Z - Documentation

Source:

index.js, line 1

Data manipulation and analysis library written in JavaScript offering the convenience of pandas or R.

Methods

(static) addCol(col, arr, dataframe) → {df}

Source:

addCol.js, line 23

Add a new column to a dataframe from an array.

Use this function to add an array as a new column in a dataframe. Make sure the array has the same length as the number of rows in the dataframe.

Example

const df = [{"label": "A", "value": 7}, {"label": "B", "value": 2}]
const series = ["2010-12-15", "2010-12-16"]
Z.addCol("date", series, df)
// [{"date": "2010-12-15", "label": "A", "value": 7}, {"date": "2010-12-16", "label": "B", "value": 2}]

Parameters:

Name	Type	Description
`col`	String	Name of the column do add
`arr`	Array	Array of values for the new column
`dataframe`	df	Zebras dataframe to add the new column to

Returns:

Type: df

(static) concat(dataframe1, dataframe2) → {df}

Source:

concat.js, line 19

Concatenate two dataframes.

Example

const df1 = [{"label": "A", "value": 7}, {"label": "B", "value": 2}]
const df2 = [{"label": "C", "value": 17}, {"label": "D", "value": 2}]
Z.concat(df1, df2)
// [{"label": "A", "value": 7}, {"label": "B", "value": 2}, {"label": "C", "value": 17}, {"label": "D", "value": 2}]

Parameters:

Name	Type	Description
`dataframe1`	df	Zebras dataframe
`dataframe2`	df	Zebras dataframe

Returns:

Zebras dataframe

Type: df

(static) corr(series1, series2) → {Number}

Source:

corr.js, line 21

Correlation between two series.

Example

const series1 = [10, 15, 20, 25, 50, 55]
const series2 = [12, 18, 34, 52, 71, 86]
Z.corr(series1, series2)
// 0.969035563335365

Parameters:

Name	Type	Description
`series1`	Array	First series
`series2`	Array	Second series

Returns:

Type: Number

(static) countUnique(arr) → {Number}

Source:

countUnique.js, line 17

Count number of unique values in a series.

Example

const series = [7, 2, 30, 30, 56, 75]
Z.countUnique(series)
// 5

Parameters:

Name	Type	Description
`arr`	Array	Array of values

Returns:

Type: Number

(static) cumulative(func, arr) → {Array}

Source:

cumulative.js, line 22

Calculate cumulative statistics.

Calculate statistics over a cumulative window from the start of the array. Works wtih z.min, z.max, z.mean, z.std, z.sum, z.prod, etc., or any other function that takes an array as a single argument.

Example

const series = [7, 2, 30, 30, 56, 75]
Z.cumulative(Z.mean, series)
// [7, 4.5, 13, 17.25, 25, 33.333333333333336]

Parameters:

Name	Type	Description
`func`	function	Function to caclulate cumulative statistics
`arr`	Array	Series to calculate cumulative statistics for

Returns:

Type: Array

(static) deriveCol(func, dataframe) → {Array}

Source:

deriveCol.js, line 21

Create a new array based on columns from existing dataframe.

Use to create new columns derived from existing columns in a dataframe.

Example

const temps = [{"date": "1990-05-06", "tempCelsius": 0}, {"date": "1990-05-07", "tempCelsius": 4}]
const fahrenheit = Z.deriveCol((r) => r.tempCelsius * 1.8 + 32, temps)
Z.addCol("tempFahrenheit", fahrenheit, temps)
// [{"date": "1990-05-06", "tempCelsius": 0, "tempFahrenheit": 32}, {"date": "1990-05-07", "tempCelsius": 4, "tempFahrenheit": 39.2}]

Parameters:

Name	Type	Description
`func`	function	Function to create the new column
`dataframe`	df	Zebras dataframe to add the new column to

Returns:

Type: Array

(static) describe(func, arr) → {Number}

Source:

describe.js, line 26

Calculate summary statistics for a numerical series.

Returns a single-row df with count, unique count, min, max, median, mean and standard deviation of a numerical series.

Example

const series = [7, 2, 30, 30, 56, 75]
Z.describe(series)
// [{"count": 6, "countUnique": 5, "max": "75.00000", "mean": "33.33333", "median": "30.00000", "min": "2.00000", "std": "28.09745"}]

Parameters:

Name	Type	Description
`func`	function	Function to caclulate cumulative statistics
`arr`	Array	Series to calculate cumulative statistics for

Returns:

Type: Number

(static) diff(arr) → {Array}

Source:

diff.js, line 18

Returns a new series with the differences between the values in the order of the input series.

