A Map of the Stack
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Hi I'm Peadar Coyle
(Pronounced PAH-DER - I'm Irish)!!
@springcoil
All views are my own and do not represent any future, current or past employers.
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Who I've worked with
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Contributor to PyMC3 and other open source software
Author and Speaker at PyData and EuroSciPy
Check out 'Interviews with Data Scientists' - 24 data scientists interviewed - proceeds go to NumFOCUS
My current Adventure
Very excited to be joining Channel 4 in London from early April to work on customer segmentation and recommendation engines
Channel 4 is an award winning not-for-profit TV channel and digital channel.
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Version 3 is the way forward!
It's March 2016 I want to do Analytics in PyData
- It depends what you want to do
- This talk includes sample code
- What is new, what is not new, etc
- Very influenced by Rob Story
- I'll talk a bit more about Statistics and ML
- There'll be no bikes in my talk :)
Why use Python for Analytics anyway?
- Although Python is not very fast for things like webservers (Go would be better) it is **very** fast for things like HPC or Numerics.
- Because of C and Fortran (and maybe others like Rust or Theano in the future)
- (HT: Rob Story)
PyData strengths
- A rich and varied ecosystem
- Lots of activities, lots of different ways of attacking the same problems
- A great history of attacking hard problems by community effort
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And many others.
Open Source can't thrive without industrial and academic support
Thanks to these guys and girls...
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The PyData Stack
And many many more...
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Our wonderful ecosystem
- I'll talk about what is new in PyData
- I'll talk about what tools to use with different localities of datasets (in memory, out-of-core and distributed)
- I'll talk about our Statistical tools including PyMC3
- I'll talk about the future!!
What's new and useful in PyData
New in the core of the stack
-
It is impossible to talk about PyData without mentioning NumPy and Pandas
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Improvements throughout the stack
Matplotlib colours, Sympy new release, improvements in NumPy
New @ operator in NumPy
Assign, and pipe in Pandas
Pandas - assign
df = pd.DataFrame({'A': range(1, 11), 'B': np.random.randn(10)})
df.assign(ln_A_plus_1=lambda x: np.log(x.A)+1)
Creates a copy of the dataframe with a nice new column.
Really useful for percentages, logarithms etc - standard Financial Analysis and Data Analysis stuff.
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I have a data problem to solve
In practice grouping and counting things :)
Adult data set
data
age workclass fnlwgt education-categorical educ marital-status
0 39 State-gov 77516 Bachelors 13 Never-married
2 38 Private 215646 HS-grad 9 Divorced
3 53 Private 234721 11th 7 Married-civ-spouse
4 28 Private 338409 Bachelors 13 Married-civ-spouse
5 37 Private 284582 Masters 14 Married-civ-spouse
6 49 Private 160187 9th 5 Married-spouse-absent
Source UCI Adult data set, csv version here: http://pymc-devs.github.io/pymc3/Bayesian_LogReg/
I can only use standard library
I'm stuck on a restricted machine and I only have Python 2.6
(Example shamelessly stolen from Rob Story and adapted for my data set)
import csv
conversion_map = {
'age': int,
'workclass': str,
'fnlwgt': int,
'education-categorical': str,
'educ': int,
'occupation': str,
'sex': str,
'capital-gain': float,
'capital-loss': float,
'hours': int,
'native-country': str,
'income': str
}
Write a conversion map and use csv
Load the csv data source
def converter(type_map, row):
"""Yep, we need to roll our own type conversions."""
