DataFrame

• pandas-like data-frame library
• Column built on typed arrays
• tries to be memory efficient
• extensions to arrays
• great for tabular data
• reads data in various formats: CSV, JSON, array of rows, array of columns, JS object, JS Map
• work in progress

See JSDoc-generated API docs see docs.

For more human-friendly docs keep reading.

Installation

npm install --save dataf

Human-Friendly API

Preliminaries

Run the node REPL.

node

Import the library (make sure it's installed).

constDF=require('dataf')

Toy Datasets (shipped with the library)

DF.dataSets

['alcohol.csv',// alcohol consumption math students'countries.csv',// geopolitical data for all countries'diabetes.csv','food.csv',// food choices'got.csv',// game of thrones deaths'happiness.csv',// world happiness 2017'iris.csv','mushrooms.csv','pokemon.csv',// stats for all from all generations'superheros.csv'...]

All have been placed in the public domain.

Load the Iris DataSet

letiris=newDF('iris')// use `let`, you will be re-assigning a lot

NOTE

the lookup of datasets happens by recursive search of each directory in DF.opts.DATASETS. You can use this and simply df.opts.DATASETS.push(yourDir) and your dataset will be discoverable. You don't need to specify the extension. .csv and .json extensions are appended if not provided (e.g. iris is actually stored in iris.csv). Dataset files must be in either CSV or JSON formats.

Selecting / Slicing Rows

Head / Tail

iris.head().print()// .head(20) for the first 20 rows// .tail() for last rows

# u8 Id f32 SepalLe... f32 SepalWi... f32 PetalLe... f32 PetalWi... s Species
- ----- -------------- -------------- -------------- -------------- -------------
0 1 5.09 3.50 1.39 0.20 Iris-setos...
1 2 4.90 3.00 1.39 0.20 Iris-setos...
2 3 4.69 3.20 1.29 0.20 Iris-setos...
3 4 4.59 3.09 1.50 0.20 Iris-setos...
4 5 5.00 3.59 1.39 0.20 Iris-setos...
- ----- -------------- -------------- -------------- -------------- -------------
5B 20B 20B 20B 20B NaN

NOTE the data types next to column names and memory indicators for every column.

Slicing

iris.slice(10,20).print()// can be .slice(5) for .slice(5, end)

# u8 Id f32 SepalLe... f32 SepalWi... f32 PetalLe... f32 PetalWi... s Species
- ----- -------------- -------------- -------------- -------------- -------------
0 11 5.40 3.70 1.50 0.20 Iris-setos...
1 12 4.80 3.40 1.60 0.20 Iris-setos...
- ----- -------------- -------------- -------------- -------------- -------------
2B 8B 8B 8B 8B NaN

NOTE the library will try to compute the width of each column

Getting a Column (Column)

We know that there are 6 columns (try running iris.nCols). To get all column names run:

iris.colNames.print(100)// make sure it prints all

Columns[Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species]

If you want to extract a column (Column, see the Column API below) from a data frame try:

iris.Species.print(5)// last column

Column s[Iris-setosa, Iris-setosa, Iris-setosa, Iris-setosa, Iris-setosa, ... 145 more]

Here s stands for STRING. You may also see: f64, f32, i32, i16, i8, u32, u16 and u8.

NOTE some column names will have spaces or/and will clash with the API and you will have to use iris.col(2) OR iris.col('SepalWidthCm'). Columns can always be referred to by their index OR name.

Selecting columns (Data Frame)

Suppose you only want the first couple of columns:

iris.select(0,1,-2).print(5)// the 1st, 2nd and the 2nd to last

This show the first 5 rows of the new data frame with only: Id, SepalLength and PetalWidth.

