而今天要给大家推荐的dython就是一款集成了诸多实用功能的数据建模工具库，帮助我们更加高效地完成数据分析过程中的诸多任务：
通过下面两种方式均可完成对dython的安装：
pip install dython
或：
conda install -c conda-forge dython
dython中目前根据功能分类划分为以下几个子模块：
data_utils子模块集成了一些基础性的数据探索性分析相关的API，如identify_columns_with_na()可用于快速检查数据集中的缺失值情况：
>> df = pd.DataFrame({'col1': ['a', np.nan, 'a', 'a'], 'col2': [3, np.nan, 2, np.nan], 'col3': [1., 2., 3., 4.]})
>> identify_columns_with_na(df)
column na_count
1 col2 2
0 col1 1
identify_columns_by_type()可快速选择数据集中具有指定数据类型的字段：
>> df = pd.DataFrame({'col1': ['a', 'b', 'c', 'a'], 'col2': [3, 4, 2, 1], 'col3': [1., 2., 3., 4.]})
>> identify_columns_by_type(df, include=['int64', 'float64'])
['col2', 'col3']
one_hot_encode()可快速对数组进行「独热编码」：
>> one_hot_encode([1,0,5])
[[0. 1. 0. 0. 0. 0.]
[1. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 1.]]
split_hist()则可以快速绘制分组直方图，帮助用户快速探索数据集特征分布：
import pandas as pd
from sklearn import datasets
from dython.data_utils import split_hist
# Load data and convert to DataFrame
data = datasets.load_breast_cancer()
df = pd.DataFrame(data=data.data, columns=data.feature_names)
df['malignant'] = [not bool(x) for x in data.target]
# Plot histogram
split_hist(df, 'mean radius', split_by='malignant', bins=20, figsize=(15,7))
nominal子模块包含了一些进阶的特征相关性度量功能，例如其中的associations()可以自适应由连续型和类别型特征混合的数据集，并自动计算出相应的Pearson、Cramer's V、Theil's U、条件熵等多样化的系数；cluster_correlations()可以绘制出基于层次聚类的相关系数矩阵图等实用功能：
model_utils子模块包含了诸多对机器学习模型进行性能评估的工具，如ks_abc()：
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from dython.model_utils import ks_abc
# Load and split data
data = datasets.load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=.5, random_state=0)
# Train model and predict
model = LogisticRegression(solver='liblinear')
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)
# Perform KS test and compute area between curves
ks_abc(y_test, y_pred[:,1])
metric_graph()：
import numpy as np
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from dython.model_utils import metric_graph
# Load data
iris = datasets.load_iris()
X = iris.data
y = label_binarize(iris.target, classes=[0, 1, 2])
# Add noisy features
random_state = np.random.RandomState(4)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
# Train a model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)
classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True, random_state=0))
# Predict
y_score = classifier.fit(X_train, y_train).predict_proba(X_test)
# Plot ROC graphs
metric_graph(y_test, y_score, 'pr', class_names=iris.target_names)
import numpy as np
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from dython.model_utils import metric_graph
# Load data
iris = datasets.load_iris()
X = iris.data
y = label_binarize(iris.target, classes=[0, 1, 2])
# Add noisy features
random_state = np.random.RandomState(4)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
# Train a model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)
classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True, random_state=0))
# Predict
y_score = classifier.fit(X_train, y_train).predict_proba(X_test)
# Plot ROC graphs
metric_graph(y_test, y_score, 'roc', class_names=iris.target_names)
sampling子模块则包含了boltzmann_sampling()和weighted_sampling()两种数据采样方法，简化数据建模流程。
dython
作为一个处于快速开发迭代过程的Python库，陆续会有更多的实用功能引入，感兴趣的朋友们可以前往https://github.com/shakedzy/dython查看更多内容或对此项目保持关注。
- EOF -
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