Data In H2O¶

A H2OFrame represents a 2D array of data where each column is uniformly typed.

The data may be local or it may be in an H2O cluster. The data are loaded from a CSV file or from a native python data structure, and is either a python-process-local file, a cluster-local file, or a list of H2OVec objects.

Loading Data From A CSV File¶

H2O’s parser supports data of various formats from multiple sources. The following formats are supported:

SVMLight
CSV (data may delimited by any of the 128 ASCII characters)
XLS

The following data sources are supported:

NFS / Local File / List of Files
HDFS
URL
A Directory (with many data files inside at the same level – no support for recursive import of data)
S3/S3N
Native Language Data Structure (c.f. the subsequent section)

>>> trainFrame = h2o.import_frame(path="hdfs://192.168.1.10/user/data/data_test.csv")
#or
>>> trainFrame = h2o.import_frame(path="~/data/data_test.csv")

Loading Data From A Python Object¶

To transfer the data that are stored in python data structures to H2O, use the H2OFrame constructor and the python_obj argument. If the python_obj argument is not None, then additional arguments are ignored.

The following types are permissible for python_obj:

tuple ()
list []
dict {}
collections.OrderedDict

The type of python_obj is inspected by performing an isinstance call. A ValueError will be raised if the type of python_obj is not one of the above types. For example, sets, byte arrays, and un-contained types are not permissible.

The subsequent sections discuss each data type in detail in terms of the “source” representation (the python object) and the “target” representation (the H2O object). Concretely, the topics of discussion will be on the following: Headers, Data Types, Number of Rows, Number of Columns, and Missing Values.

In the following documentation, H2OFrame and Frame will be used synonymously. Technically, an H2OFrame is the object-pointer that resides in the python VM and points to a Frame object inside of the H2O JVM. Similarly, H2OFrame, Frame, and H2O Frame all refer to the same kind of object. In general, though, the context is from the python VM, unless otherwise specified.

Loading A Python Tuple¶

Essentially, the tuple is an immutable list. This immutability does not map to the H2OFrame. So pythonistas beware!

The restrictions on what goes inside the tuple are fairly relaxed, but if they are not recognized, a ValueError displays.

A tuple is formatted as follows:

(i1, i2, i3, ..., iN)

Restrictions are mainly on the types of the individual iJ (1 <= J <= N).

If iJ is {} for some J, then a ValueError displays.

If iJ is a () (tuple) or [] (list), then iJ must be a () or [] for all J; otherwise a ValueError displays.

If iJ is a () or [], and if it is a nested () or nested [], then a ValueError displays. In other words, only a single level of nesting is valid and all internal arrays must be flat – H2O does not flatten them for you.

If iJ is not a () or [], then it must be of type string or a non-complex numeric type (float or int). In other words, if iJ is not a tuple, list, string, float, or int, for some J, then a ValueError displays.

Some examples of acceptable inputs are:

Example A: (1,2,3)
Example B: ((1,2,3), (4,5,6), (“cat”, “dog”))
Example C: ((1,2,3), [4,5,6], [“blue”, “yellow”], (321.239, “green”,”hi”))
Example D: (3284.123891, “dog”, 89)

Note that it is perfectly fine to mix () and [] within a tuple.

Headers, Columns, Rows, Data Types, and Missing Values:

The format of the H2OFrame is as follows:

column1 column2 column3 ... columnN

a11, a12, a13, ..., a1N

., ., ., ..., .

., ., ., ..., .

., ., ., ..., .

aM1, aM2, aM3, ..., aMN

It looks exactly like an MxN matrix with an additional header “row”. This header cannot be specified when loading data from a () (or from a [] but it is possible to specify a header with a python dictionary (see below for details).

Headers:

Since no header row can be specified for this case, H2O automatically generates a column header in the following format:

C1, C2, C3, ..., CN

Notably, these columns have a 1-based indexing (i.e. the 0th column is “C1”).

Rows, Columns, and Missing Data:

The shape of the H2OFrame is determined by two factors:

the number of arrays nested in the ()
the number of items in each array

If there are no nested arrays (as in Example A and Example D above), the resulting H2OFrame will have the following shape (rows x cols):

1 x len(tuple)

(i.e. a Frame with a single row).

