Quick Reference



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import numpy as np
from datascience import *
from pprint import pprint

Python

Names

Assign values to names with the assignment operator =. Values at the end of a cell are printed.



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ten = 3 * 2 + 4
ten









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10

You can also call the print function:



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print(ten)



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# You can also make compound expressions
height = 1.3
the_number_five = abs(-5)
absolute_height_difference = abs(height - 1.688)

Functions



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def to_percentage(proportion):
    """Converts a proportion to a percentage."""
    factor = 100
    return proportion * factor
to_percentage(0.19)









    Out[5]:





19.0

Help

One can access help and documentation info using Google-fu (i.e., using your favorite internet search engine) or built-in documentation. The cell below shows how to access a quick documentation browser withing a Jupyter notebook. Sometimes, however, the documentation is terse, and you may be better off doing an internet search.



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to_percentage?

Strings

A snippet of text is represented by a string in Python. You can create strings with single quotes (') and double quotes (") as delimiters.

Name	Example	Purpose
`""`	`s = "some text"`	Create a string
`''`	`s = 'some text'`	Create a string
`+`	`s1 = 'some'` `s2 = 'text'` `s = s1 + ' ' + s2`	Concatenate strings
`.replace`	`'Hello'.replace('o', 'a')`	Replace all instances of a substring
`.lower`	`'Hello'.lower()`	Return a lowercased version of the string
`.upper`	`'hello'.upper()`	Return an uppercased version of the string
`.capitalize`	`'unIvErSity of cOlOrAdo'.capitalize()`	Return a version with the first letter capitalized
`.title`	`'unIvErSity of cOlOrAdo'.title()`	Return a version with the first letter of every word capitalized

Tables and Arrays

Arrays



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arr = make_array(0.125, 4.75, -1.3)
arr









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array([ 0.125,  4.75 , -1.3  ])

Index into the array with the .item method:



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arr.item(1)









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4.75

Apply arithmetic functions to arrays (provided by NumPy):



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2 * (arr + 1.5)









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array([  3.25,  12.5 ,   0.4 ])



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np.log10(make_array(1, 2, 10, 1000))









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array([ 0.     ,  0.30103,  1.     ,  3.     ])



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np.sum(np.log10(make_array(1, 2, 10, 1000)))









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4.3010299956639813

Tables

Name	Example	Purpose
`Table`	`Table()`	Create an empty table, usually to extend with data
`Table.read_table`	`Table.read_table("my_data.csv")`	Create a table from a data file
`with_columns`	`tbl = Table().with_columns("N", np.arange(5), "2*N", np.arange(0, 10, 2))`	Create a copy of a table with more columns
`column`	`tbl.column("N")`	Create an array containing the elements of a column
`sort`	`tbl.sort("N")`	Create a copy of a table sorted by the values in a column
`where`	`tbl.where("N", are.above(2))`	Create a copy of a table with only the rows that match some predicate
`num_rows`	`tbl.num_rows`	Compute the number of rows in a table
`num_columns`	`tbl.num_columns`	Compute the number of columns in a table
`select`	`tbl.select("N")`	Create a copy of a table with only some of the columns
`drop`	`tbl.drop("2*N")`	Create a copy of a table without some of the columns
`take`	`tbl.take(np.arange(0, 6, 2))`	Create a copy of the table with only the rows whose indices are in the given array



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are?



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