridges Plaza (Ferry Building)	50	443	Subscriber	94107
873710	169	8/3/2015 17:20	Broadway St at Battery St	82	8/3/2015 17:23	Embarcadero at Sansome	60	532	Subscriber	94114
853087	168	7/20/2015 7:27	Temporary Transbay Terminal (Howard at Beale)	55	7/20/2015 7:30	2nd at Folsom	62	418	Subscriber	94602
863019	162	7/27/2015 8:31	Temporary Transbay Terminal (Howard at Beale)	55	7/27/2015 8:34	Mechanics Plaza (Market at Battery)	75	504	Subscriber	94111
883134	173	8/10/2015 15:11	Embarcadero at Folsom	51	8/10/2015 15:14	Beale at Market	56	363	Subscriber	94117

... (5056 rows omitted)

Miscellaneous Functions

sample_proportions()

sample_proportions(sample_size, model_proportions)

Samples sample_size objects from the distribution specified by model_proportions. sample_size should be an integer, model_proportions an array of probabilities that sum up to 1. It returns an array with the same size as model_proportions. Each item in the array corresponds to the proportion of times it was sampled out of the sample_size times.

sample_proportions(100, [.5, .3, .2])

array([0.32, 0.32, 0.36])

minimize()

minimize(function)

This function returns an array of values that minimize function. function should be a function that takes in a certain number of arguments and returns a number. The array returned by minimize is structured such that if each value in the array was passed into function as arguments, it would minimize the output value of function.

def f(x, y):
    return 0.47 * x**2 + 1.23 * np.log(y)

minimize(f)

array([ 5.17585792, -0.58835469])

Reference

Tables (datascience.tables)

Summary of methods for Table. Click a method to see its documentation.

One note about reading the method signatures for this page: each method is listed with its arguments. However, optional arguments are specified in brackets. That is, a method that’s documented like

Table.foo (first_arg, second_arg[, some_other_arg, fourth_arg])

means that the Table.foo method must be called with first_arg and second_arg and optionally some_other_arg and fourth_arg. That means the following are valid ways to call Table.foo:

some_table.foo(1, 2)
some_table.foo(1, 2, 'hello')
some_table.foo(1, 2, 'hello', 'world')
some_table.foo(1, 2, some_other_arg='hello')

But these are not valid:

some_table.foo(1) # Missing arg
some_table.foo(1, 2[, 'hi']) # SyntaxError
some_table.foo(1, 2[, 'hello', 'world']) # SyntaxError

If that syntax is confusing, you can click the method name itself to get to the details page for that method. That page will have a more straightforward syntax.

Creation

Table.__init__([labels, formatter])	Create an empty table with column labels.
Table.from_records(records)	Create a table from a sequence of records (dicts with fixed keys).
Table.from_columns_dict(columns)	Create a table from a mapping of column labels to column values.
Table.read_table(filepath_or_buffer, *args, ...)	Read a table from a file or web address.
Table.from_df(df[, keep_index])	Convert a Pandas DataFrame into a Table.
Table.from_array(arr)	Convert a structured NumPy array into a Table.

Extension (does not modify original table)

Table.with_column(label, values[, formatter])	Return a new table with an additional or replaced column.
Table.with_columns(*labels_and_values, ...)	Return a table with additional or replaced columns.
Table.with_row(row)	Return a table with an additional row.
Table.with_rows(rows)	Return a table with additional rows.
Table.relabeled(label, new_label)	Return a new table with label specifying column label(s) replaced by corresponding new_label.

Accessing values

Table.num_columns	Number of columns.
Table.columns	Return a tuple of columns, each with the values in that column.
Table.column(index_or_label)	Return the values of a column as an array.
Table.num_rows	Computes the number of rows in a table
Table.rows	Return a view of all rows.
Table.row(index)	Return a row.
Table.labels	Return a tuple of column labels.
Table.first(label)	Return the zeroth item in a column.
Table.last(label)	Return the last item in a column.
Table.values	Return data in self as a numpy array.
Table.column_index(label)	Return the index of a column by looking up its label.
Table.apply(fn, *column_or_columns)	Apply fn to each element or elements of column_or_columns.