Example

const series = [7, 2, 30, 30, 56, 75]
Z.diff(series)
// [NaN, -5, 28, 0, 26, 19]

Parameters:

Name	Type	Description
`arr`	Array	Series to calculate differences for

Returns:

Type: Array

(static) dropCol(col, dataframe) → {df}

Source:

dropCol.js, line 18

Delete a column.

Example

const df = [{"label": "A", "value": 7}, {"label": "B", "value": 2}, {"label": "C", "value": 75}]
Z.dropCol("label", df)
// [{"value": 7}, {"value": 2}, {"value": 75}]

Parameters:

Name	Type	Description
`col`	String	Name of the column to delete
`dataframe`	df	Zebras dataframe

Returns:

Zebras dataframe

Type: df

(static) fFill(df, cols) → {df}

Source:

fFill.js, line 21

Front fill a dataframe

Returns the dataframes with NaN or null values filled in. Does not treat 0 as something to fill in, but does treat "" as such

Example

const series = [{"label": "A", "value": 7}, {"label": "B", "value": NaN}, {"label": "C", "value": 75}]
Z.fFill(series)
// [{"label": "A", "value": 7}, {"label": "B", "value": 7}, {"label": "C", "value": 75}]

Parameters:

Name	Type	Description
`df`	df	Series to front fill
`cols`	Array	Array of column names to convert. Set to null or [] for all.

Returns:

Type: df

(static) fillNA(df, fillValue, cols) → {df}

Source:

fillNA.js, line 22

Front fill a dataframe

Returns the dataframes with NaN or null values filled in. Does not treat 0 as something to fill in, but does treat "" as such

Example

const series = [{"label": "A", "value": 7}, {"label": "B", "value": NaN}, {"label": "C", "value": 75}]
Z.fillNA(series, -1)
// [{"label": "A", "value": 7}, {"label": "B", "value": -1}, {"label": "C", "value": 75}]

Parameters:

Name	Type	Description
`df`	df	Dataframe to fill NAs
`fillValue`	any	Value to fill with
`cols`	Array	Array of column names to convert. Set to null or [] for all.

Returns:

Type: df

(static) filter(func, dataframe) → {df}

Source:

filter.js, line 21

Filter dataframe rows by using a filtering function.

Accepts a test function that determines which rows of the supplied dataframe are returned.

Example

const df = [{"label": "A", "value": 2}, {"label": "B", "value": 10}, {"label": "C", "value": 30}]
Z.filter(r => r.value >= 10, df)
// [{"label": "B", "value": 10}, {"label": "C", "value": 30}]

Parameters:

Name	Type	Description
`func`	function	A filtering function
`dataframe`	df	Zebras dataframe to filter

Returns:

Zebras dataframe

Type: df

(static) gbApply(func, groupByObj) → {df}

Source:

gbApply.js, line 20

Maps groups using the provided function.

Example

const df = [{"Day": "Monday", "value": 10}, {"Day": "Tuesday", "value": 5}, {"Day": "Monday", "value": 7}]
const grouped = Z.groupBy(x => x.Day, df)
Z.gbApply((group, index) => ({Day: index, value: Z.sum(Z.getCol("value", group))}), grouped)
// [{ Day: "Monday", value: 17 }, { Day: "Tuesday", value: 5 }]

Parameters:

Name	Type	Description
`func`	function	A mapper function
`groupByObj`	Object	Object grouped by a column

Returns:

Zebras dataframe

Type: df

(static) gbCount(col, groupByObj) → {df}

Source:

gbCount.js, line 21

See:

Z.groupBy, Z.gbMin, Z.gbMax, Z.gbStd, Z.gbSum, Z.gbMean, Z.gbDescribe

Calculate count for grouped objects.

Use it on groupBy objects - the output of Z.groupBy() - to analyze groups.

