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classes | Q_Score int64 0 1.72k | CreationDate stringlengths 23 23 | Users Score int64 -11 327 | AnswerCount int64 1 31 | System Administration and DevOps int64 0 1 | Title stringlengths 15 149 | Q_Id int64 5.14k 60M | Score float64 -1 1.2 | Tags stringlengths 6 90 | Answer stringlengths 18 5.54k | Question stringlengths 49 9.42k | Web Development int64 0 1 | Data Science and Machine Learning int64 1 1 | ViewCount int64 7 3.27M |
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0 | 56,563,697 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-12T12:43:00.000 | 0 | 2 | 0 | Can I avoid annotating my dataset for object recognition? | 56,562,536 | 0 | python,tensorflow,deep-learning,dataset,object-detection-api | No, not really, Deep Learning is not magic, you need bounding box annotations. The field of research that deals with this problem is Weakly Supervised Object Detection, and its a research field, there are no solutions that perform as well as using an annotated dataset. | I've been working on image classification with deep learning models (CNN with keras and tensorflow as back end) such as AlexNet and ResNet. I learned a lot about the whole dataset, learning, testing processes.
I'm now shifting to object detection and have done a lot of research. I came across R-CNN, Fast R-CNN, Faster ... | 0 | 1 | 872 |
0 | 56,839,312 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-12T14:54:00.000 | 0 | 1 | 0 | How to get a voxel array from a list of 3D points that make up a line in a voxalized volume? | 56,565,073 | 0 | python,3d,line,voxel,bresenham | I ended up solving this problem myself. For anyone who is wondering how, I'll explain it briefly.
First, since all the catheters point in the general direction of the z-axis, I got the thickness of the slices along that axis. Both input points land on a slice. I then got the coordinates of every intersection between t... | I have a list of points that represent a needle/catheter in a 3D volume. This volume is voxalized. I want to get all the voxels that the line that connects the point intersects. The line needs to go through all the points.
Ideally, since the round needle/catheter has a width I would like to be able to get the voxels t... | 0 | 1 | 628 |
0 | 56,583,582 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-12T18:44:00.000 | 0 | 2 | 0 | We have many mainframe files which are in EBCDIC format, is there a way in Python to parse or convert the mainframe file into csv file or text file? | 56,568,561 | 0 | excel,python-3.x,export-to-csv,mainframe,ebcdic | Who said anything about EBCDIC? The OP didn't.
If it is all text then FTP'ing with EBCDIC to ASCII translation is doable, including within Python.
If not then either:
The extraction and conversion to CSV needs to happen on z/OS. Perhaps with a COBOL program. Then the CSV can be FTP'ed down with
or
The data has to be... | I need to read the records from mainframe file and apply the some filters on record values.
So I am looking for a solution to convert the mainframe file to csv or text or Excel workbook so that I can easily perform the operations on the file.
I also need to validate the records count. | 0 | 1 | 1,297 |
0 | 56,592,187 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-14T04:26:00.000 | 0 | 1 | 0 | Stack Data Frames on top of one another dataframe | 56,591,367 | 0 | python,python-3.x | You could use pandas.concat() or DataFrame.append() , check pandas document its easily available with examples. | I have two dataframes :
1st dataframe
Countrey 2000 2001 2002 2003 2004 2005
canada 779 771 754 740 760 747
mexico 1311.2 1285.2 1271.2 1276.5 1270.6 1281
usa 836 814 810 800 802 799
India 914 892 888 878 880 8... | 0 | 1 | 561 |
0 | 68,484,466 | 0 | 0 | 0 | 0 | 1 | false | 9 | 2019-06-14T08:46:00.000 | 2 | 7 | 0 | Change 1's to 0 and 0's to 1 in numpy array without looping | 56,594,598 | 0.057081 | python,numpy | iverted = ~arr + 2 should do the work | Let's say I have a numpy array where I would like to swap all the 1's to 0 and all the 0's to 1 (the array will have other values, and there is nothing special about the 0's and 1's). Of course, I can loop through the array and change the values one by one.
Is there an efficient method you can recommend using? Does the... | 0 | 1 | 14,516 |
0 | 56,815,637 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-14T09:02:00.000 | 0 | 1 | 0 | Train object detection with large images | 56,594,824 | 0 | python-3.x,object-detection | Yes, you can train the object detection model with large images size 1600*1216 and obtain good accuracy. | Can I train the object detection model with large images size 1600*1216 and obtain good accuracy . Thanks | 0 | 1 | 84 |
0 | 56,596,649 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-14T10:25:00.000 | 0 | 2 | 0 | Merge column data into row in python | 56,596,244 | 0 | python,python-3.x | It's not hard at all. You have to transpose the second dataframe and add its values in the rows of the first dataframe. Use the .T command. | I have two dataframes :
1st dataframe
cat 2000 2001 2002 2003 2004 2005
1 779 771 754 740 760 747
2 1311.2 1285.2 1271.2 1276.5 1270.6 1281
3 836 814 810 800 802 799
4 914 892 888 878 880 ... | 0 | 1 | 38 |
0 | 56,603,296 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-14T17:11:00.000 | 0 | 1 | 0 | Model unable to identify distant objects | 56,602,594 | 0 | python-3.x,tensorflow,object-detection-api | You can resize and add padding using data augmentation to images with objects that are clearly visible so that they look like they are in a big distance and train your model further with those images | I have made a object recognition and detection model using tensorflow. It identifies the images which are clearly visible but its unable to identify if the same object is at a large distance. I am using Faster RCNN model. the model is able to identify the same object when it is closer but not when it is at a far dista... | 0 | 1 | 32 |
0 | 57,694,779 | 0 | 0 | 1 | 0 | 1 | false | 0 | 2019-06-14T21:09:00.000 | 1 | 1 | 0 | Design Choice: how to wrap up large files with 100k+ vectors | 56,605,145 | 0.197375 | python,performance,oop | It seems you want to "wrap paths" and store many attributes for those paths, and there are lots of "paths".
Instead of defining classes that require lots of custom objects creation just for the paths themselves, store the paths (strings I suppose) as keys in a dict, and the attributes in any appropriate form inside. | I am comparing design choice of wrapping either each "vector" into Object, or each whole "matrix" of vectors into Object.
I realize there will be more overhead if I try to wrap up each path by a class object, but it would make the system a lot easier to understand and implement.
However, I thought this might cost us t... | 0 | 1 | 20 |
0 | 56,609,093 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-15T08:50:00.000 | 0 | 3 | 0 | Which loss function to use when there are many outputs? | 56,608,876 | 0 | python,keras,neural-network,loss-function | You can use binary cross entropy loss and set the nearest n-bins to the ground truth as labels.
For example, you have 10 pixels and ground truth label is 3 and you selected 3 neighbours.
In typical categorical cross entropy, you would set label as follow using one-hot encoded vector.
[0 0 1 0 0 0 0 0 0 0]
In the soluti... | Data similar to the images of 1000 x 1 pixels come from the equipment. Somewhere in the image may be 1, 2 or more objects.
I'm going to build a neural network to detect objects. I want to make 1,000 outputs. Each output will indicate whether there is an object in that output or not.
Advise me which loss function to u... | 0 | 1 | 469 |
0 | 56,632,620 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-15T10:41:00.000 | 0 | 1 | 0 | tensor flow is not working in anaconda gettting CUDA runtime version error on window system | 56,609,576 | 0 | python,tensorflow | I uninstalled the tensorflow and then created a new environment and then reinstalled the tensorflow then the issue was resolved. | Tensorflow library was working on my system, I have installed other library now tensorflow is not working. I am using window 10. python 3.7, tensorflow 1.13.1.
I am getting following error:
File "", line 1, in
runfile('C:/Users/Yogesh/.spyder-py3/MNISTBasicClassification.py', wdir='C:/Users/Yogesh/.spyder-py3')
Fi... | 0 | 1 | 154 |
0 | 56,613,628 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-15T18:22:00.000 | 0 | 1 | 0 | from sklearn.cross_validation import KFold renaming and depreaction of cross_validation | 56,613,045 | 0 | python-3.7 | Issue resolved by importing the supported package which is from sklearn.model_selection import KFold
then modifying KFold method with correct parameters as below
KFold(n_splits=2, random_state=None, shuffle=False) | i used in my code from sklearn.cross_validation import KFold and it was working without no issue .
now i got error ModuleNotFoundError: No module named 'sklearn.cross_validation'
i google it and i found that the package function is renamed to model_selection instead of cross_validation but i have to use KFold function ... | 0 | 1 | 815 |
0 | 56,627,542 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2019-06-17T08:20:00.000 | 0 | 2 | 0 | Difference between modulus (%) and floor division(//) in NumPy? | 56,627,393 | 0 | python,numpy,integer-division | Assume a= 10, b = 6
a%b will give you the remainder, that is 4
a//b will give you the quotient, that is 1 | Recently, I read a book on Numpy which mentions different types of ufuncs, where I was encountered with two different ufuncs, namely 'modulus', denoted by % symbol and 'floor division' //. Can someone explain the difference between them and why two operators are provided to do the the same thing (display reminder of di... | 0 | 1 | 8,915 |
0 | 56,656,088 | 0 | 0 | 0 | 1 | 1 | true | 0 | 2019-06-17T14:39:00.000 | 2 | 1 | 0 | I want to write a 75000x10000 matrix with float values effectively into a database | 56,633,576 | 1.2 | python,sql,django,pandas,bigdata | SQLite is quite impressive for what it is, but it's probably not going to give you the performance you are looking for at that scale, so even though your existing project is Django on SQLite I would recommend simply writing a Python wrapper for a different data backend and just using that from within Django.