Mutation (modifies table in place)

Table.set_format(column_or_columns, formatter)	Set the pretty print format of a column(s) and/or convert its values.
Table.move_to_start(column_label)	Move a column to be the first column.
Table.move_to_end(column_label)	Move a column to be the last column.
Table.append(row_or_table)	Append a row or all rows of a table in place.
Table.append_column(label, values[, formatter])	Appends a column to the table or replaces a column.
Table.relabel(column_label, new_label)	Changes the label(s) of column(s) specified by column_label to labels in new_label.
Table.remove(row_or_row_indices)	Removes a row or multiple rows of a table in place (row number is 0 indexed).

Transformation (creates a new table)

Table.copy(*[, shallow])	Return a copy of a table.
Table.select(*column_or_columns)	Return a table with only the columns in column_or_columns.
Table.drop(*column_or_columns)	Return a Table with only columns other than selected label or labels.
Table.take()	Return a new Table with selected rows taken by index.
Table.exclude()	Return a new Table without a sequence of rows excluded by number.
Table.move_column(label, index)	Returns a new table with specified column moved to the specified column index.
Table.where(column_or_label[, ...])	Return a new Table containing rows where value_or_predicate returns True for values in column_or_label.
Table.sort(column_or_label[, descending, ...])	Return a Table of rows sorted according to the values in a column.
Table.group(column_or_label[, collect])	Group rows by unique values in a column; count or aggregate others.
Table.groups(labels[, collect])	Group rows by multiple columns, count or aggregate others.
Table.pivot(columns, rows[, values, ...])	Generate a table with a column for each unique value in columns, with rows for each unique value in rows.
Table.stack(key[, labels])	Takes k original columns and returns two columns, with col.
Table.join(column_label, other[, other_label])	Creates a new table with the columns of self and other, containing rows for all values of a column that appear in both tables.
Table.stats([ops])	Compute statistics for each column and place them in a table.
Table.percentile(p)	Return a new table with one row containing the pth percentile for each column.
Table.sample([k, with_replacement, weights])	Return a new table where k rows are randomly sampled from the original table.
Table.shuffle()	Return a new table where all the rows are randomly shuffled from the original table.
Table.sample_from_distribution(distribution, k)	Return a new table with the same number of rows and a new column.
Table.split(k)	Return a tuple of two tables where the first table contains k rows randomly sampled and the second contains the remaining rows.
Table.bin(*columns, **vargs)	Group values by bin and compute counts per bin by column.
Table.pivot_bin(pivot_columns, value_column)	Form a table with columns formed by the unique tuples in pivot_columns containing counts per bin of the values associated with each tuple in the value_column.
Table.relabeled(label, new_label)	Return a new table with label specifying column label(s) replaced by corresponding new_label.
Table.with_row(row)	Return a table with an additional row.
Table.with_rows(rows)	Return a table with additional rows.
Table.with_column(label, values[, formatter])	Return a new table with an additional or replaced column.
Table.with_columns(*labels_and_values, ...)	Return a table with additional or replaced columns.

Exporting / Displaying

Table.show([max_rows])	Display the table.
Table.as_text([max_rows, sep])	Format table as text
Table.as_html([max_rows])	Format table as HTML
Table.index_by(column_or_label)	Return a dict keyed by values in a column that contains lists of
Table.to_array()	Convert the table to a structured NumPy array.
Table.to_df()	Convert the table to a Pandas DataFrame.
Table.to_csv(filename)	Creates a CSV file with the provided filename.

