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Exam Cheat Sheet |
Cheat sheet for quizzes and exams |
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This cheat sheet has been modified from the Data 6 Python Reference and includes all of the functions and table methods that you will need for the exams.
| 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 |
| print(input, ...) | Prints the input. Multiple inputs can be passed, and they will be separated by spaces by default. | input: any inputs to print
| None|
| type(object) | Returns the type of object. | object: the object whose type is to be determined | type: the type of the object|
| 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 |
| np.cumsum(arr) | Returns the cumulative sum of the elements in arr, where each element is the sum of all preceding elements including itself | array | array: the cumulative sum of the values in the array |
| np.diff(arr) | Computes the difference between consecutive elements in arr. | array | array: the differences between consecutive elements in the array containing len(arr) - 1 elements |
| Function | Description | Input | Output |
| str.split(separator, maxsplit) | Splits str into a list of substrings using the specified separator. If separator is not provided, splits at any whitespace. You can also use the optional argument maxsplit to limit the number of splits. | 1. (Optional) separator: the delimiter used to split str
2. (Optional) maxsplit: maximum number of splits | list of substrings |
| str.join(iterable) | Concatenates the elements in iterable (usually a list or array) into a single string, with each element separated by str. | iterable: an iterable of strings to join (can be an array or list of strings) | string: a single string formed by joining the elements of iterable with the separator str |
| str.replace(old, new) | Returns a copy of the string with all occurrences of the substring old replaced by new.| old: the substring to be replaced.
new: the substring to replace old with. | string: a new string where occurrences of old have been replaced by new.|
| Function | Description | Input | Output |
| Table() | Creates an empty table, usually to extend with data | None | An empty Table |
| Table().read_table(filename) | Create a table from a data file | string: 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 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 in tbl | 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 |
| tbl.with_row(values) | Adds a new row with the specified values to tbl | 1. list or array: values to add as a new row | Table: a copy of the original table with the new row |
| tbl.with_rows(list_of_rows) | Adds multiple rows to tbl using a list of rows | 1. list of lists or arrays: each list/array represents a new row | Table: a copy of the original table with the new rows |
| 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 |
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 Ais a **string**) or an element ofA(ifAis an **array**) | |are.containing(S)| Contains the stringS` |