More impor... | thanks for hearing me out.
I have a dataset that is a matrix of shape 75000x10000 filled with float values. Think of it like heatmap/correlation matrix. I want to store this in a SQLite database (SQLite because I am modifying an existing Django project). The source data file is 8 GB in size and I am trying to use pytho... | 1 | 1 | 124 |
0 | 56,644,886 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-18T05:24:00.000 | 0 | 1 | 1 | Enabling Cuda on Azure Datascience VM | 56,642,210 | 0 | python,azure,tensorflow | what is the size of the VM you are using on Azure ? | Azure Datascience VM with Nvidia Gpu extension
Ubuntu 16.04.6 LTS
Python 3.5.5
NVCC V10.0.130
Tensorflow-gpu 1.13.1
When runnning an op, I get
E tensorflow/stream_executor/cuda/cuda_driver.cc:300] failed call to
cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected | 0 | 1 | 101 |
0 | 62,995,790 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2019-06-18T12:45:00.000 | 3 | 2 | 0 | Is there any difference between using Dataframe.columns and Dataframe.keys() to obtain the column names? | 56,649,500 | 0.291313 | python-3.x,pandas,dataframe | one difference I noticed That you can Use .keys() with Series but You can not use .columns with Series . | For the sake of curiosity is there any practical difference between getting the column names of a DataFrame (let's say df) by using df.columns or df.keys()?
I've checked the outs by type and it seems to be exactly the same. Am I missing something or these two methods are just as redundant as it seems? Is one more appr... | 0 | 1 | 1,291 |
0 | 56,665,504 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-06-19T10:20:00.000 | 2 | 2 | 0 | What happens when you transform the test set using MinMaxScaler | 56,665,409 | 1.2 | python,scikit-learn,sklearn-pandas | For a given feature x, your minmax scaling to (0,1) will effectively map:
x to (x- min_train_x)/(max_train_x - min_train_x)
where min_train_x and max_train_x are the minimum and maximum value of x in the training set.
If a value of x in the testing set is larger than the max_train_x the scaling transformation will retu... | i am currently in the process of pre-processing my data and I understand that i have to use the same scaling parameters I have used on my training set, on my test set. However, when i applied the transform method from sklearn library, i noticed something weird.
I first used preprocessing.MinMaxScaler(feature_range=(0,1... | 0 | 1 | 1,278 |
0 | 57,010,899 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-06-20T13:04:00.000 | 1 | 1 | 0 | How to display number of epochs in tensorflow object detection api with Faster Rcnn? | 56,686,630 | 0.197375 | python,tensorflow,object-detection-api | So you are right with your assumption, that you have 200 epochs.
I had a similar problem with the not showing of loss.
my solution was to go to the model_main.py file and then insert
tf.logging.set_verbosity(tf.logging.INFO)
after the import stuff.
then it shows you the loss after each 100 steps.
you could change the s... | I am using Tensorflow Object detection api. What I understood reading the faster_rcnn_inception_v2_pets.config file is that num_steps mean the total number of steps and not the epochs. But then what is the point of specifying batch_size?? Lets say I have 500 images in my training data and I set batch size = 5 and num_s... | 0 | 1 | 2,207 |
0 | 56,690,623 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-20T15:23:00.000 | 0 | 2 | 0 | Is it acceptable to scale target values for regressors? | 56,689,243 | 0 | python-3.x,regression,data-science,prediction | It is actually a common practice to scale target values in many cases.
For example a highly skewed target may give better results if it is applied log or log1p transforms. I don't know the characteristics of your data, but there could a transformation that might decrease your RMSE.
Secondly, Test set is meant to be a s... | I am getting very high RMSE and MAE for MLPRegressor , ForestRegression and Linear regression with only input variables scaled (30,000+) however when i scale target values aswell i get RMSE (0.2) , i will like to know if that is acceptable thing to do.
Secondly is it normal to have better R squared values for Test (ie.... | 0 | 1 | 4,385 |
0 | 60,075,464 | 0 | 1 | 0 | 0 | 1 | false | 2 | 2019-06-21T14:46:00.000 | 0 | 4 | 0 | i tried installing tensorflow using 'pip install tensorflow ' in anaconda prompt and command prompt. its showing following output | 56,705,686 | 0 | python-3.x,tensorflow,anaconda | I was getting this error while installing from conda environment. Always upgrade conda or pip before a new installation.
Following worked for me:
[Optional] If installing in conda environment, then temporarily remove the conda env:
conda remove --name myenv --all
Update all conda packages:
conda update --all
Create co... | Found existing installation: wrapt 1.10.11
Cannot uninstall 'wrapt'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall. | 0 | 1 | 5,569 |
0 | 56,706,524 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-06-21T15:20:00.000 | 0 | 2 | 0 | How to remove regularisation from pre-trained model? | 56,706,219 | 0 | python,python-3.x,keras,regularized | Create a model with your required hyper-parameters and load the parameters to the model using load_weight(). | I've got a partially trained model in Keras, and before training it any further I'd like to change the parameters for the dropout, l2 regularizer, gaussian noise etc. I have the model saved as a .h5 file, but when I load it, I don't know how to remove these regularizing layers or change their parameters. Any clue as to... | 0 | 1 | 356 |
0 | 56,855,229 | 0 | 0 | 0 | 0 | 2 | true | 10 | 2019-06-21T15:58:00.000 | 22 | 2 | 0 | How to convert a spark dataframe into a databrick koalas dataframe? | 56,706,860 | 1.2 | python-3.x,dataframe,databricks | To go straight from a pyspark dataframe (I am assuming that is what you are working with) to a koalas dataframe you can use:
koalas_df = ks.DataFrame(your_pyspark_df)
Here I've imported koalas as ks. | I know that you can convert a spark dataframe df into a pandas dataframe with
df.toPandas()
However, this is taking very long, so I found out about a koala package in databricks that could enable me to use the data as a pandas dataframe (for instance, being able to use scikit learn) without having a pandas dataframe... | 0 | 1 | 8,396 |
0 | 58,230,046 | 0 | 0 | 0 | 0 | 2 | false | 10 | 2019-06-21T15:58:00.000 | 3 | 2 | 0 | How to convert a spark dataframe into a databrick koalas dataframe? | 56,706,860 | 0.291313 | python-3.x,dataframe,databricks | Well. First of all, you have to understand the reason why toPandas() takes so long :
Spark dataframe are distributed in different nodes and when you run toPandas()
It will pull the distributed dataframe back to the driver node (that's the reason it takes long time)
you are then able to use pandas, or Scikit-learn in... | I know that you can convert a spark dataframe df into a pandas dataframe with
df.toPandas()
However, this is taking very long, so I found out about a koala package in databricks that could enable me to use the data as a pandas dataframe (for instance, being able to use scikit learn) without having a pandas dataframe... | 0 | 1 | 8,396 |
0 | 56,708,040 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-06-21T17:21:00.000 | 0 | 1 | 0 | .vocabulary_ vs .get_feature_names() | 56,707,957 | 1.2 | python,python-3.x,scikit-learn,tfidfvectorizer | Basically, I think that they contain exactly the same information.
However, if you have the name of the term and you look for the column position of it at the tf-idf matrix then you go for the .vocabulary_.
The .vocabulary_ has as keys the names of the terms and as values their column position at the tf-idf matrix.
Whe... | These are related to the TfidfVectorizer of sklearn.
Could some explain please the similarities and differences between these two and when each one is useful.
It is quite confusing because they look very similar to each other but also quite different.
Also the rather limited sklearn documentation does not help much in ... | 0 | 1 | 509 |
0 | 56,730,467 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-06-24T05:10:00.000 | 1 | 2 | 0 | How to efficiently delete data from Redshift? | 56,730,152 | 0.099668 | python,amazon-web-services,pyspark,bigdata,amazon-redshift | Deleting data stored in Redshift with DELETE command will take time. The reason is that you are doing a soft delete, I mean you mark existing rows as deleted and then insert new row representing updated form of the data.
So one way is executing DELETE for junks of data. Instead of deleting one by one you should try to ... | I have data in my Redshift cluster. I need to find the best and efficient way to delete the previously stored data when I re run the job.