Example

const df = [{"label": "A", "value": 7}, {"label": "A", "value": 3}, {"label": "B", "value": 2},  {"label": "B", "value": 5}, {"label": "C", "value": 75}]
Z.gbCount("value", Z.groupBy(d => d.label, df))
// [{"count": 2, "group": "A"}, {"count": 2, "group": "B"}, {"count": 1, "group": "C"}]

Parameters:

Name	Type	Description
`col`	String	Column within the groups to be analyzed
`groupByObj`	Object	Object grouped by a column

Returns:

Dataframe with the calculated statistics

Type: df

(static) gbDescribe(col, groupByObj) → {df}

Source:

gbDescribe.js, line 32

See:

Z.groupBy, Z.gbStd, Z.gbMin, Z.gbCount, Z.gbSum, Z.gbMean, Z.max

Describe grouped objects.

Use it on groupBy objects - the output of Z.groupBy() - to analyze groups.

Example

const df = [{"label": "A", "value": 7}, {"label": "A", "value": 3}, {"label": "B", "value": 2},  {"label": "B", "value": 5}, {"label": "C", "value": 75}]
Z.gbDescribe("value", Z.groupBy(d => d.label, df))
// [
//   { count: 2, group: "A", max: 7, mean: 5, min: 3, std: 2.8284271247461903, sum: 10 },
//   { count: 2, group: "B", max: 5, mean: 3.5, min: 2, std: 2.1213203435596424, sum: 7 },
//   { count: 1, group: "C", max: 75, mean: 75, min: 75, std: NaN, sum: 75 },
// ]

Parameters:

Name	Type	Description
`col`	String	Column within the groups to be analyzed
`groupByObj`	Object	Object grouped by a column

Returns:

Dataframe with the calculated statistics

Type: df

(static) gbMax(col, groupByObj) → {df}

Source:

gbMax.js, line 21

See:

Z.groupBy, Z.gbStd, Z.gbMin, Z.gbCount, Z.gbSum, Z.gbMean, Z.gbDescribe

Calculate max for grouped objects.

Use it on groupBy objects - the output of Z.groupBy() - to analyze groups.

Example

const df = [{"label": "A", "value": 7}, {"label": "A", "value": 3}, {"label": "B", "value": 2},  {"label": "B", "value": 5}, {"label": "C", "value": 75}]
Z.gbMax("value", Z.groupBy(d => d.label, df))
// [{"group": "A", "max": 7}, {"group": "B", "max": 5}, {"group": "C", "max": 75}]

Parameters:

Name	Type	Description
`col`	String	Column within the groups to be analyzed
`groupByObj`	Object	Object grouped by a column

Returns:

Dataframe with the calculated statistics

Type: df

(static) gbMean(col, groupByObj) → {df}

Source:

gbMean.js, line 22

See:

Z.groupBy, Z.gbMin, Z.gbMax, Z.gbCount, Z.gbSum, Z.gbStd, Z.gbDescribe

Calculate mean for grouped objects.

Use it on groupBy objects - the output of Z.groupBy() - to analyze groups.

Example

const df = [{"label": "A", "value": 7}, {"label": "A", "value": 3}, {"label": "B", "value": 2},  {"label": "B", "value": 5}, {"label": "C", "value": 75}]
Z.gbMean("value", Z.groupBy(d => d.label, df))
// [{"group": "A", "mean": 5}, {"group": "B", "mean": 3.5}, {"group": "C", "mean": 75}]

Parameters:

Name	Type	Description
`col`	String	Column within the groups to be analyzed
`groupByObj`	Object	Object grouped by a column

Returns:

Dataframe with the calculated statistics

Type: df

(static) gbMin(col, groupByObj) → {df}

Source:

gbMin.js, line 21

See:

Z.groupBy, Z.gbStd, Z.gbMax, Z.gbCount, Z.gbSum, Z.gbMean, Z.gbDescribe

Calculate min for grouped objects.

Use it on groupBy objects - the output of Z.groupBy() - to analyze groups.

Example

const df = [{"label": "A", "value": 7}, {"label": "A", "value": 3}, {"label": "B", "value": 2},  {"label": "B", "value": 5}, {"label": "C", "value": 75}]
Z.gbMin("value", Z.groupBy(d => d.label, df))
// [{"group": "A", "min": 3}, {"group": "B", "min": 2}, {"group": "C", "min": 75}]

Parameters:

Name	Type	Description
`col`	String	Column within the groups to be analyzed
`groupByObj`	Object	Object grouped by a column

Returns:

Dataframe with the calculated statistics

Type: df

(static) gbStd(col, groupByObj) → {df}

Source:

gbStd.js, line 24

See:

Z.groupBy, Z.gbMin, Z.gbMax, Z.gbCount, Z.gbSum, Z.gbMean, Z.gbDescribe

Calculate std for grouped objects.