 # u8 Id f32 SepalLe... f32 PetalWi...
--- ----- -------------- --------------
0 1 5.09 0.20
1 2 4.90 0.20
2 3 4.69 0.20
3 4 4.59 0.20
4 5 5.00 0.20
... ... (145 more) ...
--- ----- -------------- --------------
150B 600B 600B

If you want to select a range of column: e.g. from the 1st to the 3rd try:

iris.sliceCols(0,2).print(3)

 # u8 Id f32 SepalLe... f32 SepalWi...
--- ----- -------------- --------------
0 1 5.09 3.50
1 2 4.90 3.00
2 3 4.69 3.20
... ... (147 more) ...
--- ----- -------------- --------------
150B 600B 600B

This is the same as:

iris.sliceCols('Id','SepalWidthCm').print(3)

Only Numeric Columns (remove string columns)

iris.numeric// all BUT the "Species" column (getter)

Only String Columns (remove numeric columns)

iris.nominal// just the "Species" column (getter)

Removing (Dropping) Columns

If you want to remove the 2nd and the second to last columns:

iris.drop(1,-2).print(3)

 # f32 SepalLe... f32 SepalWi... f32 PetalLe... s Species
--- -------------- -------------- -------------- -------------
0 5.09 3.50 1.39 Iris-setos...
1 4.90 3.00 1.39 Iris-setos...
2 4.69 3.20 1.29 Iris-setos...
... ... (147 more) ... ...
--- -------------- -------------- -------------- -------------
600B 600B 600B NaN

NOTE those operations are not in-place meaning dropping produces a new data frame without specified columns.

Selecting Rows

With Specific Value

Signature: iris.where(val, colId, op). Where op is one of {"=" (default), ">", "<", ">=", "<="}.

iris.Species[0]'Iris-setosa'iris.where('Iris-setosa',-1)// -1 for last col// ... DataFrame with subset of rows with just Iris-setosa

Matching Predicate (Test)

Signature: iris.filter(rowTestFunc).
Signature: iris.filter(valTestFunc, colId).

iris.where('Iris-setosa',-1)// ORiris.filter(species=>species==='Iris-setosa',-1)

Accessing Values (preferred way)

iris.val(10,'Species')// val(rowIdx, colId)'Iris-setosa'

Accessing Rows

One Row

Accessing a single row:

constrow=iris.row(20)// 21st row[21,5.400000095367432,3.4000000953674316,1.7000000476837158,0.20000000298023224,'Iris-setosa']

Iterating Over Values in a Single Row

constirow=iris.irow(10);Array.from(irow)[5.400000095367432,3.700000047683716,1.5,0.20000000298023224,'Iris-setosa']

Iterating Over Rows

If you want to iterate over all the rows (not efficient) try:

constrowIt=iris.slice(0,3).rowsIter// (getter)for(constrofrowIt){console.log(r)}// you may also iterate over the dataframe (equivalent method)for(constrofiris){console.log(r)}[1,5.099999904632568,3.5,1.399999976158142,0.20000000298023224,'Iris-setosa'][2,4.900000095367432,3,1.399999976158142,0.20000000298023224,'Iris-setosa'][3,4.699999809265137,3.200000047683716,1.2999999523162842,0.20000000298023224,'Iris-setosa']

Manipulation

In-Place Modification of Columns

Just assign:

// 2nd coliris[1]=iris[1].map(s=>s>=5 ? 0 : 1)// equivalent to:iris.SepalLengthCm=iris.SepalLengthCm.map(s=>s>=5 ? 0 : 1)

NOTE this might have to be dataset[' Col With Spaces'] = newCol.

Mapping Columns

Apply function to each element is selected column:

iris.map(-1,label=>{// there is an easier way to do this (see `DataFrame.labelEncode()`)if(label==='Iris-versi'){return0;}elseif(label==='Iris-virgi'){return1;}else{return2;}});

NOTE use iris.map(null, f) to apply to all columns.

Mapping Shortcuts

null means it will be applied to all.

• .trunc(colId | null)
• .floor(colId | null)
• .ceil(colId | null)
• .round(colId | null)
• .abs(colId | null)
• .sqrt(colId | null)
• .cbrt(colId | null)
• .square(colId | null)
• .cube(colId | null)
• .add(colId | null, n)
• .sub(colId | null, n)
• .mul(colId | null, n)
• .div(colId | null, n)

It's smart enough to know not to apply them to string columns if they don't make sense (e.g. .abs()). String columns are ignored.

