Unnamed: 0 int64 0 378k | id int64 49.9k 73.8M | title stringlengths 15 150 | question stringlengths 37 64.2k | answer stringlengths 37 44.1k | tags stringlengths 5 106 | score int64 -10 5.87k |
|---|---|---|---|---|---|---|
2,000 | 55,870,212 | How to convert a numpy array dtype=object to a sparse matrix? | <p>I have an numpy array of dtype = object containing multiple other arrays for elements and I need to convert it to a sparse matrix.</p>
<p>Ex:</p>
<pre><code>a = np.array([np.array([1,0,2]),np.array([1,3])])
array([array([1, 0, 2]), array([1, 3])], dtype=object)
</code></pre>
<p>I have tried the solution given by ... | <p>You can't. This error arises when it tries to find the nonzero elements of <code>a</code>. A sparse matrix just stores the nonzero elements of a matrix. Try</p>
<pre><code>np.nonzero(a)
</code></pre>
<p>If your array contained lists instead of arrays, it would work - sort of:</p>
<pre><code>In [615]: a = np.... | python|numpy|scipy | 2 |
2,001 | 64,815,718 | Filter column value from other columns' values and turn the results into multiple lists Pandas | <pre><code> import pandas as pd
data = {"Country": ["AA", "BB","CC","DD","EE","FF","GG"],
"1990": [0,1,1,1,0,1,1],
"1991": [0,0,1,1,1,0,1],
"1992": [1,1,1,1,1,0,0],
"1... | <p>One way using dict comprehension with <code>groupby</code> on <code>axis=1</code>:</p>
<pre><code>res = {name: i.index[i[name]].tolist() for name, i in df.set_index("Country").astype(bool).groupby(level=0, axis=1)}
print (res)
{'1990': ['BB', 'CC', 'DD', 'FF', 'GG'],
'1991': ['CC', 'DD', 'EE', 'GG'],
'... | python|pandas|list | 0 |
2,002 | 64,945,683 | Walk along 2D numpy array as long as values remain the same | <p><strong>Short description</strong><br>
I want to walk along a numpy 2D array starting from different points in specified directions (either 1 or -1) until a column changes (see below)</p>
<p><strong>Current code</strong></p>
<p>First let's generate a dataset:</p>
<pre><code># Generate big random dataset
# first colu... | <p>You don't have to whole input array in while loop. You could just use the column that values you want to check.</p>
<p>I refactored a little bit your code as well so there is no <code>while True</code> statement and so there is no <code>if</code> that raises error for no particular reason.</p>
<p>Code:</p>
<pre clas... | python|arrays|numpy | 0 |
2,003 | 64,731,533 | Great accuracy on IMDB Sentiment Analysis. Is there any train data leakage I'm missing? | <p>I'm getting an unusual high accuracy on a sentiment analysis classifier I'm testing with python <code>sklearn</code> library. This is usually some sort of training data leakage but I can't figure out if that's the case.</p>
<p>My dataset has ~50k nonduplicated IMDB reviews.</p>
<pre><code>import pandas as pd
import ... | <p>A good way to test if there is data leakage would be to check the performance on the validation set in the repository you linked, <a href="https://github.com/ricardorei/lightning-text-classification/blob/master/data/imdb_reviews_test.csv" rel="nofollow noreferrer">here</a>.</p>
<p>I downloaded the dataset and tried ... | python|pandas|machine-learning|scikit-learn|confusion-matrix | 1 |
2,004 | 64,865,618 | Create ID column in a pandas dataframe | <p>I have a dataframe containing a trading log. My problem is that I do not have any ID to match buy and sell of a stock. The stock could be traded many times and I would like to have an ID to match each finished trade.