I have these two column to determine previous data previous_key (column that corresponds to run_dt) and creat_ts (time when we load the data)
I found two approaches so far but they d... | 0 | 1 | 6,918 |
0 | 56,775,827 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-06-24T14:19:00.000 | 0 | 1 | 0 | Plotting heat-map in python with multiple data sets | 56,738,558 | 1.2 | python,heatmap | The problem was caused due to the fact that the Data was of non-uniform grid, now I created a common domain (x-axis value) for all the functions and then interpolated all the Data set to the new X-value.
I used the numpy.interpol() function to achieve this. | I have a list FFT data which I want to plot in a single heat map. Each data set has its own X and Y. Usually I make use of Seaborn and Panda dataframe to plot the heatmap as the Frequency will be common for all the sets, but now the frequency column is unique for each data set. How can we plot a heatmap from this kind ... | 0 | 1 | 497 |
0 | 56,748,008 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-25T06:18:00.000 | 0 | 1 | 0 | Distance of the centre of the object from the centre of the image | 56,747,903 | 0 | python,opencv,image-processing | If the bounding box has bottom-left coordinate (x1, y1) and top-right (x2, y2), then its center'll be ((x1+x2)/2, (y1+y2)/2). Same in the case of the whole image.
Now, determine the distance by using sqrt((c1x-c2x)^2 + (c1y-c2y)^2)
Here, (c1x, c1y) is the center of the image and (c2x, c2y) is that of the bounding box | If we apply object detection on an image containing a playing card, then it will make bounding box around that card. Now my question is this that is there any way to find the distance between the center of the card(bounding box) and the center of the whole image? | 0 | 1 | 580 |
0 | 56,755,246 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2019-06-25T13:23:00.000 | 0 | 1 | 0 | No module named 'numpy' Even When Installed | 56,755,112 | 1.2 | python,numpy,import,package | python3 is not supported under NumPy 1.16.4. Try to install a more recent version of NumPy:
pip uninstall numpy
pip install numpy | I'm using windows with Python 3.7.3, I installed NumPy via command prompt with "pip install NumPy", and it installed NumPy 1.16.4 perfectly. However, when I run "import numpy as np" in a program, it says "ModuleNotFoundError: No module named 'numpy'"
I only have one version of python installed, and I don't know how I c... | 0 | 1 | 696 |
0 | 56,757,810 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-25T15:22:00.000 | 0 | 1 | 0 | Does the BBox labelling for object detection has to be done manually on Images or is there any ways to automate it | 56,757,346 | 0 | python,tensorflow | Short answer: You will probably have to do a lot of it manually, or maybe hire an intern.
If you already have a way to automatically annotate your data, you don't have to build another.
You can try to use another way as an initial Solution, which you still need to go through by hand afterwards and finetune or correct... | I'm doing a project on custom object detection. I have to use a Bbox labelling tool and yolov3 weights to train my dataset. Still I'm confused about where to start and proceed with the same. | 0 | 1 | 25 |
0 | 56,760,620 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-25T17:41:00.000 | 0 | 1 | 0 | Python/Pandas - Read multiple files in a folder based on dates in filenames | 56,759,396 | 0 | python,pandas | Just put the read command in a try except statement:
try:
read_file()
append_file()
except:
pass | I have a folder with hundreds of csv files with the filenames in the format of file_nameddmmyyyy (e.g. data25062019.csv).
I am using python/pandas to read the files and manipulating the dataframes. I want to be able to pull multiple csv files based on the dates on the file names.
e.g. I want to open the daily files fr... | 0 | 1 | 141 |
0 | 56,769,956 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-06-26T09:33:00.000 | 0 | 2 | 0 | How to set up data collection for small-scale algorithmic trading software | 56,769,559 | 1.2 | python,database,algorithmic-trading | CSV is a nice exchange format, but as it is based on a text file, it is not good for real-time updates. Only my opinion but I cannot imagine a reason to prefere that to database.
In order to handle real time conflicts, you will later need a professional grade database. PostgreSQL has the reputation of being robust, Mar... | This is a question on a conceptual level.
I'm building a piece of small-scale algorithmic trading software, and I am wondering how I should set up the data collection/retrieval within that system. The system should be fully autonomous.
Currently my algorithm that I want to trade live is doing so on a very low frequen... | 1 | 1 | 88 |
0 | 56,778,812 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-26T17:58:00.000 | 1 | 2 | 0 | When is it better to use npz files instead of csv? | 56,778,614 | 0.099668 | python,csv,numpy | It depends of the expected usage. If a file is expected to have broad use cases including direct access from an ordinary client machines, then csv is fine because it can be directly loaded in Excel or LibreOffice calc which are widely deployed. But it is just an good old text file with no indexes nor any additional fea... | I'm looking at some machine learning/forecasting code using Keras, and the input data sets are stored in npz files instead of the usual csv format.
Why would the authors go with this format instead of csv? What advantages does it have? | 0 | 1 | 817 |
0 | 59,459,541 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2019-06-26T18:26:00.000 | 0 | 1 | 0 | How to fix 'KeyError: dtype('float32')' in LDAviz | 56,779,011 | 0 | python,lda,amazon-sagemaker | I know this is late but I just fixed a similar problem by updating my gensim library from 3.4 to the current version which for me is 3.8. | I use LDAvis library to visualize my LDA topics. It works fine before, but it gets me this error when I download the saved model files from Sagemaker to the local computer. I don't know why does this happen? Does that relate to Sagemaker?
If I run from the local, and saved the model from local, and then run LDAviz libr... | 0 | 1 | 520 |
0 | 56,781,883 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-26T21:37:00.000 | 1 | 2 | 0 | How to reshape a 4 dimensional Numpy array into different dimensions? | 56,781,237 | 0.099668 | python,numpy | np.squeeze will collapse all the dimensions having length 1, or you can use the reshape function | I have a 4 dimensional Numpy array, of (8, 1, 1, 102). Now, for instance, I simply want to ignore the middle two dimensions and have an array of shape (8,102), what may be the suitable way to accomplish this? | 0 | 1 | 358 |
0 | 56,785,434 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2019-06-26T22:26:00.000 | 3 | 3 | 0 | Number of epochs to be used in a Keras sequential model | 56,781,680 | 0.197375 | python,keras,conv-neural-network | If the number of epochs are very high, your model may overfit and your training accuracy will reach 100%. In that approach you plot the error rate on training and validation data. The horizontal axis is the number of epochs and the vertical axis is the error rate. You should stop training when the error rate of validat... | I'm building a Keras sequential model to do a binary image classification. Now when I use like 70 to 80 epochs I start getting good validation accuracy (81%). But I was told that this is a very big number to be used for epochs which would affect the performance of the network.
My question is: is there a limited number... | 0 | 1 | 2,256 |
0 | 59,754,774 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-27T08:57:00.000 | 0 | 1 | 0 | Multiple header in Pandas DataFrame to_excel | 56,787,460 | 0 | python,pandas,export-to-excel | Had a similar issue. Solved by persisting cell-by-cell using worksheet.write(i, j, df.iloc[i,j]), with i starting after the header rows. | I need to export my DataFrame to Excel. Everything is good but I need two "rows" of headers in my output file. That mean I need two columns headers. I don't know how to export it and make double headers in DataFrame. My DataFrame is created with dictionary but I need to add extra header above.
I tried few dumb things b... | 0 | 1 | 1,093 |
0 | 56,789,899 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-06-27T09:51:00.000 | 1 | 4 | 0 | How can I find the index of a tuple inside a numpy array? | 56,788,435 | 0.049958 | python,numpy | It's not means search a tuple('Species2', 'Species3') from groups when you use
np.where(groups == ('Species2', 'Species3'))
it means search 'Species2' and 'Species3' separately if you have a Complete array like this
groups=np.array([('Species1',''), ('Species2', 'Species3')], dtype=object) | I have a numpy array as:
groups=np.array([('Species1',), ('Species2', 'Species3')], dtype=object).
When I ask np.where(groups == ('Species2', 'Species3')) or even np.where(groups == groups[1]) I get an empty reply: (array([], dtype=int64),)
Why is this and how can I get the indexes for such an element? | 0 | 1 | 1,220 |
0 | 58,783,825 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-27T12:15:00.000 | 0 | 1 | 0 | Aggregate Ranking using Khatri-Rao product | 56,790,761 | 0 | python,data-mining,matrix-multiplication,data-analysis,ranking | Rank both graphs separately each node gets a rank in both graphs then do simple matrix addition. Now normalize the rank. This should keep the relationship like rank1>rank2>rank3>rank4 true and relationships like rank1+rank1>rank1+rank2 true. I don't know how it would help you taking the Khatri-Rao product of the matrix... | I have constructed 2 graphs and calculated the eigenvector centrality of each node. Each node can be considered as an individual project contributor. Consider 2 different rankings of project contributors. They are ranked based on the eigenvector of the node.