Visualizations

Table.plot([column_for_xticks, select, ...])	Plot line charts for the table.
Table.bar([column_for_categories, select, ...])	Plot bar charts for the table.
Table.group_bar(column_label, **vargs)	Plot a bar chart for the table.
Table.barh([column_for_categories, select, ...])	Plot horizontal bar charts for the table.
Table.group_barh(column_label, **vargs)	Plot a horizontal bar chart for the table.
Table.pivot_hist(pivot_column_label, ...[, ...])	Draw histograms of each category in a column.
Table.hist(*columns[, overlay, bins, ...])	Plots one histogram for each column in columns.
Table.hist_of_counts(*columns[, overlay, ...])	Plots one count-based histogram for each column in columns.
Table.scatter(column_for_x[, select, ...])	Creates scatterplots, optionally adding a line of best fit.
Table.scatter3d(column_for_x, column_for_y)	Convenience wrapper for Table#iscatter3d
Table.boxplot(**vargs)	Plots a boxplot for the table.
Table.interactive_plots()	Redirects plot, barh, hist, and scatter to their plotly equivalents
Table.static_plots()	Turns off redirection of plot, barh, hist, and scatter to their plotly equivalents

Maps (datascience.maps)

Draw maps using folium.

class datascience.maps.Circle(lat, lon, popup='', color='blue', area=314.1592653589793, **kwargs)

A marker displayed with either Folium’s circle_marker or circle methods.

The circle_marker method draws circles that stay the same size regardless of map zoom, whereas the circle method draws circles that have a fixed radius in meters. To toggle between them, use the radius_in_meters flag in the draw_on function.

popup – text that pops up when marker is clicked color – fill color area – pixel-squared area of the circle

Defaults from Folium:

fill_opacity: float, default 0.6

Circle fill opacity

More options can be passed into kwargs by following the attributes listed in https://leafletjs.com/reference-1.4.0.html#circlemarker or https://leafletjs.com/reference-1.4.0.html#circle.

For example, to draw three circles with circle_marker:

t = Table().with_columns([
        'lat', [37.8, 38, 37.9],
        'lon', [-122, -122.1, -121.9],
        'label', ['one', 'two', 'three'],
        'color', ['red', 'green', 'blue'],
        'area', [3000, 4000, 5000],
    ])
Circle.map_table(t)

To draw three circles with the circle methods, replace the last line with:

Circle.map_table(t, radius_in_meters=True)

draw_on(folium_map, radius_in_meters=False)

Add feature to Folium map object.

class datascience.maps.Map(features=(), ids=(), width=960, height=500, **kwargs)

A map from IDs to features. Keyword args are forwarded to folium.

color(values, ids=(), key_on='feature.id', palette='YlOrBr', **kwargs)

Color map features by binning values.

values – a sequence of values or a table of keys and values ids – an ID for each value; if none are provided, indices are used key_on – attribute of each feature to match to ids palette – one of the following color brewer palettes:

‘BuGn’, ‘BuPu’, ‘GnBu’, ‘OrRd’, ‘PuBu’, ‘PuBuGn’, ‘PuRd’, ‘RdPu’, ‘YlGn’, ‘YlGnBu’, ‘YlOrBr’, and ‘YlOrRd’.

Defaults from Folium:

threshold_scale: list, default None

Data range for D3 threshold scale. Defaults to the following range of quantiles: [0, 0.5, 0.75, 0.85, 0.9], rounded to the nearest order-of-magnitude integer. Ex: 270 rounds to 200, 5600 to 6000.

fill_opacity: float, default 0.6

Area fill opacity, range 0-1.

line_color: string, default ‘black’

GeoJSON geopath line color.

line_weight: int, default 1

GeoJSON geopath line weight.

line_opacity: float, default 1

GeoJSON geopath line opacity, range 0-1.

legend_name: string, default None

Title for data legend. If not passed, defaults to columns[1].

copy()

Copies the current Map into a new one and returns it. Note: This only copies rendering attributes. The underlying map is NOT deep-copied. This is as a result of no functionality in Folium. Ref: https://github.com/python-visualization/folium/issues/1207

property features

format(**kwargs)

Apply formatting.

geojson()

Render features as a FeatureCollection.

overlay(feature, color='Blue', opacity=0.6)

Overlays feature on the map. Returns a new Map.