Use it on groupBy objects - the output of Z.groupBy() - to analyze groups.

Example

const df = [{"label": "A", "value": 7}, {"label": "A", "value": 3}, {"label": "B", "value": 2},  {"label": "B", "value": 5}, {"label": "C", "value": 75}]
Z.gbStd("value", Z.groupBy(d => d.label, df))
// [{"group": "A", "std": 2.8284271247461903}, {"group": "B", "std": 2.1213203435596424}, {"group": "C", "std": NaN}]

Parameters:

Name	Type	Description
`col`	String	Column within the groups to be analyzed
`groupByObj`	Object	Object grouped by a column

Returns:

Dataframe with the calculated statistics

Type: df

(static) gbSum(col, groupByObj) → {df}

Source:

gbSum.js, line 24

See:

Z.groupBy, Z.gbMin, Z.gbMax, Z.gbCount, Z.gbMean, Z.gbStd, Z.gbDescribe

Calculate sums for grouped objects.

Use it on groupBy objects - the output of Z.groupBy() - to analyze groups.

Example

const df = [{"label": "A", "value": 7}, {"label": "A", "value": 3}, {"label": "B", "value": 2},  {"label": "B", "value": 5}, {"label": "C", "value": 75}]
Z.gbSum("value", Z.groupBy(d => d.label, df))
// [{"group": "A", "sum": 10}, {"group": "B", "sum": 7}, {"group": "C", "sum": 75}]

Parameters:

Name	Type	Description
`col`	String	Column within the groups to be analyzed
`groupByObj`	Object	Object grouped by a column

Returns:

Dataframe with the calculated statistics

Type: df

(static) getCol(col, dataframe) → {Array}

Source:

getCol.js, line 18

Extract a series to an array from a dataframe.

Example

const df = [{"label": "A", "value": "2010-12-13"}, {"label": "B", "value": "2010-12-15"}, {"label": "C", "value": "2010-12-17"}]
Z.getCol("value", df)
// ["2010-12-13", "2010-12-15", "2010-12-17"]

Parameters:

Name	Type	Description
`col`	String	Name of the column to extract
`dataframe`	df	Zebras dataframe

Returns:

Series array

Type: Array

(static) getRange(arr) → {Array}

Source:

getRange.js, line 20

Range of series.

Example

const series = [7, 2, 30, 56, 75]
Z.getRange(series)
// [2, 75]

Parameters:

Name	Type	Description
`arr`	Array	Array of values

Returns:

Array with min and max

Type: Array

(static) groupBy(func, dataframe) → {Object}

Source:

groupBy.js, line 18

Create an object grouped by according to the supplied function.

Example

const df = [{"Day": "Monday", "value": 10}, {"Day": "Tuesday", "value": 5}, {"Day": "Monday", "value": 7}]
Z.groupBy(x => x.Day, df)
// {"Monday": [{"Day": "Monday", "value": 10}, {"Day": "Monday", "value": 7}], "Tuesday": [{"Day": "Tuesday", "value": 5}]}

Parameters:

Name	Type	Description
`func`	function	Function returning string key
`dataframe`	df	Zebras dataframe

Returns:

Type: Object

(static) head(n, dataframe) → {df}

Source:

head.js, line 19

See:

Z.slice, Z.tail

Return dataframe with first n rows of input dataframe.

Example

Z.head(3, df)
// returns a new dataframe with the first 3 lines of `df`

Parameters:

Name	Type	Description
`n`	Number	Number of rows to select from start of df
`dataframe`	df

Returns:

Zebras dataframe

Type: df

(static) kurt(arr) → {Number}

Source:

kurt.js, line 20

Kurtosis of a series.

Example

const series = [7, 2, 30, 56, 75]
Z.kurt(series)
// -2.040541067936147

Parameters:

Name	Type	Description
`arr`	Array	Series to calculate kurtosis for

Returns:

Type: Number

(static) max(arr) → {Number}

Source:

max.js, line 19

Max of series.