converted_row = {}
for col, val in row.items():
converter = type_map.get(col)
if converter:
converted_row[col] = converter(val)
else:
converted_row[col] = val
return converted_row
with open('adult.csv', 'r') as f:
reader = csv.DictReader(f)
adult2 = [converter(conversion_map, r) for r in reader]
How does it look
>>> adult2[:2]
[{'': '0',
'age': 39,
'capital-loss': 0.0,
'captial-gain': '2174',
'educ': 13,
'education-categorical': ' Bachelors',
'fnlwgt': 77516,
'hours': 40,
'income': ' <=50K',
'marital-status': ' Never-married',
'native-country': ' United-States',
'occupation': ' Adm-clerical',
'relationship': ' Not-in-family',
'sex': ' Male',
'workclass': ' State-gov'},
I want to get the maximum age in my dataset
def get_max_age():
max_age = 0
for row in adult2:
if row['age'] > 1 and row['age'] > max_age:
max_age = row['age']
return max_age
>>> get_max_age()
90
# Or you could do it like this generator expression
>>> max(row['age'] for row in adult2 if row['age'] > 1)
90
Let's say you wanted to group things
# defaultdict is awesome. defaultdict is awesome.
from collections import defaultdict
def grouper(grouping_col, seq):
"""People have definitely written a faster version than what I'm about to write
Thanks to Rob Story for this one"""
groups = defaultdict(lambda: defaultdict(list))
for row in seq:
group = groups[row[grouping_col]]
for k, v in row.items():
if k != grouping_col:
group[k].append(v)
return groups
>>> groups = grouper('occupation', adult2)
A natural question is the mean number of hours by occupation
summary = {}
for group, values in groups.items():
summary[group] = sum(values['hours']) / len(values['hours'])
>>> summary
{' ?': 31.90613130765057,
' Adm-clerical': 37.55835543766578,
' Armed-Forces': 40.666666666666664,
' Craft-repair': 42.30422054159551,
' Exec-managerial': 44.9877029021151,
' Farming-fishing': 46.989939637826964,
' Handlers-cleaners': 37.947445255474456,
' Machine-op-inspct': 40.755744255744254,
' Other-service': 34.70166919575114,
' Priv-house-serv': 32.88590604026846,
' Prof-specialty': 42.38671497584541,
' Protective-serv': 42.87057010785824,
' Sales': 40.78109589041096,
' Tech-support': 39.432112068965516,
' Transport-moving': 44.65623043206011}
Interlude: Itertools
It is common advice but it's worth being aware of itertools if you want to write something like this.
I wanna count things - in a functional way
PyToolz
PSA: PyToolz is awesome allows you to use functional programming techniques in Python.
http://toolz.readthedocs.org/en/latest/index.html
I want to make it faster - I'll use CyToolz
PyToolz example
#I wanna see the frequencies of ages in the dataset
>>> tz.frequencies([r['age'] for r in adult2])
# Toolz has currying!
#I want to count by all of the occupations with greater than 15 years of education
import toolz.curried as tzc
>>> tzc.pipe(adult2,
tzc.filter(lambda r: r['educ'] > 15),
tzc.map(lambda r: (r['occupation'],)),
tzc.countby(lambda r: r[0]),
dict)
{' ?': 15,
' Adm-clerical': 5,
' Craft-repair': 2,
' Exec-managerial': 55,
' Farming-fishing': 1,
' Machine-op-inspct': 1,
' Other-service': 1,
' Prof-specialty': 321,
' Sales': 8,
' Tech-support': 3,
' Transport-moving': 1}
Summary: Toolz
Toolz has some great virtues
-
Composability: They interoperate due to core data structures
-
Purity: They don't change their input or rely on external state
-
Lazy: Only evaluated when needed
- They also support serializability so they're easy to accelerate or parallelize
Interlude
Pandas
Not going to talk too much about Pandas in this talk.