Ranking #1:
Rank 1 - A
Rank 2 - B
Rank 3 - C
Ranking #2:
Ran... | 0 | 1 | 83 |
0 | 56,797,251 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-27T15:56:00.000 | 0 | 1 | 0 | How to get cluster of lines using python | 56,794,801 | 0 | python,cluster-analysis | Yes, almost all can be used. It's just probably not the most efficient way. Depending on how you'd formalize your problem, which is not clear enough. Do the points pairwise have to fulfill this property, or is it enough if they "mostly" do, or if there are some that do?
In many cases it will be easiest (but slow) to ma... | I have a lines dataset where each lines have several points (minimum 2). All the coordinates are known and on the same metric reference.
I would like to merge the lines with the same azimut +/- 10° and a maximal distance of 5cm between lines.
I think a clustering algorithm can do that, I found on web some clustering al... | 0 | 1 | 577 |
0 | 56,815,137 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-28T07:14:00.000 | 0 | 1 | 0 | How to customize a pre-trained model with our own dataset? | 56,802,475 | 0 | python,deep-learning,conv-neural-network,transfer-learning | Load the saved model with pretrained weights. Remove last dense and 2 class softmax layer and add a new dense with 4 class softmax as your low level features are already trained. Now train this model with modified data. | I am currently working on Object detection. I am using Amazon Workspace for training my model. I am using the model to detect cars and bikes. I have used a pre-trained model (which is Faster-RCNN-Inception-V2 model) and customized it with my own dataset for 2 labels namely car and bike. It took me 5 hours to complete t... | 0 | 1 | 469 |
0 | 56,817,681 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-29T12:40:00.000 | 0 | 1 | 0 | How to "push and shift" an element in a numpy array most efficiently? | 56,817,603 | 0 | python,numpy | You're describing a ring buffer. Just use a normal NumPy array plus an integer which tells you which row is the "top." To push, simply replace the "top" row with a new row and increment the top index. | I am trying to add a numpy array x = [1,2,3,4] to "the end" of a numpy array of shape (n,4) (i.e. "push" x onto the array) while removing the first element (i.e. "shift" the array). So basically, after adding my array x, the (4) shaped array that was added last should be removed.
How does one do this in the computation... | 0 | 1 | 140 |
0 | 56,822,417 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-29T22:55:00.000 | 0 | 1 | 0 | How to calculate the number of queues needed in a box classification/packaging problem? | 56,821,368 | 0 | python,queue,classification,bins | Your question is frustratingly vague. I think I understand what you're asking for, but a little more detail would help.
As I understand it, you have something like an order processing facility, where each order is a queue in which you hold items until all the items for the order arrive. Then you release that queue at s... | I need to size a box classification system where boxes get on a queue until the packaging quantity is reached. Then they all leave the queue at a fixed rate.
This is done real time with production. I can estimate the volume for each SKU but I can't predict the order in which they will arrive at the classification/sorti... | 0 | 1 | 35 |
0 | 56,825,630 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-06-30T14:14:00.000 | 0 | 1 | 0 | the way to do the cross validation | 56,825,542 | 0 | python,cross-validation,k-fold | It depends what is your end goal. K-Fold CV is used for finding model hyperparameters.
After this phase you may change your validation dataset and train your model with it.
If you want to harness as much data as you can (performing predictions) it might be a good idea to train N models on N different folds and ensemble... | Let say I have fold1, fold2 , fold3.
I trained fold1,fold2,fold3 with modelA.
A) modelA(fold1) -> modelA(fold2) -> modelA(fold3)
B) modelA(fold1) -> saved weight modelA(fold1) -> modelA(fold2)->
saved weight modelA(fold2) -> modelA(fold3)-> saved weight
modelA(fold3) -> ensemble 3 weight
which way is the rig... | 0 | 1 | 33 |
0 | 57,441,048 | 0 | 0 | 0 | 0 | 1 | true | 3 | 2019-07-02T11:17:00.000 | 0 | 1 | 0 | Pandas numpy.lookfor equivalent | 56,851,264 | 1.2 | python,pandas,numpy | There is probably no such thing for Pandas library as of today. However useful tip I found out is that you can press tab while typing pandas method in Jupyter Notebook, so it will suggest some possible methods.
Also by pressing shift and tab you can get information about the method within jupyter notebook. Pressing sh... | Numpy has great help function that allows me to search through the numpy documentation from terminal.
Is there something similar to numpy.lookfor function that works for pandas library?
I know that numpy.info works with pandas functions and methods but I have to know the name of the method beforehand. | 0 | 1 | 79 |
0 | 56,852,299 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-02T11:56:00.000 | 1 | 1 | 0 | NLP Structure Question (best way for doing feature extraction) | 56,851,945 | 0.197375 | python-3.x,pandas,nlp,jupyter-notebook,spacy | The preprocessing pipeline depends mainly upon your problem which you are trying to solve. The use of TF-IDF, word embeddings etc. have their own restrictions and advantages.
You need to understand the problem and also the data associated with it. In order to make the best use of the data, we need to implement the pro... | I am building an NLP pipeline and I am trying to get my head around in regards to the optimal structure. My understanding at the moment is the following:
Step1 - Text Pre-processing [a. Lowercasing, b. Stopwords removal, c. stemming, d. lemmatisation,]
Step 2 - Feature extraction
Step 3 - Classification - using the... | 0 | 1 | 52 |
0 | 56,921,855 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-03T10:13:00.000 | 0 | 1 | 0 | Disparity value difference in Stereo SGBM vs. WLS disparity maps | 56,867,814 | 0 | python-3.x | Anyone who might have an idea please? | I have been working on a stereo imaging project for a few months now. The goal is to track a defined object, compute its position (by using triangulation with stereo camera) and its orientation (by using an IMU). I get really good results, but there is something I don't understand and it's bothering me : the minimum de... | 0 | 1 | 151 |
0 | 56,869,092 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2019-07-03T11:04:00.000 | 0 | 1 | 0 | Pros and cons of an object with big dataframe vs a list of lots of class objects in Python | 56,868,707 | 0 | python,list,dataframe,object | The question is rather broad, so I will not go beyond generalities.
If you intend to process user by user, then it makes sense to have one object per user.
On the other hand, if you mainly process all users at the same time and attribute by attribute, then it makes sense to have classes for attributes each object conta... | I'm writing a python program to perform certain processing on data for couple of millions (N) users. Outcome of the process would be several 1D arrays for each user (the process would apply on each user separately). I need set and get functions for each user output data.
I have two options for implementing this:
1. Cre... | 0 | 1 | 90 |
0 | 58,441,617 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-07-03T18:40:00.000 | 0 | 1 | 0 | How to apply lstm in speech emotion feature | 56,876,204 | 1.2 | python,speech-recognition,lstm | After my internship I learn how to fix out this error and where to look.
Here's what you have to take care.
Unexpected error input form
If the reported layer is the first it is a cause of the input data for the train of a model as a same shape for a create your model.
If this is the last layer that bug then it is the l... | I'd like to apply lstm in my speech emotion datasets (dataset of features in numeric values with one column of targets).
I've done split_train_test. Do I need some other transformation to do in the data set before the model?
I ask this question because when I compile and fit the model I've got one error in the last de... | 0 | 1 | 26 |
0 | 56,882,432 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-07-03T22:16:00.000 | 1 | 1 | 0 | How to write each dataframe partition into different tables | 56,878,553 | 1.2 | python-3.x,pyspark,azure-databricks | If your Id has distinct number of values (kind of type/country column) you can use partitionBy to store and thereby saving them to different table will be faster.
Otherwise create a derive column(using withColumn) from you id column by using the logic same as you want to use while deviding data across tables. Then you ... | I am using Databricks to connect to an Eventhub, where each message comming from the EventHub may be very different from another.
In the message, I have a body and an id.
I am looking for performance, so I am avoiding collecting data or doing unecessary processings, also I want to do the saving in parallel by partition... | 0 | 1 | 134 |
0 | 56,925,320 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-07-04T06:33:00.000 | -1 | 1 | 0 | Find the outliers or anomaly in gps data (time, latitude, longitude, altitude) | 56,881,873 | -0.197375 | python-3.x,machine-learning,data-science | First of all, if you use Python, then use scikit-learn.
For this problem, there is multiple possibilities.
One way is indeed to use a clustering algorithm. For this purpose to get the anomaly too, you can use DBSCAN. It is an algorithm designed to get cluster and the outliers.
Another way would be (assuming you have fo... | I have data. Based on the data (time, latitude, longitude, altitude) determine what are the typical routes that device makes during a full week.
After determining the baseline routes or typical area frequented by device we can start determining an anomaly based on the device traveling outside it’s frequent route/area.
