GUI and Desktop Applications int64 0 1 | A_Id int64 5.3k 72.5M | Networking and APIs int64 0 1 | Python Basics and Environment int64 0 1 | Other int64 0 1 | Database and SQL int64 0 1 | Available Count int64 1 13 | is_accepted bool 2
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 | 35,362,451 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2016-02-12T12:05:00.000 | 1 | 1 | 0 | numpy.arange floating point errors | 35,362,241 | 1.2 | python,python-2.7,numpy,floating-point | That's because floats (most of the time) cannot represent the exact value you put in. Try print("%.25f" % np.float64(0.1)) which returns 0.1000000000000000055511151 that's not exactly 0.1.
Numpy already provides a good workaround for almost-equal (floating point) comparisons: np.testing.assert_almost_equal so you can t... | Why does np.arange(5, 60, 0.1)[150] yield 19.999999999999947. But np.arange(5, 60, 0.5)[30] yield 20.0?
Why does this happen? | 0 | 1 | 1,265 |
0 | 35,412,636 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2016-02-12T13:25:00.000 | 0 | 2 | 0 | Error Importing Python Packages into Jupyter | 35,363,795 | 0 | python,windows,python-2.7 | Running this solved the problem: pip install scipy-0.16.1-cp27-none-win_amd64.whl After doing this, all other packages were able to be re-installed and successfully imported. | I've been working in Jupyter IPython notebook (using Python 2.7) and haven't had any issues before this importing and installing packages. Most of my packages were installed via Anaconda. Now I'm randomly having problems importing packages that I've always been able to import. I have an example below. Please help. I'm ... | 0 | 1 | 1,319 |
0 | 35,383,780 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2016-02-13T13:56:00.000 | 1 | 3 | 0 | Making Dataframe Analysis faster | 35,380,802 | 0.066568 | python,pandas | Use cProfile and lineprof to figure out where the time is being spent.
To get help from others, post your real code and your real profile results.
Optimization is an empirical process. The little tips people have are often counterproductive. | I am using three dataframes to analyze sequential numeric data - basically numeric data captured in time. There are 8 columns, and 360k entries. I created three identical dataframes - one is the raw data, the second a "scratch pad" for analysis and a third dataframe contains the analyzed outcome. This runs really slowl... | 0 | 1 | 69 |
0 | 35,383,875 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2016-02-13T13:56:00.000 | 0 | 3 | 0 | Making Dataframe Analysis faster | 35,380,802 | 0 | python,pandas | Most probably it doesn't matter because pandas stores each column separately anyway (DataFrame is a collection of Series). But you might get better data locality (all data next to each other in memory) by using a single frame, so it's worth trying. Check this empirically. | I am using three dataframes to analyze sequential numeric data - basically numeric data captured in time. There are 8 columns, and 360k entries. I created three identical dataframes - one is the raw data, the second a "scratch pad" for analysis and a third dataframe contains the analyzed outcome. This runs really slowl... | 0 | 1 | 69 |
0 | 35,384,823 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2016-02-13T16:29:00.000 | 0 | 1 | 0 | dumping several objects into the same file | 35,382,336 | 1.2 | python,numpy,scipy,pickle | You could use dill. dill.dump accesses and uses the dump method from numpy to store an array or matrix object, so it's stored the same way it would be if you did it directly from the method on the numpy object. You'd just dill.dump the dictionary.
dill also has the ability to store pickles in compressed format, but i... | Let's say I have a dictionary of about 100k pairs of strings, and a numpy matrix of shape (100k, 500). I would like to save them to the disk in a same file.
What I'm doing right now is using cPickle to dump the dictionary, and scipy.io.savemat to dump the matrix. This way, the dump / load is very fast. But the problem ... | 0 | 1 | 498 |
0 | 35,423,410 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-02-15T08:12:00.000 | 0 | 1 | 0 | pygal data series of different lengths | 35,404,323 | 0 | python,pygal | Never mind, I got it sorted, I was using the wrong graph type | How can I plot multiple data series with different number of elements and have them fill the graph along the x axis?
At the moment if I graph a = [1,2,3,4,5] and b = [1,2,3] the b lines only covers half the graph. Is this possible or do I need to somehow combine the graphs after plotting/rendering them? | 0 | 1 | 110 |
0 | 35,420,774 | 0 | 1 | 1 | 0 | 1 | false | 2 | 2016-02-15T13:56:00.000 | 0 | 2 | 0 | Python logger.debug converting arguments to string without logging | 35,411,265 | 0 | python,logging,optimization | Use logger.debug('%s', myArray) rather than logger.debug(myArray). The first argument is expected to be a format string (as all the documentation and examples show) and is not assumed to be computationally expensive. However, as @dwanderson points out, the logging will actually only happen if the logger is enabled for ... | I'm optimizing a Python program that performs some sort of calculation. It uses NumPy quite extensively. The code is sprinkled with logger.debug calls (logger is the standard Python log object).
When I run cProfile I see that Numpy's function that converts an array to string takes 50% of the execution time. This is sur... | 0 | 1 | 2,623 |
0 | 35,438,322 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-02-16T15:32:00.000 | 1 | 3 | 0 | can i make linear regression predict like a classification? | 35,436,599 | 0.066568 | python,scikit-learn,linear-regression | There is a linear classifier sklearn.linear_model.RidgeClassifer(alpha=0.) that you can use for this. Setting the Ridge penalty to 0. makes it do exactly the linear regression you want and set the threshold to divide between classes. | I trained a linear regression model(using sklearn with python3),
my train set was with 94 features and the class of them was 0 or 1..
than i went to check my linear regression model on the test set and it gave me those results:
1.[ 0.04988957] its real value is 0 on the test set
2.[ 0.00740425] its real value is 0 on t... | 0 | 1 | 2,479 |
0 | 35,441,725 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-02-16T19:22:00.000 | 1 | 1 | 0 | pandas to_csv on dataframe with a column that has dates below 1900 | 35,441,310 | 1.2 | python-2.7,csv,pandas | Upgrading pandas from 0.15.1 to 0.17.1 resolved this issue. | I need to save to csv, but have date values in the series that are below 1900 (ie Mar 1 1899), which is preventing this from happening. I get ValueError: year=1899 is before 1900; the datetime strftime() methods require year >= 1900. It seems a little absurd for a function like this to work only for dates above 1900s, ... | 0 | 1 | 136 |
0 | 35,464,658 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-02-17T14:21:00.000 | 1 | 2 | 0 | Creating a 3d numpy array matrix using append method | 35,464,138 | 0.099668 | python,numpy,memory,arrays | By definition you cannot append anything to an array because when the array is declared in memory it has to reserve as much space as it is going to need.
What you can do is to either declare an array with the known geometry and initial values and then rewrite the new values per row keeping a counter of the rows "append... | Is there a way to create a 3d numpy array by appending 2d numpy arrays? What I currently do is append my 2d numpy array into an initialized list of pre determined 2d numpy array, i.e., List=[np.zeros((600,600))]. After appending all my 2d numpy arrays I use numpy.dstack to create 3d numpy array. I think this is not a v... | 0 | 1 | 1,796 |
0 | 44,714,952 | 0 | 1 | 0 | 0 | 2 | false | 10 | 2016-02-17T19:53:00.000 | 7 | 5 | 0 | OpenCV for Python 3.5.1 | 35,466,429 | 1 | python,python-3.x,opencv | For anyone who would like to install OpenCV on Python 3.5.1,use this library called
opencv-contrib-python
This library works for Python 3.5.1 | I have searched quite a bit regarding this and I've tried some of these methods myself but I'm unable to work with OpenCV.So can anyone of you help me install OpenCV for python 3.5.1?
I'm using anaconda along with Pycharm in windows
Or is this not possible and i have to use python 2.7?
Thanks in advance | 0 | 1 | 79,914 |
0 | 63,226,795 | 0 | 1 | 0 | 0 | 2 | false | 10 | 2016-02-17T19:53:00.000 | 1 | 5 | 0 | OpenCV for Python 3.5.1 | 35,466,429 | 0.039979 | python,python-3.x,opencv | For OS:Windows 10 and Python version: 3.5.1 & 3.6, this worked for me
pip install opencv-contrib-python | I have searched quite a bit regarding this and I've tried some of these methods myself but I'm unable to work with OpenCV.So can anyone of you help me install OpenCV for python 3.5.1?
I'm using anaconda along with Pycharm in windows
Or is this not possible and i have to use python 2.7?
Thanks in advance | 0 | 1 | 79,914 |
0 | 41,201,223 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-02-18T04:23:00.000 | 1 | 3 | 0 | Can we predict time-series single-dimensional data using H2O? | 35,472,785 | 0.066568 | r,python-2.7,machine-learning,prediction,h2o | I have tried to use many of the default methods inside H2O with time series data. If you treat the system as a state machine where the state variables are a series of lagged prior states, it's possible, but not entirely effective as the prior states don't maintain their causal order. One way to alleviate this is to as... | We have hourly time series data having 2 columns, one is the timestamp and other is the error rate. We used H2O deep-learning model to learn and predict future error-rate but looks like it requires at least 2 features (except timestamp) for creating the model.