Example

const series = [7, 2, 30, 56, 75]
Z.max(series)
// 75

Parameters:

Name	Type	Description
`arr`	Array	Array of values

Returns:

Type: Number

(static) mean(arr) → {Number}

Source:

mean.js, line 19

Mean of series.

Example

const series = [7, 2, 30, 56, 75]
Z.mean(series)
// 34

Parameters:

Name	Type	Description
`arr`	Array	Series to calculate mean for

Returns:

Type: Number

(static) median(arr) → {Number}

Source:

median.js, line 19

Median of series.

Example

const series = [7, 2, 30, 56, 75]
Z.median(series)
// 30

Parameters:

Name	Type	Description
`arr`	Array	Series to calculate median for

Returns:

Type: Number

(static) merge(dfLeft, dfRight, leftOn, rightOn, leftSuffix, rightSuffix) → {df}

Source:

merge.js, line 62

Join two dataframes on a column.

Performs a left join on two dataframes. The 'On' arguments set which column in each df to join on. The 'Suffix' arguments determine what the suffix should be when the two dataframes have overlapping column names besides the one being joined on.

Example

const df1 = [{"label": "A", "value": 7}, {"label": "B", "value": 2}, {"label": "C", "value": 75}]
const df2 = [{"label": "A", "value": "2010-12-13"}, {"label": "B", "value": "2010-12-15"}, {"label": "C", "value": "2010-12-17"}]
Z.merge(df1, df2, "label", "label", "_df1", "_df2")
// [
//   { label: "A", value_df1: 7, value_df2: "2010-12-13" },
//   { label: "B", value_df1: 2, value_df2: "2010-12-15" },
//   { label: "C", value_df1: 75, value_df2: "2010-12-17" },
// ]

Parameters:

Name	Type	Description
`dfLeft`	df	First dataframe
`dfRight`	df	Second dataframe
`leftOn`	String	Left column to join on
`rightOn`	String	Right column to join on
`leftSuffix`	String	Left suffix for overlapping column names
`rightSuffix`	String	Right suffix for overlapping column names

Returns:

Joined dataframe

Type: df

(static) min(arr) → {Number}

Source:

min.js, line 19

Min of series.

Example

const series = [7, 2, 30, 56, 75]
Z.min(series)
// 2

Parameters:

Name	Type	Description
`arr`	Array	Array of values

Returns:

Type: Number

(static) parseDates(cols, dataframe) → {df}

Source:

parseDates.js, line 18

Convert columns to datestamp.

Example

const df = [{"label": "A", "value": "2010-12-13"}, {"label": "B", "value": "2010-12-15"}, {"label": "C", "value": "2010-12-17"}]
Z.parseDates(["value"], df)
// [{"label": "A", "value": 1292198400000}, {"label": "B", "value": 1292371200000}, {"label": "C", "value": 1292544000000}]

Parameters:

Name	Type	Description
`cols`	Array	Array of column names to convert
`dataframe`	df	Zebras dataframe to parse

Returns:

Zebras dataframe

Type: df

(static) parseNums(columnNames, dataframe) → {df}

Source:

parseNums.js, line 18

Convert columns to numerical type (floats).

Example

const df = [{"label": "A", "value": "7"}, {"label": "B", "value": "2"}, {"label": "C", "value": "75"}]
Z.parseNums(["value"], df)
// [{"label": "B", "value": 2}, {"label": "A", "value": 7}, {"label": "C", "value": 75}]

Parameters:

Name	Type	Description
`columnNames`	Array	Array of column names to convert
`dataframe`	df	Zebras dataframe to parse

Returns:

Zebras dataframe

Type: df

(static) pctChange(arr) → {Array}

Source:

pctChange.js, line 20

Percent changes

Returns a new series with the percent changes between the values in order of the input series.

Example

const series = [10, 15, 20, 25, 50, 55]
Z.pctChange(series)
// [NaN, 0.5, 0.33333333333333326, 0.25, 1, 0.10000000000000009]

Parameters:

Name	Type	Description
`arr`	Array	Series to calculate percent changes for

Returns:

Type: Array

(static) pickCols(cols, dataframe) → {df}

Source:

pickCols.js, line 20

Select a subset of columns.

Accepts an array with the names of the columns to retain.

