小编use*_*077的帖子

我已经安装了pandas-daatreader但是已经弃用了用于下载历史股票价格数据的Google和Yahoo API.

import pandas_datareader.data as web
start_date = '2018-01-01'
end_date = '2018-06-08'
panel_data = web.DataReader('SPY', 'yahoo', start_date, end_date)

ImmediateDeprecationError: 
Yahoo Daily has been immediately deprecated due to large breaks in the API without the
introduction of a stable replacement. Pull Requests to re-enable these data
connectors are welcome.

See https://github.com/pydata/pandas-datareader/issues

你能告诉我如何使用Python访问历史股票价格吗？事实上,我有兴趣在研究时尽可能地提价.

谢谢.

我已经安装了pytorchwithconda和transformerswith pip.

我可以import transformers毫无问题，但当我尝试时，import pipeline from transformers我得到一个例外：

from transformers import pipeline
---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-4-69a9fd07ccac> in <module>
----> 1 from transformers import pipeline

ImportError: cannot import name 'pipeline' from 'transformers' (C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\transformers\__init__.py)

这是搜索init .py 文件的目录视图：

是什么导致了这个问题？我该如何解决它？

我正在使用以下代码使用 PyMC3 创建一个简单的模型：

import pymc3 as pm
import theano.tensor as tt

with pm.Model() as model:
    p = pm.Uniform("freq_cheating", 0, 1)
    p_skewed = pm.Deterministic("p_skewed", 0.5*p + 0.25)

    yes_responses = pm.Binomial("number_cheaters", 100, p_skewed, observed= 50) 

    step = pm.Metropolis()
    trace = pm.sample(25000, step=step)
    burned_trace50 = trace[2500:]

是否可以将此模型绘制为 DAG？

我正在尝试使用 Python 中的 SQLAlchemy 连接到 Azure 数据库。

我的代码如下：

engine_azure = \
create_engine('mssql+pyodbc://{Server admin login}:{password}@{Server name}.database.windows.net:1433/{AdventureWorksLT}', echo=True)

我收到以下消息：

C:\ProgramData\Anaconda3\lib\site-packages\sqlalchemy\connectors\pyodbc.py:92: SAWarning: No driver name specified; this is expected by PyODBC when using DSN-less connections
  "No driver name specified; "

然后我运行以下代码：

print(engine_azure.table_names())

我收到以下消息：

DBAPIError: (pyodbc.Error) ('01S00', '[01S00] [Microsoft][ODBC Driver Manager] Invalid connection string attribute (0) (SQLDriverConnect)')

我运行 Windows10，使用最新的 Anaconda3 (2019)，并通过 conda 安装了 Jupyter Notebook。

我曾经通过导航到保存我的 Jupyter Notebooks 的文件夹\jupyter_notebook_files 并从该目录运行 cmd 命令jupyter notebook来运行 Jupyter Notebook。

几个小时前，出于节省空间的原因，我删除了一个名为 ABC 的文件夹。

现在，当我尝试运行 Jupyter Notebook 时，我收到消息：

[C 20:57:04.610 NotebookApp] Bad config encountered during initialization:
[C 20:57:04.611 NotebookApp] No such notebook dir: ''D:/ABC''

我通过输入以下内容处理了这个问题：

C:\WINDOWS\system32>jupyter notebook --notebook-dir='D:\DIGITAL_LIBRARY\Jupyter_Notebook_Files'

有没有办法使该文件夹成为默认文件夹，而不必每次都重复该子句：-notebook-dir='D:\DIGITAL_LIBRARY\Jupyter_Notebook_Files' ？

我正在尝试实施我在网上找到的建议，但我已经走到了我想去的地方。

这是一个可重现的示例：

library(tidyverse)
library(dplyr)
library(rlang)

data(mtcars)

filter_expr = "am == 1"

mutate_expr = "gear_carb = gear*carb"

select_expr = "mpg , cyl"

mtcars %>% filter_(filter_expr) %>% mutate_(mutate_expr) %>% select_(select_expr)

