pandas python sklearn中的“ValueError：无法将字符串转换为浮点数”
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/58040556/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
'ValueError: could not convert string to float' in python sklearn
提问by XCeptable
I have a Pandas DataFrame with date columns. The data is imported from a csv file. When I try to fit the regression model, I get the error ValueError: could not convert string to float: '2019-08-30 07:51:21.
.
我有一个带有日期列的 Pandas DataFrame。数据是从 csv 文件导入的。当我尝试拟合回归模型时，出现错误ValueError: could not convert string to float: '2019-08-30 07:51:21。.
How can I get rid of it?
我怎样才能摆脱它？
Here is dataframe.
这是数据框。
source.csv
源文件.csv
event_id tsm_id rssi_ts rssi batl batl_ts ts_diff
0 417736018 4317714 2019-09-05 20:00:07 140 100.0 2019-09-05 18:11:49 01:48:18
1 417735986 4317714 2019-09-05 20:00:07 132 100.0 2019-09-05 18:11:49 01:48:18
2 418039386 4317714 2019-09-06 01:00:08 142 100.0 2019-09-06 00:11:50 00:48:18
3 418039385 4317714 2019-09-06 01:00:08 122 100.0 2019-09-06 00:11:50 00:48:18
4 420388010 4317714 2019-09-07 15:31:07 143 100.0 2019-09-07 12:11:50 03:19:17
Here is my code:
这是我的代码：
model = pd.read_csv("source.csv")
model.describe()
event_id tsm_id. rssi batl
count 5.000000e+03 5.000000e+03 5000.000000 3784.000000
mean 3.982413e+08 4.313492e+06 168.417200 94.364429
std 2.200899e+07 2.143570e+03 35.319516 13.609917
min 3.443084e+08 4.310312e+06 0.000000 16.000000
25% 3.852882e+08 4.310315e+06 144.000000 97.000000
50% 4.007999e+08 4.314806e+06 170.000000 100.000000
75% 4.171803e+08 4.314815e+06 195.000000 100.000000
max 4.258451e+08 4.317714e+06 242.000000 100.000000
labels_b = np.array(model['batl'])
features_r= model.drop('batl', axis = 1)
features_r = np.array(features_r)
from sklearn.model_selection import train_test_split
train_features, test_features, train_labels, test_labels = train_test_split(features_r,
labels_b, test_size = 0.25, random_state = 42)
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_estimators = 1000, random_state = 42)
rf.fit(train_features, train_labels);
Here is error msg:
这是错误消息：
ValueError Traceback (most recent call last)
<ipython-input-28-bc774a9d8239> in <module>
4 rf = RandomForestRegressor(n_estimators = 1000, random_state = 42)
5 # Train the model on training data
----> 6 rf.fit(train_features, train_labels);
~/ml/env/lib/python3.7/site-packages/sklearn/ensemble/forest.py in fit(self, X, y, sample_weight)
247
248 # Validate or convert input data
--> 249 X = check_array(X, accept_sparse="csc", dtype=DTYPE)
250 y = check_array(y, accept_sparse='csc', ensure_2d=False, dtype=None)
251 if sample_weight is not None:
~/ml/env/lib/python3.7/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
494 try:
495 warnings.simplefilter('error', ComplexWarning)
--> 496 array = np.asarray(array, dtype=dtype, order=order)
497 except ComplexWarning:
498 raise ValueError("Complex data not supported\n"
~/ml/env/lib/python3.7/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
536
537 """
--> 538 return array(a, dtype, copy=False, order=order)
539
540
ValueError: could not convert string to float: '2019-08-30 07:51:21'
采纳答案by ndclt
You have to convert time date from string to pandas timestamp. This can be done with the following line (everything else is kept as you write):
您必须将时间日期从字符串转换为Pandas时间戳。这可以通过以下行完成（其他所有内容都在您编写时保留）：
model = (
pd.read_csv("source.csv", parse_dates=['rssi_ts', 'batl_ts'], date_parser=lambda x: pd.to_datetime(x))
.assign(
rssi_ts=lambda x: x.loc[:, 'rssi_ts'].astype(int) / 10 ** 9,
batl_ts=lambda x: x.loc[:, 'batl_ts'].astype(int) / 10 ** 9,
ts_diff=lambda x: pd.to_timedelta(x.loc[:, 'ts_diff']).astype(int) / 10 ** 9
)
)
Timestampobjects created by the parse_datesarguments can be converted into float.
Timestamp由parse_dates参数创建的对象可以转换为浮点数。
Edit: a bracket was missing.
编辑：缺少括号。
Edit2: for other timestamp and the delta time.
Edit2：用于其他时间戳和增量时间。
回答by ludovic
Try a thing like this, after reading the model
在阅读模型后尝试这样的事情
import datetime
to_timestamp_fct = lambda x: datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S').timestamp()
model['rssi_ts'] = model['rssi_ts'].apply(to_timestamp_fct)
回答by seunmi
It is reading your value as a string... For the model to work it has to read it has an integer or float, so use this function :
它正在将您的值作为字符串读取...要使模型工作，它必须读取它具有整数或浮点数，因此请使用此函数：
model = (
pd.read_csv("source.csv", parse_dates=['rssi_ts', 'batl_ts'], date_parser=lambda x: pd.to_datetime(x))
.assign(rssi_ts=lambda x: x.loc[:, 'rssi_ts'].astype(int) / 10 ** 9)
