Python Reference 🐍

Understanding the Python Reference
1. The Anatomy of a Function
2. How to Read These Tables
Built-In Python Functions
NumPy Array Functions
Tables and Table Methods
Visualization Functions
Table.where Predicates
More Documentation

Understanding the Python Reference

If you’re new to reading documentation, looking at the Data 6 Python Reference might feel a little overwhelming, but don’t worry — the point of this class is not to memorize all of these functions or their arguments. For exams and quizzes, you will be provided with a reference sheet that contains all of the functions you may need on the exam.

However, we do expect you to understand how to use the Python Reference to understand new functions, and to help with debugging when things go wrong. Learning how to read and understand documentation is a key to becoming a good data scientist. In fact, even course staff continue to use the Python Reference to refresh their memory about certain functions.

Of course, the Python Reference can only provide information about the basics of the functions you’ll use in Data 6. The best knowledge about functions comes from using these functions in code you write in labs or homeworks. If you get stuck when using a certain function, we encourage you to come to office hours or ask a question on Ed.

The Anatomy of a Function

 def my_function(num):
 return num ** 3

In the function above, my_function is the name of the function, which takes one argument called num. The data type of the input is an int or float, and the function returns the number raised to the power 3, which is also an int or float.

How to Read These Tables

The Function column tells you how to call the function and what arguments it accepts. Everything written in this font is code or refers to a particular argument in the function (e.g. num in np.sqrt(num)).
The Description column gives you a brief description of what the function does, including what each argument is used for
The Input column tells you what data type each argument needs to be. If you’re getting a TypeError, it might be because your inputs are of the wrong type. Data types are indicated in bold (e.g. string or Table).
The Output column tells you what the function returns and what data type it is.

Built-In Python Functions

Function	Description	Input	Output
`str(val)`	Converts `val` to a string	A value of any type (int, float, NoneType, etc.)	The value as a string
`int(num)`	Converts `num` to an int	A numerical value (represented as a string or float)	The value as an int
`float(num)`	Converts `num` to a float	A numerical value (represented as a string or int)	The value as a float
`len(arr)`	Returns the length of `arr`	array or list	int: the length of the array or list
`max(arr)`	Returns the maximum value in `arr`	array or list	The maximum value the array (usually an int)
`min(arr)`	Returns the minimum value in `arr`	array or list	The minimum value the array (usually an int)
`sum(arr)`	Returns the sum of the values in `arr`	array or list	int or float: the sum of the values in the array
`abs(num)`	Returns the absolute value of `num`	int or float	int or float

NumPy Array Functions

Function	Description	Input	Output
`make_array(val1, val2, ...)`	Makes a NumPy array with the inputted values	A sequence of values	An array with those values
`np.mean(arr)` or `np.average(arr)`	Calculates the average value of `arr`	An array of numbers	float: The average of the array
`np.sum(arr)`	Returns the sum of the values in `arr`	array	int or float: the sum of the values in the array
`np.prod(arr)`	Returns the product of the values in `arr`	array	int or float: the product of the values in the array
`np.sqrt(num)`	Calculates the square root of `num`	int or float	float : the square root of the number
`np.arange(stop)`, `np.arange(start, stop)`, or `np.arange(start, stop, step)`	Creates an array of sequential numbers starting at `start`, going up in increments of `step`, and going up to but excluding `stop`. Default `start` is 0, default `step` is 1	int or float	array
`np.count_nonzero(arr)`	Returns the number of non-zero (or `True`) elements in an array	An array of values	int: the number of non-zero values in `arr`
`np.append(arr, item)`	Appends `item` to the end of `arr`. Does not modify the original array.	1. array to append to 2. item to append (any type)	array: a new array with the appended item