... | 0 | 1 | 493 |
0 | 57,056,265 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-07-04T11:42:00.000 | 0 | 2 | 0 | Is there an option like generator in keras with scikit to process large records of data? | 56,887,202 | 0 | python,machine-learning,keras,scikit-learn,training-data | The Gaussian process implementation(Regression/classification) from scikit is'nt capable of handling big dataset. It can run only upto 15000 rows of data. So I decided to use a different algorithm instead as this seems to be a problem with algorithm. | I have a training dataset of shape(90000,50) and I trying to fit this in model(Gaussian process regression). This errors out with memory error. I do understand the computation, but is there way to pass data in batches using scikit? I am using the scikit implementation of the GPR algorithm. | 0 | 1 | 350 |
0 | 56,888,637 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2019-07-04T12:38:00.000 | 0 | 1 | 0 | jupyter notebook - problem no module named 'pandas' | 56,888,145 | 0 | python,pandas,numpy,jupyter-notebook,anaconda | let's try:
in jupiter
!pip install pandas --upgrade
import pandas as pd
print(pd.__version__)
what do you see | I am trying to learn pandas and I haven't been able to import it into my code. I have looked at other answers on this site and none of them have worked.
jupyter notebook is not importing my module. | 0 | 1 | 76 |
0 | 56,973,276 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-07-04T12:44:00.000 | 0 | 1 | 0 | Keras preprocessing for 3D semantic segmentation task | 56,888,245 | 1.2 | python,image,keras,deep-learning,conv-neural-network | The Input() function defines the shape of the input tensor of a given model. For 3D images, often a 5D Tensor is expected, e.g. (None, 32, 32, 32, 1), where None refers to the batch size. Therefore the training images and labels have to be reshaped. Keras offers the to_categorical function to one-hot encode the label d... | For semantic image segmentation, I understand that you often have a folder with your images and a folder with the corresponding masks. In my case, I have gray-scale images with the dimensions (32, 32, 32). The masks naturally have the same dimensions. The labels are saved as intensity values (value 1 = label 1, value 2... | 0 | 1 | 207 |
0 | 56,893,072 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-04T16:46:00.000 | 0 | 1 | 0 | Uploading images folders from my PC to Google Colab | 56,891,871 | 0 | python,deep-learning,dataset,google-colaboratory | You can try zipping and the unzip on collab
Step 1: Zip the whole folder
Step 2:Upload the folder
Step 3: !unzip myfoldername.zip
Step 4:type ls and see folder names to see if successful
It would be better if you compress or resize the images to reduce file size using opencv or something | I want to train a deep learning (CNN) model on a dataset containing around 100000 images. Since the dataset is huge (approx 82 Gb), I want to use Google colab since it's GPU supported. How do I upload this full image folder into my notebook and use it?
I can not use google drive or git hub since my dataset is too large... | 0 | 1 | 308 |
0 | 56,897,110 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-07-05T03:31:00.000 | 0 | 3 | 0 | Early Stopping, Model has gone through how many epochs? | 56,896,221 | 0 | python-3.x,tensorflow,keras,neural-network | Yes, you get the model (weights) corresponding to the epoch when it stops. A commonly used strategy is to save the model whenever the validation loss/acc improves. | I am using Keras. I am training my Neural Network and using Early Stopping. My patience is 10 and the epoch with the lowest validation loss is 15. My network runs til 25 epochs and stops however my model is the one with 25 epochs not 15 if I understand correctly
Is there an easy way to revert to the 15 epoch model or d... | 0 | 1 | 1,076 |
0 | 56,896,809 | 0 | 0 | 0 | 0 | 2 | false | 2 | 2019-07-05T04:50:00.000 | 0 | 2 | 0 | How to represent the Null class in Multilabel Classification with Convolutional Neural Nets? | 56,896,712 | 0 | python,tensorflow,machine-learning,keras | The model outputs a probability to each class, and we assign the input to the class which has the highest probability during prediction. The last layer is normally a softmax layer for multiclass classification and sigmoid for binary classification. Both of them squash the input into a range of 0 to 1. We can interpret ... | I'm trying to label images with the various categories that they belong to with a convolutional neural net. For my problem, the image can be in a single category, multiple categories, or zero categories. Is it standard practice to set the zero category as all zeroes or should I add an additional null class neuron to th... | 0 | 1 | 791 |
0 | 56,897,025 | 0 | 0 | 0 | 0 | 2 | true | 2 | 2019-07-05T04:50:00.000 | 5 | 2 | 0 | How to represent the Null class in Multilabel Classification with Convolutional Neural Nets? | 56,896,712 | 1.2 | python,tensorflow,machine-learning,keras | Yes, the "null" category should be represented as just zeros. In the end multi-label classification is a set of C binary classification problems, where C is the number of classes, and if all C problems output "no class", then you get a vector of just zeros. | I'm trying to label images with the various categories that they belong to with a convolutional neural net. For my problem, the image can be in a single category, multiple categories, or zero categories. Is it standard practice to set the zero category as all zeroes or should I add an additional null class neuron to th... | 0 | 1 | 791 |
0 | 59,382,368 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-07-06T18:05:00.000 | 1 | 1 | 0 | Adanet running out of memory | 56,916,504 | 0.197375 | python,tensorflow,tensorflow-estimator,adanet | Three hypotheses:
You might have too many DNNs in your ensemble, which can happen if max_iteration_steps is too small and max_iterations is not set (both of those are constructor arguments to AutoEnsembleEstimator). If you want to train each DNN for N steps, and you want an ensemble with 2 DNNs, you should set max_ite... | I tried training an AutoEnsembleEstimator with two DNNEstimators (with hidden units of 1000,500, 100) on a dataset with around 1850 features (after feature engineering), and I kept running out of memory (even on larger 400G+ high-mem gcp vms).
I'm using the above for binary classification. Initially I had trained vari... | 0 | 1 | 39 |
0 | 56,926,165 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2019-07-07T07:51:00.000 | 0 | 1 | 0 | Is there a pre-defined custom indexed array in python? | 56,920,278 | 0 | python-3.x | No.
Feel free to write a wrapper class that adjusts slices by such an offset. | I am a beginner for python programming, specifically for scientific computing. In python, the index of a np.array, by default, starts from 0. Is it possible to change the index starting from any number, such as -1 ..., like what can do in fortran? thanks a lot. | 0 | 1 | 36 |
0 | 59,574,175 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-07-07T10:05:00.000 | 0 | 1 | 0 | How to create a CNN cross filter for Tensorflow in python? | 56,921,148 | 0 | python,tensorflow,filter,conv-neural-network | If I understand your question correctly, you want to use a Cross Filter, like the one whose shape is3*4 rather than a Square Filter, whose shape is 3*3.
If that is the case, that is allowed using the Function, tf.keras.layers.Conv2D.
In the argument, kernel_size, you need to provide (3,4) instead of just 3 or (3,3). | In general, the CNN model uses a square filter. I would like to use a cross filter or X filter. The function of creating a square filter in tensorflow is provided, but information on other filter-types is not available.
How can I make a cross filter and/or an X-filter? | 0 | 1 | 35 |
0 | 56,931,866 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-08T09:04:00.000 | 0 | 1 | 0 | Predict the probability of someone leaving based on two conditions | 56,931,584 | 0 | python,dataframe,statistics,probability | A direct and simple approach would be to use the mean of the percentage you get from the age and tax bracket that they are in. The downsides is that you consider that both those variables are independant and have the same weight in deciding wether an employee will quit or not.
A better approach would be to use a classi... | I am currently working on a dataset that provides information over several years about employees at a big company. Information includes whether or not the employee quit that year (True or False for every year), what Tax Bracket they're in and what age they are. Based on the dataset, I have plottet the percentage of peo... | 0 | 1 | 33 |
0 | 56,950,836 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-08T10:31:00.000 | 1 | 2 | 0 | Which algorithm to use for percentage features in my DV and IV, in regression? | 56,933,059 | 0.099668 | python,statistics,regression,percentage,feature-extraction | Having a mix of predictors doesn't matter for any form of regression, this will only change how you interpret the coefficients. What does matter, however, is the type/distribution of your Y variable
Case 1: Predict a continuous valued Y
a.Will using a Lasso regression suffice?
Regular OLS regression will work fine fo... | I am using regression to analyze server data to find feature importance.
Some of my IVs (independent variables) or Xs are in percentages like % of time, % of cores, % of resource used, while others are in numbers like number of bytes, etc.
I standardized all my Xs with (X-X_mean)/X_stddev. (Am I wrong in doing so?)
Whi... | 0 | 1 | 173 |
0 | 56,952,479 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2019-07-08T18:02:00.000 | 2 | 2 | 1 | Remote video streaming with Pepper and NAO robots | 56,940,339 | 1.2 | python,opencv,gstreamer,nao-robot,pepper | gstreamer should already be installed on the robot, so you could run it on the robot with a command like this:
gst-launch-0.10 -v v4l2src device=/dev/video-top ! video/x-raw-yuv,width=640,height=480,framerate=30/1 ! ffmpegcolorspace ! jpegenc ! multipartmux! tcpserversink port=3000
... and then you can open the stre... | I am trying to remotely stream videos and images from the cameras of the Pepper and NAO robots to my laptop.
