Fairly new to coding and have been browsing this site for a while, not clued up enough to give anything back yet though.
I'm trying to calculate the Hurst exponent using this code originally from QuantStart but modified to import data from Yahoo. Daily Hurst Exponent
When running in Powershell I return these errors:
C:\Program Files\Anaconda2\lib\site-packages\pandas\io\data.py:35: FutureWarning:
The pandas.io.data module is moved to a separate package (pandas-datareader) and will be removed from pandas in a future
version.
After installing the pandas-datareader package (https://github.com/pydata/pandas-datareader), you can change the import
from pandas.io import data, wb to from pandas_datareader import data, wb.
FutureWarning)
When changing from pandas.io import data, wb to from pandas_datareader import data, wb:
Traceback (most recent call last):
File "hurst.py", line 23, in
aapl = DataReader("AAPL", "yahoo", datetime(2012,1,1), datetime(2015,9,18))
NameError: name 'DataReader' is not defined
Please can someone help and guide me in what changes I'm missing to get the script to run properly.
Thanks,
James
from pandas_datareader.data import DataReader
...
ts1 = DataReader(symbol, "yahoo", start_date, end_date)
See the usage in the documentation
for pandas datareader:
from pandas_datareader import data
import datetime
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2013, 1, 27)
f = data.DataReader("F", 'yahoo', start, end)
Related
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-1-2cacdf187bba> in <module>
6 import numpy as np
7
----> 8 from pytorchcv import load_mnist, train, plot_results, plot_convolution, display_dataset
9 load_mnist(batch_size=128)
ImportError: cannot import name 'load_mnist' from 'pytorchcv' (/anaconda/envs/py37_pytorch/lib/python3.7/site-packages/pytorchcv/__init__.py)
How can fix this bug?
I use python 3.7 and Jupiter notebook. The code in Pytorch Fundamental of Microsoft: Link https://learn.microsoft.com/en-gb/learn/modules/intro-computer-vision-pytorch/5-convolutional-networks
import torch
import torch.nn as nn
import torchvision
import matplotlib.pyplot as plt
from torchinfo import summary
import numpy as np
from pytorchcv import load_mnist, train, plot_results, plot_convolution, display_dataset
load_mnist(batch_size=128)
I installed PyTorch by command: pip install pytorchcv
I assume you might have the wrong pytorchcv package. The one in pypy does not contain load_mnist
Starting from scratch you could download mnist as such:
data_train = torchvision.datasets.MNIST('./data',
download=True,train=True,transform=ToTensor()) data_test = torchvision.datasets.MNIST('./data',
download=True,train=False,transform=ToTensor())
They missed one command before importing pytorchcv.
This pytorchcv is different from pytorchcv in PyPI.
Run this before importing pytorchcv.
!wget https://raw.githubusercontent.com/MicrosoftDocs/pytorchfundamentals/main/computer-vision-pytorch/pytorchcv.py
Then it should work.
please download the prepared py file from the link https://raw.githubusercontent.com/MicrosoftDocs/pytorchfundamentals/main/computer-vision-pytorch/pytorchcv.py and put it into current folder, then VS Code could recognize it
Following Pickle figures from matplotlib, I am trying to load a figure from a pickle. I am using the same code with the modifications that are suggested in the responses.
Saving script:
import numpy as np
import matplotlib.pyplot as plt
import pickle as pl
# Plot simple sinus function
fig_handle = plt.figure()
x = np.linspace(0,2*np.pi)
y = np.sin(x)
plt.plot(x,y)
# plt.show()
# Save figure handle to disk
pl.dump(fig_handle,file('sinus.pickle','wb'))
Loading script:
import matplotlib.pyplot as plt
import pickle as pl
import numpy as np
# Load figure from disk and display
fig_handle = pl.load(open('sinus.pickle', 'rb'))
fig_handle.show()
The saving script produces a file named "sinus.pickle" but the loading file does not show the anticipated figure. Any suggestions?
Python 2.7.13
matplotlib 2.0.0
numpy 1.12.1
p.s. following a suggestion replaced fig_handle.show() with pat.show() which produced an error:
Traceback (most recent call last):
File "/usr/local/lib/python2.7/
site-packages/matplotlib/backends/backend_macosx.py", line 109,
in_set_device_scale
self.figure.dpi = self.figure.dpi / self._device_scale * value
File "/usr/local/lib/python2.7/site-packages/matplotlib/figure.py",
line 416, in _set_dpi
self.callbacks.process('dpi_changed', self)
File "/usr/local/lib/python2.7/site-packages/matplotlib/cbook.py",
line 546, in process
if s in self.callbacks:
AttributeError: 'CallbackRegistry' object has no attribute 'callbacks'
What you call your "loading script" doesn't make any sense.
From the very link that you provided in your question, loading the picked figure is as simple as:
# Load figure from disk and display
fig_handle2 = pl.load(open('sinus.pickle','rb'))
fig_handle2.show()
Final solution included modification of
fig_handle.show()
to
plt.show()
and modification of the backend to "TkAgg", based to an advice given by ImportanceOfBeingErnest
Here is my code
from pandas import read_csv
from pandas.tools.plotting import scatter_matrix
from matplotlib import pyplot
filename = 'iris.data.csv'
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = read_csv(filename, names=names)
print(dataset.shape)
print(dataset.head(20))
# Data visualizations
dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
pyplot.show()
When i run above code. Then following error is shown
Traceback (most recent call last):
File "/media/k/UBUNTU2/Work and stuff/coding language/Python/Machine learning/exp.py", line 43, in <module>
dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
File "/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.py", line 2090, in plot_frame
raise ValueError('Invalid chart type given %s' % kind)
ValueError: Invalid chart type given box
Any idea ? What should i do? Please help
Your pandas version (0.14) is already 3 years old. The "box" kind was introduced in version 0.15. Now we are at version 0.20.