My original dataframe a sequential timeseries dataframe with timestamps. The below example illustrat... | <p>Try this:</p>
<pre><code>m = df1['deal'] == 'buy'
df1['ID'] = m.cumsum().where(m)
df1['ID'] = df1.groupby('stock')['ID'].ffill()
df1
</code></pre>
<p>Output:</p>
<pre><code> stock deal ID
0 A buy 1.0
1 B buy 2.0
2 C buy 3.0
3 A sell 1.0
4 C sell 3.0
5 A buy 4.0
6 A s... | python|pandas|dataframe | 3 |
2,005 | 69,534,875 | Exclude df rows where a dates field: time/seconds are between a specific period | <p>Morning All,</p>
<p>I have a very large df but need to strip out data NOT between 8.30am AEST to 5pm UTC.</p>
<pre><code># Dates are dd/mm/yyyy
df ={ 'rfq_create_date_time': ['01/10/2021 00:00:00 AM',
'02/10/2021 01:01:01 AM',
'03/10/2021 05:00:00 AM... | <p>To use <code>between_time</code>, as you've probably realised, the date/time needs to be the index of the dataframe.</p>
<p>When the date/time is a column in the dataframe you can use 'standard' filtering.</p>
<pre><code>from datetime import time
import pandas as pd
# Dates are dd/mm/yyyy
data = {
"rfq_cr... | python|pandas | 1 |
2,006 | 69,599,377 | Python Congressional Plotly TypeError: Object of type MultiPolygon is not JSON serializable for Congressional Districts | <pre><code>import pandas as pd
from census import Census
import geopandas as gpd
import numpy as np
import plotly.io as pio
import plotly.express as px
pio.renderers.default='browser'
file_path = "Path"
# Load Census Median Age by District Data
c = Census("KEY")
district_df = c.acs1.state_congre... | <ul>
<li>it's not clear to me if your <em>geojson</em> is valid. Given you are plotting US census data, may as well use US census mapping data <a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html" rel="nofollow noreferrer">https://www.census.gov/geographies/mapping-files/ti... | python|pandas|plotly|choropleth | 0 |
2,007 | 69,314,600 | Python - Error in String literal str.replace | <p>I have attempted to replace a <code>string</code> in a column with either of the two commands below. For both of them, I am getting the "SyntaxError: EOL while scanning string literal" error. Please help/guide. Thanks.</p>
<pre><code>df['filename'] = df['filename'].str.replace("H:\May2017\hb_ymvid\HB_... | <p><code>\</code> denotes escape sequence in <code>python</code>, if you mean literal <code>\</code> then use <code>\\</code>, i.e. replace</p>
<pre><code>"H:\May2017\hb_ymvid\HB_ED_S\Pictures1\05cropped_PC\"
</code></pre>
<p>using</p>
<pre><code>"H:\\May2017\\hb_ymvid\\HB_ED_S\\Pictures1\\05cropped_PC\\... | python|pandas|dataframe | 0 |
2,008 | 41,175,797 | How to create a list of dictionaries | <p>I want to calculate data on the frequencies of words in documents grouped by year, and then place the data in a pandas dataframe. </p>
<p>My routine creates a dictionary for each row, containing words and frequencies as keys and values. I then want to loop through years, appending the dictionaries to each other to ... | <p>As the previous poster mentioned, append() is a list method but not a dict method. This should work, though:</p>
<pre><code>import pandas
word_data = [] # list type
word_counts_1 = {'year': '1965', 'word1':20, 'word2': 250, 'word3': 125} # dict type
word_counts_2 = {'year':'1966','word1':150, 'word4': 250, 'word... | python|pandas|dictionary | 1 |
2,009 | 53,899,752 | How can I create a custom connection between two different keras layers in LeNet5 architecture? | <p>I am working on <a href="https://engmrk.com/lenet-5-a-classic-cnn-architecture/" rel="nofollow noreferrer">LeNet5</a> architecture. I want to implement a custom connection between the layers C3 and S2 as explained <a href="https://i.stack.imgur.com/UBhya.png" rel="nofollow noreferrer">here</a>. How do I have to defi... | <p><strong>You can create the following custom layer classclass:</strong></p>
<pre><code>CustomLayer(tf.keras.layers.Layer):