First, I used a while loop to repeatedly capture images from the NAO's camera and processed the images through opencv. However, as you can imagine, this only provided me with a framerate of about 1 fps.
Then I... | 0 | 1 | 1,257 |
0 | 56,969,631 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2019-07-10T11:12:00.000 | 0 | 4 | 0 | how do I produce unique random numbers as an array in Python? | 56,969,477 | 0 | python,arrays,numpy,random | If you need all possible arrays in random order, consider enumerating them in any arbitrary deterministic order and then shuffling them to randomize the order. If you don't want all arrays in memory, you could write a function to generate the array at a given position in the deterministic list, then shuffle the positio... | I have an array with size (4,4) that can have values 0 and 1, so I can have 65536 different arrays. I need to produce all these arrays without repeating. I use wt_random=np.random.randint(2, size=(65536,4,4)) but I am worried they are not unique. could you please tell me this code is correct or not and what should I do... | 0 | 1 | 452 |
0 | 56,970,162 | 0 | 1 | 0 | 0 | 2 | false | 0 | 2019-07-10T11:43:00.000 | 0 | 3 | 0 | How to add a column of seconds to a column of times in python? | 56,970,023 | 0 | python,datetime,time,add,timedelta | Probably the easiest way is to read your file into a pandas data-frame and parse each row as a datetime object. Then you create a datetime.timedelta object passing the fractional seconds.
A datetime object + a timedelta handles wrapping around for days quite nicely so this should work without any additional code. Final... | I have a file contain some columns which the second column is time. Like what I show below. I need to add a column of time which all are in seconds like this: "2.13266 2.21784 2.20719 2.02499 2.16543", to the time column in the first file (below). My question is how to add these two time to each oth... | 0 | 1 | 673 |
0 | 56,973,402 | 0 | 1 | 0 | 0 | 2 | false | 0 | 2019-07-10T11:43:00.000 | 0 | 3 | 0 | How to add a column of seconds to a column of times in python? | 56,970,023 | 0 | python,datetime,time,add,timedelta | Ok. Finally it is done via this code:
d= 2.13266
dd= pd.to_timedelta (int(d), unit='s')
df= pd.Timestamp('2014-08-26 19:49:32')
new = df + dd | I have a file contain some columns which the second column is time. Like what I show below. I need to add a column of time which all are in seconds like this: "2.13266 2.21784 2.20719 2.02499 2.16543", to the time column in the first file (below). My question is how to add these two time to each oth... | 0 | 1 | 673 |
0 | 56,981,888 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-07-10T15:34:00.000 | 0 | 1 | 0 | how to average in a specific dimension with numpy.mean? | 56,974,114 | 1.2 | python,numpy | The solution is to specify the correct axis and use keepdims=True which is noted by several commenters (If you add your answer I will delete mine).
This can be done with either pos.mean(axis = 0,keepdims=True) or np.mean(pos,axis=0,keepdims=True) | I have a matrix called POS which has form (10,132) and I need to average those first 10 elements in such a way that my averaged matrix has the form of (1,132)
I have tried doing
means = pos.mean (axis = 1)
or
menas = np.mean(pos)
but the result in the first case is a matrix of (10,) and in the second it is a simple nu... | 0 | 1 | 95 |
0 | 56,978,241 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-10T18:50:00.000 | 1 | 2 | 0 | Can I obtain Word2Vec and Doc2Vec matrices to calculate a cosine similarity? | 56,976,941 | 0.099668 | python,gensim,word2vec,doc2vec | Yes, you could train a Word2Vec or Doc2Vec model on your texts. (Though, your data is a bit small for these algorithms.)
Afterwards, with a Word2Vec model (or some modes of Doc2Vec), you would have word-vectors for all the words in your texts. One simple way to then create a vector for a longer text is to average toget... | I am working with text data and at the moment I have put my data into a term document matrix and calculated the TF, term frequency and TF-IDF, term frequency inverse document frequency. From here my matrix looks like:
columns = document names
rownames = words
filled with their TF and TF-IDF scores.
I have been using t... | 0 | 1 | 1,708 |
0 | 56,980,001 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-07-10T21:59:00.000 | 1 | 1 | 0 | Is it bad to not remove stopwords when I've already set a ceiling on document frequency? | 56,979,185 | 1.2 | python,scikit-learn,nlp,text-mining,text-processing | You are right. It could be the definition of stop words. However, do not forget that one reason to remove the stop words in the first phase, is to prevent counting them and reduce the computation time.
Notice that your intuition behind stop words is correct. | I'm using sklearn.feature_extraction.text.TfidfVectorizer. I'm processing text. It seems standard to remove stop words. However, it seems to me that if I already have a ceiling on document frequency, meaning I will not include tokens that are in a large percent of the document (eg max_df=0.8), dropping stop words doesn... | 0 | 1 | 40 |
0 | 56,988,032 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2019-07-11T10:03:00.000 | 0 | 2 | 0 | Applying "reinforcement learning" on a supervised learning model | 56,986,663 | 0 | python,linear-regression,reinforcement-learning,supervised-learning | Most (or maybe all) iterative supervised learning methods already use a feedback loop on the outputs of the prediction. If fact, this feedback is very informative since it provides information with the exact amount of error in each sample. Think for example in stochastic gradient descent, where you compute the error of... | Is it possible to use "reinforcement learning" or a feedback loop on a supervised model?
I have worked on a machine learning problem using a supervised learning model, more precisely a linear regression model, but I would like to improve the results by creating a feedback loop on the outputs of the prediction, i.e, tel... | 0 | 1 | 361 |
0 | 57,064,604 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2019-07-11T10:03:00.000 | 0 | 2 | 0 | Applying "reinforcement learning" on a supervised learning model | 56,986,663 | 0 | python,linear-regression,reinforcement-learning,supervised-learning | Reinforcement Learning has been used to tune hyper-parameters and/or select optimal Supervised Learning Models. There's also a paper on it: "Learning to optimize with Reinforcement Learning".
Reading Pablo's answer you may want to read up on "back propagation". It may be what you are looking for. | Is it possible to use "reinforcement learning" or a feedback loop on a supervised model?
I have worked on a machine learning problem using a supervised learning model, more precisely a linear regression model, but I would like to improve the results by creating a feedback loop on the outputs of the prediction, i.e, tel... | 0 | 1 | 361 |
0 | 68,777,974 | 0 | 0 | 0 | 0 | 1 | false | 10 | 2019-07-11T11:48:00.000 | 1 | 1 | 0 | Tensorflow 2.0: Accessing a batch's tensors from a callback | 56,988,498 | 0.197375 | python,tensorflow,keras,tensorflow2.0,tf.keras | No, there is no way to access the actual values for input and output in a callback. That's not just part of the design goal of callbacks. Callbacks only have access to model, args to fit, the epoch number and some metrics values. As you found, model.input and model.output only points to the symbolic KerasTensors, not a... | I'm using Tensorflow 2.0 and trying to write a tf.keras.callbacks.Callback that reads both the inputs and outputs of my model for the batch.
I expected to be able to override on_batch_end and access model.inputs and model.outputs but they are not EagerTensor with a value that I could access. Is there anyway to access ... | 0 | 1 | 354 |
0 | 57,031,365 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-11T15:48:00.000 | 0 | 1 | 0 | VTK ObbTree.IntersectWIthLine too slow for large-scale collision detection | 56,993,004 | 0 | python,gpu,vtk | IntersectWithLine only intersects a segment to a polygonal mesh afaik.
You can maybe make a convex hull of the first cloud and then ask for the internal points of the second (?)
In this case one would use vtkSelectEnclosedPoints. | I want to check the collision between a set of points to a point cloud (contain around 1M pts). I actually want to know which parts of the point cloud is collided with those outside points, and store those collided pts in the point cloud.
I loop through each outside point and use the Obbtree.IntersectWithLine to check ... | 0 | 1 | 173 |
0 | 57,639,340 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-11T16:21:00.000 | 0 | 1 | 0 | Python Streamplot from large 1D arrays | 56,993,521 | 0 | python,arrays,python-3.x,matplotlib,plot | try to vectorize your loops that may help greatly with large scale datas. | I need to plot streamtraces from CFD analysis with Python, over a 2D contour plot. My problem is that I'm dealing with 4 large 1D arrays (x,y coordinates and u,v velocity components), say over 100k points, arising from an external CFD simulation (so I cannot manipulate them). Creating 2D arrays from them (e.g. with sci... | 0 | 1 | 48 |
0 | 58,046,297 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-11T17:26:00.000 | 0 | 2 | 0 | Save colab results | 56,994,495 | 0 | python-2.7 | After mounting your Google drive. You can do the following to save your work on the drive:
1 Click File.
2 Click Save File in Drive. | I am using google colab but I can not save the results directly from colab to my google drive or even in my computer. Can anybody give me a hand?
