Z

Z

Source:

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

Methods

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

Source:

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:

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:

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:

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:

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:

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.deriveCol("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:

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:

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:

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) filter(func, dataframe) → {df}

Source:

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) gbCount(col, groupByObj) → {df}

Source:
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:
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:
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:
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:
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:
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:
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:

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:

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:

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:
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:

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 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 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 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:

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 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:

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:

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:

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:

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 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:

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:
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:
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:

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) readCSV(filepath) → {df}

Source:

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) rolling(func, n, arr) → {Array}

Source:

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 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:

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 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:

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:

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 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:
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:

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:

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:

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