It is fast becoming a stable and core member of the PyData stack
Really useful for indexed data like time series data or csv file data
Statsmodels and seaborn already consider it a core member of the stack
- I won't talk about Numpy either
- Xarray and Dask are all either dependent on it or strongly influenced by it
- Pandas depends on it
- Many other projects like Scipy depend on it
- The speed optimizations and the ability to release the GIL allow this to be very fast for modern hardware
- Recent improvements include the '@' operator making it a lot easier to write good linear algebra code in NumPy
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End of Interlude
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Labelled heterogenous data
NumPy arrays plus labels - excellent for 'Scientific data' :)
I have weather forecasting data in NetCDF - this is what you use
Xarray looks like this
arr = np.array([[1, 2, 3, 4],
[10, 20, 30, 40],
[100, 200, 300, 400]])
dim0_coords = ['a', 'b', 'c']
dim1_coords = ['foo', 'bar', 'baz', 'qux']
da = xray.DataArray(arr, [('x', dim0_coords), ('y', dim1_coords)])
da
da.loc['b']
There are plenty of examples in the notebooks
>> da[0:3]
<xarray.DataArray (x: 3, y: 4)>
array([[ 1, 2, 3, 4],
[ 10, 20, 30, 40],
[100, 200, 300, 400]])
Coordinates:
* x (x) <U1 'a' 'b' 'c'
* y (y) <U3 'foo' 'bar' 'baz' 'qux'
>>> da.dims
('x', 'y')
>> da.coords
Coordinates:
* x (x) <U1 'a' 'b' 'c'
* y (y) <U3 'foo' 'bar' 'baz' 'qux'
# Get a mean by label
>> da.mean(dim='y')
<xarray.DataArray (x: 3)>
array([ 2.5, 25. , 250. ])
Coordinates:
* x (x) <U1 'a' 'b' 'c'
- I want to disconnect or 'decouple' my expressions for computations from my backend
- Why can't I do Pandas like things on Postgresql?
- I have some data in CSV, some in HDF5 (PyTables), some in my SQL database
- I still run into in-memory problems
- I have bigger-than-I-can-RAM-data
- Getting a bigger machine is overkill
- Why are my analytical expressions tied to my data structure?
- Can I have expressions that work across data structure *and* storage?
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Blaze: An interface to query data on different storage systems
-
Dask: Parallel computing through task scheduling and blocked algorithms
-
Datashape: A data description language
- DyND: A C++ library for dynamic, multidimensional arrays
- Odo: Data migration between different storage systems
Blaze Ecosystem
We'll talk about Dask later
- We'll use Odo and Blaze in this demo
- Datashape and DyND are awesome but I won't talk about them
import blaze as bz
bz_adult = bz.symbol('adult2', bz.discover(adult))
>>> type(bz_adult)
blaze.expr.expressions.Symbol
>>> mean_age = bz.by(bz_adult.occupation,
price=bz_adult.age.mean())
>>> hours_count = bz.by(bz_adult[bz_adult.hours > 35].educ,
count=bz_adult.workclass.count())
# We haven't actually computed anything yet!
# Let's make Pandas compute it.
bz.compute(mean_age, adult)
# We have here the count of number of years of education
# by a certain filter of greater than 35 hours of work per week.
>>> bz.compute(hours_count, adult)
educ count
0 1 51
1 2 168
2 3 333
3 4 646
4 5 514
5 6 933
6 7 1175
7 8 433
Let's compute in Postgres!