Rename Columns

iris.rename(0,'First').rename(-2,'Second to Last')// or justiris.rename(0,'First',-2,'Second to Last')

Merging Data Frames

iris.concat(iris)// append all rows (axis 0)iris.concat(iris,1)// append all columns (axis 1)

NOTE this library will manage duplicate column names.

iris.concat(iris,1).colNames['Id','SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm','Species','Id2','SepalLengthCm2','SepalWidthCm2','PetalLengthCm2','PetalWidthCm2','Species2']

Appending a Column

iris.appendCol(iris.Id,'Id2')// .appendCol(col, colName)

Shuffle, Reverse

iris.shuffle()iris.reverse()

Both are safe in that the won't modify in place.

Sort

Signature: iris.sort(colId, 'asc' (default) | 'des' ).

iris.sort('SepalWidthCm')// default is iris.sort(colId, 'asc')

iris.sort('SepalWidthCm','des')// descending sort

NOTE

constants such as 'des' are defined in the constants module which you can import:

const{ DataType, LoggingLevel, PrintingPreset, SortingOrder, What }=require('dataf/constants')

Statistics & Math

Aggregate operations, each is DataFrame -> DataFrame

MATH

• .add()
• .sub()
• .mul()
• .div()

STATS

• .min()
• .max()
• .range()
• .mean()
• .var() variance
• .stdev() standard deviation
• .median()
• .Q3()
• .Q1()
• .IQR() inter-quartile range
• .skewness()
• .kurtosis()
• .mad() mean absolute deviation

E.g.:

iris.IQR()

# s column f32 IQR
- ------------- -------
0 Id 75.00
1 SepalLengt... 1.30
2 SepalWidth... 0.50
3 PetalLengt... 3.50
4 PetalWidth... 1.50
- ------------- -------
NaN 20B

Sample (get a random subset of rows)

Signatures:

Signature	Description
`.sample(0.15)`	for random 15% of the dataset
`iris.sample(30)`	for random 30 sample of the dataset
`iris.sample(0.5, true)`	with replacement (default)
`iris.sample(100, false)`	**without** replacement

Summary

iris.summary()// this will produce a summary data frame with info for every column

# s column s dtype f32 min f32 max f32 range f32 mean f32 stdev
- ------------- ------- ------- ------- --------- -------- ---------
0 Id u8 1.00 150.00 149.00 75.50 43.30
1 SepalLengt... f32 4.30 7.90 3.59 5.84 0.82
2 SepalWidth... f32 2.00 4.40 2.40 3.05 0.43
3 PetalLengt... f32 1.00 6.90 5.90 3.75 1.75
4 PetalWidth... f32 0.10 2.50 2.40 1.19 0.76
5 Species s NaN NaN NaN NaN NaN
- ------------- ------- ------- ------- --------- -------- ---------
NaN NaN 24B 24B 24B 24B 24B

Aggregates

Counts (of unique values)

This is particularly useful for nominal / discrete attributes that take on a small amount of values. E.g. Gender is one of {M, F} or Salary is one of {Low, Med, High}.

iris.counts(-1)// for the last column// iris.ps(-1) // for normalized values

# s Species u8 count
- ------------- --------
0 Iris-setos... 50
1 Iris-versi... 50
2 Iris-virgi... 50
- ------------- --------
NaN 3B

Correlations (A Matrix Operation)

For a correlation of each column with each other column (matrix):

iris.corr(/* `false` to *not* print the first column */)

# s column f64 Id f64 SepalLe... f64 SepalWi... f64 PetalLe... f64 PetalWi...
- ------------- ------ -------------- -------------- -------------- --------------
0 Id 1.00 0.71 -0.39 0.88 0.89
1 SepalLengt... 0.71 1.00 -0.10 0.87 0.81
2 SepalWidth... -0.39 -0.10 1.00 -0.42 -0.35
3 PetalLengt... 0.88 0.87 -0.42 1.00 0.96
4 PetalWidth... 0.89 0.81 -0.35 0.96 1.00
- ------------- ------ -------------- -------------- -------------- --------------
NaN 40B 40B 40B 40B 40B

Other matrix operations:

• iris.cov()
• iris.dot()
• iri.distance()

Pre-Processing

Remove NaN / Infinity / other

To remove all rows that have some value:

// from all cols i.e. remove all rows where any of the value is NaNiris.removeAll(NaN)// from 1th and 3rd cols and from col 'PetalLengthCm'iris.removeAll(NaN,0,2,'PetalLengthCm')

Discretize (Bin)

iris.kBins('SepalLengthCm');// 5 bins for this columniris.kBins(null);// 3 bins for all columnsiris.kBins(2)// 3rd (2 idx) col, 3 bins.col(2)// select ONLY 3rd column (index is 2), which is of type Column.print(10)

Column u8[2, 1, 2, 1, 2, 2, 2, 2, 1, 1, ... 40 more]

NOTE this is smart enough only to target numeric attributes so string columns will be ignored (no need to run .numeric).