Is there any way h2o can learn this type of data (time, val... | 0 | 1 | 2,186 |
0 | 35,486,318 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2016-02-18T15:29:00.000 | 0 | 2 | 0 | Removing decimals on export python | 35,485,629 | 0 | python,pandas | figured it out. specified the data type on import with dtype = {"phone" : str, "other_phone" : str}) | I'm using pandas to input a list of names and phone numbers, clean that list, then export it. When I export the list, all of the phone numbers have '.0' tacked on to the end. I tried two solutions:
A: round()
B: converting to integer then converting to text (which has worked in the past)
For some reason when I tried A,... | 0 | 1 | 79 |
0 | 35,492,991 | 0 | 0 | 0 | 0 | 1 | true | 7 | 2016-02-18T21:14:00.000 | 12 | 1 | 0 | How to update an SVM model with new data | 35,492,556 | 1.2 | python,numpy,machine-learning,computer-vision,scikit-learn | In sklearn you can do this only for linear kernel and using SGDClassifier (with appropiate selection of loss/penalty terms, loss should be hinge, and penalty L2). Incremental learning is supported through partial_fit methods, and this is not implemented for neither SVC nor LinearSVC.
Unfortunately, in practise fitting... | I have two data set with different size.
1) Data set 1 is with high dimensions 4500 samples (sketches).
2) Data set 2 is with low dimension 1000 samples (real data).
I suppose that "both data set have the same distribution"
I want to train an non linear SVM model using sklearn on the first data set (as a pre-training )... | 0 | 1 | 4,307 |
0 | 37,745,749 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-02-20T11:45:00.000 | 1 | 1 | 0 | Installation of nltk and scikit-learn | 35,522,767 | 0.197375 | python,numpy,scikit-learn | I met the same problem today. Now I have solved it.
Because I have installed numPy manually, and I use the command "pip" to install the else package.
Solve way:
find the old version of numPy.
You can import numPy and print the path of it.
delete the folder.
use pip to install again. | I have been trying to install and use scikit-learn and nltk. However, I get the following error while importing anything:
Traceback (most recent call last):
File "", line 1, in
File "/usr/local/lib/python2.7/site-packages/sklearn/init.py", line 57, in
from .base import clone
File "/usr/local/lib/p... | 0 | 1 | 474 |
0 | 35,531,393 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-02-21T01:51:00.000 | 1 | 4 | 0 | In pandas, how to get 2nd mode | 35,531,367 | 0.049958 | python,pandas | Try this method:
Create a duplicate data set.
Use .mode() to find the most common value.
Pop all items with that value from the set.
Run .mode() again on the modified data set. | So I'm generating a summary report from a data set. I used .describe() to do the heavy work but it doesn't generate everything I need i.e. the second most common thing in the data set.
I noticed that if I use .mode() it returns the most common value, is there an easy way to get the second most common? | 0 | 1 | 2,825 |
0 | 38,318,621 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-02-21T08:39:00.000 | 0 | 1 | 0 | Include django rest pandas dataframe Index field in json response | 35,534,039 | 0 | python,django,django-rest-framework | One simple way would be to just reset the index in transform_dataframe method.
df = df.reset_index()
This would just add a new index and set your old index as a column, included in the output. | Backgroud
I am using django-rest-pandas for serving json & xls.
Observation
When I hit url with format=xls, I get complete data in the downloaded file. But for format=josn, the index field of dataframe is not part of the records.
Question
How can I make django-rest-pandas to include dataframe's index field in json re... | 1 | 1 | 363 |
0 | 41,886,452 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-02-21T17:03:00.000 | 1 | 1 | 0 | how to convert a RGB image to its YIQ components in python , and then apply dct transform to compress it? | 35,539,077 | 0.197375 | python,matplotlib,dct | Are you willing to use a library outside of numpy, scipy, and matplotlib?
If so you can use skimage.color.rgb2yiq() from the scikit-image library. | In matlab we have rgb2ntsc() function to get YIQ components of a RGB image. Is there a similar function available in python (numpy ,matplotlib or scipy libraries)?
Also to apply discrete cosine transform (compress it) we can use dct2() , in matlab , is there a similar function in python? | 0 | 1 | 1,415 |
0 | 43,568,782 | 0 | 0 | 0 | 0 | 1 | false | 63 | 2016-02-22T10:31:00.000 | 27 | 3 | 0 | Tensorflow Tensorboard default port | 35,551,326 | 1 | python,tensorflow,tensorboard | You should provide a port flag (--port=6007).
But I am here to explain how you can find it and other flags without any documentation. Almost all command line tools have a flag -h or --help which shows all possible flags this tool allows.
By running it you will see information about a port flag and that --logdir allows... | Is there a way to change the default port (6006) on TensorBoard so we could open multiple TensorBoards? Maybe an option like --port="8008"? | 0 | 1 | 72,323 |
0 | 35,576,424 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2016-02-22T11:39:00.000 | 0 | 2 | 0 | Ignoring NaN when interpolating grid in Python | 35,552,667 | 0 | python,scipy,interpolation,nan | Spline fitting/interpolation is global, so it's likely that even a single nan is messing up the whole mesh. | I have a gridded velocity field that I want to interpolate in Python. Currently I'm using scipy.interpolate's RectBivariateSpline to do this, but I want to be able to define edges of my field by setting certain values in the grid to NaN. However, when I do this it messes up the interpolation of the entire grid, effecti... | 0 | 1 | 776 |
0 | 35,552,764 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2016-02-22T11:39:00.000 | 1 | 2 | 0 | Ignoring NaN when interpolating grid in Python | 35,552,667 | 0.099668 | python,scipy,interpolation,nan | All languages that implement floating point correctly (which includes python) allow you to test for a NaN by comparing a number with itself.
x is not equal to x if, and only if, x is NaN.
You'll be able to use that to filter your data set accordingly. | I have a gridded velocity field that I want to interpolate in Python. Currently I'm using scipy.interpolate's RectBivariateSpline to do this, but I want to be able to define edges of my field by setting certain values in the grid to NaN. However, when I do this it messes up the interpolation of the entire grid, effecti... | 0 | 1 | 776 |
0 | 48,353,933 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2016-02-23T12:06:00.000 | 0 | 1 | 0 | getting error for importing numpy at Python 3.5.1 | 35,577,179 | 0 | python,numpy | It may be possible that you have have installed pip for some lower version of python. To check it first look for your default python version by:
$python
Now check for your linked version of python with pip
$pip --version
Now see if the two python versions match.
If they don't match then, you need to upgrade you pip... | I have installed numpy-1.11.0b3 by, pip install "numpy-1.11.0b3+mkl-cp35-cp35m-win32.whl". The installation became successful.
But, when I write "import numpy" at the Python Shell (3.5.1), I am getting the error as - ImportError: No module named 'numpy'.
Can anyone suggest me regarding this ?
Regards, Arpan Ghose | 0 | 1 | 429 |
0 | 35,583,196 | 0 | 0 | 0 | 1 | 2 | false | 2 | 2016-02-23T15:28:00.000 | 1 | 3 | 0 | Fastest approach for geopandas (reading and spatialJoin) | 35,581,528 | 0.066568 | python,multithreading,pandas,geopandas | I am assuming you have already implemented GeoPandas and are still finding difficulties?
you can improve this by further hashing your coords data. similar to how google hashes their search data. Some databases already provide support for these types of operations (eg mongodb). Imagine if you took the first (left) digit... | I have about a million rows of data with lat and lon attached, and more to come. Even now reading the data from SQLite file (I read it with pandas, then create a point for each row) takes a lot of time.
Now, I need to make a spatial joint over those points to get a zip code to each one, and I really want to optimise t... | 0 | 1 | 939 |
0 | 35,786,998 | 0 | 0 | 0 | 1 | 2 | true | 2 | 2016-02-23T15:28:00.000 | 1 | 3 | 0 | Fastest approach for geopandas (reading and spatialJoin) | 35,581,528 | 1.2 | python,multithreading,pandas,geopandas | As it turned out, the most convenient solution in my case is to use pandas.read_SQL function with specific chunksize parameter. In this case, it returns a generator of data chunks, which can be effectively feed to the mp.Pool().map() along with the job;
In this (my) case job consists of 1) reading geoboundaries, 2) s... | I have about a million rows of data with lat and lon attached, and more to come. Even now reading the data from SQLite file (I read it with pandas, then create a point for each row) takes a lot of time.