Example

const df = [{"label": "A", "value": 7}, {"label": "B", "value": 2}, {"label": "C", "value": 75}]
Z.pickCols(["value"], df)
// [{"value": 7}, {"value": 2}, {"value": 75}]

Parameters:

Name	Type	Description
`cols`	Array	Array of column names to pick
`dataframe`	df	Zebras dataframe

Returns:

Zebras dataframe

Type: df

(static) pipe(functions, dataframe) → {any}

Source:

pipe.js, line 27

Pipe functions together by performing left-to-right function composition.

Example

const data = [
  {"Date": "1997-01-01", "Value": "12"},
  {"Date": "1997-01-02", "Value": "14"},
  {"Date": "1997-01-03", "Value": "7"},
  {"Date": "1997-01-04", "Value": "112"}
]
Z.pipe([
  Z.parseNums(["Value"]), // converts "Value" column to floats
  Z.getCol("Value"), // extracts "Value" column to array
  Z.mean() // calculates mean of "Value" array
])(data)
// 36.25

Parameters:

Name	Type	Description
`functions`	Array	Array of functions to compose
`dataframe`	df	Zebras dataframe

Returns:

Result of the composed functions applied to dataframe

Type: any

(static) print(dataframe) → {String}

Source:

print.js, line 36

Prints dataframe.

Returns the entire dataframe as an ASCII table. If working in a local Node environment, wrap this and other printing functions in console.log() to display ASCII tables.

Example

Z.print(df)

// will output an ASCII table like this:
┌────────────┬───────┬───────┬───────┬───────┬───────────┬─────────┐
│ Date       │ Open  │ High  │ Low   │ Close │ Adj Close │ Volume  │
├────────────┼───────┼───────┼───────┼───────┼───────────┼─────────┤
│ 1950-01-03 │ 16.66 │ 16.66 │ 16.66 │ 16.66 │ 16.66     │ 1260000 │
├────────────┼───────┼───────┼───────┼───────┼───────────┼─────────┤
│ 1950-01-04 │ 16.85 │ 16.85 │ 16.85 │ 16.85 │ 16.85     │ 1890000 │
├────────────┼───────┼───────┼───────┼───────┼───────────┼─────────┤
│ 1950-01-05 │ 16.93 │ 16.93 │ 16.93 │ 16.93 │ 16.93     │ 2550000 │
├────────────┼───────┼───────┼───────┼───────┼───────────┼─────────┤
│ 1950-01-06 │ 16.98 │ 16.98 │ 16.98 │ 16.98 │ 16.98     │ 2010000 │
├────────────┼───────┼───────┼───────┼───────┼───────────┼─────────┤
│ 1950-01-09 │ 17.08 │ 17.08 │ 17.08 │ 17.08 │ 17.08     │ 2520000 │
└────────────┴───────┴───────┴───────┴───────┴───────────┴─────────┘

Parameters:

Name	Type	Description
`dataframe`	df	to print

Returns:

Entire dataframe as an ASCII table

Type: String

(static) printHead(n, dataframe) → {String}

Source:

printHead.js, line 31

See:

Z.print, Z.printTail

Print first n rows of dataframe.

Example

Z.printHead(3, df)

// will output an ASCII table like this:
┌────────────┬───────┬───────┬───────┬───────┬───────────┬─────────┐
│ Date       │ Open  │ High  │ Low   │ Close │ Adj Close │ Volume  │
├────────────┼───────┼───────┼───────┼───────┼───────────┼─────────┤
│ 1950-01-05 │ 16.93 │ 16.93 │ 16.93 │ 16.93 │ 16.93     │ 2550000 │
├────────────┼───────┼───────┼───────┼───────┼───────────┼─────────┤
│ 1950-01-06 │ 16.98 │ 16.98 │ 16.98 │ 16.98 │ 16.98     │ 2010000 │
├────────────┼───────┼───────┼───────┼───────┼───────────┼─────────┤
│ 1950-01-09 │ 17.08 │ 17.08 │ 17.08 │ 17.08 │ 17.08     │ 2520000 │
└────────────┴───────┴───────┴───────┴───────┴───────────┴─────────┘

Parameters:

Name	Type	Description
`n`	Number	Number of rows to print
`dataframe`	df

Returns:

First n rows of dataframe as an ASCII table

Type: String

(static) printTail(n, dataframe) → {String}

Source:

printTail.js, line 31

See:

Z.print, Z.printHead

Print last n rows of dataframe.