If there are nested arrays (as in Example B and Example C above), then (given the rules stated above) the resulting H2OFrame will have ROWS equal to the number of arrays nested within and COLUMNS equal to the maximum sub-array:

max( [len(l) for l in tuple] ) x len(tuple)

Note that this addresses the issue with ragged sub-arrays by assuming that shorter sub-arrays will pad themselves with NA (missing values) at the end so that they become the correct length.

Because the Frame is uniformly typed, combining data types within a column may produce unexpected results. Please read up on the H2O parser for details on how a column type is determined for mixed-type columns.

Loading A Python List¶

The same principles that apply to tuples also apply to lists. Lists are mutable objects, so there is no semantic difference regarding mutability between an H2OFrame and a list (as there is for a tuple).

Additionally, a list [] is ordered the same way as a tuple (), with the data appearing within the brackets.

Loading A Python Dictionary Or collections.OrderedDict¶

Each entry in the {} is expected to represent a single column. Keys in the {} must be character strings following the pattern: ^[a-zA-Z_][a-zA-Z0-9_.]*$ without restriction on length. A valid column name may begin with any letter (capital or not) or an “_”, followed by any number of letters, digits, “_”s, or ”.”s.

Values in the {} may be a flat [], a flat (), or a single int, float, or string value. Nested [] and () will raise a ValueError. This is the only additional restriction on [] and () that applies in this context.

Note that the built-in dict does not provide any guarantees on ordering. This has implications on the order of columns in the eventual H2OFrame, since they may be written out of order from which they were initially put into the dict.

collections.OrderedDict preserves the order of the key-value pairs in which they were entered.

`H2OFrame`¶

class h2o.frame.H2OFrame(python_obj=None, local_fname=None, remote_fname=None, vecs=None, text_key=None)[source]¶

anyfactor()[source]¶

Returns:	Whether or not the frame has any factor columns

as_date(format)[source]¶: Return the column with all elements converted to millis since the epoch. :param format: The date time format string :return: H2OFrame

asnumeric()[source]¶

Returns:	A frame with factor columns converted to numbers (numeric columns untouched).

cbind(data)[source]¶

Parameters:	data – H2OFrame or H2OVec to cbind to self
Returns:	void

col_names()[source]¶

Retrieve the column names (one name per H2OVec) for this H2OFrame.

Returns:	A character list[] of column names.

ddply(cols, fun)[source]¶

Parameters:	cols – Column names used to control grouping fun – Function to execute on each group. Right now limited to textual Rapids expression
Returns:	New frame with 1 row per-group, of results from ‘fun’

describe()[source]¶

Generate an in-depth description of this H2OFrame.

The description is a tabular print of the type, min, max, sigma, number of zeros, and number of missing elements for each H2OVec in this H2OFrame.

Returns:	None (print to stdout)

dim()[source]¶

Get the number of rows and columns in the H2OFrame.

Returns:	The number of rows and columns in the H2OFrame as a list [rows, cols].

drop(i)[source]¶

Column selection via integer, string(name) returns a Vec Column selection via slice returns a subset Frame

Parameters:	i – Column to select
Returns:	Returns an H2OVec or H2OFrame.

filterNACols(frac=0.2)[source]¶: Filter columns with prportion of NAs >= frac. :param frac: Fraction of NAs in the column. :return: A list of column indices.

group_by(cols, a, order_by=None)[source]¶

GroupBy :param cols: The columns to group on. :param a: A dictionary of aggregates having the following shape: {“colname”:[aggregate, column, naMethod]} e.g.: {“bikes”:[“count”, 0, “all”]} The naMethod is one of “all”, “ignore”, or “rm”, which specifies how to handle NAs that appear in columns that are being aggregated.

“all” - include NAs “rm” - exclude NAs “ignore” - ignore NAs in aggregates, but count them (e.g. in denominators for mean, var, sd, etc.) :param order_by: A list of column names or indices on which to order the results. :return: The group by frame.

gsub(pattern, replacement, ignore_case=False)[source]¶: sub and gsub perform replacement of the first and all matches respectively. Of note, mutates the frame. :return: H2OFrame

head(rows=10, cols=200, **kwargs)[source]¶

Analgous to R’s head call on a data.frame. Display a digestible chunk of the H2OFrame starting from the beginning.