Args:

feature: a Table of map features, a list of map features,

a Map, a Region, or a circle marker map table. The features will be overlayed on the Map with specified color.

color (str): Color of feature. Defaults to ‘Blue’

opacity (float): Opacity of overlain feature. Defaults to

0.6.

Returns:

A new Map with the overlain feature.

classmethod read_geojson(path_or_json_or_string_or_url)

Read a geoJSON string, object, file, or URL. Return a dict of features keyed by ID.

class datascience.maps.Marker(lat, lon, popup='', color='blue', **kwargs)

A marker displayed with Folium’s simple_marker method.

popup – text that pops up when marker is clicked color – The color of the marker. You can use: [‘red’, ‘blue’, ‘green’, ‘purple’, ‘orange’, ‘darkred’, ’lightred’, ‘beige’, ‘darkblue’, ‘darkgreen’, ‘cadetblue’, ‘darkpurple’, ‘white’, ‘pink’, ‘lightblue’, ‘lightgreen’, ‘gray’, ‘black’, ‘lightgray’] to use standard folium icons. If a hex color code is provided, (color must start with ‘#’), a folium.plugin.BeautifyIcon will be used instead.

Defaults from Folium:

marker_icon: string, default ‘info-sign’

icon from (http://getbootstrap.com/components/) you want on the marker

clustered_marker: boolean, default False

boolean of whether or not you want the marker clustered with other markers

icon_angle: int, default 0

angle of icon

popup_width: int, default 300

width of popup

The icon can be further customized by by passing in attributes into kwargs by using the attributes listed in https://python-visualization.github.io/folium/modules.html#folium.map.Icon.

copy()

Return a deep copy

draw_on(folium_map)

Add feature to Folium map object.

format(**kwargs)

Apply formatting.

geojson(feature_id)

GeoJSON representation of the marker as a point.

property lat_lons

Sequence of lat_lons that describe a map feature (for zooming).

classmethod map(latitudes, longitudes, labels=None, colors=None, areas=None, other_attrs=None, clustered_marker=False, **kwargs)

Return markers from columns of coordinates, labels, & colors.

The areas column is not applicable to markers, but sets circle areas.

Arguments: (TODO) document all options

index_map: list of integers, default None (when not applicable)

list of indices that maps each marker to a corresponding label at the index in cluster_labels (only applicable when multiple marker clusters are being used)

cluster_labels: list of strings, default None (when not applicable)

list of labels used for each cluster of markers (only applicable when multiple marker clusters are being used)

colorbar_scale: list of floats, default None (when not applicable)

list of cutoffs used to indicate where the bins are for each color (only applicable when colorscale gradient is being used)

include_color_scale_outliers: boolean, default None (when not applicable)

boolean of whether or not outliers are included in the colorscale gradient for markers (only applicable when colorscale gradient is being used)

radius_in_meters: boolean, default False

boolean of whether or not Circles should have their radii specified in meters, scales with map zoom

clustered_marker: boolean, default False

boolean of whether or not you want the marker clustered with other markers

other_attrs: dictionary of (key) property names to (value) property values, default None

A dictionary that list any other attributes that the class Marker/Circle should have

classmethod map_table(table, clustered_marker=False, include_color_scale_outliers=True, radius_in_meters=False, **kwargs)

Return markers from the columns of a table.

The first two columns of the table must be the latitudes and longitudes (in that order), followed by ‘labels’, ‘colors’, ‘color_scale’, ‘radius_scale’, ‘cluster_by’, ‘area_scale’, and/or ‘areas’ (if applicable) in any order with columns explicitly stating what property they are representing.