Now, I need to make a spatial joint over those points to get a zip code to each one, and I really want to optimise t... | 0 | 1 | 939 |
0 | 35,589,047 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-02-23T18:41:00.000 | 1 | 1 | 0 | Bokeh: add a % sign to axis ticks without changing numeric value | 35,585,580 | 0.197375 | python,formatting,bokeh | To elaborate on what bigreddot proposed: PrintfTickFormatter(format='%0.0f %%') worked. One thing to note is the %% to properly escape the %. | I am creating a plot in Bokeh with percentages on the y-axis. The data is represented as a percent (e.g. '99.0') as opposed to a likelihood (e.g. '0.990'). I want to add a '%' sign after each number on the axis, but when using NumeralTickFormatter(format='0 %') my values are multiplied by 100 because it expects a likel... | 0 | 1 | 1,425 |
0 | 35,604,894 | 0 | 0 | 0 | 0 | 1 | false | 4 | 2016-02-24T13:57:00.000 | 1 | 2 | 0 | Split numpy array into similar array based on its content | 35,604,173 | 0.099668 | python,arrays,numpy,curves,curvesmoothing | take the length of the line on every axes, the split as you want.
example:
point 1: [0,0]
point 2: [1,1]
then:
length of the line on X axes: 1-0 = 1
also in the Y axes.
now, if you want to split it in two, just divide these lengths, and create a new array.
[0,0],[.5,.5],[1,1] | I have a 2D numpy array that represents the coordinates (x, y) of a curve, and I want to split that curve into parts of the same length, obtaining the coordinates of the division points.
The most easy example is a line defined for two points, for example [[0,0],[1,1]], and if I want to split it in two parts the result ... | 0 | 1 | 279 |
0 | 35,629,019 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2016-02-25T13:32:00.000 | 1 | 2 | 0 | Hide wrong values of a graph | 35,628,756 | 0.099668 | python-3.x,matplotlib | Forgive me for I'm not familiar with matplotlib but I'm presuming that you're reading the csv file directly into matplotlib. If so is there an option to read the csv file into your app as a list of ints or as a string and then do the data validation before passing that string to the library?
Apologies if my idea is n... | I am building graphics using Matplotlib and I sometimes have wrong values in my Csv files, it creates spikes in my graph that I would like to suppress, also sometimes I have lots of zeros ( when the sensor is disconnected ) but I would prefer the graph showing blank spaces than wrong zeros that could be interpreted as ... | 0 | 1 | 21 |
0 | 35,629,514 | 0 | 0 | 0 | 0 | 2 | true | 0 | 2016-02-25T13:32:00.000 | 0 | 2 | 0 | Hide wrong values of a graph | 35,628,756 | 1.2 | python-3.x,matplotlib | I found a way that works:
I used the Xlim to set my max and min x values and then i set all the values that i didnt want to nan ! | I am building graphics using Matplotlib and I sometimes have wrong values in my Csv files, it creates spikes in my graph that I would like to suppress, also sometimes I have lots of zeros ( when the sensor is disconnected ) but I would prefer the graph showing blank spaces than wrong zeros that could be interpreted as ... | 0 | 1 | 21 |
0 | 35,638,383 | 0 | 0 | 0 | 0 | 1 | false | 5 | 2016-02-25T18:53:00.000 | 1 | 1 | 0 | Is it better to store Pandas Data Frames in a dictionary or in a Panel? | 35,635,870 | 0.197375 | python,dictionary,pandas,dataframe,panel | I don't think you need a panel. I recommend a nested dataframe approach. | I hope this doesn't sound as an open question for discussion. I am going to give some details for my specific case.
I am new to Pandas and I need to store several 2D arrays, where columns represent frequencies and rows represent directions (2D waves spectra, if you are curious). Each array represent a specific time.
I ... | 0 | 1 | 1,554 |
0 | 35,664,986 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-02-25T20:19:00.000 | 0 | 2 | 0 | Rounding up a jitter in psychopy | 35,637,501 | 1.2 | python,psychopy | difference = 1.0 - (RT - int(RT)) | I'm building a fMRI paradigm and I have a stimulus that disappears when a user presses a button (up to 4s), then a jitter (0-12s), then another stimulus presentation. I'm locking the stimuli presentation to the 1s TR of the scanner so I'm curious how I can round up the jitter time to the nearest second.
So, the task i... | 0 | 1 | 188 |
0 | 35,638,661 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-02-25T20:46:00.000 | 1 | 1 | 0 | Force classifer to select a fixed number of targets | 35,637,968 | 1.2 | python,machine-learning,classification,random-forest | Just do it yourself. Each classifier in scikit-learn gives you access to decision_function or predict_proba, both of which are support for predict operation (predict is just argmax of these). Thus - just select 100 with the highest support. | If I have a dataset with events where each event has data with 1000 possible items with only 100 being correct for each event. How do I force my classifier to select only 100 for each event?
After I run it through my training model (with 18 features and always has 100 targets/event flagged as 1) the classifier selects ... | 0 | 1 | 33 |
0 | 35,646,195 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-02-26T07:20:00.000 | 1 | 1 | 0 | Statistic to consider while analysing a value with previous set of values | 35,645,776 | 1.2 | python,statistics,mathematical-optimization | As I'm understanding it, you want some measure of central tendency. There are three of these: mean, median, and mode. Which one you want to use depends on your goals and priorities. Mean is very popular and understandable to people. It has a lot of useful statistical properties. However, it is subject to outliers. On t... | I am trying to find a good statistical method to compare a given value with an existing set of values. Currently I am considering mean of the existing numbers and comparing it with the given value. If the value is off by 50% of the mean then I would say it is off the flow. I am using python programming language for all... | 0 | 1 | 37 |
0 | 35,835,011 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-02-26T09:42:00.000 | 0 | 2 | 0 | Find all roots of a nonlinear function | 35,648,212 | 0 | python,numerical-methods,nonlinear-functions,nonlinear-optimization | You can square the function and use global optimization software to locate all the minima inside a domain and pick those with a zero value.
Stochastic multistart based global optimization methods with clustering are quite proper for this task. | Let us assume I have a smooth, nonlinear function f: R^n -> R with the (known) maximum number of roots N. How can I find the roots efficiently? Right now I have calculated the function on the grid on a preselected area, refined the grid where the function is below a predefined threshold and continued that routine, but ... | 0 | 1 | 866 |
0 | 35,658,350 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-02-26T15:32:00.000 | 2 | 1 | 0 | Python packages for Spark on datanodes | 35,655,693 | 0.379949 | python,numpy,apache-spark,pyspark | Yes, putting the packages on a NAS mount to which all the datanodes are connected will work up to dozens and perhaps 100 nodes if you have a good NAS. However, this solution will break down at scale as all the nodes try to import the files they need. The Python import mechanism usese a lot of os.stat calls to the f... | We want to use Python 3.x with packages like NumPy, Pandas,etc. on top of Spark.
We know the Python distribution with these packages needs to be present/distributed on all the datanodes for Spark to use these packages.
Instead of setting up this Python distro on all the datanodes, will putting it on a NAS mount to whic... | 0 | 1 | 144 |
0 | 35,663,902 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2016-02-26T20:46:00.000 | 1 | 2 | 0 | How to detect objects in a video opencv with Python? | 35,661,419 | 0.099668 | python,opencv,frames | How are your objects filled or just an outline?
In either case the approach I would take is to detect the vertices by finding the maximum gradient or just by the bounding box. The vertices will be on the bounding box. Once you have the vertices, you can say whether the object is a square or a rectangle just by finding ... | I have a video consisting of different objects such as square, rectangle , triangle. I somehow need to detect and show only square objects. So in each frame, if there is a square, it is fine but if there is a triangle or rectangle then it should display it. I am using background subtraction and I am able to detect all ... | 0 | 1 | 948 |
0 | 35,788,514 | 0 | 0 | 0 | 0 | 2 | true | 0 | 2016-02-26T20:46:00.000 | 3 | 2 | 0 | How to detect objects in a video opencv with Python? | 35,661,419 | 1.2 | python,opencv,frames | You can use the following algorithm:
-Perform Background subtraction, as you're doing currently
-enclose foreground in contours (using findContours(,,,) then drawContours(,,,) function)
-enclose obtained contours in bounding boxes (using boundingRect(,,,) function)
-if area of bounding box is approximately equal to tha... | I have a video consisting of different objects such as square, rectangle , triangle. I somehow need to detect and show only square objects. So in each frame, if there is a square, it is fine but if there is a triangle or rectangle then it should display it. I am using background subtraction and I am able to detect all ... | 0 | 1 | 948 |
0 | 35,670,515 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-02-27T13:24:00.000 | 0 | 2 | 0 | python pandas ... alternatives to iterrows in pandas to get next rows value (NEW) | 35,670,348 | 0 | python,pandas,apply,next | While it's not the most "fancy" way - I would just use a numeric iterator and access lines i and i+1 | I have a df in pandas
import pandas as pd
import pandas as pd
df = pd.DataFrame(['AA', 'BB', 'CC'], columns = ['value'])
I want to iterate over rows in df. For each row i want rows value and next rows value.
Here is the desired result.
0 1 AA BB
1 2 BB CC
I have tried a pairwise() function with itertools.
from itertool... | 0 | 1 | 908 |
0 | 35,677,835 | 0 | 1 | 0 | 0 | 1 | false | 39 | 2016-02-28T01:47:00.000 | 0 | 3 | 0 | Understanding matplotlib: plt, figure, ax(arr)? | 35,677,767 | 0 | python,matplotlib,plot,figure | pyplot is matlab like API for those who are familiar with matlab and want to make quick and dirty plots
figure is object-oriented API for those who doesn't care about matlab style plotting
So you can use either one but perhaps not both together. | I'm not really new to matplotlib and I'm deeply ashamed to admit I have always used it as a tool for getting a solution as quick and easy as possible. So I know how to get basic plots, subplots and stuff and have quite a few code which gets reused from time to time...but I have no "deep(er) knowledge" of matplotlib.