Regards | 0 | 1 | 67 |
0 | 56,997,616 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2019-07-11T21:17:00.000 | 1 | 2 | 0 | check for consecutive values membership in arrays | 56,997,410 | 0.099668 | python,arrays | best way to maintain a mapping of elements in an array to a value is a hash table.
as @JacobIRR mentioned {[4, 5, 6] : 6} is not valid because you cant have multiple keys associated to a value. i cant give you an exact solution to your problem as you didnt mention how your function works.
if your algorithm dosent depen... | There are several similar questions around that suggest using any() with sets, however mine is a bit different in a way that I want to get the most effective and Pythonic way of forming a new array out of existing arrays based on membership.
I have the following array [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 4, 5, 6], [2, 4, 5... | 0 | 1 | 54 |
0 | 57,009,082 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-12T14:00:00.000 | 0 | 1 | 0 | How does fsolve in scipy work? What methods does it use to find the roots? | 57,008,554 | 0 | python,scipy | So, after looking up the source code it seems that the solver "fsolve" uses the powell method to find the roots. | I'm trying to find out how fsolve in scipy works. So far, all I have found is that it is a numerical solver that finds the root of non linear sets of equations. But I can't find what method it uses to find the roots anywhere. Does anyone know how the roots are found? | 0 | 1 | 122 |
0 | 57,012,785 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-07-12T17:34:00.000 | 1 | 2 | 0 | Force scipy.stats.cauchy between an interval | 57,011,662 | 1.2 | python,python-3.x,scipy | Short answer: Not directly, no.
The issue is that a distribution is normalized, the integral of the pdf over the support is unity. When you change the support, you are effectively changing the distribution.
For the truncated Cauchy distribution, you can easily roll your own little generator, using the inverse function ... | I would like to force scipy.stats.cauchy probability density function to only generate values between -1 and 1.
Currently I am doing the hacky way by running y=cauchy.rvs(center,sigma) inside a while loop and while its lower than -1 or higher than 1 it recomputes it and after it enters the desired interval it returns y... | 0 | 1 | 507 |
0 | 57,016,163 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-13T01:06:00.000 | 0 | 2 | 0 | Quickest way to insert zeros into numpy array | 57,015,429 | 0 | python-3.x,numpy | u can use np.zeros and append it to your existing array like
newid=np.append(np.zeros((4,), dtype=int),ids)
Good Luck! | I have a numpy array ids = np.array([1,1,1,1,2,2,2,3,4,4])
and another array of equal length vals = np.array([1,2,3,4,5,6,7,8,9,10])
Note: the ids array is sorted by ascending order
I would like to insert 4 zeros before the beginning of each new id - i.e.
new array = np.array([0,0,0,0,1,2,3,4,0,0,0,0,5,6,7,0,0,0,0,8,0... | 0 | 1 | 126 |
0 | 57,053,572 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2019-07-13T23:37:00.000 | 0 | 1 | 0 | How can I put my curvilinear coordinate data on a map projection? | 57,023,555 | 1.2 | python,interpolation,netcdf | I am pretty sure you can already use methods like contour, contourf, pcolormesh from Python's matplotlib without re-gridding the data. The same methods work for Basemap. | I'm working with NetCDF files from NCAR and I'm trying to plot sea-ice thickness. This variable is on a curvilinear (TLAT,TLON) grid. What is the best way to plot this data on a map projection? Do I need to re-grid it to a regular grid or is there a way to plot it directly? I'm fairly new to Python so any help would be... | 0 | 1 | 276 |
0 | 57,029,334 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-14T12:11:00.000 | 0 | 2 | 0 | Add coordinates to points after changing a df CRS in Geopandas | 57,027,276 | 0 | python,pandas,gis,qgis,geopandas | Alright, so problem solved. It was a simple espg issue. Here's the solution if that can help any other newbie like me:
Even if Google uses espg 3857, if you need to convert the X,Y of your point to something which could be exploitable (such as a url parameter), you need to use espg 4326.
You'll then get your long lat t... | Basically the idea is to automate some workflows without using Qgis.
I'm failing to achieve similar results to the Qgis feature 'Add coordinates to points' in Geopandas, which allows you to grab the x,y coordinates of points in its current projection and create new attributes to the table.
So I have a set of points wit... | 0 | 1 | 659 |
0 | 57,031,293 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-07-14T14:49:00.000 | 0 | 2 | 0 | Create a non-hdfs csv from spark dataframe | 57,028,445 | 0 | python-3.x,pandas,apache-spark,pyspark,pyspark-sql | If you have 45 million records you will likely need to create a set of csv files which spark will do automatically. Depending on where you want to save the data the path will vary. For example if you wanted to write to S3 you would provide a path like this.
df.write.csv("s3://my-bucket/path/to/folder/")
You may als... | I want to create a non-hdfs .csv file using a spark DataFrame. How can do it ?
The purpose of this non-hdfs .csv file is to use read_csv() on it and load it back to a pandas DataFrame.
I tried using toPandas() but I have 45 million records in my spark DataFrame and its very slow. | 0 | 1 | 438 |
0 | 57,029,035 | 0 | 1 | 0 | 0 | 1 | true | 1 | 2019-07-14T15:37:00.000 | 1 | 1 | 0 | Grid Search and computer in sleep mode | 57,028,814 | 1.2 | python,machine-learning,grid-search,gridsearchcv | Sleep mode suspends all processor activities and places RAM in a low power state only enough to retain the state. So, yes grid search or any process for that matter is suspended. | Does the grid search cv stops when my computer is in sleep mode? Should I turn off sleep mode when running it?
Regards
Vikrant | 0 | 1 | 68 |
0 | 57,142,523 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-15T07:29:00.000 | -1 | 1 | 0 | how to use natural language generation from a csv file input .which python module we should use.can any one share a sample tutorial? | 57,035,069 | -0.197375 | python,nlp | There are not much python libraries for NLG!!. Try out nlglib a python wrapper around SimpleNLG. For tutorial purposes, you could read Building Natural Language Generation systems by e.reiter. | take a input as a csv file and generate text/sentence using nlg. I have tried with pynlg and markov chain.But nothing worked .What else I can use? | 0 | 1 | 311 |
0 | 57,045,925 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2019-07-15T18:30:00.000 | 0 | 2 | 0 | Maximizing GCD with some condition | 57,045,356 | 0 | java,c++,python-3.x,python-2.7,greatest-common-divisor | Here is one way of doing it.
Create a mutable class MinMax for storing the min. and max. index.
Create a Map<Integer, MinMax> for storing the min. and max. index for a particular divisor.
For each value in a, find all divisors for a[i], and update the map accordingly, such that the MinMax object stores the min. and max... | This is a problem given in HackWithInfy2019 in hackerrank.
I am stuck with this problem since yesterday.
Question:
You are given array of N integers.You have to find a pair (i,j)
which maximizes the value of GCD(a[i],a[j])+(j - i)
and 1<=i< j<=n
Constraints are:
2<= N <= 10^5
1<= a[i] <= 10^5
I've tried this problem u... | 0 | 1 | 289 |
0 | 57,048,809 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-07-15T19:03:00.000 | 0 | 1 | 0 | How to do multi-objective optimization using neural networks? | 57,045,756 | 1.2 | python,matlab,neural-network,artificial-intelligence | There are many methods, but the easiest way is "linear scalarization".
You can add objectives to make single objective.
While doing this, you can weight objectives considering priority.
(Making linear combination of multiple objectives)
See examples:
Variational AE loss (regularization loss + reconstruction loss)
Assoc... | I have five decision variables, each having a specific range. I need to find a combination of these variables so as to maximize one of my objectives while minimizing the other at the same time. I have prepared a datasheet of randomly generated variables with respective values of the 2 objective functions. Please sugges... | 0 | 1 | 244 |
0 | 57,053,491 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-16T08:24:00.000 | 0 | 1 | 0 | How to include quarterly regressor in Prophet for monthly time series? | 57,053,017 | 0 | python,forecasting,facebook-prophet | You have to evaluate based on your business problem, but there are some questions you can ask yourself.
How are the external regressors making their predictions? Are they trained on completely different data?
If not, are they worth including?
How quickly do we expect those regressors to get "stale"? How far in the f... | I have a monthly time series which I want to forecast using Prophet. I also have external regressors which are only available on a quarterly basis.
I have thought of following possibilities -
repeat the quarterly values to make it monthly and then include
linearly interpolate for the months
What other options I ca... | 0 | 1 | 189 |
0 | 67,595,461 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-16T13:11:00.000 | 0 | 1 | 0 | Keras, Tensorflow are reserving all GPU memory on model build | 57,058,071 | 0 | python-3.x,tensorflow,keras | I meet a similar problem when I load pre-trained ResNet50. The GPU memory usage just surges to 11GB while ResNet50 usually only consumes less than 150MB.