""" Custom layer with initialize matrix = connect_matrix. """
def __init__(self, activation, units, connect_matrix):
super(CustomLayer, self).__init__()
sel... | tensorflow|machine-learning|keras|neural-network|data-science | 0 |
2,010 | 53,858,902 | How to save Tensorflow encoder decoder model? | <p>I followed <a href="https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb#" rel="nofollow noreferrer">this tutorial</a> about building an encoder-decoder language translation model and built one for my native la... | <p>You can save a Keras model in Keras's HDF5 format, see:</p>
<p><a href="https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model" rel="nofollow noreferrer">https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model</a></p>
<p>You will want to do something like:</p>
<pre><code>import tf.keras
mod... | tensorflow|keras|google-cloud-ml|encoder-decoder | 0 |
2,011 | 54,221,484 | Select data based on multiple criteria using Pandas | <p>I am new to using Pandas. I want to select rows from a dataframe where multiple columns match in value. Along the lines of:</p>
<p>if column A equals column AB and column B equals column BC </p>
<p>then I want those values. </p>
<p>I haven't actually used an if statement, I read iteration was not good to use with... | <p>You'll simply need to add the conditions inside parenthesis inside your <code>.loc</code> and not repeat a DF filter inside the df filter:</p>
<p>First, creating a crude datasample, as you didn't provide one besides the image:</p>
<pre><code># creating the values, first one will be ID, then next 4 will be the values... | python|pandas|select | 3 |
2,012 | 54,057,338 | Weight decay loss | <p>I need to write a code to gradually decay the weight of my loss function by computes lambda with given steps, But I don't have any idea. Any help will be appreciated.</p>
<p>This is my Loss function:</p>
<pre><code>loss_A = criterion(recov_A, real_A)
loss_Final = lambda_A * loss_A + #lambda_A is a fixed number: 10... | <p>To decay the fixed number depends on the number of steps or even the number of epochs you can use the following code or you can write the code as a function and call it whenever you want.</p>
<pre><code>final_value = 1e-3 # Small number because dont end up with 0
initial_value = 20
starting_step = 25
total_step =... | python|python-3.x|pytorch | 1 |
2,013 | 53,970,733 | i want to compute the distance between two numpy histogram | <p>i'm creating an image processing program and i want to measure the wasserstein distance between two numpy histograms.
the two histogram are created with the function <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html" rel="nofollow noreferrer">numpy.histogram</a></p>
<p>i tried the... | <p>thank to SpghttCd the solution was simple ... i had just to replace</p>
<pre><code>wasserstein_distance(histogram1, histogram2)
</code></pre>
<p>with</p>
<pre><code>wasserstein_distance(histogram1[0], histogram2[0])
</code></pre> | python|numpy|opencv|histogram|distance | 0 |
2,014 | 53,803,676 | Binary Markov-K Random Generator | <p>Hello Stackoverflow Community, </p>
<p>currently I'm working on an entropy encoder (MQ-coder) implementation (cython wrapper and internal c source code). To create a test setting, I want to use a binary markov-k random generator, that outputs numpy arrays as input for the encoder. What would be the easiest way to i... | <p>FYI: this generates a table[256] of probabilities, based on the bits of it's (ascii) input.</p>
<p>Usage: <code>cat*.c| ./a.out</code></p>
<p>;-)</p>
<hr>
<pre><code>#include <stdio.h>
struct cell {
unsigned nhit;
unsigned ones;
} cells[256] ={{0,0},};
int main(void)
{
unsigne... | python|numpy|random|scipy|generator | 0 |
2,015 | 38,179,248 | Absolute difference of two NumPy arrays | <p>Is there an efficient way/function to subtract one matrix from another and writing the absolute values in a new matrix?