Feature (Column) Selection

Feature selection (i.e. select best columns, by default uses "var" -- variance):

Signature: iris.nBest(n, metric) where metric is one of:

• "var"
• "stdev"
• "mean"
• "mad"
• "IQR"
• "median"
• "Q1"
• "Q3"
• "skewness"
• "min"
• "range"
• "max"

OR a function from Column (one column) to a number (Column -> Num).

iris.drop('Id')// `Id` column is not useful.numeric// select all numeric cols.nBest(2)// best 2 features using variance as score.print(3)// show first 3 rows// try: iris.drop('Id').numeric.nBest(2, 'mad').print(3)

 # f32 PetalLe... f32 SepalLe...
--- -------------- --------------
0 1.39 5.09
1 1.39 4.90
2 1.29 4.69
... (147 more) ...
--- -------------- --------------
600B 600B

Normalization

Using .nBest() in this way is naive and you might want to normalize (scale to the same range) the values:

iris.drop('Id')// `Id` column is not useful.numeric// select all numeric cols.normalize()// bring them to range [0, 1].nBest(2)// best 2 features using variance as score.print(3)

As you can see you might get different results:

 # f32 PetalWi... f32 PetalLe...
--- -------------- --------------
0 0.04 0.06
1 0.04 0.06
2 0.04 0.05
... (147 more) ...
--- -------------- --------------
600B 600B

Label Encoding

It's a bit awkward to constantly have to drop the 'Species' column because it's a string column...

You can easily convert it to a numeric column:

From:

iris.select(-2,-1).print(48,52)

 # f32 PetalWi... s Species
--- -------------- -------------
... (48 more) ...
48 0.20 Iris-setos...
49 0.20 Iris-setos...
50 1.39 Iris-versi...
51 1.50 Iris-versi...
... (98 more) ...
--- -------------- -------------
600B NaN

To:

iris.select(-2,-1).labelEncode().print(48,52)

 # f32 PetalWi... u8 Species
--- -------------- ----------
... (48 more) ...
48 0.20 0
49 0.20 0
50 1.39 1
51 1.50 1
... (98 more) ...
--- -------------- ----------
600B 150B

By default all string columns will be label encoded (numeric columns will be ignored). You may specify the colIds e.g. df.labelEncode(0, -3, 'Target').

One-Hot Encoding

Signature: iris.oneHot(colId)

// expects the column to be unsigned intiris.labelEncode('Species').oneHot('Species').print(48,52)

 # u8 0 u8 1 u8 2
--- ---- --------- ----
... ... (48 more) ...
48 1 0 0
49 1 0 0
50 0 1 0
51 0 1 0
... ... (98 more) ...
--- ---- --------- ----
150B 150B 150B

Clipping (ensuring value is in range)

For demonstration let's make a 1-col data frame:

iris.select(1).print(3)

# f32 SepalLe...
- --------------
0 5.09
1 4.90
2 4.69
- --------------
12B

To clip:

iris.select(1).clip(null,4.88,5)// null == all cols.print(3)

 # f32 SepalLe...
--- --------------
0 5.00
1 4.90
2 4.88
... (147 more)
--- --------------
600B

Notice that 5.09 got clipped to 5.00!

Outliers

To remove outliers (outside of Q1 to Q3) run:

iris.dropOutliers()// consider all colsiris.dropOutliers(0,-2)// consider just 1st and second to last cols

Advanced Human-Friendly API

Data Types

Data Type	String
string	s
32-bit signed integer	i32
16-bit signed integer	i16
8-bit signed integer	i8
32-bit unsigned integer	u32
16-bit unsigned integer	u16
8-bit unsigned integer	u8
32-bit float (single precision)	f32
64-bit float (double precision)	f64

If you want to get the data type for all columns try:

iris.dtypes['u8','f32','f32','f32','f32','s']// read-only

Or for a prettier output make a meta data frame with information about the previous data frame!

iris.dtype()// note difference between `iris.dtype()` (method) and `iris.dtypes` (getter)

SIDENOTE.dtype() is an aggregate! This means it produces a data frame from applying a Column -> * operation to all columns.