Parameters:	rows – Number of rows to display. cols – Number of columns to display. kwargs – Extra arguments passed from other methods.
Returns:	None

impute(column, method, combine_method, by, inplace)[source]¶

Impute a column in this H2OFrame.

Parameters:	column – The column to impute method – How to compute the imputation value. combine_method – For even samples and method=”median”, how to combine quantiles. by – Columns to group-by for computing imputation value per groups of columns. inplace – Impute inplace?
Returns:	the imputed frame.

insert_missing_values(fraction=0.1, seed=None)[source]¶

Inserting Missing Values to an H2OFrame This is primarily used for testing. Randomly replaces a user-specified fraction of entries in a H2O dataset with missing values. WARNING: This will modify the original dataset. Unless this is intended, this function should only be called on a subset of the original.

Parameters:	fraction – A number between 0 and 1 indicating the fraction of entries to replace with missing. seed – A random number used to select which entries to replace with missing values. Default of seed = -1 will

automatically generate a seed in H2O. :return: H2OFrame with missing values inserted

interaction(factors, pairwise, max_factors, min_occurrence, destination_frame=None)[source]¶: Categorical Interaction Feature Creation in H2O. Creates a frame in H2O with n-th order interaction features between categorical columns, as specified by the user. :param factors: factors Factor columns (either indices or column names). :param pairwise: Whether to create pairwise interactions between factors (otherwise create one higher-order interaction). Only applicable if there are 3 or more factors. :param max_factors: Max. number of factor levels in pair-wise interaction terms (if enforced, one extra catch-all factor will be made) :param min_occurrence: Min. occurrence threshold for factor levels in pair-wise interaction terms :param destination_frame: A string indicating the destination key. If empty, this will be auto-generated by H2O. :return: H2OFrame

keys()[source]¶

Retrieve the keys for each of the H2OVec objects comrpising this H2OFrame.

Returns:	the array of keys.

levels(col=0)[source]¶

Get the factor levels for this frame and the specified column index.

Parameters:	col – A column index in this H2OFrame.
Returns:	a list of strings that are the factor levels for the column.

logical_negation()[source]¶

max()[source]¶

Returns:	The maximum value of all frame entries

merge(other, allLeft=False, allRite=False)[source]¶

Merge two datasets based on common column names

Parameters:

other – Other dataset to merge. Must have at least one column in common with self, and all columns in common are used as the merge key. If you want to use only a subset of the columns in common, rename the other columns so the columns are unique in the merged result.
allLeft – If true, include all rows from the left/self frame
allRite – If true, include all rows from the right/other frame

Returns:

Original self frame enhanced with merged columns and rows

min()[source]¶

Returns:	The minimum value of all frame entries

names()[source]¶

Retrieve the column names (one name per H2OVec) for this H2OFrame.

Returns:	A character list[] of column names.

ncol()[source]¶

Get the number of columns in this H2OFrame.

Returns:	The number of columns in this H2OFrame.

nlevels(col=0)[source]¶

Get the number of factor levels for this frame and the specified column index.

Parameters:	col – A column index in this H2OFrame.
Returns:	an integer.

nrow()[source]¶

Get the number of rows in this H2OFrame.

Returns:	The number of rows in this dataset.

static py_tmp_key()[source]¶

Returns:	a unique h2o key obvious from python

quantile(prob=None, combine_method='interpolate')[source]¶

Compute quantiles over a given H2OFrame.

Parameters:	prob – A list of probabilties, default is [0.01,0.1,0.25,0.333,0.5,0.667,0.75,0.9,0.99]. You may provide any sequence of any length. combine_method – For even samples, how to combine quantiles. Should be one of [“interpolate”, “average”, “low”, “hi”]
Returns:	an H2OFrame containing the quantiles and probabilities.

rep_len(length_out)[source]¶: Replicates the values in data in the H2O backend :param length_out: the number of columns of the resulting H2OFrame :return: an H2OFrame

round(digits=0)[source]¶

Returns:	The rounded values in the H2OFrame to the specified number of decimal digits.

scale(center=True, scale=True)[source]¶: Centers and/or scales the columns of the H2OFrame :return: H2OFrame :param center: either a ‘logical’ value or numeric list of length equal to the number of columns of the H2OFrame :param scale: either a ‘logical’ value or numeric list of length equal to the number of columns of H2OFrame.

send_frame()[source]¶

Send a frame description to H2O, returns a key.

