Python 将字符串转换为分类 - numpy

Question

我正在拼命尝试更改以下数据集中的字符串变量 day、car2。

<class 'pandas.core.frame.DataFrame'>
Int64Index: 23653 entries, 0 to 23652
Data columns (total 7 columns):
day              23653 non-null object
clustDep         23653 non-null int64
clustArr         23653 non-null int64
car2             23653 non-null object
clustRoute       23653 non-null int64
scheduled_seg    23653 non-null int64
delayed          23653 non-null int64
dtypes: int64(5), object(2)
memory usage: 1.4+ MB
None

我已经尝试了 SO 上的所有内容，如您在下面的代码示例中所见。我正在运行 Python 2.7，numpy 1.11.1。我尝试了 scikits.tools.categorical 但没有成功，它不会事件加载命名空间。这是我的代码:

import numpy as np
#from scikits.statsmodels import sm

trainId = np.random.choice(range(df.shape[0]), size=int(df.shape[0]*0.8), replace=False)
train = df[['day', 'clustDep', 'clustArr', 'car2', 'clustRoute', 'scheduled_seg', 'delayed']]

#for col in ['day', 'car2', 'scheduled_seg']:
#    train[col] = train.loc[:, col].astype('category')

train['day'] = train['day'].astype('category')
#train['day'] = sm.tools.categorical(train, cols='day', drop=True)
#train['car2C'] = train['car2'].astype('category')
#train['scheduled_segC'] = train['scheduled_seg'].astype('category')


train = df.loc[trainId, train.columns]
testId = np.in1d(df.index.values, trainId, invert=True)
test = df.loc[testId, train.columns]


#from sklearn import tree
#clf = tree.DecisionTreeClassifier()
#clf = clf.fit(train.drop(['delayed'], axis=1), train['delayed'])

这会产生以下错误:

/Users/air/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:11: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

如有任何帮助，我们将不胜感激。非常感谢!

---更新---示例数据:

             day  clustDep  clustArr car2  clustRoute  scheduled_seg  delayed
0   Saturday        12        15   AA           1              5        1
1    Tuesday        12        15   AA           1              1        1
2    Tuesday        12        15   AA           1              5        1
3   Saturday        12        13   AA           4              3        1
4   Saturday         2        13   AB           6              3        1
5  Wednesday         2        13   IB           6              3        1
6     Monday         2        13   EY           6              3        0
7     Friday         2        13   EY           6              3        1
8   Saturday        11        13   AC           6              5        1
9     Friday        11        13   DL           6              5        1

Answer 1

它对我来说很好用(Pandas 0.19.0):

In [155]: train
Out[155]:
         day  clustDep  clustArr car2  clustRoute  scheduled_seg  delayed
0   Saturday        12        15   AA           1              5        1
1    Tuesday        12        15   AA           1              1        1
2    Tuesday        12        15   AA           1              5        1
3   Saturday        12        13   AA           4              3        1
4   Saturday         2        13   AB           6              3        1
5  Wednesday         2        13   IB           6              3        1
6     Monday         2        13   EY           6              3        0
7     Friday         2        13   EY           6              3        1
8   Saturday        11        13   AC           6              5        1
9     Friday        11        13   DL           6              5        1

In [156]: train.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 7 columns):
day              10 non-null object
clustDep         10 non-null int64
clustArr         10 non-null int64
car2             10 non-null object
clustRoute       10 non-null int64
scheduled_seg    10 non-null int64
delayed          10 non-null int64
dtypes: int64(5), object(2)
memory usage: 640.0+ bytes

In [157]: train.day = train.day.astype('category')

In [158]: train.car2 = train.car2.astype('category')

In [159]: train.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 7 columns):
day              10 non-null category
clustDep         10 non-null int64
clustArr         10 non-null int64
car2             10 non-null category
clustRoute       10 non-null int64
scheduled_seg    10 non-null int64
delayed          10 non-null int64
dtypes: category(2), int64(5)
memory usage: 588.0 bytes