Example

Z.printTail(3, df)

// will output an ASCII table like this:
┌────────────┬───────┬───────┬───────┬───────┬───────────┬─────────┐
│ Date       │ Open  │ High  │ Low   │ Close │ Adj Close │ Volume  │
├────────────┼───────┼───────┼───────┼───────┼───────────┼─────────┤
│ 1950-01-05 │ 16.93 │ 16.93 │ 16.93 │ 16.93 │ 16.93     │ 2550000 │
├────────────┼───────┼───────┼───────┼───────┼───────────┼─────────┤
│ 1950-01-06 │ 16.98 │ 16.98 │ 16.98 │ 16.98 │ 16.98     │ 2010000 │
├────────────┼───────┼───────┼───────┼───────┼───────────┼─────────┤
│ 1950-01-09 │ 17.08 │ 17.08 │ 17.08 │ 17.08 │ 17.08     │ 2520000 │
└────────────┴───────┴───────┴───────┴───────┴───────────┴─────────┘

Parameters:

Name	Type	Description
`n`	Number	Number of rows to print
`dataframe`	df

Returns:

Last n rows of dataframe as an ASCII table

Type: String

(static) prod(arr) → {Number}

Source:

prod.js, line 19

Product of series.

Example

const series = [7, 2, 30, 56, 75]
Z.prod(series)
// 1764000

Parameters:

Name	Type	Description
`arr`	Array	Array of values

Returns:

Type: Number

(static) qcut(Integer, array) → {Array}

Source:

qcut.js, line 22

Quantile Cut: For input array X, return each values quantile where Q is the number of quantiles to use. Based on pandas qcut. Uses midpoint interpolation.

Example

const series = [...Array(275).keys()]
Z.valueCounts(Z.quantile(series))
// {"0":55, "1":55, "2":55, "3":55, "4":55, "5":55}

Parameters:

Name	Type	Description
`Integer`	Number	for the number of quantiles to use
`array`	Array	of values to calculate

Returns:

Type: Array

(static) quantile(quantile, array) → {Number}

Source:

quantile.js, line 20

Quantile of a value in a series of series using midpoint interpolation. see https://numpy.org/doc/stable/reference/generated/numpy.percentile.html

Example

const series = [7, 2, 30, 56, 75, 150]
Z.quantile(.95, series)
// 112.5

Parameters:

Name	Type	Description
`quantile`	Number	to calculate as represented by a number from 0 to 1
`array`	Array	of values to calculate percentile from

Returns:

Type: Number

(static) randomSample(n, dataframe) → {df}

Source:

randomSample.js, line 19

See:

Z.slice, Z.tail

Return dataframe with first n rows of input dataframe.

Example

Z.randomSample(3, df)
// returns a new dataframe with the 3 random rows of `df`

Parameters:

Name	Type	Description
`n`	Number	Number of rows to select from start of df
`dataframe`	df

Returns:

Zebras dataframe

Type: df

(static) readCSV(filepath) → {df}

Source:

readCSV.js, line 17

Synchronously reads a CSV file.

Example

Z.readCSV(filepath)

Parameters:

Name	Type	Description
`filepath`	String	File path for the CSV file to read

Returns:

Zebras dataframe

Type: df

(static) renameCols(mapper, dataframe) → {df}

Source:

renameCols.js, line 18

Rename columns.

Example

const df = [{"label": "A", "value": 7}, {"label": "B", "value": 2}, {"label": "C", "value": 75}]
Z.renameCols({"label": "name"}, df)
// [{"name": "A", "value": 7}, {"name": "B", "value": 2}, {"name": "C", "value": 75}]

Parameters:

Name	Type	Description
`mapper`	Object	Dictionary in the format {"oldColName": "newColName"}
`dataframe`	df	Zebras dataframe

Returns:

Zebras dataframe

Type: df

(static) rolling(func, n, arr) → {Array}

Source:

rolling.js, line 23

Calculate rolling statistics

Calculate statistics over a moving window. Works with Z.min, Z.max, Z.mean, Z.std, Z.sum, Z.prod, or any other function that takes an array as a single argument.

Example

const series = [7, 2, 30, 30, 56, 75]
Z.rolling(Z.mean, 2, series)
// ["NotANumber", 4.5, 16, 30, 43, 65.5]

Parameters:

Name	Type	Description
`func`	function	Function to caclulate rolling statistics
`n`	Number	Range (?)
`arr`	Array	Series to calculate rolling statistics for

Returns:

Type: Array

(static) skew(arr) → {Number}

Source:

skew.js, line 20

Skew of a series.