Args:

cls: Type of marker being drawn on the map {Marker, Circle}.

table: Table of data to be made into markers. The first two columns of the table must be the latitudes and longitudes (in that order), followed by ‘labels’, ‘colors’, ‘cluster_by’, ‘color_scale’, ‘radius_scale’, ‘area_scale’, and/or ‘areas’ (if applicable) in any order with columns explicitly stating what property they are representing. Additional columns for marker-specific attributes such as ‘marker_icon’ for the Marker class can be included as well.

clustered_marker: Boolean indicating if markers should be clustered with folium.plugins.MarkerCluster.

include_color_scale_outliers: Boolean indicating if outliers should be included in the color scale gradient or not.

radius_in_meters: Boolean indicating if circle markers should be drawn to map scale or zoom scale.

class datascience.maps.Region(geojson, **kwargs)

A GeoJSON feature displayed with Folium’s geo_json method.

copy()

Return a deep copy

draw_on(folium_map)

Add feature to Folium map object.

format(**kwargs)

Apply formatting.

geojson(feature_id)

Return GeoJSON with ID substituted.

property lat_lons

A flat list of (lat, lon) pairs.

property polygons

Return a list of polygons describing the region.

• Each polygon is a list of linear rings, where the first describes the exterior and the rest describe interior holes.

• Each linear ring is a list of positions where the last is a repeat of the first.

• Each position is a (lat, lon) pair.

property properties

property type

The GEOJSON type of the regions: Polygon or MultiPolygon.

datascience.maps.get_coordinates(table, replace_columns=False, remove_nans=False)

Adds latitude and longitude coordinates to table based on other location identifiers. Must be in the United States.

Takes table with columns “zip code” or “city” and/or “county” and “state” in column names and adds the columns “lat” and “lon”. If a county is not found inside the dataset, that row’s latitude and longitude coordinates are replaced with np.nans. The ‘replace_columns’ flag indicates if the “city”, “county”, “state”, and “zip code” columns should be removed afterwards. The ‘remove_nans’ flag indicates if rows with nan latitudes and longitudes should be removed. Robust to capitalization in city and county names. If a row’s location with multiple zip codes is specified, the latitude and longitude pair assigned to the row will correspond to the smallest zip code.

Dataset was acquired on July 2, 2020 from https://docs.gaslamp.media/download-zip-code-latitude-longitude-city-state-county-csv. Found in geocode_datasets/geocode_states.csv. Modified column names and made city/county columns all in lowercase.

Args:

table: A table with counties that need to mapped to coordinates replace_columns: A boolean that indicates if “county”, “city”, “state”, and “zip code” columns should be removed remove_nans: A boolean that indicates if columns with invalid longitudes and latitudes should be removed

Returns:

Table with latitude and longitude coordinates

Predicates (datascience.predicates)

Predicate functions.

class datascience.predicates.are

Predicate functions. The class is named “are” for calls to where.

For example, given a table, predicates can be used to pick rows as follows.

>>> from datascience import Table
>>> t = Table().with_columns([
...    'Sizes', ['S', 'M', 'L', 'XL'],
...    'Waists', [30, 34, 38, 42],
... ])
>>> t.where('Sizes',  are.equal_to('L'))
Sizes | Waists
L     | 38
>>> t.where('Waists', are.above(38))
Sizes | Waists
XL    | 42
>>> t.where('Waists', are.above_or_equal_to(38))
Sizes | Waists
L     | 38
XL    | 42
>>> t.where('Waists', are.below(38))
Sizes | Waists
S     | 30
M     | 34
>>> t.where('Waists', are.below_or_equal_to(38))
Sizes | Waists
S     | 30
M     | 34
L     | 38
>>> t.where('Waists', are.strictly_between(30, 38))
Sizes | Waists
M     | 34
>>> t.where('Waists', are.between(30, 38))
Sizes | Waists
S     | 30
M     | 34
>>> t.where('Waists', are.between_or_equal_to(30, 38))
Sizes | Waists
S     | 30
M     | 34
L     | 38
>>> t.where('Sizes',  are.equal_to('L'))
Sizes | Waists
L     | 38
>>> t.where('Waists', are.not_above(38))
Sizes | Waists
S     | 30
M     | 34
L     | 38
>>> t.where('Waists', are.not_above_or_equal_to(38))
Sizes | Waists
S     | 30
M     | 34
>>> t.where('Waists', are.not_below(38))
Sizes | Waists
L     | 38
XL    | 42
>>> t.where('Waists', are.not_below_or_equal_to(38))
Sizes | Waists
XL    | 42
>>> t.where('Waists', are.not_strictly_between(30, 38))
Sizes | Waists
S     | 30
L     | 38
XL    | 42
>>> t.where('Waists', are.not_between(30, 38))
Sizes | Waists
L     | 38
XL    | 42
>>> t.where('Waists', are.not_between_or_equal_to(30, 38))
Sizes | Waists
XL    | 42
>>> t.where('Sizes', are.containing('L'))
Sizes | Waists
L     | 38
XL    | 42
>>> t.where('Sizes', are.not_containing('L'))
Sizes | Waists
S     | 30
M     | 34
>>> t.where('Sizes', are.contained_in('MXL'))
Sizes | Waists
M     | 34
L     | 38
XL    | 42
>>> t.where('Sizes', are.contained_in('L'))
Sizes | Waists
L     | 38
>>> t.where('Sizes', are.not_contained_in('MXL'))
Sizes | Waists
S     | 30