Returns:	A key

setLevel(level)[source]¶: A method to set all column values to one of the levels. :param level: The level at which the column will be set (a string) :return: An H2OFrame with all entries set to the desired level

setLevels(levels)[source]¶: Works on a single categorical vector. New domains must be aligned with the old domains. This call has SIDE EFFECTS and mutates the column in place (does not make a copy). :param level: The level at which the column will be set (a string) :param x: A single categorical column. :param levels: A list of strings specifying the new levels. The number of new levels must match the number of old levels. :return: None

setNames(names)[source]¶

Change the column names to names.

Parameters:	names – A list of strings equal to the number of columns in the H2OFrame.
Returns:	None. Rename the column names in this H2OFrame.

show()[source]¶

signif(digits=6)[source]¶

Returns:	The rounded values in the H2OFrame to the specified number of significant digits.

strsplit(pattern)[source]¶: Split the strings in the target column on the given pattern :return: H2OFrame

sub(pattern, replacement, ignore_case=False)[source]¶: sub and gsub perform replacement of the first and all matches respectively. Of note, mutates the frame. :return: H2OFrame

sum()[source]¶

Returns:	The sum of all frame entries

summary()[source]¶: Generate summary of the frame on a per-Vec basis. :return: None

table(data2=None)[source]¶

Returns:	a frame of the counts at each combination of factor levels

tail(rows=10, cols=200, **kwargs)[source]¶

Analgous to R’s tail call on a data.frame. Display a digestible chunk of the H2OFrame starting from the end.

Parameters:	rows – Number of rows to display. cols – Number of columns to display. kwargs – Extra arguments passed from other methods.
Returns:	None

tolower()[source]¶: Translate characters from upper to lower case for a particular column Of note, mutates the frame. :return: H2OFrame

toupper()[source]¶: Translate characters from lower to upper case for a particular column Of note, mutates the frame. :return: H2OFrame

transpose()[source]¶

Returns:	The transpose of the H2OFrame.

trim()[source]¶: Trim the edge-spaces in a column of strings (only operates on frame with one column) :return: H2OFrame

var()[source]¶

Returns:	The covariance matrix of the columns in this H2OFrame.

vecs()[source]¶

Retrieve the array of H2OVec objects comprising this H2OFrame.

Returns:	The array of H2OVec objects.

`H2OVec`¶

class h2o.frame.H2OVec(name, expr)[source]¶

A single column of data that is uniformly typed and possibly lazily computed.

anyfactor()[source]¶

Returns:	Same as isfactor for a vec

append(data)[source]¶

Append a value during CSV read, convert to float.

Parameters:	data – An element being appended to the end of this H2OVec
Returns:	void

as_date(format)[source]¶: Return the column with all elements converted to millis since the epoch. :param format: The date time format string :return: H2OVec

ascharacter()[source]¶

Returns:	A lazy Expr representing this vec converted to characters

asfactor()[source]¶

Returns:	A lazy Expr representing this vec converted to a factor

asnumeric()[source]¶

Returns:	A vec with factor columns converted to numbers.

cbind(data)[source]¶

Parameters:	data – H2OFrame or H2OVec
Returns:	new H2OFrame with data cbinded to the end

cut(breaks, labels=None, include_lowest=False, right=True, dig_lab=3)[source]¶

day()[source]¶

Returns:	Returns a new day column from a msec-since-Epoch column

dayOfWeek()[source]¶

Returns:	Returns a new Day-of-Week column from a msec-since-Epoch column

dim()[source]¶

Returns:	The length of the H2OVec

floor()[source]¶

Returns:	A lazy Expr representing the Math.floor() of this H2OVec.

get_expr()[source]¶

Helper method to obtain the expr object in self. Can also get it directly @ ._expr.