The solution is thus to install a newer version of pandas in order to be able to use kind="box" in the plotting wrapper.
If you need to use version 0.14 you can get boxplot using the DataFrame.boxplot() method. The usage according to documentation would be:
df = DataFrame(rand(10,5))
plt.figure();
bp = df.boxplot()
I'm trying to run the code below in virtual machine for a homework practice problem. I'm getting the error message below, and I'm trying to figure out if it's an issue with my code or the site. If anyone can point out if it's an error with my code an how to fix it I'd be grateful. If my code looks ok, then I'll let the course know they have a bug.
Code:
import numpy as np
import pandas as pd
# Load the dataset
X = pd.read_csv('titanic_data.csv')
# Limit to categorical data
X = X.select_dtypes(include=[object])
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
# TODO: Create a LabelEncoder object, which will turn all labels present in
# in each feature to numbers.
# HINT: Use LabelEncoder()
df=pd.DataFrame(X)
le = preprocessing.labelEncoder()
# TODO: For each feature in X, apply the LabelEncoder's fit_transform
# function, which will first learn the labels for the feature (fit)
# and then change the labels to numbers (transform).
df2=df.apply(le.fit_transform)
#for feature in X:
# HINT: use fit_transform on X[feature] using the LabelEncoder() object
#X[feature] = label_encoder.fit_transform(X[feature])
# TODO: Create a OneHotEncoder object, which will create a feature for each
# label present in the data.
# HINT: Use OneHotEncoder()
ohe = preprocessing.OneHotEncoder()
# TODO: Apply the OneHotEncoder's fit_transform function to all of X, which will
# first learn of all the (now numerical) labels in the data (fit), and then
# change the data to one-hot encoded entries (transform).
# HINT: Use fit_transform on X using the OneHotEncoder() object
onehotlabels = enc.fit_transform(df2)
Error:
Traceback (most recent call last):
File "vm_main.py", line 33, in <module>
import main
File "/tmp/vmuser_zrkfroofmi/main.py", line 2, in <module>
import studentMain
File "/tmp/vmuser_zrkfroofmi/studentMain.py", line 3, in <module>
import OneHot
File "/tmp/vmuser_zrkfroofmi/OneHot.py", line 21, in <module>
le = preprocessing.labelEncoder()
NameError: name 'preprocessing' is not defined
Call OneHotEncoder without preprocessing before the name. So just do ohe = OneHotEncoder(). The problem is in your import, what you have in your script would work if you did from sklearn import preprocessing.
From the code, 'from sklearn.preprocessing import OneHotEncoder', user was trying to import OneHotEncoder from preprocessing without importing preprocessing first. Preprocessing is a package from sklearn and OneHotEncoder is a preprocessor. That caused the error. So import preprocessing and then try with OneHotEncoder
The StanfordSegmenter does not have an interface in nltk, different from the case of StanfordPOStagger or StanfordNER. So to use it, basically I have to create an interface manually for StanfordSegmenter, namely stanford_segmenter.py under ../nltk/tokenize/. I follow the instructions here http://textminingonline.com/tag/chinese-word-segmenter
However, when I tried to run this from nltk.tokenize.stanford_segmenter import stanford_segmenter, I got an error
msg Traceback (most recent call last):
File "C:\Users\qubo\Desktop\stanfordparserexp.py", line 48, in <module>
from nltk.tokenize.stanford_segmenter import stanford_segmenter
ImportError: No module named stanford_segmenter
[Finished in 0.6s]
The instructions mentioned to reinstall nltk after creating the stanford_segmenter.py. I don't quite get the point but so I did. However, the process can hardly be called 'reinstall', but rather a detaching and reconnecting nltk to python libs.
I'm using Windows 64 and Python 2.7.11. NLTK and all relevant pkgs are updated to the latest version. Wonder if you guys can shed some light on this. Thank you all so much.
I was able to import the module by running the following code:
import imp
yourmodule = imp.load_source("module_name.py", "/path/to/module_name.py")
yourclass = yourmodule.TheClass()
yourclass is an instance of the class and TheClass is the name of the class you want to create the obj in. This is similar to the use of:
from pkg_name.module_name import TheClass
So in the case of StanfordSegmenter, the complete lines of code is as follows:
# -*- coding: utf-8 -*-
import imp
import os
ini_path = 'D:/jars/stanford-segmenter-2015-04-20/'
os.environ['STANFORD_SEGMENTER'] = ini_path + 'stanford-segmenter-3.5.2.jar'
stanford_segmenter = imp.load_source("stanford_segmenter", "C:/Users/qubo/Miniconda2/pkgs/nltk-3.1-py27_0/Lib/site-packages/nltk/tokenize/stanford_segmenter.py")
seg = stanford_segmenter.StanfordSegmenter(path_to_model='D:/jars/stanford-segmenter-2015-04-20/data/pku.gz', path_to_jar='D:/jars/stanford-segmenter-2015-04-20/stanford-segmenter-3.5.2.jar', path_to_dict='D:/jars/stanford-segmenter-2015-04-20/data/dict-chris6.ser.gz', path_to_sihan_corpora_dict='D:/jars/stanford-segmenter-2015-04-20/data')
sent = '我有一只小毛驴我从来也不骑。'
text = seg.segment(sent.decode('utf-8'))