Created by Nishant Kheterpal
In the examples in the left column, np refers to the NumPy module, as usual. Everything else is a function, a method, an example of an argument to a function or method, or an example of an object we might call the method on. For example, tbl refers to a table, array refers to an array, and num refers to a number. array.item(0) is an example call for the method item, and in that example, array is the name previously given to some array.
| Name | Chapter | Description |
|---|---|---|
Table() |
6 | Create an empty table, usually to extend with data |
Table().read_table(filename) |
6 | Create a table from a data file |
tbl.with_columns(name, values) tbl.with_columns(n1, v1, n2, v2,...) |
6 | A table with an additional or replaced column or columns. name is a string for the name of a column, values is an array |
tbl.column(column_name_or_index) |
6 | The values of a column (an array) |
tbl.num_rows |
6 | Compute the number of rows in a table |
tbl.num_columns |
6 | Compute the number of columns in a table |
tbl.labels |
6 | Lists the column labels in a table |
tbl.select(col1, col2, ...) |
6 | Create a copy of a table with only some of the columns. Each column is the column name or index. |
tbl.drop(col1, col2, ...) |
6 | Create a copy of a table without some of the columns. Each column is the column name or index. |
tbl.relabel(old_label, new_label) |
6 | Modifies the existing table in place, changing the column heading in the first argument to the second |
tbl.relabeled(old_label, new_label) |
6 | Returns a new table with the column heading in the first argument changed to the second |
tbl.sort(column_name_or_index) |
6.1 | Create a copy of a table sorted by the values in a column. Defaults to ascending order unless "descending = True" is included |
tbl.where(column, predicate) |
6.2 | Create a copy of a table with only the rows that match some predicate See Table.where predicates below |
tbl.take(row_indices) |
6.2 | A table with only the rows at the given indices. row_indices is an array of indices. |
tbl.scatter(x_column, y_column) |
7 | Draws a scatter plot consisting of one point for each row of the table. Note that x_column and y_column must be strings specifying column names. |
tbl.plot(x_column, y_column) |
7 | Draw a line graph consisting of one point for each row of the table. |
tbl.barh(categories) tbl.barh(categories, values) |
7.1 | Displays a bar chart with bars for each category in a column, with height proportional to the corresponding frequency. values argument unnecessary if table has only a column of categories and a column of values. |
tbl.hist(column, unit, bins) |
7.2 | Generates a histogram of the numerical values in a column. unit and bins are optional arguments, used to label the axes and group the values into intervals (bins), respectively. Bins have the form [a, b). |
tbl.apply(function, column) |
8.1 | Returns an array of values resulting from applying a function to each item in a column. |
tbl.group(column_or_columns, func) |
8.2 | Group rows by unique values or combinations of values in a column(s). Multiple columns must be entered in array or list form. Other values aggregated by count (default) or optional argument func. |
tbl.pivot(col1, col2, vals, collect) tbl.pivot(col1, col2) |
8.3 | A pivot table where each unique value in col1 has its own column and each unique value in ccol2 has its own row. Count or aggregate values from a third column, collect with some function. Default vals and collect return counts in cells. |
tblA.join(colA, tblB, colB) tblA.join(colA, tblB) |
8.4 | Generate a table with the columns of tblA and tblB, containing rows for all values of a column that appear in both tables. Default colB is colA. colA and colB must be strings specifying column names. |
tbl.sample(n) tbl.sample(n, with_replacement) |
10 | A new table where n rows are randomly sampled from the original table. Default is with replacement. For sampling without replacement, use argument with_replacement=False. For a non-uniform sample, provide a third argument weights=distribution where distribution is an array or list containing the probability of each row. |
sample_proportions(sample_size, model_proportions |
11.1 | Sample_size should be an integer, model_proportions an array of probabilities that sum up to 1. The function samples sample_size objects from the distribution specified by model_proportions. 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. |
| Name | Chapter | Description |
|---|---|---|
max(array) |
3.3 | Returns the maximum value of an array |
min(array) |
3.3 | Returns the minimum value of an array |
sum(array) |
3.3 | Returns the sum of the values in an array |
abs(num), np.abs(array) |
3.3 | Take the absolute value of number or each number in an array. |
round(num), np.round(array) |
3.3 | Round number or array of numbers to the nearest integer. |
len(array) |
3.3 | Returns the length (number of elements) of an array |
make_array(val1, val2, ...) |
5 | Makes a numpy array with the values passed in |
np.average(array) np.mean(array) |
5.1 | Returns the mean value of an array |
np.diff(array) |
5.1 | Returns a new array of size len(arr)-1 with elements equal to the difference between adjacent elements; val_2 - val_1, val_3 - val_2, etc. |
np.sqrt(array) |
5.1 | Returns an array with the square root of each element |
np.arange(start, stop, step) np.arange(start, stop) np.arange(stop) |
5.2 | An array of numbers starting with start, going up in increments of step, and going up to but excluding stop. When start and/or step are left out, default values are used in their place. Default step is 1; default start is 0. |
array.item(index) |
5.3 | Returns the i-th item in an array (remember Python indices start at 0!) |
np.random.choice(array, n) np.random.choice(array) |
9 | Picks one (by default) or some number 'n' of items from an array at random. By default, with replacement. |
np.count_nonzero(array) |
9 | Returns the number of non-zero (or True) elements in an array. |
np.append(array, item) |
9.2 | 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. |
percentile(percentile, array) |
12.1 | Returns the corresponding percentile of an array. |