安利一個Python大數(shù)據(jù)分析神器！

conda?install?dask

conda?install?dask-core

git?clone?https://github.com/dask/dask.git
cd?dask
python?-m?pip?install?.

import?dask.array?as?da
x?=?da.random.uniform(low=0,?high=10,?size=(10000,?10000),??#?normal?numpy?code
??????????????????????chunks=(1000,?1000))??#?break?into?chunks?of?size?1000x1000

y?=?x?+?x.T?-?x.mean(axis=0)??#?Use?normal?syntax?for?high?level?algorithms

#?DataFrames
import?dask.dataframe?as?dd
df?=?dd.read_csv('2018-*-*.csv',?parse_dates='timestamp',??#?normal?Pandas?code
?????????????????blocksize=64000000)??#?break?text?into?64MB?chunks

s?=?df.groupby('name').balance.mean()??#?Use?normal?syntax?for?high?level?algorithms

#?Bags?/?lists
import?dask.bag?as?db
b?=?db.read_text('*.json').map(json.loads)
total?=?(b.filter(lambda?d:?d['name']?==?'Alice')
??????????.map(lambda?d:?d['balance'])
??????????.sum())

def?inc(x):
????return?x?+?1

def?double(x):
????return?x?*?2

def?add(x,?y):
????return?x?+?y

data?=?[1,?2,?3,?4,?5]

output?=?[]
for?x?in?data:
????a?=?inc(x)
????b?=?double(x)
????c?=?add(a,?b)
????output.append(c)

total?=?sum(output)
45

import?dask

output?=?[]
for?x?in?data:
????a?=?dask.delayed(inc)(x)
????b?=?dask.delayed(double)(x)
????c?=?dask.delayed(add)(a,?b)
????output.append(c)

total?=?dask.delayed(sum)(output)

total.visualize()??

>>>?total.compute()
45

#?from?sklearn.grid_search?import?GridSearchCV
??from?dklearn.grid_search?import?GridSearchCV
#?from?sklearn.pipeline?import?Pipeline
??from?dklearn.pipeline?import?Pipeline

from?sklearn.datasets?import?make_classification

X,?y?=?make_classification(n_samples=10000,
???????????????????????????n_features=500,
???????????????????????????n_classes=2,
???????????????????????????n_redundant=250,
???????????????????????????random_state=42)

from?sklearn?import?linear_model,?decomposition
from?sklearn.pipeline?import?Pipeline
from?dklearn.pipeline?import?Pipeline

logistic?=?linear_model.LogisticRegression()
pca?=?decomposition.PCA()
pipe?=?Pipeline(steps=[('pca',?pca),
???????????????????????('logistic',?logistic)])


grid?=?dict(pca__n_components=[50,?100,?150,?250],
????????????logistic__C=[1e-4,?1.0,?10,?1e4],
????????????logistic__penalty=['l1',?'l2'])

#?from?sklearn.grid_search?import?GridSearchCV
from?dklearn.grid_search?import?GridSearchCV

estimator?=?GridSearchCV(pipe,?grid)

estimator.fit(X,?y)

from?dask.distributed?import?Client
c?=?Client('scheduler-address:8786')

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