Example

const series = [7, 2, 30, 56, 75]
Z.skew(series)
// 0.17542841315728933

Parameters:

Name	Type	Description
`arr`	Array	Series to calculate skew for

Returns:

Type: Number

(static) slice(start, end, dataframe) → {df}

Source:

slice.js, line 19

Get dataframe rows by index.

Example

const df = [{"label": "A", "value": 7}, {"label": "B", "value": 2}, {"label": "C", "value": 75}]
Z.slice(1, 2, df)
// [{"label": "B", "value": 2}]

Parameters:

Name	Type	Description
`start`	Number	The start index (inclusive).
`end`	Number	The end index (exclusive).
`dataframe`	df	Zebras dataframe

Returns:

Zebras dataframe

Type: df

(static) sort(func, dataframe) → {df}

Source:

sort.js, line 21

Sort dataframe rows using custom sorting function.

Accepts a sorting function that determines the order of rows in the returned dataframe.

Example

const df = [{"label": "A", "value": 7}, {"label": "B", "value": 2}, {"label": "C", "value": 75}]
Z.sort((a, b) => b.value - a.value, df)
// [{ label: "C", value: 75 },{ label: "A", value: 7 },{ label: "B", value: 2 }]

Parameters:

Name	Type	Description
`func`	function	A sorting function
`dataframe`	df	Zebras dataframe to sort

Returns:

Zebras dataframe

Type: df

(static) sortByCol(col, direction, dataframe) → {df}

Source:

sortByCol.js, line 19

Sort dataframe rows by a column.

Example

const df = [{"label": "A", "value": 7}, {"label": "B", "value": 2}, {"label": "C", "value": 75}]
Z.sortByCol("value", "asc", df)
// [{"label": "B", "value": 2}, {"label": "A", "value": 7}, {"label": "C", "value": 75}]

Parameters:

Name	Type	Description
`col`	String	Name of the column to sort by
`direction`	String	Determines direction, pass `asc` for ascending and `desc` for descending
`dataframe`	df	Zebras dataframe to sort

Returns:

Zebras dataframe

Type: df

(static) std(arr) → {Number}

Source:

std.js, line 19

Standard deviation of series.

Example

const series = [7, 2, 30, 56, 75]
Z.std(series)
// 31.36080356113344

Parameters:

Name	Type	Description
`arr`	Array	Series to calculate standard deviation for

Returns:

Type: Number

(static) sum(arr) → {Number}

Source:

sum.js, line 19

Sum of series.

Example

const series = [7, 2, 30, 56, 75]
Z.sum(series)
// 170

Parameters:

Name	Type	Description
`arr`	Array	Array of values

Returns:

Type: Number

(static) tail(n, dataframe) → {df}

Source:

tail.js, line 19

See:

Z.slice, z.head

Return a dataframe with the last n rows of input dataframe.

Example

Z.tail(3, df)
// returns a new dataframe with the last 3 lines of `df`

Parameters:

Name	Type	Description
`n`	Number	Number of rows to select from bottom of df
`dataframe`	df

Returns:

Zebras dataframe

Type: df

(static) toCSV(df, filepath) → {undefined}

Source:

toCSV.js, line 18

Synchronously writes a dataframe to a CSV file.

Example

Z.toCSV(filepath, df)

Parameters:

Name	Type	Description
`df`	df	Zebras dataframe to write
`filepath`	String	File path for the CSV file to write

Returns:

Type: undefined

(static) unique(arr) → {Array}

Source:

unique.js, line 17

Get unique values in a series.

Example

const series = [7, 7, 2, 30, 30, 56, 75]
Z.unique(series)
// [7, 2, 30, 56, 75]

Parameters:

Name	Type	Description
`arr`	Array	Array of values

Returns:

Type: Array

(static) valueCounts(arr) → {Object}

Source:

valueCounts.js, line 17

Count number of occurences of each value in a series.

Example

const series = [7, 2, 30, 30, 56, 75]
Z.valueCounts(series)
// {"2": 1, "30": 2, "56": 1, "7": 1, "75": 1}

Parameters:

Name	Type	Description
`arr`	Array	Array of values

Returns:

Type: Object