From 6e378452f248f7dd280b153f70928e6d6d20a1df Mon Sep 17 00:00:00 2001
From: Icyblade Dai <icyblade@users.noreply.github.com>
Date: Sun, 1 Oct 2017 05:42:15 -0700
Subject: [PATCH] coding style update (#2752)

* coding style update

Current coding style varies(for example: the mixed use of single quote and double quote), and it will be confusing, especially for new users.
This PR will try to follow proposal of PEP8, make the documents more readable.

* minor fix
---
 doc/python/python_intro.md | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/doc/python/python_intro.md b/doc/python/python_intro.md
index 6aa490742..2dd389c41 100644
--- a/doc/python/python_intro.md
+++ b/doc/python/python_intro.md
@@ -37,9 +37,9 @@ dtest = xgb.DMatrix('test.svm.buffer')
 ```
 * To load a numpy array into ```DMatrix```:
 ```python
-data = np.random.rand(5,10) # 5 entities, each contains 10 features
-label = np.random.randint(2, size=5) # binary target
-dtrain = xgb.DMatrix( data, label=label)
+data = np.random.rand(5, 10)  # 5 entities, each contains 10 features
+label = np.random.randint(2, size=5)  # binary target
+dtrain = xgb.DMatrix(data, label=label)
 ```
 * To load a scpiy.sparse array into ```DMatrix```:
 ```python
@@ -49,16 +49,16 @@ dtrain = xgb.DMatrix(csr)
 * Saving ```DMatrix``` into a XGBoost binary file will make loading faster:
 ```python
 dtrain = xgb.DMatrix('train.svm.txt')
-dtrain.save_binary("train.buffer")
+dtrain.save_binary('train.buffer')
 ```
 * Missing values can be replaced by a default value in the ```DMatrix``` constructor:
 ```python
-dtrain = xgb.DMatrix(data, label=label, missing = -999.0)
+dtrain = xgb.DMatrix(data, label=label, missing=-999.0)
 ```
 * Weights can be set when needed:
 ```python
 w = np.random.rand(5, 1)
-dtrain = xgb.DMatrix(data, label=label, missing = -999.0, weight=w)
+dtrain = xgb.DMatrix(data, label=label, missing=-999.0, weight=w)
 ```
 
 Setting Parameters
@@ -66,7 +66,7 @@ Setting Parameters
 XGBoost can use either a list of pairs or a dictionary to set [parameters](../parameter.md). For instance:
 * Booster parameters
 ```python
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
+param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
 param['nthread'] = 4
 param['eval_metric'] = 'auc'
 ```
@@ -81,7 +81,7 @@ param['eval_metric'] = ['auc', 'ams@0']
 
 * Specify validations set to watch performance
 ```python
-evallist  = [(dtest,'eval'), (dtrain,'train')]
+evallist = [(dtest, 'eval'), (dtrain, 'train')]
 ```
 
 Training
@@ -90,7 +90,7 @@ Training
 Training a model requires a parameter list and data set.
 ```python
 num_round = 10
-bst = xgb.train( plst, dtrain, num_round, evallist )
+bst = xgb.train(plst, dtrain, num_round, evallist)
 ```
 After training, the model can be saved.
 ```python
@@ -101,12 +101,12 @@ The model and its feature map can also be dumped to a text file.
 # dump model
 bst.dump_model('dump.raw.txt')
 # dump model with feature map
-bst.dump_model('dump.raw.txt','featmap.txt')
+bst.dump_model('dump.raw.txt', 'featmap.txt')
 ```
 A saved model can be loaded as follows:
 ```python
-bst = xgb.Booster({'nthread':4}) #init model
-bst.load_model("model.bin") # load data
+bst = xgb.Booster({'nthread': 4})  # init model
+bst.load_model('model.bin')  # load data
 ```
 
 Early Stopping
@@ -134,7 +134,7 @@ ypred = bst.predict(dtest)
 
 If early stopping is enabled during training, you can get predictions from the best iteration with `bst.best_ntree_limit`:
 ```python
-ypred = bst.predict(dtest,ntree_limit=bst.best_ntree_limit)
+ypred = bst.predict(dtest, ntree_limit=bst.best_ntree_limit)
 ```
 
 Plotting