该过滤器表达式工作正常。

在发生变异表达的作品很好，但新的变量名称gear_carb =齿轮*碳水化合物，而不是预期的gear_carb。

最后，select表达式返回一个异常。

当我尝试从多索引 Pandas 数据帧中删除一个级别时，我收到一条奇怪的（据我所知）消息。

对于可重现的示例：

toy.to_json()
'{"["ISRG","EPS_diluted"]":{"2004-12-31":0.33,"2005-01-28":0.33,"2005-03-31":0.25,"2005-04-01":0.25,"2005-04-29":0.25},"["DHR","EPS_diluted"]":{"2004-12-31":0.67,"2005-01-28":0.67,"2005-03-31":0.67,"2005-04-01":0.58,"2005-04-29":0.58},"["BDX","EPS_diluted"]":{"2004-12-31":0.75,"2005-01-28":0.75,"2005-03-31":0.72,"2005-04-01":0.72,"2005-04-29":0.72},"["SYK","EPS_diluted"]":{"2004-12-31":0.4,"2005-01-28":0.4,"2005-03-31":0.42,"2005-04-01":0.42,"2005-04-29":0.42},"["BSX","EPS_diluted"]":{"2004-12-31":0.35,"2005-01-28":0.35,"2005-03-31":0.42,"2005-04-01":0.42,"2005-04-29":0.42},"["BAX","EPS_diluted"]":{"2004-12-31":0.18,"2005-01-28":0.18,"2005-03-31":0.36,"2005-04-01":0.36,"2005-04-29":0.36},"["EW","EPS_diluted"]":{"2004-12-31":0.4,"2005-01-28":0.4,"2005-03-31":0.5,"2005-04-01":0.5,"2005-04-29":0.5},"["MDT","EPS_diluted"]":{"2004-12-31":0.44,"2005-01-28":0.45,"2005-03-31":0.45,"2005-04-01":0.45,"2005-04-29":0.16},"["ABT","EPS_diluted"]":{"2004-12-31":0.63,"2005-01-28":0.63,"2005-03-31":0.53,"2005-04-01":0.53,"2005-04-29":0.53}}'

toy.droplevel(level = 1, axis = 1)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-33-982eee5ba162> in <module>()
----> 1 toy.droplevel(level = 1, axis = 1)

C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
   4370             if self._info_axis._can_hold_identifiers_and_holds_name(name):
   4371                 return self[name]
-> 4372             return object.__getattribute__(self, name)
   4373 
   4374     def __setattr__(self, name, value):

AttributeError: 'DataFrame' object has no attribute 'droplevel'

我已经训练了一个 lightgbm 模型，我想绘制学习曲线。我怎样才能做到这一点？在 Keras 示例中，history 返回指标，以便我可以在训练结束后绘制它们。这里是如何处理这个任务的？

我的代码如下：

def f_lgboost(data, params):

    model = lgb.LGBMClassifier(**params)


    X_train = data['X_train']

    y_train = data['y_train']

    X_dev = data['X_dev']

    y_dev = data['y_dev']

    X_test = data['X_test']

    categorical_feature= ['Ticker_code', 'Category_code']

    X_train[categorical_feature] = X_train[categorical_feature].astype('category')

    X_dev[categorical_feature] = X_dev[categorical_feature].astype('category')

    X_test[categorical_feature] = X_test[categorical_feature].astype('category')


    feature_name = X_train.columns.to_list()

    model.fit(X_train, y_train, eval_set = [(X_dev, y_dev)], eval_metric = 'auc', early_stopping_rounds = 20, 
              categorical_feature = categorical_feature, feature_name = feature_name)

    y_pred_train = model.predict_proba(X_train)[:, 1].ravel()

    y_pred_dev = model.predict_proba(X_dev)[:, 1].ravel()

    from sklearn.metrics import roc_auc_score

    auc_train = roc_auc_score(y_train, y_pred_train)

    auc_dev = roc_auc_score(y_dev, …

我正在尝试使用 matplotlib 可视化多元正态分布。我想生产这样的东西：

我使用以下代码：

from mpl_toolkits import mplot3d
x = np.linspace(-1, 3, 100)
y = np.linspace(0, 4, 100)
X, Y = np.meshgrid(x, y)
Z = np.random.multivariate_normal(mean = [1, 2], cov = np.array([[0.5, 0.25],[0.25, 0.50]]), size = 100000)
ax = plt.axes(projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1,
                cmap='viridis', edgecolor='none')
ax.set_title('surface');

但我收到以下错误消息：

...
      7 ax.plot_surface(X, Y, Z, rstride=1, cstride=1,
----> 8                 cmap='viridis', edgecolor='none')
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape

错误的原因是什么以及如何更正我的代码？

我在Windows 10上运行Python 3.6.

我的代码如下:

data1
     Loan_ID    Gender
1   LP001003    Male
2   LP001005    Male
3   LP001006    Male
4   LP001008    Male
5   LP001011    Male

data2
    Loan_ID2    LoanAmount
1   LP001003    128.0
2   LP001005    66.0
3   LP001006    120.0
4   LP001008    141.0
5   LP001011    267.0

data_merged = data1.merge(right= data2, how='inner',left_on='Loan_ID', right_on = 'Loan_ID2',right_index=True, sort=False)

data_merged.shape
(0, 4)

您的建议将不胜感激.