Re... | 0 | 1 | 8,825 |
0 | 35,731,697 | 0 | 0 | 0 | 0 | 2 | true | 0 | 2016-03-01T19:08:00.000 | 0 | 2 | 0 | Efficiency of passing arrays to a function vs unpickling the array inside the function in Python? | 35,731,339 | 1.2 | python,arrays,performance | It depends on your requirement. If you pass an array to the function, python passes the reference of that array. Whereas if you pass the array in the form of unpacked arguments, objects comprising the array is passed.
Within the function if the order of your original array is not getting modified, it is better to pass... | Sorry in advance, I know almost nothing about efficiency, so I might need a little extra help.
I have a function that calls another function and it passes in pretty big arrays. Due to limitations in memory, I've just been saving the arrays to pickle files and unpickling inside the second function. Just for context, I ... | 0 | 1 | 45 |
0 | 35,732,141 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2016-03-01T19:08:00.000 | 0 | 2 | 0 | Efficiency of passing arrays to a function vs unpickling the array inside the function in Python? | 35,731,339 | 0 | python,arrays,performance | If you memory limitations doesn't allow to keep all files in memory, you will read the file each time and pass list to another function (it will be passed by reference). | Sorry in advance, I know almost nothing about efficiency, so I might need a little extra help.
I have a function that calls another function and it passes in pretty big arrays. Due to limitations in memory, I've just been saving the arrays to pickle files and unpickling inside the second function. Just for context, I ... | 0 | 1 | 45 |
0 | 60,751,250 | 0 | 0 | 0 | 0 | 3 | false | 17 | 2016-03-02T03:54:00.000 | 0 | 9 | 0 | Matplotlib.pyplot.hist() very slow | 35,738,199 | 0 | python,matplotlib,histogram | If you are working with pandas, make sure the data you passed in plt.hist() is a 1-d series rather than a dataframe. This helped me out. | I'm plotting about 10,000 items in an array. They are of around 1,000 unique values.
The plotting has been running half an hour now. I made sure rest of the code works.
Is it that slow? This is my first time plotting histograms with pyplot. | 0 | 1 | 21,439 |
0 | 58,707,409 | 0 | 0 | 0 | 0 | 3 | false | 17 | 2016-03-02T03:54:00.000 | 0 | 9 | 0 | Matplotlib.pyplot.hist() very slow | 35,738,199 | 0 | python,matplotlib,histogram | For me it took calling figure.canvas.draw() after the call to hist to update immediately, i.e. hist was actually fast (discovered that after timing it), but there was a delay of a few seconds before figure was updated. I was calling hist inside a matplotlib callback in a jupyter lab cell (qt5 backend). | I'm plotting about 10,000 items in an array. They are of around 1,000 unique values.
The plotting has been running half an hour now. I made sure rest of the code works.
Is it that slow? This is my first time plotting histograms with pyplot. | 0 | 1 | 21,439 |
0 | 56,879,388 | 0 | 0 | 0 | 0 | 3 | false | 17 | 2016-03-02T03:54:00.000 | 2 | 9 | 0 | Matplotlib.pyplot.hist() very slow | 35,738,199 | 0.044415 | python,matplotlib,histogram | For me, the problem is that the data type of pd.Series, say S, is 'object' rather than 'float64'. After I use S = np.float64(S), then plt.hist(S) is very quick. | I'm plotting about 10,000 items in an array. They are of around 1,000 unique values.
The plotting has been running half an hour now. I made sure rest of the code works.
Is it that slow? This is my first time plotting histograms with pyplot. | 0 | 1 | 21,439 |
0 | 35,886,350 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-03-03T23:30:00.000 | 0 | 1 | 0 | Does pcolormesh plot the 2D array rightside-up or upside-down? | 35,785,178 | 1.2 | python,image,matplotlib,plot | The array is plotted upside-down, meaning the index (0, 0) is at the bottom left. | Every time I go to plot a 2D array in matplotlib using, for example, pcolormesh, I have the same question: Is the resultant image showing the array rightside-up or upside-down? That is, is index (0, 0) at the top left of the plot or the bottom left?
It's tedious to write a test every six months to remind myself. This... | 0 | 1 | 335 |
0 | 35,881,615 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2016-03-04T16:17:00.000 | 3 | 2 | 0 | Trouble Installing OpenFace in Python | 35,800,893 | 1.2 | python,deep-learning,face-recognition,torch | As I posted in the comments, this segfault was caused by compiling dlib with one Python version and running it with another. This was resolved by manually installing dlib rather than using their pip package. | I am new to deeplearning and face recognition. After searching, I found this python package about deeplearning applied to face recognition called OpenFace. From its documentation, I think it is build on top of Torch for the neural nets computation.
I want to install the package in a virtual environment, so basically th... | 0 | 1 | 4,278 |
0 | 35,953,956 | 0 | 0 | 0 | 0 | 1 | true | 4 | 2016-03-04T16:52:00.000 | 4 | 1 | 0 | Which dtype performs better when training a randomforest in python? | 35,801,638 | 1.2 | python,pandas,scikit-learn | Nearly all scikit-learn estimators will convert input data to float before running the algorithm, regardless of the original types in the array. This holds for the random forest implementation. | I was trying to train a randomforest classifier in python. However, in my original pandas.dataframe, there are float64, object, datetime64, int64 and bool dtypes(nearly all kinds of dtypes allowed in pandas).
Is it necessary to convert a bool to float or int?
For a two-value object column, should I convert it to bool... | 0 | 1 | 1,673 |
0 | 35,827,519 | 0 | 1 | 0 | 0 | 1 | true | 3 | 2016-03-06T05:47:00.000 | 1 | 1 | 0 | iPython Notebook for big / complex analysis. Good idea or not? | 35,823,618 | 1.2 | python,matplotlib,neural-network,ipython,jupyter-notebook | It doesn't matter. It just runs a Python kernel in the background which is no different from one you would run from the command line.
The only thing you should avoid, obviously, is displaying huge amounts of data in your notebook (like plotting your whole image set at once). | I'm working on a project involving Neural Networks (using theano) with a big data set 50,000 images of 3072 pixels. The computational process gets expensive when training the Neural Network as you may expect.
I was using PyCharm to debug and write the code but since I had some trouble using matplotlib and other librar... | 0 | 1 | 265 |
0 | 35,825,863 | 0 | 1 | 0 | 0 | 1 | true | 13 | 2016-03-06T10:34:00.000 | 10 | 1 | 0 | What is the difference between a NumPy array and a python list? | 35,825,802 | 1.2 | python,arrays,list,numpy | Numpy arrays is a typed array, the array in memory stores a homogenous, densely packed numbers.
Python list is a heterogeneous list, the list in memory stores references to objects rather than the number themselves.
This means that Python list requires dereferencing a pointer every time the code needs to access the nu... | Why do we use numpy arrays in place of lists in python? What is the main difference between them? | 0 | 1 | 17,191 |
0 | 35,827,413 | 0 | 0 | 0 | 0 | 4 | false | 28 | 2016-03-06T12:38:00.000 | 1 | 7 | 0 | What is a good heuristic to detect if a column in a pandas.DataFrame is categorical? | 35,826,912 | 0.028564 | python,pandas,scikit-learn | IMO the opposite strategy, identifying categoricals is better because it depends on what the data is about. Technically address data can be thought of as unordered categorical data, but usually I wouldn't use it that way.
For survey data, an idea would be to look for Likert scales, e.g. 5-8 values, either strings (whic... | I've been developing a tool that automatically preprocesses data in pandas.DataFrame format. During this preprocessing step, I want to treat continuous and categorical data differently. In particular, I want to be able to apply, e.g., a OneHotEncoder to only the categorical data.
Now, let's assume that we're provided a... | 0 | 1 | 13,722 |
0 | 35,827,781 | 0 | 0 | 0 | 0 | 4 | false | 28 | 2016-03-06T12:38:00.000 | 5 | 7 | 0 | What is a good heuristic to detect if a column in a pandas.DataFrame is categorical? | 35,826,912 | 0.141893 | python,pandas,scikit-learn | There's are many places where you could "steal" the definitions of formats that can be cast as "number". ##,#e-# would be one of such format, just to illustrate. Maybe you'll be able to find a library to do so.
I try to cast everything to numbers first and what is left, well, there's no other way left but to keep them ... | I've been developing a tool that automatically preprocesses data in pandas.DataFrame format. During this preprocessing step, I want to treat continuous and categorical data differently. In particular, I want to be able to apply, e.g., a OneHotEncoder to only the categorical data.
Now, let's assume that we're provided a... | 0 | 1 | 13,722 |
0 | 35,828,098 | 0 | 0 | 0 | 0 | 4 | false | 28 | 2016-03-06T12:38:00.000 | 1 | 7 | 0 | What is a good heuristic to detect if a column in a pandas.DataFrame is categorical? | 35,826,912 | 0.028564 | python,pandas,scikit-learn | I think the real question here is whether you'd like to bother the user once in a while or silently fail once in a while.