# Blaze/Odo make it easy to move data between containers
# Note that we have an empty table already created
pg_datasource = bz.odo(adult,
"postgresql://peadarcoyle@localhost/pydata::adult2")
# Now we're going to use Postgres as our computation engine
result = bz.compute(hours_count, pg_datasource)
result
<sqlalchemy.sql.selectable.Select at 0x113ae4390; Select object>
# I don't want a selectable. I want a DataFrame
# odo again
bz.odo(bz.compute(hours_count, pg_datasource), pd.DataFrame)
educ count
0 8 433
1 16 413
2 15 576
3 4 646
4 1 51
Let's store in Bcolz (we'll see Bcolz and ctable- the storage format later)
import bcolz
>> %time bz.odo(adult, 'adult.bcolz')
CPU times: user 10.3 s, sys: 18.1 s, total: 28.4 s
Wall time: 28.8 s
Out[55]:
ctable((32561,), [('age', '<i8'), ('workclass', 'O'), ('fnlwgt', '<i8'),
('educationcategorical', 'O'), ('educ', '<i8'), ('maritalstatus', 'O'),
('occupation', 'O'), ('relationship', 'O'), ('sex', 'O'), ('captialgain', '<i8'),
('capitalloss', '<i8'), ('hours', '<i8'), ('nativecountry', 'O'), ('income', 'O')])
nbytes: 7.76 MB; cbytes: 43.54 MB; ratio: 0.18
cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
rootdir := 'adult.bcolz'
[ (39, ' State-gov', 77516, ' Bachelors', 13, ' Never-married', ' Adm-clerical',
' Not-in-family', ' Male', 2174, 0, 40, ' United-States', ' <=50K')
(50, ' Self-emp-not-inc', 83311, ' Bachelors', 13, ' Married-civ-spouse',
' Exec-managerial', ' Husband', ' Male', 0, 0, 13, ' United-States', ' <=50K')
(38, ' Private', 215646, ' HS-grad', 9, ' Divorced', ' Handlers-cleaners',
' Not-in-family', ' Male', 0, 0, 40, ' United-States', ' <=50K')
...,
(58, ' Private', 151910, ' HS-grad', 9, ' Widowed', ' Adm-clerical',
' Unmarried', ' Female', 0, 0, 40, ' United-States', ' <=50K')
(22, ' Private', 201490, ' HS-grad', 9, ' Never-married', ' Adm-clerical',
' Own-child', ' Male', 0, 0, 20, ' United-States', ' <=50K')
(52, ' Self-emp-inc', 287927, ' HS-grad', 9, ' Married-civ-spouse',
' Exec-managerial', ' Wife', ' Female', 15024, 0, 40, ' United-States', ' >50K')]
What else?
You can use any SQL supported by SQLAlchemy as your computation. It also supports Python lists, Spark DataFrames, MongoDB, Numpy arrays...
I want to maximize my speed of reading/writing on a single computer
-
bcolz is a columnar data store for fast data storage and retrieval with built-in high performance compression. It supports both in-memory and out-of-memory storage and operations. Cf. http://bcolz.blosc.org/.
Bcolz
- Fast IO and leverages Blosc for compression
- For certain problems like reading timeseries and doing analytics this can be useful
- We've seen immature projects like Castra built on top of it.
- We'll see more and more tools leveraging fast compression structures.
- Here I use POIWorld a dataset of 'Points of Interest' from OpenStreetMap
- Has some great synergies with binary formats like HDF5
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df_poiworld = pd.read_csv('POIWorld.csv', usecols=columns)
dc = bcolz.ctable.fromdataframe(df_poiworld)
dc
ctable((9140052,), [('name', 'O'), ('amenity', 'O'),
('Longitude', '<f8'), ('Latitude', '<f8')])
nbytes: 575.61 MB; cbytes: 3.00 GB; ratio: 0.19
cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[(nan, 'post_box', -0.20698000000000003, 51.9458753)
(nan, 'post_box', -0.268633, 51.938183)
(nan, 'post_box', -0.274278, 51.930209999999995) ...,
(nan, nan, -77.2697855, 39.24023820000001)
(nan, nan, -77.2777191, 39.237238399999995)
