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Danfo.js Documentation
Getting Started
API reference
General Functions
Input/Output
Series
Creating a Series
Series.append
Series.cumSum
Series.cumMax
Series.cumProd
Series.cumMin
Series.str.split
Series.str.len
Series.str.join
Series.str.trim
Series.str.substring
Series.str.substr
Series.str.slice
Series.str.search
Series.str.repeat
Series.str.replace
Series.str.lastIndexOf
Series.str.indexOf
Series.str.includes
Series.str.endsWith
Series.str.startsWith
Series.str.concat
Series.str.charAt
Series.str.toUpperCase
Series.str.toLowerCase
Series.str.capitalize
Series.dt.seconds
Series.dt.minutes
Series.dt.dayOfMonth
Series.dt.monthName
Series.dt.hours
Series.dt.dayOfWeek
Series.dt.dayOfWeek
Series.dt.month
Series.dt.year
Series.argMax
Series.argMin
Series.argSort
Series.replace
Series.isNa
Series.fillNa
Series.dropNa
Series.dropDuplicates
Series.valueCounts
Series.nUnique
Series.unique
Series.abs
Series.ne
Series.eq
Series.ge
Series.le
Series.gt
Series.lt
Series.iloc
Series.loc
Series.at
Series.iat
Series.ndim
Series.shape
Series.dtype
Series.values
Series.tensor
Series.index
Series.apply
Series.map
Series.setIndex
Series.resetIndex
Series.describe
Series.copy
Series.sortValues
Series.var
Series.std
Series.round
Series.minimum
Series.maximum
Series.count
Series.sum
Series.max
Series.min
Series.mode
Series.median
Series.mean
Series.mod
Series.pow
Series.div
Series.mul
Series.sub
Series.add
Series.sample
Series.tail
Series.head
Series.and
Series.or
Dataframe
Configuration Options
Plotting
Groupby
User Guides
Building Data Driven Applications with Danfo.js - Book
Contributing Guide
Release Notes
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Series
One-dimensional ndarray with axis labels (including time series).
Series(data, {columns: [ Array ], dtypes: [ Array ], index: [Array]}) [source]

The index (axis labels) of the Series.
The Tensorflow tensor of the data backing this Series or Index.
Return Series as ndarray or ndarray-like depending on the dtype.
Return the dtype object of the underlying data.
Return a tuple of the shape of the underlying data.
Number of dimensions of the underlying data, by definition 1.
Return the number of elements in the underlying data.

Cast a Series object to a specified dtype
Make a copy of this object’s indices and data.

[`Series.loc`](series.loc.md)
Access a group of rows and columns by label(s) or a boolean array.
Purely integer-location based indexing for selection by position.

Return Addition of series and other, element-wise (binary operator add).
Return Subtraction of series and other, element-wise (binary operator sub).
Return Multiplication of series and other, element-wise (binary operator mul).
Return Floating division of series and other, element-wise (binary operator truediv).
Return Modulo of series and other, element-wise (binary operator mod).
Return Exponential power of series and other, element-wise (binary operator pow).
Round each value in a Series to the given number of decimals.
​Series.lt​
Return Less than of series and other, element-wise (binary operator lt).
​Series.gt​
Return Greater than of series and other, element-wise (binary operator gt).
​Series.le​
Return Less than or equal to of series and other, element-wise (binary operator le).
​Series.ge​
Return Greater than or equal to of series and other, element-wise (binary operator ge).
​Series.ne​
Return Not equal to of series and other, element-wise (binary operator ne).
​Series.eq​
Return Equal to of series and other, element-wise (binary operator eq).

Invoke function on values of Series.
Map values of Series according to input correspondence.

Return a Series with absolute numeric value of each element.
Compute correlation with other Series, excluding missing values.
Return number of non-NaN observations in the Series.
Return cumulative maximum over a DataFrame or Series axis.
Return cumulative minimum over a DataFrame or Series axis.
Return cumulative product over a DataFrame or Series axis.
Return cumulative sum over a DataFrame or Series axis.
Generate descriptive statistics.
Return the maximum of the values for the requested axis.
Return the mean of the values for the requested axis.
Return the median of the values for the requested axis.
Return the minimum of the values for the requested axis.
Return the mode(s) of the dataset.
Return sample standard deviation over requested axis.
Return the sum of the values for the requested axis.
Return unbiased variance over requested axis.
Return unique values of Series object.
Return number of unique elements in the object.
Return a Series containing counts of unique values.

Return Series with duplicate values removed.
Return the first n rows.
Generate a new DataFrame or Series with the index reset.
Return a random sample of items from an axis of object.
Return the last n rows.

Return a new Series with missing values removed.
Fill NaN values using the specified method.
Detect missing values.
Replace values given in to_replace with value.

​Series.or​
Returns the logical OR between Series and other. Supports element-wise operations and broadcasting.
Returns the logical AND between Series and other. Supports element-wise operations and broadcasting.

Return the integer indices that would sort the Series values.
Return int position of the smallest value in the Series.
Return int position of the largest value in the Series.
Sort by the values.

Danfo provides dtype-specific methods under various accessors. These are separate namespaces within Series that only apply to specific data types.
Data Type
Accessor
​Datetime​
dt
​String​
str

Series.dt can be used to access the values of the series as datetime and return several properties. These can be accessed like Series.dt.<property>.
Datetime methods
The year of the datetime.
Returns a numeric representation of the month. January=0 - December=11.
​
Returns the day of the week, in local time
The hours of the datetime.
The minutes of the datetime.
The seconds of the datetime.
Returns the name of the day, of the week, in local time
Returns the day of the month, in local time

Series.str can be used to access the values of the series as strings and apply several methods to it. These can be accessed like Series.str.<function/property>.
Capitalize the first character of each string
Converts all characters to uppercase.
Converts all characters to lowercase.
Returns the character at the specified index (position).
Joins two or more strings/arrays.
Checks whether a string begins with specified characters.
Checks whether a string ends with specified characters
Checks whether a string contains the specified string/characters.
Returns the position of the first found occurrence of a specified value in a string.
Returns the position of the last found occurrence of a specified value in a string.
Returns a new string with a specified number of copies of an existing string.
Searches a string for a specified value, or regular expression, and returns the position of the match.
Extracts a part of a string and returns a new string.
Splits a string into an array of substrings.
Extracts the characters from a string, beginning at a specified start position, and through the specified number of character.
Extracts the characters from a string, between two specified indices.
Counts the number of characters in each string.
Removes whitespace from both ends of a string.
Joins strings to specified value.
Replace each occurrence of pattern/regex in the Series/Index.

Series.plot is both a callable method and a namespace attribute for specific plotting methods of the form Series.plot.<kind>.
Vertical bar plot.
Make a box plot of the DataFrame columns.
Make a violin plot of the DataFrame columns.
Draw one histogram of the DataFrame’s columns.
Generate Kernel Density Estimate plot using Gaussian kernels.
Plot Series or DataFrame as lines.
Generate a pie plot.
Time series plots
​Table​
Display Series as Interactive table in a Div

Convert DataFrame or Series to CSV.
Convert DataFrame or Series to a JSON.
Series.toExcel
Convert DataFrame or Series to an excel file
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Creating a Series
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On this page
Attributes
Conversion
Indexing, iteration
Binary operator functions
Function application
Computations / descriptive stats
Reindexing / selection / label manipulation
Missing data handling
Logical Comparison
Reshaping, sorting
Accessors
Plotting
Serialization / IO / conversion