If you don't mind bothering the user, maybe detecting ambiguity and raising an error is the way to go.
If you don't mind failing silently, then your heuristics are ok. I don't think you'll find any... | I've been developing a tool that automatically preprocesses data in pandas.DataFrame format. During this preprocessing step, I want to treat continuous and categorical data differently. In particular, I want to be able to apply, e.g., a OneHotEncoder to only the categorical data.
Now, let's assume that we're provided a... | 0 | 1 | 13,722 |
0 | 38,108,924 | 0 | 0 | 0 | 0 | 4 | false | 28 | 2016-03-06T12:38:00.000 | 1 | 7 | 0 | What is a good heuristic to detect if a column in a pandas.DataFrame is categorical? | 35,826,912 | 0.028564 | python,pandas,scikit-learn | I've been thinking about a similar problem and the more that I consider it, it seems that this itself is a classification problem that could benefit from training a model.
I bet if you examined a bunch of datasets and extracted these features for each column / pandas.Series:
% floats: percentage of values that are fl... | I've been developing a tool that automatically preprocesses data in pandas.DataFrame format. During this preprocessing step, I want to treat continuous and categorical data differently. In particular, I want to be able to apply, e.g., a OneHotEncoder to only the categorical data.
Now, let's assume that we're provided a... | 0 | 1 | 13,722 |
0 | 35,847,976 | 0 | 0 | 0 | 0 | 1 | false | 6 | 2016-03-06T13:30:00.000 | 2 | 4 | 0 | How can SciKit-Learn Random Forest sub sample size may be equal to original training data size? | 35,827,446 | 0.099668 | python,scikit-learn,random-forest,subsampling | Certainly not all samples are selected for each tree. Be default each sample has a 1-((N-1)/N)^N~0.63 chance of being sampled for one particular tree and 0.63^2 for being sampled twice, and 0.63^3 for being sampled 3 times... where N is the sample size of the training set.
Each bootstrap sample selection is in average ... | In the documentation of SciKit-Learn Random Forest classifier , it is stated that
The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default).
What I dont understand is that if the sample size is always the same as the input sample ... | 0 | 1 | 3,033 |
0 | 35,836,274 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-03-07T03:39:00.000 | 0 | 1 | 0 | How is "accuracy" calculated using Libsvm - SVM_Predict.exe | 35,835,787 | 0 | python,machine-learning,svm,libsvm,prediction | The file "trainingdata.svm.prediction" is predicting the labels 1 and 0 for your set (1 means the sample was predicted to be male, 0 is female).
It assumes all the labels belong to class index 0, I believe. | I am using the LIBSVM for the first time.
I was able to train a data(for images) and my model is ready "trainingdata.svm.model"
Now, when I run my classification against an unknown test data it is giving me two files:
1. trainingdata.svm.prediction (This file contains 1's and 0's) against my each of test data.
2. It is... | 0 | 1 | 190 |
0 | 46,668,033 | 0 | 0 | 0 | 0 | 1 | false | 8 | 2016-03-07T06:09:00.000 | 6 | 1 | 0 | HDF5 possible data corruption or loss? | 35,837,243 | 1 | python,python-2.7,hdf5,h5py,hdf | Declaration up front: I help maintain h5py, so I probably have a bias etc.
The wikipedia page has changed since the question was posted, here's what I see:
Criticism
Criticism of HDF5 follows from its monolithic design and lengthy specification.
Though a 150-page open standard, the only other C implementation of HDF5... | On wikipedia one can read the following criticism about HDF5:
Criticism of HDF5 follows from its monolithic design and lengthy
specification. Though a 150-page open standard, there is only a single
C implementation of HDF5, meaning all bindings share its bugs and
performance issues. Compounded with the lack of j... | 0 | 1 | 1,980 |
0 | 35,880,616 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2016-03-07T17:09:00.000 | 0 | 2 | 0 | I am trying to read several csv files in python 2.7 and assign to a list variable | 35,849,754 | 0 | python,list,csv | OK, I should close this. My comment resolved the question above. I reformatted my input csv files to be values, each on a separate "line", I was able to read the lines in one by one and append them to a list. This seems really sloppy and wasteful. I was hoping for a method to read a csv file and in one line assign it ... | I know this has been asked many times, but when I try this, I always get a list of lists.
The data in my input file (col.csv) looks like:
1,2,"black", "orange"
There are NO hard returns in the data (\n)(it's a csv file, right?). When I use the csv module in python to import to a list using reader, I end up with list o... | 0 | 1 | 55 |
0 | 39,803,190 | 0 | 1 | 0 | 0 | 1 | false | 4 | 2016-03-08T12:07:00.000 | 0 | 1 | 0 | Issues with importing vpython for anaconda | 35,866,967 | 0 | python,anaconda,vpython | The graph functions now live in the main vpython library when using Jupyter. So,
from vpython import *
should be sufficient. (P.S. I'd recommend not importing * but rather importing the functions you plan to use or just import vpython.)
Note however that some functions change name in the Jupyter-compatible version of... | I try to import vpython into anaconda. It seems to work so far, but if I call
from visual import * it gives me an error.
However, it does work when I type from vpython import * ,which is really weird since in all programs I only see the from visual import * command.
Now to the real problem: I can't draw graphs. I have... | 0 | 1 | 3,831 |
0 | 35,882,190 | 0 | 1 | 0 | 0 | 3 | false | 1 | 2016-03-09T02:30:00.000 | 0 | 3 | 0 | Can someone consolidate the definition and the differences between a list, an array, a numpy array, a pandas dataframe , series? | 35,881,832 | 0 | python,arrays,numpy,pandas,dataframe | Here's a general overview (partial credit to online documentation and Mark Lutz and Wes McKinney O'Reilly books):
list: General selection object available in Python's standard
library. Lists are positionally ordered collections of arbitrarily
typed objects, and have no fixed size. They are also mutable (str
for exampl... | I am a Python beginner and getting confused by these different forms of storing data? When should one use which. Also which of these is suitable to store a matrix (and a vector)? | 0 | 1 | 80 |
0 | 35,882,181 | 0 | 1 | 0 | 0 | 3 | false | 1 | 2016-03-09T02:30:00.000 | 0 | 3 | 0 | Can someone consolidate the definition and the differences between a list, an array, a numpy array, a pandas dataframe , series? | 35,881,832 | 0 | python,arrays,numpy,pandas,dataframe | list - the original Python way of storing multiple values
array - a little used Python module (let's ignore it)
numpy array - the closest thing in Python to the arrays, matrices and vectors used in mathematics and languages like MATLAB
dataframe, datseries - pandas structures, generally built on numpy, better suited fo... | I am a Python beginner and getting confused by these different forms of storing data? When should one use which. Also which of these is suitable to store a matrix (and a vector)? | 0 | 1 | 80 |
0 | 35,882,258 | 0 | 1 | 0 | 0 | 3 | false | 1 | 2016-03-09T02:30:00.000 | 0 | 3 | 0 | Can someone consolidate the definition and the differences between a list, an array, a numpy array, a pandas dataframe , series? | 35,881,832 | 0 | python,arrays,numpy,pandas,dataframe | Lists: lists are very flexible and can hold completely heterogeneous, arbitrary data, and they can be appended to very efficiently.
Array: The array.array type, on the other hand, is just a thin wrapper on C arrays. It can hold only homogeneous data, all of the same type, and so it uses only sizeof(one object) * lengt... | I am a Python beginner and getting confused by these different forms of storing data? When should one use which. Also which of these is suitable to store a matrix (and a vector)? | 0 | 1 | 80 |
0 | 43,664,585 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-03-09T02:45:00.000 | 0 | 1 | 0 | the input shape of array about Keras on Tensorflow | 35,881,949 | 0 | python,tensorflow,keras | Keras assumes that if you are using tensorflow, you are going with (samples, channels, rows, cols) | I have a question about the 4D tensor on keras about Convolution2D Layers.
The Keras doc says:
4D tensor with shape: (samples, channels, rows, cols) if dim_ordering='th' or 4D tensor with shape: (samples, rows, cols, channels) if dim_ordering='tf'.
I use 'tf', how about my input? When I use (samples, channels, rows, co... | 0 | 1 | 1,225 |
0 | 35,889,624 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-03-09T02:58:00.000 | 0 | 1 | 0 | Vectorizer where or how fit information is stored? | 35,882,062 | 0 | python,scikit-learn | In the test phase you should use the same model names as you used in the trainings phase. In this way you will be able to use the model parameters which are derived in the training phase. Here is an example below;
First give a name to your vectorizer and to your predictive algoritym (It is NB in this case)
vectorize... | in text mining/classification when a vectorizer is used to transform a text into numerical features, in the training TfidfVectorizer(...).fit_transform(text) or TfidfVectorizer(...).fit(text) is used. In testing it supposes to utilize former training info and just transform the data following the training fit.