static above(y)

Greater than y.

static above_or_equal_to(y)

Greater than or equal to y.

static below(y)

Less than y.

static below_or_equal_to(y)

Less than or equal to y.

static between(y, z)

Greater than or equal to y and less than z.

static between_or_equal_to(y, z)

Greater than or equal to y and less than or equal to z.

static contained_in(superstring)

A string that is part of the given superstring.

static containing(substring)

A string that contains within it the given substring.

static equal_to(y)

Equal to y.

static not_above(y)

Is not above y

static not_above_or_equal_to(y)

Is neither above y nor equal to y

static not_below(y)

Is not below y

static not_below_or_equal_to(y)

Is neither below y nor equal to y

static not_between(y, z)

Is equal to y or less than y or greater than z

static not_between_or_equal_to(y, z)

Is less than y or greater than z

static not_contained_in(superstring)

A string that is not contained within the superstring

static not_containing(substring)

A string that does not contain substring

static not_equal_to(y)

Is not equal to y

static not_strictly_between(y, z)

Is equal to y or equal to z or less than y or greater than z

static strictly_between(y, z)

Greater than y and less than z.

Formats (datascience.formats)

String formatting for table entries.

class datascience.formats.CurrencyFormatter(symbol='$', *args, **vargs)

Format currency and convert to float.

convert_value(value)

Convert value to float. If value is a string, ensure that the first character is the same as symbol ie. the value is in the currency this formatter is representing.

format_value(value)

Format currency.

class datascience.formats.DateFormatter(format='%Y-%m-%d %H:%M:%S.%f', *args, **vargs)

Format date & time and convert to UNIX timestamp.

convert_value(value)

Convert 2015-08-03 to a Unix timestamp int.

format_value(value)

Format timestamp as a string.

class datascience.formats.DistributionFormatter(decimals=2, *args, **vargs)

Normalize a column and format as percentages.

convert_column(values)

Normalize values.

class datascience.formats.Formatter(min_width=None, max_width=None, etc=None)

String formatter that truncates long values.

convert_column(values)

Convert each value using the convert_value method.

static convert_value(value)

Identity conversion (override to convert values).

property converts_values

Whether this Formatter also converts values.

etc = ' ...'

format_column(label, column)

Return a formatting function that pads & truncates values.

static format_value(value)

Pretty-print an arbitrary value.

max_width = 60

min_width = 4

class datascience.formats.NumberFormatter(decimals=2, decimal_point='.', separator=',', int_to_float=False, *args, **vargs)

Format numbers that may have delimiters.

convert_value(value)

Convert string 93,000.00 to float 93000.0.

format_value(value)

Pretty-print an arbitrary value.

class datascience.formats.PercentFormatter(decimals=2, *args, **vargs)

Format a number as a percentage.

format_value(value)

Format number as percentage.