# s column s dtype
- ------------- -------
0 Id u8
1 SepalLengt... f32
2 SepalWidth... f32
3 PetalLengt... f32
4 PetalWidth... f32
5 Species s
- ------------- -------
NaN NaN

You can force-cast columns:

iris.cast(2,'u8')// passing `null` instead of `2` would run cast on all cols

Down-Casting

You can also run iris.downcast() and let the library figure out the most efficient data type for each column so that data is not lost. This is especially useful after truncating (floats are converted to integers).

Default:

# u8 Id f32 SepalLe... f32 SepalWi... f32 PetalLe... f32 PetalWi... s Species
- ----- -------------- -------------- -------------- -------------- -------------
0 1 5.09 3.50 1.39 0.20 Iris-setos...
1 2 4.90 3.00 1.39 0.20 Iris-setos...
2 3 4.69 3.20 1.29 0.20 Iris-setos...
- ----- -------------- -------------- -------------- -------------- -------------
3B 12B 12B 12B 12B NaN

Now see how much memory can be saved:

iris.trunc().downcast().head(3)

# u8 Id u8 SepalLe... u8 SepalWi... u8 PetalLe... u8 PetalWi... s Species
- ----- ------------- ------------- ------------- ------------- -------------
0 1 5 3 1 0 Iris-setos...
1 2 4 3 1 0 Iris-setos...
2 3 4 3 1 0 Iris-setos...
- ----- ------------- ------------- ------------- ------------- -------------
3B 3B 3B 3B 3B NaN

Memory

Although this information is by default printed, you may produce a data frame with information about memory consumption of each column.

iris.memory()

# s column u16 memory
- ------------- ----------
0 Id 150
1 SepalLengt... 600
2 SepalWidth... 600
3 PetalLengt... 600
4 PetalWidth... 600
- ------------- ----------
NaN 10B

NOTE it's not calculated for string columns (notice that "Species" is missing).

To figure out how much your data frame is taking in total try:

iris.memory().col(-1).add()2550// bytes

Copies

Deep Copy

If for some reason you need a deep-copy try (expensive):

iris.clone()

Shallow Copy

Shallow copies are cheap:

iris.copy()

Generalized Row Slicing

Sometimes you may want to get rows from 10th to 20th and e.g. 50th to 65th:

// [F, T],[F, T] // FROM - TOiris.slice(9,19,49,64)

Generalized Column Slicing

The same applies to column slices:

iris.sliceCols(-3,-2,0,2)

# f32 PetalLe... f32 PetalWi... u8 Id f32 SepalLe... f32 SepalWi...
- -------------- -------------- ----- -------------- --------------
0 1.39 0.20 1 5.09 3.50
1 1.39 0.20 2 4.90 3.00
2 1.29 0.20 3 4.69 3.20
- -------------- -------------- ----- -------------- --------------
12B 12B 3B 12B 12B

Exporting

HTML

iris.head(2).toHTML(/* optional file name */)

<table><tr><th>Id</th><th>SepalLengthCm</th><th>SepalWidthCm</th><th>PetalLengthCm</th><th>PetalWidthCm</th><th>Species</th></tr><tr><td>1</td><td>5.099999904632568</td><td>3.5</td><td>1.399999976158142</td><td>0.20000000298023224</td><td>Iris-setosa</td></tr><tr><td>2</td><td>4.900000095367432</td><td>3</td><td>1.399999976158142</td><td>0.20000000298023224</td><td>Iris-setosa</td></tr></table>

JSON

iris.head(2).toJSON(/* optional file name */)

{
"Id": [1, 2],
"SepalLengthCm": [5.099999904632568, 4.900000095367432],
"SepalWidthCm": [3.5, 3],
"PetalLengthCm": [1.399999976158142, 1.399999976158142],
"PetalWidthCm": [0.20000000298023224, 0.20000000298023224],
"Species": ["Iris-setosa", "Iris-setosa"]
}

CSV

iris.head(2).toCSV(/* optional file name */)

Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species1,5.099999904632568,3.5,1.399999976158142,0.20000000298023224,Iris-setosa2,4.900000095367432,3,1.399999976158142,0.20000000298023224,Iris-setosa

SQL Table

iris.head(2).toSQLTableDef('MyIrisTable',/* optional file name */)

CREATETABLEIF NOT EXISTS MyIrisTable (
Id INT,
SepalLengthCm REAL,
SepalWidthCm REAL,
PetalLengthCm REAL,
PetalWidthCm REAL,
Species TEXT
)

SQL Updates

iris.toSQLUpdates('MyIrisTable',/* optional file name */)

UPDATE MyIrisTable SET Id =1, SepalLengthCm =5.099999904632568, SepalWidthCm =3.5, PetalLengthCm =1.399999976158142, PetalWidthCm =0.20000000298023224, Species = Iris-setosa;
UPDATE MyIrisTable SET Id =2, SepalLengthCm =4.900000095367432, SepalWidthCm =3, PetalLengthCm =1.399999976158142, PetalWidthCm =0.20000000298023224, Species = Iris-setosa;
UPDATE MyIrisTable SET Id =3, SepalLengthCm =4.699999809265137, SepalWidthCm =3.200000047683716, PetalLengthCm =1.2999999523162842, PetalWidthCm =0.20000000298023224, Species = Iris-setosa;
UPDATE MyIrisTable SET Id =4, SepalLengthCm =4.599999904632568, SepalWidthCm =3.0999999046325684, PetalLengthCm =1.5, PetalWidthCm =0.20000000298023224, Species = Iris-setosa;

SQL Inserts

iris.head(4).toSQLInserts('MyIrisTable',/* optional file name */)

INSERT INTO MyIrisTable (Id, SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, Species) VALUES (1, 5.099999904632568, 3.5, 1.399999976158142, 0.20000000298023224, Iris-setosa);
INSERT INTO MyIrisTable (Id, SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, Species) VALUES (2, 4.900000095367432, 3, 1.399999976158142, 0.20000000298023224, Iris-setosa);
INSERT INTO MyIrisTable (Id, SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, Species) VALUES (3, 4.699999809265137, 3.200000047683716, 1.2999999523162842, 0.20000000298023224, Iris-setosa);
INSERT INTO MyIrisTable (Id, SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, Species) VALUES (4, 4.599999904632568, 3.0999999046325684, 1.5, 0.20000000298023224, Iris-setosa);

Settings

Option	Default	Sensible Alternatives	Description
`PRINT_PREC`	2	3, 4, 5, 6, 7, 8	how many float digits after the radix point to print
`FLOAT_PREC`	32	64	-
`MIN_COL_WIDTH`	10	12, 15, 20	constrain width of columns when printing
`HEAD_LEN`	5	7, 10, 20	by default print this number of rows when running `.head()`, `.tail()` etc.

To set:

DF.opts.OPTION=VALUE;

More Advanced Examples

Fix Column Names With Spaces

constargs=df.colNames// replace spaces with '_'.map(c=>[c,c.replace(/\s+/,'_')])// flatten.reduce((pair1,pair2)=>pair1.concat(pair2),[]);df=df.rename(...args)

Matrix of Normalized Differences Between Means of Columns

This would normally take a lot of code:

iris.normalize().matrix((col1,col2)=>Math.abs(col1.mean()-col2.mean()),true,// show colstrue,// halves the computation time when f(c2, c1) == f(c1, c2)0)// saves computation on the diagonal, when f(c, c) == id

Save Memory

df=df.labelEncode()// string cols => unsigned int.kBins(null)// f64, f32, ... => unsigned int.downcast()// optimize// see memorydf.memory()// see dtypesdf.dtype()// megabytesB=df.memory()// mem for each col.add()// add up.val(0,1)// get totalMB=B/1e6

Column Human-Friendly API

TODO

Disclaimer

1. I am not a statistician
2. Unit tests for DataFrame are not done yet
3. Alpha-stage
4. I would not use it in production (yet)
5. This isn't supposed to be an exact copy of pandas
6. In some places it's not efficient
7. Date columns / mixed data types not supported. Every column must be either numeric OR string. A single DataFrame may have a combination of numeric and string columns.
8. I am a student.

License

MIT