In gene... | 0 | 1 | 172 |
0 | 35,887,508 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-03-09T09:09:00.000 | 1 | 1 | 0 | Sorting words into categories in Python | 35,887,212 | 1.2 | python,machine-learning,deep-learning | for deep learning to work on this you would have to develop a large dataset, most likely manually. the largest natural language processing dataset was, in fact, created manually.
BUT even if you were able to find a dataset which a model could learn off. THEN a model such as gradient boosted trees would be one, amongst ... | I have about 3,000 words and I would like to group them into about 20-50 different categories. My words are typical phrases you might find in company names. "Face", "Book", "Sales", "Force", for example.
The libraries I have been looking at so far are pandas and scikit-learn. I'm wondering if there is a machine-learnin... | 0 | 1 | 717 |
1 | 35,901,608 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2016-03-09T19:48:00.000 | 2 | 1 | 0 | Show image using OpenCV in Python on a Raspberry Pi terminal | 35,901,246 | 0.379949 | python,linux,opencv,terminal,raspberry-pi | You need to use a windowing system to display images using imshow.
(That can be enabled in settings running sudo raspi-config)
If you absolutely, positively need to display images without using a windowing system, consider providing an html/web interface. Two options that come to mind when serving a web interface are:
... | I get a gtk-WARNING when trying:
cv2.imshow("WindowName", image)
I'm using this to watch a live stream one frame at a time. Are there any alternative libraries I could use? I tried several other options like PIL and Tkinter as well as wand, but could get none of them to work for various different reason. | 0 | 1 | 1,687 |
0 | 35,923,537 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-03-10T14:57:00.000 | 0 | 1 | 0 | Inject custom cost function for linear regression | 35,919,948 | 0 | python,python-2.7,machine-learning,scipy,scikit-learn | I suspect there is no a readily available module that suits your needs.
If I were you I would:
partition the features into 2 groups: one for simple linear regression and another one for regularized regression. Train two models on two different (maybe overlapping?) sets of features. When you cross-validate your models,... | I want to run a lasso or ridge regression, but where the L1 or L2 constraint on the coefficients is on some of the coefficients, not all.
Another way to say it: I would like to use my own custom cost function inside the lasso or ridge algorithm.
I would like to avoid having to rewrite the whole algorithm. Is there a mo... | 0 | 1 | 787 |
0 | 35,939,091 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2016-03-11T11:18:00.000 | 0 | 1 | 0 | matplotlib & seaborn: how to get rid of lines? | 35,938,891 | 1.2 | python,matplotlib,seaborn | Passing arguments into ax.yaxis.grid() and ax.xaxis.grid() will include or omit grids in the graphs | I'm using the whitegrid style and it's fine except for the vertical lines in the background.
I just want to retain the horizontal lines. | 0 | 1 | 202 |
0 | 35,948,671 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2016-03-11T16:01:00.000 | 3 | 1 | 0 | random_state parameter in classification models | 35,944,725 | 1.2 | python,random,machine-learning,scikit-learn,evaluation | If the random_state affects your results it means that your model has a high variance. In case of Random Forest this simply means that you use too small forest and should increase number of trees (which due to bagging - reduce variance). In scikit-learn this is controlled by n_estimators parameters in the constructor.
... | Can someone explain why does the random_state parameter affects the model so much?
I have a RandomForestClassifier model and want to set the random_state (for reproducibility pourpouses), but depending on the value I use I get very different values on my overall evaluation metric (F1 score)
For example, I tried to fit ... | 0 | 1 | 743 |
0 | 35,964,006 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2016-03-12T21:34:00.000 | 0 | 1 | 0 | Can't find pandas in data nitro | 35,963,580 | 0 | python,pandas,datanitro | DataNitro is probably using a different copy of Python on your machine. Go to Settings in the DataNitro ribbon, uncheck "use default Python", and select the Canopy python directory manually. Then, restart Excel and see if importing works. | when I try to import pandas using the data nitro shell, I get the error that there is no module named pandas.
I have pandas through the canopy distribution, but somehow the data nitro shell isn't "finding" it.
I suspect this has to do with the directory in which pandas is stored, but I don't know how to "extract" pan... | 0 | 1 | 193 |
0 | 47,130,715 | 0 | 1 | 0 | 0 | 4 | false | 2 | 2016-03-13T00:16:00.000 | 0 | 5 | 0 | pycharm error while importing, even though it works in the terminal | 35,964,994 | 0 | python,macos,scikit-learn,pycharm,tensorflow | I had a similar problem. My code was was not working on PyCharm professional. I had PyCharm CE previously installed and it worked from there. I had configured PyCharm CE a while ago and I had forgotten what setup I used but if issues persist, make sure that the packages are installed under Preferences > Project > Proje... | I have installed the packages TensorFlow and scikit_learn and all its dependencies. When I try importing them using python 2.7.6 or 2.7.10 (I have tried both) in the terminal, it works fine. However, when I do it using pycharm it gives an error.
In the case of scikit_learn with the launcher 2.7.6 says:
ImportError: dy... | 0 | 1 | 2,043 |
0 | 40,260,973 | 0 | 1 | 0 | 0 | 4 | true | 2 | 2016-03-13T00:16:00.000 | 0 | 5 | 0 | pycharm error while importing, even though it works in the terminal | 35,964,994 | 1.2 | python,macos,scikit-learn,pycharm,tensorflow | At the end, I ended up creating a virtual environment, reinstalling everything in there, and calling it through pycharm. I am not entirely sure what was the problem between conda and pycharm, I probably messed up somewhere.
I am now using a different virtual environment depending on the project and I am happier than e... | I have installed the packages TensorFlow and scikit_learn and all its dependencies. When I try importing them using python 2.7.6 or 2.7.10 (I have tried both) in the terminal, it works fine. However, when I do it using pycharm it gives an error.
In the case of scikit_learn with the launcher 2.7.6 says:
ImportError: dy... | 0 | 1 | 2,043 |
0 | 40,121,055 | 0 | 1 | 0 | 0 | 4 | false | 2 | 2016-03-13T00:16:00.000 | 0 | 5 | 0 | pycharm error while importing, even though it works in the terminal | 35,964,994 | 0 | python,macos,scikit-learn,pycharm,tensorflow | Add this 'DYLD_LIBRARY_PATH=/usr/local/cuda/lib' to Python environment variable. Run-> Edit Configurations -> Environment variables. Hope it works. | I have installed the packages TensorFlow and scikit_learn and all its dependencies. When I try importing them using python 2.7.6 or 2.7.10 (I have tried both) in the terminal, it works fine. However, when I do it using pycharm it gives an error.
In the case of scikit_learn with the launcher 2.7.6 says:
ImportError: dy... | 0 | 1 | 2,043 |
0 | 38,749,298 | 0 | 1 | 0 | 0 | 4 | false | 2 | 2016-03-13T00:16:00.000 | 0 | 5 | 0 | pycharm error while importing, even though it works in the terminal | 35,964,994 | 0 | python,macos,scikit-learn,pycharm,tensorflow | you should start PyCharm from terminal
cd /usr/lib/pycharm-community/bin
./pycharm.sh | I have installed the packages TensorFlow and scikit_learn and all its dependencies. When I try importing them using python 2.7.6 or 2.7.10 (I have tried both) in the terminal, it works fine. However, when I do it using pycharm it gives an error.
In the case of scikit_learn with the launcher 2.7.6 says:
ImportError: dy... | 0 | 1 | 2,043 |
0 | 44,932,978 | 0 | 0 | 0 | 0 | 1 | false | 11 | 2016-03-13T17:45:00.000 | -1 | 2 | 0 | pyspark partitioning data using partitionby | 35,973,590 | -0.099668 | python,apache-spark,pyspark,partitioning,rdd | I recently used partitionby. What I did was restructure my data so that all those which I want to put in same partition have the same key, which in turn is a value from the data. my data was a list of dictionary, which I converted into tupples with key from dictionary.Initially the partitionby was not keeping same keys... | I understand that partitionBy function partitions my data. If I use rdd.partitionBy(100) it will partition my data by key into 100 parts. i.e. data associated with similar keys will be grouped together
Is my understanding correct?
Is it advisable to have number of partitions equal to number of
available cores? Doe... | 0 | 1 | 23,512 |
0 | 37,115,845 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2016-03-13T19:48:00.000 | 0 | 2 | 0 | Backpropagation with Momentum using Scikit-Learn | 35,974,957 | 1.2 | python,scikit-learn,backpropagation,momentum | If anybody ever needs an answer for this, I actually decided to run everything on a Linux VM. I then followed the instructions to install the dev version and everything(well almost) worked perfectly. Running it on Linux is way easier than Windows because you can just install the package from git and run it without havi... | I'm trying to use Scikit-Learn's Neural Network to classify my dataset using a Backpropagation with Momentum. I need to specify these parameters: Hidden neurons, Hidden layers, Training set, Learning rate and Momentum.