Tables and Table Methods

Function	Description	Input	Output
`Table()`	Creates an empty table, usually to extend with data	None	An empty Table
`tbl.with_column(name, values)` or `tbl.with_columns(n1, v1, n2, v2, ...)`	Adds an extra column onto `tbl` with the label `name` and `values` as the column values	1. string: name of the new column 2. array: values in the column	Table: a copy of the original table with the new column(s)
`tbl.column(col)`	Returns the values in a column	string or int: the column name or index	array: the values in that column
`tbl.num_rows`	Compute the number of rows in `tbl`	None	int: the number of rows in the table
`tbl.num_columns`	Compute the number of columns in `tbl`	None	int: the number of columns in the table
`tbl.labels`	Returns the labels in `tbl`	None	array: the names of each column as strings
`tbl.select(col1, col2, ...)`	Creates a copy of `tbl` only with the selected columns	string or int: the column name(s) or index(es) to be included in the table	Table with the selected columns
`tbl.drop(col1, col2, ...)`	Creates a copy of `tbl` without the selected columns	string or int: the column name(s) or index(es) to be dropped from the table	Table without the selected columns
`tbl.relabeled(old_label, new_label)`	Creates a new table, changing the column name specified by `old_label` to `new_label`, and leaves the original table unchanged.	1. string: the old column name 2. string the new column name	Table: a copy of the original table with the changed column name
`tbl.show(n)`	Displays the first `n` rows of `tbl`. If no argument is specified, the function defaults to showing the entire table	(Optional) int: number of rows to be displayed	None (table is displayed)
`tbl.sort(column_name)`	Sorts the rows of `tbl` by the values in the `column_name` column. Defaults to ascending order unless the optional argument `descending=True` is included.	1. string or int: name or index of the column to sort 2. (Optional) `descending=True`	Table: a copy of the original table with the column sorted
`tbl.where(column, predicate)`	Creates a copy of `tbl` containing only the rows where the value of `column` matches the `predicate`. See `Table.where` predicates below.	1. string or int: column name or index 2. `are.(...)` predicate	Table: a copy of the original table with only the rows that match the predicate
`tbl.take(row_indices)`	Creates a table with only the rows at the given indices. `row_indices` is either an array of indices or an integer corresponding to one index.	int or array: indices of rows to be included in the table	Table: a copy of the original table with only the rows at the given indices
`tbl.apply(function)` or `tbl.apply(function, col1, col2, ...)`	Returns an array of values resulting from applying a function to each item in a column.	1. Function: function to apply to column 2. (Optional) string or int: the column name(s) or index(es) to apply the function to	array containing an element for each value in the original column after applying the function to it
`tbl.group(column_or_columns, function)`	Groups rows in `tbl` by unique values or combinations of values in a column(s). Multiple columns must be entered as an array of strings. Values in the other columns are aggregated by count (by default) or the optional argument `function`. You can visualize the `group` function here.	1. string or array of strings: column(s) on which to group 2. (Optional) Function: function to aggregate values in cells (defaults to counting rows)	Table a new groupped table
`tbl.pivot(col1, col2)` or `tbl.pivot(col1, col2, values, collect)`	Creates a pivot table where each unique value in `col1` has its own column and each unique value in `col2` has its own row. Counts or aggregates values from a third column, collected with some function. If the `values` and `collect` arguments are not included, `pivot` defaults to returning counts in the cells. You can visualize the `pivot` function here.	1. string: name of the column in `tbl` whose unique values will make up the columns of the pivot table 2. string: name of column in `tbl` whose unique values will make up the rows of the pivot table 3. (Optional) string: name of the column in `tbl` that describes the values of cells in the pivot table 4. (Optional) Function: how the values are collected (e.g. `sum` or `np.mean`)	Table: a new pivot table
`tblA.join(colA, tblB)` or `tblA.join(colA, tblB, colB)`	Generate a table with the columns of `tblA` and `tblB`, containing rows for all values in `colA` and `colB` that appear in `tblA` and `tblB`, respectively. By default, `colB` is the same value as `colA`. `colA` and `colB` must be strings specifying column names.	1. string: name of column in `tblA` with values to join on 2. Table: the other table 3. (Optional) string: the name of the shared column in `tblB`, if column names are different between the tables	Table: a new combined table

Visualization Functions

Function	Description	Input	Output
`tbl.barh(categories)` or `tbl.barh(categories, values)`	Displays a horizontal bar chart with bars for each category in the column `categories`. `values` specifies the column corresponding to the size of each bar, but is unnecessary if the table only has two columns. Optional argument `overlay` (default is `True`) specifies whether grouped bar charts should be overlaid or on separate plots.	1. string: name of the column with categories 2. (Optional) string: name of the column with values corresponding to the categories	None: draws a bar chart
`tbl.hist(column)`	Generates a histogram of the numerical values in `column`. Optional arguments `group` (to specify categorical column to group on), `bins` (to specify custom bins), and `overlay` to specify overlaid or separate histograms.	string: name of the column	None: draws a histogram
`tbl.plot(x_column, y_column)` or `tbl.plot(x_column)`	Draws a line plot consisting of one point for each row in `tbl`. If only `x_column` is specified, `plot` will plot the rest of the columns on the y-axis with different colored lines. Optional argument `overlay` (default is `True`) specifies whether multiple lines should be overlaid or on separate plots.	1. string: name of the column on the x-axis 2. string: name of the column on the y-axis	None: draws a line graph
`tbl.scatter(x_column, y_column)`	Draws a scatter plot consisting of one point for each row in `tbl`. The optional argument `fit_line=True` can be included to draw a line of best fit through the scatter plot. The optional arguments `group` (to specify categorical column to group on) and `sizes` (to specify a numerical column for bubble sizes) can also be used to encode additional variables.	1. string: name of the column on the x-axis 2. string: name of the column on the y-axis 3. (Optional) `fit_line=True`	None: draws a scatter plot

Table.where Predicates

These functions can be passed in as the second argument to tbl.where(..) and act as a condition by which to select rows from tbl.

Predicate	Description
`are.equal_to(Z)`	Equal to `Z` (can be an int, float or string)
`are.not_equal_to(Z)`	Not equal to ‘Z’ can be a number (int or float) or a string)
`are.above(x)`	Greater than `x`
`are.above_or_equal_to(x)`	Greater than or equal to `x`
`are.below(x)`	Less than `x`
`are.below_or_equal_to(x)`	Less than or equal to `x`
`are.between(x,y)`	Greater than or equal to `x` and less than `y`
`are.between_or_equal_to(x,y)`	Greater than or equal to `x`, and less than or equal to `y`
`are.strictly_between(x,y)`	Greater than `x` and less than `y`
`are.contained_in(A)`	True if it is a substring of `A (if` A `is a string) or an element of` A `(if` A` is an array)
`are.containing(S)`	Contains the string `S`