Construct a DataFrame from the columns given by cols with the index
generated by colnames. A DataFrame inherits from
Associative{Any,Any}, so Associative operations should work. Columns
are vector-like objects. Normally these are AbstractDataVector's (DataVector's
or PooledDataVector's), but they can also (currently) include standard
Julia Vectors.
Construct a DataFrame from the columns given by cols with default
column names.
An empty DataFrame.
A shallow copy of df. Columns are referenced, not copied.
A deep copy of df. Copies of each column are made.
A new DataFrame with nrow rows and the same column names and types as df.
Same meanings as for Arrays.
Same meanings as Associative operations. keys are column names;
values are column contents.
Methods to iterate over columns.
Number of columns in df.
Number of rows in df.
Number of columns in df.
Whether the number of columns equals zero.
First i rows of df. Defaults to 6.
Last i rows of df. Defaults to 6.
Standard pretty-printer of df. Called by print() and the REPL.
Show the structure of df. Like R's str.
Show a description of each column of df.
A Vector{Bool} of indexes of complete cases in df (rows with no
NA's).
A Vector{Bool} of indexes indicating rows that are duplicates of prior rows.
DataFrame with unique rows in df.
DataFrames are indexed like a Matrix and like an Associative. Columns
may be indexed by column name. Rows do not have names. Referencing
with one argument normally indexes by columns: df["col"],
df[["col1","col3"]] or df[i]. With two arguments, rows and columns
are selected. Indexing along rows works like Matrix indexing. Indexing
along columns works like Matrix indexing with the addition of column
name access.
Returns a subset of the columns of df as specified by ind, which
may be an Int, a Range, a Vector{Int}, ByteString, or
Vector{ByteString}. Columns are referenced, not copied. For a
single-element ind, the column by itself is returned.
Returns a subset of df as specified by irow and icol. irow may
be an Int, a Range, or a Vector{Int}. icol may be an Int, a
Range, or a Vector{Int}, ByteString, or, ByteString, or
Vector{ByteString}. For a single-element ind, the column subset by
itself is returned.
Returns the column Index for df.
See the Indexing section for these operations on column indexes.
The column names as an Array{ByteString}
Replace or add a new column with name colname and contents newcol.
Arrays are converted to DataVector's. Values are recycled to match the
number of rows in df.
Insert a column of name name and with contents item into df at
position index.
Insert columns of df2 into df1.
Delete columns in df at positions given by cols (noted with any
means that columns can be referenced).
Nondestructive version. Return a DataFrame based on the columns in
df after deleting columns specified by cols.
Delete rows at positions specified by inds from the given DataFrame.
Concatenate columns. Duplicated column names are adjusted.
Concatenate rows.
Return a DataFrame from file filename. Options o include
colnames ("true", "false", or "check" (the default)) and
poolstrings ("check" (default) or "never").
Evaluate expression ex with the columns in df.
Return a copy of df after evaluating expression ex with the
columns in df.
Modify df by evaluating expression ex with the columns in df.
Return a new DataFrame based on evaluating expression ex with the
columns in df. Often used for summarizing operations.
Apply f to each column of df, and return the results as an
Array{Any}.
Apply the function specified by Symbol s to each column of df, and
return the results as a DataFrame.
Return a SubDataFrame with references to rows and columns of df.
Return a SubDataFrame with references to rows and columns of df.
Referencing should work the same as DataFrames.
Return a GroupedDataFrame based on unique groupings indicated by the
columns with one or more names given in cols.
Methods to iterate over GroupedDataFrame groupings.
Reference a particular grouping. Referencing returns a SubDataFrame.
Evaluate expression ex with the columns in gd in each grouping.
Return a DataFrame with the results of evaluating expression ex with
the columns in gd in each grouping.
Sweeps along groups and applies based_on to each group. Returns a
DataFrame.
Apply f to each grouping of gd and return the results in an Array.
Apply f to each column in each grouping of gd, and return the
results as an Array{Any}.
Apply the function specified by Symbol s to each column of in each
grouping of gd, and return the results as a DataFrame.
Return a DataFrame with the results of grouping on cols and
colwise evaluation based on s. Equivalent to colwise(groupby(df,
cols), s).
Return a DataFrame with the results of grouping on cols and
evaluation of e in each grouping. Equivalent to based_on(groupby(df,
cols), e).
For conversion from wide to long format. Returns a DataFrame with
stacked columns indicated by cols. The result has column "key"
with column names from df and column "value" with the values from
df. Columns in df not included in cols are duplicated along the
stack.
For conversion from long to wide format. Returns a DataFrame. ikey
indicates the key column--unique values in column ikey will be
column names in the result. ivalue indicates the value column.
irefkey is the column with a unique identifier for that . Columns
not given by ikey, ivalue, or irefkey are currently ignored.
Return the database join of df1 and df2 based on the column bycol.
Currently only a single merge key is supported. Supports jointype of
"inner" (the default), "left", "right", or "outer".
An Index with names s. An Index is like an Associative type. An
Index is used for column indexing of DataFrames. An Index maps
ByteStrings and Vector{ByteStrings} to Indices.
Normal meanings.
Delete the name s or name at position idx in x.
A Vector{ByteString} with the names of x.
Set names nm in x.
Replace names in x, by applying function f to each name,
by mapping old to new names with a dictionary (Associative), or using
from and to vectors.
This does the mapping from name(s) to Indices (positions). idx may
be ByteString, Vector{ByteString}, Int, Vector{Int}, Range{Int},
Vector{Bool}, AbstractDataVector{Bool}, or AbstractDataVector{Int}.
Add a group to idx with name newgroup that includes the names in
the vector names.
A Dict that maps the name of each group to the names in the group.
Set groups in idx based on the mapping given by gr.
Missing value behavior is implemented by instantiations of the AbstractDataVector
abstract type.
A constant indicating a missing value.
Return a Bool or Array{Bool} (if x is an AbstractDataVector)
that is true for elements with missing values.
Return a copy of x after removing missing values.
Return a copy of x after replacing missing values with val.
Return an object based on x such that future operations like mean
will not include missing values. This can be an iterator or other
object.
Return an object based on x such that future operations like mean
will replace NAs with val.
Return an NA value appropriate for the type of x.
Return an object like x filled with NA values with size dim.
Create a DataVector from x, with m optionally indicating which values
are NA. DataVector's are like Julia Vectors with support for NA's. x may
be any type of Vector.
Create a PooledDataVector from x, with m optionally indicating which
values are NA. PooledDataVector's contain a pool of values with references
to those values. This is useful in a similar manner to an R array of
factors.
All normal Vector operations including array referencing should work.
All NA-related methods are supported.
Returns a PooledDataVector with length equal to x that divides values in x
based on the divisions given by breaks.
Return a Formula object based on ex. Formulas are two-sided
expressions separated by ~, like :(y ~ w*x + z + i&v).
A ModelFrame.
A ModelMatrix based on mf, f and d, or ex and d.
Linear model results (type OLSResults) based on formula ex and df.