I found MLPClassifier in Sklearn.neural_network package. The problem is that this package is part of... | 0 | 1 | 903 |
0 | 35,976,436 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2016-03-13T21:38:00.000 | 0 | 1 | 0 | vector changes to matrix at computation | 35,976,192 | 1.2 | python,python-2.7,matrix,vector,linear-algebra | I managed to fix it by using a temp variable, setting it to correct size, and iterating over dsdb1. i still don't know what caused the bug. | This is probably a rookie mistake that I'm missing somewhere but I can't for the life of me find anything related to my problem on the web.
I have a vector b1 of size 5 by 1, and i have another vector dsdb1 which is also 5 by 1.
When I write b1 += tau*dsdb1 I get the error "non-broadcastable output operand with shape (... | 0 | 1 | 23 |
0 | 52,134,247 | 0 | 0 | 0 | 0 | 1 | false | 8 | 2016-03-14T12:39:00.000 | 0 | 3 | 1 | Using Scikit-learn google app engine | 35,987,785 | 0 | python,python-2.7,google-app-engine,scikit-learn | The newly-released 2nd Generation Python 3.7 Standard Environment (experimental) can run all modules. It's still in beta, though. | I am trying to deploy a python2.7 application on google app engine. It uses few modules like numpy,flask,pandas and scikit-learn. Though I am able to install and use other modules. Installing scikit-learn in lib folder of project give following error:-
Traceback (most recent call last): File "/base/data/home/runtimes/p... | 0 | 1 | 1,664 |
0 | 36,008,452 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-03-15T06:15:00.000 | 0 | 1 | 0 | How do I add a quadratic constraint using coeff in the scipsuite python interface | 36,003,924 | 0 | python,scip | This is currently not supported. You need to loop through your quadratic constraints and add them one after the other using the expression method. | How do I add a quadratic constraint using the scip python interface?
In one of the examples, I see something like model.addCons(x*x+y*y<=6)
However, since I have a lot of variables(x1..xn and my constraint is of the form x'Qx<=0.2, where x is n*1 and Q is n*n), this method is rather impossible. How can I put the quadra... | 0 | 1 | 103 |
0 | 36,019,131 | 0 | 0 | 0 | 0 | 1 | true | 3 | 2016-03-15T17:51:00.000 | 3 | 1 | 0 | Identifying overfitting in a cross validated SVM when tuning parameters | 36,018,586 | 1.2 | python,scikit-learn,svm,cross-validation,grid-search | Overfitting is generally associated with high variance, meaning that the model parameters that would result from being fitted to some realized data set have a high variance from data set to data set. You collected some data, fit some model, got some parameters ... you do it again and get new data and now your parameter... | I have an rbf SVM that I'm tuning with gridsearchcv. How do I tell if my good results are actually good results or whether they are overfitting? | 0 | 1 | 1,516 |
0 | 43,882,395 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-03-15T23:57:00.000 | 0 | 2 | 0 | Appending to NumPy (Python) Array | 36,024,460 | 0 | python,arrays,numpy | You should not append arrays, if you can avoid, due to efficiency issues. Appending means changing the allocated memory size, which can run into non-contiguous memory space, hence inefficient allocation or reallocation would be necessary. These can slow down your program a lot, specially for large arrays.
If you are im... | For starters, I am doing a Runge-Kutta on a three-DOF NumPy array. My array looks like this:
states = [[X], [Vx], [Y], [Vy], [Z], [Vz]]
I run my Runge-Kutta, and get my four K values, which I transpose with [newaxis]. So when I try to append the new states to my states array as follows:
states = append(states, states[:... | 0 | 1 | 536 |
0 | 36,033,515 | 0 | 1 | 0 | 0 | 3 | false | 1 | 2016-03-16T10:33:00.000 | 1 | 3 | 0 | DeprecationWarning with matplotlib and dateutil | 36,033,095 | 0.066568 | python,datetime,matplotlib,python-dateutil | First of all it is not an error. It's a warning.
Second, most likely the problem is not your problem rather a problem in Matplotlib, which need to fix how they call a function or some method form python-dateutil. Most likely, you can ignore this warning, and it will be fixed in the next Matplotlib version. | Since i've installed the last version of matplotlib (1.5.1), I have the following error when i try to plot values with datetime as X.
DeprecationWarning: Using both 'count' and 'until' is inconsistent
with RFC 2445 and has been deprecated in dateutil. Future versions
will raise an error.
Does someone met ths erro... | 0 | 1 | 842 |
0 | 36,626,778 | 0 | 1 | 0 | 0 | 3 | false | 1 | 2016-03-16T10:33:00.000 | 1 | 3 | 0 | DeprecationWarning with matplotlib and dateutil | 36,033,095 | 0.066568 | python,datetime,matplotlib,python-dateutil | Best solution :
in the file /matplotlib/dates.py, the line 830 :
self.rule.set(dtstart=start, until=stop, count=self.MAXTICKS + 1)
shall be commented | Since i've installed the last version of matplotlib (1.5.1), I have the following error when i try to plot values with datetime as X.
DeprecationWarning: Using both 'count' and 'until' is inconsistent
with RFC 2445 and has been deprecated in dateutil. Future versions
will raise an error.
Does someone met ths erro... | 0 | 1 | 842 |
0 | 36,423,287 | 0 | 1 | 0 | 0 | 3 | false | 1 | 2016-03-16T10:33:00.000 | 3 | 3 | 0 | DeprecationWarning with matplotlib and dateutil | 36,033,095 | 0.197375 | python,datetime,matplotlib,python-dateutil | The issue have been fixed on matplotlib, but not released in a finalised version (>= 1.5.2)
I had to install the current working version with
pip install git+https://github.com/matplotlib/matplotlib | Since i've installed the last version of matplotlib (1.5.1), I have the following error when i try to plot values with datetime as X.
DeprecationWarning: Using both 'count' and 'until' is inconsistent
with RFC 2445 and has been deprecated in dateutil. Future versions
will raise an error.
Does someone met ths erro... | 0 | 1 | 842 |
0 | 52,089,783 | 0 | 1 | 0 | 0 | 1 | false | 22 | 2016-03-17T03:26:00.000 | 30 | 4 | 0 | Pandas row to json | 36,051,134 | 1 | python,json,pandas | Looping over indices is very inefficient.
A faster technique:
df['json'] = df.apply(lambda x: x.to_json(), axis=1) | I have a dataframe in pandas and my goal is to write each row of the dataframe as a new json file.
I'm a bit stuck right now. My intuition was to iterate over the rows of the dataframe (using df.iterrows) and use json.dumps to dump the file but to no avail.
Any thoughts? | 0 | 1 | 43,885 |
0 | 36,081,837 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2016-03-18T10:00:00.000 | 1 | 1 | 0 | Random forest tree growing algorithm | 36,081,370 | 0.197375 | python,algorithm,machine-learning,random-forest,decision-tree | Q1
You shouldn't remove the features from M. Otherwise it will not be able to detect some types of relationships (ex: linear relationships)
Maybe you can stop earlier, in your condition it might go up to leaves with only 1 sample, this will have no statistical significance. So it's better to stop at say, when the numb... | I'm doing a Random Forest implementation (for classification), and I have some questions regarding the tree growing algorithm mentioned in literature.
When training a decision tree, there are 2 criteria to stop growing a tree:
a. Stop when there are no more features left to split a node on.
b. Stop when the node has ... | 0 | 1 | 586 |
0 | 36,104,821 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2016-03-18T13:58:00.000 | 1 | 1 | 0 | Scipy nnls algorithm does not have termination tolerance option | 36,086,330 | 0.197375 | python,scipy,spyder | You can use lsq_linear which is available in scipy version 0.17. | I am using the nnls algorithm from scipy and am shocked to find that I cannot control the final tolerance, as can be done in Matlab using Tolx i.e, termination tolerance.
Modifying the Fortran code or even reading it is extremely hard. I am only new to python since a month.
I need to do $\| A x - b\|_2 \leq \epsilon\... | 0 | 1 | 129 |
0 | 36,143,671 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2016-03-21T21:04:00.000 | 3 | 2 | 0 | Spark running on EC2 vs EMR | 36,141,570 | 1.2 | python,amazon-web-services,amazon-ec2,apache-spark,amazon-emr | EMR provides a simple to use Hadoop/spark as service. You just have to select the components you want to be installed (spark, hadoop), their versions, how many machines you want to use and a couple other options and then it installs everything for you. Since you are students I assume you don't have experience in automa... | We are students that are working on a graduation project related to the Data Science, we are developing a Recommender Engine using Spark with python (Pyspark) with Android Application (Interface for the users) and we have a faced a lot of roadblocks, one of them was how to keep the Spark script up and running on a clou... | 1 | 1 | 3,688 |
0 | 36,157,336 | 0 | 0 | 1 | 0 | 1 | false | 0 | 2016-03-22T12:59:00.000 | 0 | 1 | 0 | How to use freqz to get response of complex FIR filter | 36,155,061 | 0 | python,numpy,scipy | Never mind, it is absolutely possible to pass a set of complex coefficients to freqz, I got confused because I tried to plot the response without specifying I wanted the absolute value of h, which rendered the warning:
ComplexWarning: Casting complex values to real discards the imaginary part. Trap for young players l... | I'm trying to analyse an asymmetric FIR filter with complex coefficients. I know I can use the numpy function freqz to analyse the frequency response of an FIR or IIR-filter with real coefficients. At the moment I'm just using a regular FFT of the FIR filter and I use fftshift to put the negative frequencies in front o... | 0 | 1 | 631 |
0 | 50,800,687 | 0 | 0 | 0 | 0 | 1 | false | 40 | 2016-03-24T07:56:00.000 | 0 | 6 | 0 | How to get the samples in each cluster? | 36,195,457 | 0 | python,scikit-learn,cluster-analysis,k-means | You can Simply store the labels in an array. Convert the array to a data frame. Then Merge the data that you used to create K means with the new data frame with clusters.