Returns:	the _expr member of this H2OVec

gsub(pattern, replacement, ignore_case=False)[source]¶: sub and gsub perform replacement of the first and all matches respectively. Of note, mutates the vec. :return: H2OFrame

hour()[source]¶

Returns:	Returns a new Hour-of-Day column from a msec-since-Epoch column

isfactor()[source]¶

Returns:	A lazy Expr representing the truth of whether or not this vec is a factor.

isna()[source]¶

Returns:	Returns a new boolean H2OVec.

key()[source]¶

Returns:	Return the H2O Key for this Vec.

levels()[source]¶

Get the factor levels for this vec.

Returns:	a list of strings that are the factor levels for the column.

logical_negation()[source]¶

match(table, nomatch=0)[source]¶: Makes a vector of the positions of (first) matches of its first argument in its second. :return: bit H2OVec

max()[source]¶

Returns:	Max value of the H2OVec elements.

mean()[source]¶

Returns:	Mean of this H2OVec.

median()[source]¶

Returns:	Median of this H2OVec.

min()[source]¶

Returns:	Min value of the H2OVec elements.

static mktime(year=1970, month=0, day=0, hour=0, minute=0, second=0, msec=0)[source]¶

All units are zero-based (including months and days). Missing year is 1970.

Returns:	Returns msec since the Epoch.

month()[source]¶

Returns:	Returns a new month column from a msec-since-Epoch column

name()[source]¶

Returns:	Return the column name for this H2OVec

static new_vecs(vecs=None, rows=-1)[source]¶

nlevels()[source]¶

Get the number of factor levels for this frame and the specified column index.

Returns:	an integer.

quantile(prob=None, combine_method='interpolate')[source]¶

Returns:	A lazy Expr representing the quantiles of this H2OVec.

rep_len(length_out)[source]¶: Replicates the values in data in the H2O backend :param length_out: the length of the resulting H2OVec :return: an H2OVec

round(digits=0)[source]¶

Returns:	The rounded values in the H2OFrame to the specified number of decimal digits.

row_select(vec)[source]¶

Boolean column select lookup

Parameters:	vec – An H2OVec.
Returns:	A new H2OVec.

runif(seed=None)[source]¶

Parameters:	seed – A random seed. If None, then one will be generated.
Returns:	A new H2OVec filled with doubles sampled uniformly from [0,1).

scale(center=True, scale=True)[source]¶: Centers and/or scales the column (H2OVec) :return: H2OVec :param center: either a ‘logical’ value or numeric value. :param scale: either a ‘logical’ value or numeric value.

sd()[source]¶

Returns:	Standard deviation of the H2OVec elements.

setLevel(level)[source]¶: A method to set all column values to one of the levels. :param level: The level at which the column will be set (a string). Must be a member of self.levels(). :return: An H2OVec with all entries set to the desired level

setLevels(levels)[source]¶: Works on a single categorical vector. New domains must be aligned with the old domains. This call has SIDE EFFECTS and mutates the column in place (does not make a copy). :param level: The level at which the column will be set (a string) :param x: A single categorical column. :param levels: A list of strings specifying the new levels. The number of new levels must match the number of old levels. :return: None

setName(name)[source]¶

Set the column name for this column.

Parameters:	name – The new name for this column.
Returns:	None

show(noprint=False)[source]¶

Pretty print this H2OVec, or return values up to an iterator on an enclosing Frame

Parameters:	noprint – A boolean stating whether to print or to return data.
Returns:	If noprint is False, then self._expr is returned.

signif(digits=6)[source]¶

Returns:	The rounded values in the H2OFrame to the specified number of significant digits.

strsplit(pattern)[source]¶: Split the strings in the target column on the given pattern :return: H2OFrame

sub(pattern, replacement, ignore_case=False)[source]¶: sub and gsub perform replacement of the first and all matches respectively. Of note, mutates the vec. :return: H2OFrame

sum()[source]¶

Returns:	Sum of the H2OVec elements.

summary()[source]¶

Compute the rollup data summary (min, max, mean, etc.)

Returns:	the summary from this Expr object