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Python Reference 🐍

Table of contents

  1. Understanding the Python Reference
    1. The Anatomy of a Function
    2. How to Read These Tables
  2. Built-In Python Functions
  3. NumPy Array Functions
  4. Tables and Table Methods
  5. Visualization Functions
  6. Table.where Predicates
  7. 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

FunctionDescriptionInputOutput
str(val)Converts val to a stringA value of any type (int, float, NoneType, etc.)The value as a string
int(num)Converts num to an intA numerical value (represented as a string or float)The value as an int
float(num)Converts num to a floatA numerical value (represented as a string or int)The value as a float
len(arr)Returns the length of arrarray or listint: the length of the array or list
max(arr)Returns the maximum value in arrarray or listThe maximum value the array (usually an int)
min(arr)Returns the minimum value in arrarray or listThe minimum value the array (usually an int)
sum(arr)Returns the sum of the values in arrarray or listint or float: the sum of the values in the array
abs(num)Returns the absolute value of numint or floatint or float

NumPy Array Functions

FunctionDescriptionInputOutput
make_array(val1, val2, ...)Makes a NumPy array with the inputted valuesA sequence of valuesAn array with those values
np.mean(arr) or np.average(arr)Calculates the average value of arrAn array of numbersfloat: The average of the array
np.sum(arr)Returns the sum of the values in arrarrayint or float: the sum of the values in the array
np.prod(arr)Returns the product of the values in arrarrayint or float: the product of the values in the array
np.sqrt(num)Calculates the square root of numint or floatfloat : 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 1int or floatarray
np.count_nonzero(arr)Returns the number of non-zero (or True) elements in an arrayAn array of valuesint: 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

FunctionDescriptionInputOutput
Table()Creates an empty table, usually to extend with dataNoneAn empty Table
Table().read_table(filename)Create a table from a data filestring: the name of the file 
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 values1. 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 in tblstring or int: the column name or indexarray: the values in that column
tbl.num_rowsCompute the number of rows in tblNoneint: the number of rows in the table
tbl.num_columnsCompute the number of columns in tblNoneint: the number of columns in the table
tbl.labelsReturns the labels in tblNonearray: the names of each column as strings
tbl.select(col1, col2, ...)Creates a copy of tbl only with the selected columnsstring or int: the column name(s) or index(es) to be included in the tableTable with the selected columns
tbl.drop(col1, col2, ...)Creates a copy of tbl without the selected columnsstring or int: the column name(s) or index(es) to be dropped from the tableTable 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 displayedNone (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 tableTable: 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

FunctionDescriptionInputOutput
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 columnNone: 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.

PredicateDescription
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

More Documentation

The Data 6 Python reference guide is based on the Data 8 Python Reference. More detailed Python documentation is available here.