Display the dataframe. Now you should see the row with corresponding cluster. If you want to list all the data with specific cluster, use something ... | I am using the sklearn.cluster KMeans package. Once I finish the clustering if I need to know which values were grouped together how can I do it?
Say I had 100 data points and KMeans gave me 5 cluster. Now I want to know which data points are in cluster 5. How can I do that.
Is there a function to give the cluster id a... | 0 | 1 | 62,619 |
0 | 36,207,559 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2016-03-24T12:18:00.000 | 1 | 1 | 0 | Running tensorflow model training from dataflow | 36,200,052 | 0.197375 | python,tensorflow,google-cloud-dataflow | There is nothing planned at this time.
If you can run the Tensorflow training on a single machine (it sounds like this is what you were doing with Spark) then it should be possible to do the training within a DoFn of a Dataflow pipeline. | I am playing around with tensorflow and today I have noticed that google also open-sourced Python SDK for their dataflow.
Currently when I need to train and evaluate several networks in parallel I usually use either luigi and run one model training after another or I use spark and I am performing each model training wi... | 0 | 1 | 310 |
0 | 36,209,490 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2016-03-24T20:23:00.000 | 1 | 1 | 0 | No internet access but Pip wants to look for newer version | 36,209,363 | 0.197375 | python,matplotlib,installation,pip | On the computer with the internet, use pip wheel matplotlib to download all the wheel files needed for the installation (including dependencies).
Then, on the computer without the internet, use pip install matplotlib --find-links="<directory of the wheel files>". This will install matplotlib from the local files. | I'm trying to install matplotlib a server that is sequestered and has no internet connection. When I run pip with this command:
pip.exe install matplotlib-1.5.1-cp27-none-win_amd64.whl --no-index
I get this message:
Could not find a version that satisfies the requirement pyparsing!=2.0.4,>=1.5.6 (from matplotlib==1.5.1... | 0 | 1 | 518 |
0 | 36,227,222 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-03-25T01:44:00.000 | 2 | 1 | 0 | H2OFrame converts dict to all zeros | 36,212,815 | 0.379949 | python,django,pandas,scikit-learn,h2o | It seems the Pandas DataFrame to H2OFrame conversion works fine outside Django, but fails inside Django. The problem might be with Django's pre_save not allowing the writing/reading of the temporary .csv file that H2O creates when ingesting a python object. A possible workaround is to explicitly write the Pandas DataFr... | I am taking input values from a django model admin screen and on pre_save calling h2o to do predictions for other values and save them.
Currently I convert my input from pandas (trying to work with sklearn preprocessing easily here) by using:
modelH2OFrame = h2o.H2OFrame(python_obj = model_data_frame.to_dict('list')) ... | 1 | 1 | 276 |
0 | 66,138,989 | 0 | 0 | 0 | 0 | 2 | false | 27 | 2016-03-25T10:42:00.000 | 0 | 4 | 0 | Parameters of detectMultiScale in OpenCV using Python | 36,218,385 | 0 | python,opencv,object-detection | detectMultiScale function is used to detect the faces. This function will return a rectangle with coordinates(x,y,w,h) around the detected face.
It takes 3 common arguments — the input image, scaleFactor, and minNeighbours.
scaleFactor specifies how much the image size is reduced with each scale. In a group photo, ther... | I am not able to understand the parameters passed to detectMultiScale. I know that the general syntax is detectMultiScale(image, rejectLevels, levelWeights)
However, what do the parameters rejectLevels and levelWeights mean? And what are the optimal values used for detecting objects?
I want to use this to detect pupil... | 0 | 1 | 78,382 |
0 | 55,628,240 | 0 | 0 | 0 | 0 | 2 | false | 27 | 2016-03-25T10:42:00.000 | 20 | 4 | 0 | Parameters of detectMultiScale in OpenCV using Python | 36,218,385 | 1 | python,opencv,object-detection | Amongst these parameters, you need to pay more attention to four of them:
scaleFactor – Parameter specifying how much the image size is reduced at each image scale.
Basically, the scale factor is used to create your scale pyramid. More explanation, your model has a fixed size defined during training, which is visible... | I am not able to understand the parameters passed to detectMultiScale. I know that the general syntax is detectMultiScale(image, rejectLevels, levelWeights)
However, what do the parameters rejectLevels and levelWeights mean? And what are the optimal values used for detecting objects?
I want to use this to detect pupil... | 0 | 1 | 78,382 |
0 | 38,489,976 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-03-25T14:13:00.000 | 0 | 1 | 0 | How to resume training from *.meta in tensorflow? | 36,221,588 | 0 | python,tensorflow | Not sure if this will work for you, but at least for DNNCLassifiers you can specify the model_dir parameter when creating it and that will construct the model from the files and then you can continue the training.
For the DNNClassifiers you specify model_dir when first creating the object and the training will store ch... | In the latest version of tensorflow, when I save the model I find two files are produced: model_xxx and model_xxx.meta.
Does model_xxx.meta specify the network? Can I resume training using model_xxx and model_xxx.meta without specify the network in the code? What about training queue structure, are they stored in model... | 1 | 1 | 429 |
0 | 36,226,280 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2016-03-25T18:42:00.000 | 0 | 1 | 0 | Why does to_datetime produce only missing values for a float column in pandas? | 36,225,950 | 0 | python,datetime,numpy,pandas,types | The best way might be to avoid floats altogether. Preempt the conversion to numerics in read_table by specifying dtype, with the column in question being kept an object. to_datetime handles that as intended.
HT: BrenBarn in the comments. | I am confused by something in pandas 0.18.0. In my input csv data, a field is supposed to consist dates as YYYYMMDD strings, but some rows have this missing or misformatted. I want to represent this column as datetime with dates where possible, and missing where not.
I tried several options, and what got me furthest wa... | 0 | 1 | 61 |
0 | 64,681,838 | 0 | 0 | 0 | 0 | 3 | false | 231 | 2016-03-25T18:50:00.000 | 0 | 14 | 0 | How to find which columns contain any NaN value in Pandas dataframe | 36,226,083 | 0 | python,pandas,dataframe,nan | df.isna() return True values for NaN, False for the rest. So, doing:
df.isna().any()
will return True for any column having a NaN, False for the rest | Given a pandas dataframe containing possible NaN values scattered here and there:
Question: How do I determine which columns contain NaN values? In particular, can I get a list of the column names containing NaNs? | 0 | 1 | 303,847 |
0 | 68,703,088 | 0 | 0 | 0 | 0 | 3 | false | 231 | 2016-03-25T18:50:00.000 | 0 | 14 | 0 | How to find which columns contain any NaN value in Pandas dataframe | 36,226,083 | 0 | python,pandas,dataframe,nan | features_with_na=[features for features in dataframe.columns if dataframe[features].isnull().sum()>0]
for feature in features_with_na:
print(feature, np.round(dataframe[feature].isnull().mean(), 4), '% missing values')
print(features_with_na)
it will give % of missing value for each column in dataframe | Given a pandas dataframe containing possible NaN values scattered here and there:
Question: How do I determine which columns contain NaN values? In particular, can I get a list of the column names containing NaNs? | 0 | 1 | 303,847 |
0 | 47,418,973 | 0 | 0 | 0 | 0 | 3 | false | 231 | 2016-03-25T18:50:00.000 | 40 | 14 | 0 | How to find which columns contain any NaN value in Pandas dataframe | 36,226,083 | 1 | python,pandas,dataframe,nan | You can use df.isnull().sum(). It shows all columns and the total NaNs of each feature. | Given a pandas dataframe containing possible NaN values scattered here and there:
Question: How do I determine which columns contain NaN values? In particular, can I get a list of the column names containing NaNs? | 0 | 1 | 303,847 |
0 | 36,270,781 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2016-03-28T17:57:00.000 | 1 | 1 | 0 | Easy way to classify words like "A lot", "A few", "some" | 36,267,978 | 0.197375 | python,nlp | Similarity is actually a difficult problem in NLP. I recommend you to use Word2Vec and generate word embeddings of each word. Then you can compare the distance of each word pair and see if could word better than your way. The key to improve the effectiveness of word embedding is to pick a corpus which is large enough a... | I am working on a project that needs to be able to classify modifiers like "a lot", "a few", "lots", "some" etc. into minimum percentages
For example "a lot" -> 80%
Right now I'm thinking of simply creating a large dictionary that relates these modifiers and numerical values e.g.
a few -> 15%
some -> 10%
lots -> 80%
Ho... | 0 | 1 | 76 |
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