(nan, 'drinking_water', -5.8, nan)]
>>> dc.cols
age : carray((32561,), int64)
nbytes: 254.38 KB; cbytes: 256.00 KB; ratio: 0.99
cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[39 50 38 ..., 58 22 52]
workclass : carray((32561,), |S17)
nbytes: 540.56 KB; cbytes: 303.83 KB; ratio: 1.78
cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[b' State-gov' b' Self-emp-not-inc' b' Private' ..., b' Private'
b' Private' b' Self-emp-inc']
educ : carray((32561,), int64)
nbytes: 254.38 KB; cbytes: 256.00 KB; ratio: 0.99
cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[13 13 9 ..., 9 9 9]
occupation : carray((32561,), |S18)
nbytes: 572.36 KB; cbytes: 338.49 KB; ratio: 1.69
cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[b' Adm-clerical' b' Exec-managerial' b' Handlers-cleaners' ...,
b' Adm-clerical' b' Adm-clerical' b' Exec-managerial']
sex : carray((32561,), |S7)
nbytes: 222.58 KB; cbytes: 256.00 KB; ratio: 0.87
cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[b' Male' b' Male' b' Male' ..., b' Female' b' Male' b' Female']
hours : carray((32561,), int64)
nbytes: 254.38 KB; cbytes: 256.00 KB; ratio: 0.99
cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[40 13 40 ..., 40 20 40]
%%time
#Generate 1GB of data
>> N = 100000 * 1000
>> import bcolz
>> ct = bcolz.fromiter(((i, i ** 2) for i in range(N)),
dtype="i4, i8",
count=N,
cparams=bcolz.cparams(clevel=9))
CPU times: user 59.6 s, sys: 1.08 s, total: 1min
Wall time: 59.1 s
>> ct
ctable((100000000,), [('f0', '<i4'), ('f1', '<i8')])
nbytes: 1.12 GB; cbytes: 151.84 MB; ratio: 7.54
cparams := cparams(clevel=9, shuffle=True, cname='blosclz')
[(0, 0) (1, 1) (2, 4) ..., (99999997, 9999999400000009)
(99999998, 9999999600000004) (99999999, 9999999800000001
That is 7 times compression in-memory
You can also store on disk and read it fast
>> %time ct.eval('f0 ** 2 + sqrt(f1)')
CPU times: user 4.38 s, sys: 1.96 s, total: 6.34 s
Wall time: 1.26 s
Out[36]:
carray((100000000,), float64)
nbytes: 762.94 MB; cbytes: 347.33 MB; ratio: 2.20
cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[ 0.00000000e+00 2.00000000e+00 6.00000000e+00 ..., 1.37491943e+09
1.57491943e+09 1.77491942e+09]
Fast numerical calculations
Integration with Numexpr to handle expressions
Intelligent use of caching and multithreading to optimize numerical calcuations
Let's look at Adult dataset again
- With Bcolz you can do Pandas like things
- Based on NumPy but has support for PyTables/HDF5 (which may be faster)
- Uses chunking. The chunked nature of bcolz objects, together with buffered I/O, makes appends very cheap
- This makes this ideal for say storing and retrieving market data.
- This is for fast fetch, and write rarely...
dc['workclass' == ' State-gov']
#dc.cols
# You can do DataFrame-like stuff
dc['workclass' == ' State-gov']
Out[117]:
(39, b' State-gov', 13, b' Adm-clerical', b' Male', 40)
PSA: Bcolz version 1 release candidate is out
There are some challenges with integration into the rest of PyData, this should stabilize.
Quantopian Inc a crowd-sourced hedge fund uses Bcolz
- Each Column Is Stored Separately
- Escapes the GIL
- Better compression ratio for binary data
- Allows you to compress in-memory/ on disk and retrieve fast
- https://quantopian.github.io/talks/NeedForSpeed/slides.html
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My data is bigger than RAM or in a cluster
Use Dask or specifically dask.array
Dask looks like this!