Utility Functions (datascience.util)

Utility functions

datascience.util.is_non_string_iterable(value)

Returns a boolean value representing whether a value is iterable.

datascience.util.make_array(*elements)

Returns an array containing all the arguments passed to this function. A simple way to make an array with a few elements.

As with any array, all arguments should have the same type.

Args:

elements (variadic): elements

Returns:

A NumPy array of same length as the provided varadic argument elements

>>> make_array(0)
array([0])
>>> make_array(2, 3, 4)
array([2, 3, 4])
>>> make_array("foo", "bar")
array(['foo', 'bar'],
      dtype='<U3')
>>> make_array()
array([], dtype=float64)

datascience.util.minimize(f, start=None, smooth=False, log=None, array=False, **vargs)

Minimize a function f of one or more arguments.

Args:

f: A function that takes numbers and returns a number

start: A starting value or list of starting values

smooth: Whether to assume that f is smooth and use first-order info

log: Logging function called on the result of optimization (e.g. print)

vargs: Other named arguments passed to scipy.optimize.minimize

Returns either:

1. the minimizing argument of a one-argument function

2. an array of minimizing arguments of a multi-argument function

datascience.util.percentile(p, arr=None)

Returns the pth percentile of the input array (the value that is at least as great as p% of the values in the array).

If arr is not provided, percentile returns itself curried with p

>>> percentile(74.9, [1, 3, 5, 9])
5
>>> percentile(75, [1, 3, 5, 9])
5
>>> percentile(75.1, [1, 3, 5, 9])
9
>>> f = percentile(75)
>>> f([1, 3, 5, 9])
5

datascience.util.plot_cdf_area(rbound=None, lbound=None, mean=0, sd=1)

Plots a normal curve with specified parameters and area below curve shaded between lbound and rbound.

Args:

rbound (numeric): right boundary of shaded region

lbound (numeric): left boundary of shaded region; by default is negative infinity

mean (numeric): mean/expectation of normal distribution

sd (numeric): standard deviation of normal distribution

datascience.util.plot_normal_cdf(rbound=None, lbound=None, mean=0, sd=1)

Plots a normal curve with specified parameters and area below curve shaded between lbound and rbound.

Args:

rbound (numeric): right boundary of shaded region

lbound (numeric): left boundary of shaded region; by default is negative infinity

mean (numeric): mean/expectation of normal distribution

sd (numeric): standard deviation of normal distribution

datascience.util.proportions_from_distribution(table, label, sample_size, column_name='Random Sample')

Adds a column named column_name containing the proportions of a random draw using the distribution in label.

This method uses np.random.Generator.multinomial to draw sample_size samples from the distribution in table.column(label), then divides by sample_size to create the resulting column of proportions.

Args:

table: An instance of Table.

label: Label of column in table. This column must contain a

distribution (the values must sum to 1).

sample_size: The size of the sample to draw from the distribution.

column_name: The name of the new column that contains the sampled

proportions. Defaults to 'Random Sample'.

Returns:

A copy of table with a column column_name containing the sampled proportions. The proportions will sum to 1.

Throws:

ValueError: If the label is not in the table, or if

table.column(label) does not sum to 1.

datascience.util.sample_proportions(sample_size: int, probabilities)

Return the proportion of random draws for each outcome in a distribution.

This function is similar to np.random.Generator.multinomial, but returns proportions instead of counts.

Args:

sample_size: The size of the sample to draw from the distribution.

probabilities: An array of probabilities that forms a distribution.

Returns:

An array with the same length as probability that sums to 1.

datascience.util.table_apply(table, func, subset=None)

Applies a function to each column and returns a Table.

Args:

table: The table to apply your function to.

func: The function to apply to each column.

subset: A list of columns to apply the function to; if None,

the function will be applied to all columns in table.

Returns:

A table with the given function applied. It will either be the shape == shape(table), or shape (1, table.shape[1])