The problem in my case is that I also import PyTorch without actually used it in my code. After commented it, everything works fine.
But I have another PC with the sa... | my GPU is NVIDIA RTX 2080 TI
Keras 2.2.4
Tensorflow-gpu 1.12.0
CUDA 10.0
Once I load build a model ( before compilation ), I found that GPU memory is fully allocated
[0] GeForce RTX 2080 Ti | 50'C, 15 % | 10759 / 10989 MB | issd/8067(10749M)
What could be the reason, how can i debug it?
I don't have spare memory to lo... | 0 | 1 | 502 |
0 | 57,084,810 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-16T20:08:00.000 | 0 | 1 | 0 | Using pandas.merge_asof when one merge column contains NaNs | 57,064,741 | 0 | python,pandas,merge | I did two methods:
1) I set the Nan's to -1. (there was no id that had -1 in the other dataset). Then put them back to Nan after.
2) I removed the records associated with the Nan's for that column, and put the records back in after.
I tried to compare results (and reset the indices, sort by timestamp), but I kept getti... | New to pandas - I have been trying to use the pandas.merge_asof to join two datasets together by shared ID first, then merge by nearest timestamp to the timestamp in df1.
The issue is that I have discovered is that both left_on and right_on must be int. I have one column that contains NaNs and they must remain. Floats... | 0 | 1 | 486 |
0 | 57,066,365 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-07-16T22:30:00.000 | 0 | 1 | 0 | How to apply trained model on images of shape/size larger than what the model was trained on (in Tensorflow)? | 57,066,263 | 1.2 | python,python-2.7,tensorflow | I once have the same question about how to deal with larger or smaller images. According to my experience, a possible method is to resize images to the input size of the network. For example, if your current image size is 982x982 and the network input size is 512x512, then you can just use some libraries like Pillow or... | I trained a model on size 512x512 images. I currently have size 982x982 (or any other size) images that I need to have the model predict on. When I run on these images, I get errors about unexpected input array shape.
Does Tensorflow provide any way to conveniently deploy a model on images of size/shape larger than wha... | 0 | 1 | 234 |
0 | 57,085,156 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-17T04:15:00.000 | 0 | 1 | 0 | Converting multiple string columns to enum in H2o Flow | 57,068,435 | 0 | python,pyspark,h2o | I was not able to find a coding solution. But found an easier way - saved the pyspark df as a parquet in hdfs and imported it into h2o. All string columns were auto-recognised as Enum. | I'm having more than 100 String Columns which I need to be converting into enum so that the ML model identifies theses columns as categories.
In Pyspark, there is no Category type (as in Pandas) and hence I casted all categories as 'String'. I don't want to click 'convert to enum' > 100 times and I'm sure there is an ... | 0 | 1 | 228 |
0 | 57,077,151 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-07-17T11:43:00.000 | 2 | 1 | 0 | Training Keras model without validation set and normalization of images | 57,075,037 | 1.2 | python,keras,conv-neural-network,keras-2,standardized | Yes, you can train a keras model without validation data, but its not a good practice, because then you would not know if the model can generalize or not. The same applies for autoencoders, they can overfit to the training set.
It is always recommended to normalize your inputs, specially if the ranges are large or smal... | I'm using Keras on Python to train a CNN autoencoder. In the fit() method I have to provide validation_split or validation_data. First, I would like to use 80% of my data as training data and 20% as validation data (random split). As soon as I have found the best parameters, I would like to train the autoencoder on al... | 0 | 1 | 3,996 |
0 | 57,209,941 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2019-07-17T20:35:00.000 | 1 | 1 | 0 | How to display a DataFrame without Jupyter Notebook crashing? | 57,083,727 | 0.197375 | python,pandas,numpy | I think your machine is not powerful enough to handle so much data. I can display 57623 rows for my data (with 16 columns) without any problem. But my machine has 252GB memory, and it still took about 1 minute to display these data in jupyter notebook.
If I tried to scroll through the data, it's slow, and sometimes st... | Everytime I try to display a complete DataFrame in my jupyter notebook, the note book crashes. The file wont start up so I had to make a new jupyter notebook file. When i do display(df) it only shows a couple of the rows, when I need to show 57623 rows. I need to show results for all of these rows and put them into an ... | 0 | 1 | 534 |
0 | 62,209,904 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2019-07-18T02:38:00.000 | 0 | 5 | 0 | How do you clear Google Colab's output periodically | 57,086,398 | 0 | python,tensorflow,google-colaboratory,object-detection-api | This is a partial solution, but you could also specify your verbose parameter in your fit method to 0 to get no output, or to 2 to get a reduced output.
And you can use TensorBoard to still see what is going on. | I am using Google Colab to train an object detection model, using the tensorflow object detection api. When I run the cell train.py, it keeps printing diegnostic output. After 30 minutes or so the browser crashes, because of the high number of lines printed in the cell's output.
Is there any script which one can use to... | 0 | 1 | 8,171 |
0 | 57,088,849 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2019-07-18T07:01:00.000 | 0 | 2 | 0 | how to pass in 'training' argument to tf,keras.Model when using model.fit | 57,088,786 | 0 | python,tensorflow,keras | As far as i know, there is no argument for this. Model.fit simply trains the model on whatever training data provided, and at the end of each epoch evaluates the training on either provided validation data, OR by the use of validation_split. | So I have this model written by the subclassing API, the call signature looks like call(x, training), where training argument is needed to differentiate between training and non-training when doing batchnorm and dropout. How do I make the model forward pass know I am in training mode or eval mode when I use model.fit?
... | 0 | 1 | 1,713 |
0 | 57,093,852 | 0 | 0 | 0 | 0 | 1 | false | 6 | 2019-07-18T11:24:00.000 | 0 | 2 | 0 | how to add custom Keras model in OpenCv in python | 57,093,345 | 0 | python,opencv,keras | You would save the model to H5 file model.save("modelname.h5") , then load it in OpenCV code load_model("modelname.h5"). Then in a loop detect the objects you find via model.predict(ImageROI) | i have created a model for classification of two types of shoes
now how to deploy it in OpenCv (videoObject detection)??
thanks in advance | 0 | 1 | 5,883 |
0 | 57,110,360 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2019-07-18T19:41:00.000 | 0 | 1 | 0 | Can my training and testing data for neural network be of different lengths? | 57,101,619 | 0 | python,keras,neural-network,time-series,lstm | You have 2 options:
First option, as @stormzhou commented above, you can pad you test data with zeros (not recommended)
Second option, you can use for training and validation only 200 timesteps. | I am trying to use a recurrent neural network for a regression problem. I have 50 samples of 600 timesteps each. I am planning to use 40 for training and 10 for validation.
After my network gets trained, can I use it to predict for a timeseries of a smaller length (200 timesteps)?
The input and output dimensions (i.e... | 0 | 1 | 182 |
0 | 57,103,655 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2019-07-18T22:58:00.000 | 1 | 1 | 0 | How to deal with infrequent data in a time series prediction model | 57,103,601 | 0.197375 | python,deep-learning,dataset,missing-data | I would be tempted to put the last quarter revenue in a separate table, with a date field representing when that quarter began (or ended, it doesn't really matter). Then you can write queries to work the way that most suits your application. You could certainly reconstitute the view you mention above using that table, ... | I am trying to create a basic model for stock price prediction and some of the features I want to include come from the companies quarterly earnings report (every 3 months) so for example; if my data features are Date, OpenPrice, Close Price, Volume, LastQrtrRevenue how do I include LastQrtrRevenue if I only have a va... | 0 | 1 | 161 |
0 | 57,112,843 | 0 | 1 | 0 | 0 | 1 | true | 13 | 2019-07-19T04:54:00.000 | 28 | 2 | 0 | ModuleNotFoundError: No module named 'plotly.graph_objects' | 57,105,747 | 1.2 | python,plotly,plotly-python | You should use instead:
from plotly import graph_objs as go | Trying to use 'plotly.graph_objects' but I get this error
ModuleNotFoundError: No module named 'plotly.graph_objects'
How do I download module and apply using anaconda navigator | 0 | 1 | 24,260 |
0 | 57,165,757 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2019-07-19T08:13:00.000 | 0 | 2 | 0 | How to Bokeh plot a combination of Miles and Metres on axis (e.g. 7.5 Miles 100m, 7.5 Miles 200m, 8 Miles 100m....) | 57,108,242 | 1.2 | python,pandas,bokeh | I figured it out in the end, I just converted the Miles(e.g. 7.5) and Meters(e.g. 400m) into Miles (e.g. 7.75miles). Then I used the tickformatter to display the specific points as meters. Thanks for the help | I need to plot some data with Bokeh which has distance as the x-axis. However the distance data I have been provided with is in the format "7 Miles 100 Meters, 7 Miles 200m" etc.
What's the best way to do this? | 0 | 1 | 123 |
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