Basically the Pandas/NumPy API
import dask.array as da
# create a dask array from the above array
a2 = da.from_array(a, chunks=200)
# multiply this array by a factor
b2 = a2 * 4
# find the minimum value
b2_min = b2.min()
print(b2_min)
#I want to tell if this is a School
#or not and then plot it on a graph
>> is_school = with_amenity.amenity.str.contains('[Ss]chool')
>> school = with_amenity[is_school]
#Very similar to pandas but you need to
#call compute on the dask objects
>> dd.compute(school.amenity.count())
(342025,)
# So we have about 342k schools in
# UK and Ireland in the OpenStreetMap project
import dask.dataframe as dd
lon, lat = dd.compute(school.Longitude,
school.Latitude)
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
fig, ax = plt.subplots(figsize=(10, 15))
m = Basemap(projection='mill',
lon_0=-5.23636, lat_0=53.866772,
llcrnrlon=-10.65073, llcrnrlat=49.16209,
urcrnrlon=1.76334, urcrnrlat=60.860699)
m.drawmapboundary(fill_color='#ffffff', linewidth=.0)
x, y = m(lon.values, lat.values)
m.scatter(x, y, s=1, marker=',', color="steelblue", alpha=0.6);
Compute in Dask and plot in Matplotlib
Notice how similar to Pandas and NumPy the API is.
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UK and Irish schools in Open Street Map
When do you use Dask?
- Medium data is greater than RAM size
- Generally Dask comes into it's own on around 16GB datasets
- Anything around the 1TB - 1PB range probably needs either a good SQL database or something like Spark
- Benchmark: My own Laptop has 4 cores and about 8GB of RAM
Distributed Arrays
- Bolt - distributed arrays backed by Spark
- DistArray - other distributed arrays
- Biggus - another kind of distributed array (virtual)
- Dask.array - Distributed arrays using task scheduling
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Very exciting technology for the JVM community
Improvements in PySpark and interoperability
Improvements in Machine Learning libraries
Comes into it's own with lots of JSON blobs on many nodes
Interlude
I want to speed up my code
- Numba (A fast LLVM based JIT compiler that is easy to use via decorators)
- Cython (A language that allows you to blend C objects for great speedup)
- PyPy (Another compiler but without support for NumPy code) Recent improvements in PyPy
- Plenty of tutorials online, and new tools are arriving...
End of Interlude
Recent improvements in dealing with 'Big Data'
- Distributed computing has improved in Dask
- See Matt Rocklins website
- PyData will just get better and better at dealing with 'big data'
- Soon you may not need to use the JVM to deal with HDFS.
- Spark is improving too. Spark is very exciting and I could give an entire talk in Spark. Other people are doing that!
Arrow and Ibis
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Source: Wes McKinney
Ibis Example
rounds = con.table('pokemon_types')
rounds.info()
#This is a Pokemon table in SQLite
rounds.slot.value_counts()
slot count
0 1 784
1 2 395
SQLite in the background but could be Impala - all with a pandas like API
PyData Map
Wouldn't it be great to have a map for the stack
I had a a go
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I wanna do Stats/ML
There's lots of cool stuff in PyData Land!
PyMC3
- Recent improvements to documentation!!
- Written on top of Theano
- Timeseries examples, Bayesian Logistic Regression
- Model evaluation functions
- PSA: It's now in Beta
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Bayesian LogReg
Subtitle
data[data['native-country']==" United-States"]
income = 1 * (data['income'] == " >50K")
age2 = np.square(data['age'])
data = data[['age', 'educ', 'hours']]
data['age2'] = age2
data['income'] = income
with pm.Model() as logistic_model:
pm.glm.glm('income ~ age + age2 + educ + hours',
data, family=pm.glm.families.Binomial())
trace_logistic_model = pm.sample(2000,
pm.NUTS(), progressbar=True)
Statsmodels
PSA: If you want to help PyData a lot - work on Statsmodels
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Scikit-Learn
- The best documentation in PyData
- Lots of cool improvements
- Chat to Andreas about this - he's at PyData Amsterdam
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I want to analyze text
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Production ready NLP toolkits all under open source
The future...
What is coming next
- Substantially improved data access speeds
- Closer to native performance Python extensions like Apache Spark
- New in-memory analytics functionality for nested/JSON-like data
Theano
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Computational toolkits
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Lasagne
So cite, send pull requests and/or help NumFOCUS!
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Copy of The PyData Map of the Stack
By springcoil
Copy of The PyData Map of the Stack
Keynote from PyData Amsterdam
- 5,464