I can do it entry by entry but for big matrices, this will be fairly slow...</p>
<p>For example:</p>
<pre><code>X = [[12,7,3],
[4 ,5,6],
[7 ,8,9]]
Y = [[5,8,1],
[6,7,3],
[4,5,9]]
for i in range... | <p>If you want the absolute element-wise difference between both matrices, you can easily subtract them with NumPy and use <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.absolute.html" rel="noreferrer"><code>numpy.absolute</code></a> on the resulting matrix. </p>
<pre><code>import numpy as np
X = ... | python|arrays|numpy | 27 |
2,016 | 38,368,500 | What's the most efficient way to sum up an ndarray in numpy while minimizing floating point inaccuracy? | <p>I have a big matrix with values that vary greatly in orders of magnitude. To calculate the sum as accurate as possible, my approach would be to reshape the ndarray into a 1-dimensional array, sort it and then add it up, starting with the smallest entries. Is there a better / more efficient way to do this?</p> | <p>I think that, given floating point precision problems, the best known algorithm for your task is <a href="https://en.wikipedia.org/wiki/Kahan_summation_algorithm" rel="noreferrer">Kahan summation</a>. For practical purposes, Kahan summation has an error bound that is independent of the number of summands, while nai... | python|numpy|precision | 6 |
2,017 | 38,324,603 | How to keep a slice of a numpy array and clear the rest from memory? | <p>I have a list which contains several large <code>numpy arrays</code></p>
<p>I want to only keep a slice of each of those arrays, and clear my system memory. I have tried using the keywords <code>del</code> and <code>None</code> but those do not seem to have any effect (I use the fedora system monitor to monitor RAM... | <p>You can add zero to the slice:</p>
<p><code>smallSlice = bigArray[...,::10]</code></p>
<p><code>del bigArray</code></p>
<p>will leave bigArray in memory, as there is a copy the slice points to.</p>
<p><code>smallSlice = bigArray[...,::10] + 0</code></p>
<p><code>del bigArray</code></p>
<p>will create a new array, an... | python|python-3.x|numpy | 0 |
2,018 | 66,091,666 | Move one column to another dataframe pandas | <p>I have a DataFrame <code>df1</code> that looks like this:</p>
<pre><code>userId movie1 movie2 movie3
0 4.1 0.0 1.0
1 3.1 1.1 3.4
2 2.8 0.0 1.7
3 0.0 5.0 0.0
4 0.0 0.0 0.0
5 2.3 0.0 2.0
</code></pre>
<p>and a... | <pre><code> df1=pd.concat([df1,df2['movie6']],axis=0)
</code></pre> | python|pandas|dataframe | 0 |
2,019 | 66,311,611 | When and How Keras calculate metrics for each batch of samples? | <p>I was seeing how Keras custom metrics working, and calculation doesn't match between <code>tf.print</code> in metric function and callback print of <code>model.fit</code>.</p>
<pre class="lang-py prettyprint-override"><code>import tensorflow as tf # tf2.4.1
import numpy as np
model = tf.keras.models.Sequential(
... | <p>In keras, the training loss/metric is calculated at the end of each epoch as the mean of loss/metric in each batch. so in your case:</p>
<pre><code>EPOCH 1: (0.02672063 + 0.109848022) / 2 = 0.068284326
EPOCH 2: (0.0456855185 + 0.088704437) / 2 = 0.06719497775
</code></pre>
<p>which correspond to:</p>
<pre><code>hist... | python|tensorflow|machine-learning|keras|deep-learning | 1 |
2,020 | 65,910,850 | How can I get a value from other dataframe's column based on other index? | <p>Take this dataframe <code>df</code> fragment:</p>
<pre><code> col_1 col_2 col_3
0 aaa !!! sss
1 bbb @@@ jjj
2 ccc !!! NaN
3 ddd $$$ nnn
4 eee %%% xxx
</code></pre>
<p>I need to run a <code>fillna()</code> on <code>col_3</code> to get the value of <code>col_1</code> based on the first o... | <p>Here's how to get this done:</p>
<ul>
<li><p>Step 1: Do a Groupby of <code>col_2</code> and find the values of <code>col_1</code> but
pick only the first entry of this value</p>
</li>
<li><p>Step 2: Convert this into a dictionary Both of these steps can be
accomplished by doing:</p>
<p><code>df.groupby('col_2')['col... | python|pandas|dataframe | 1 |
2,021 | 66,316,981 | How to build a custom accuracy metric with tolerance in TF2? | <p>I want to build a custom accuracy metric with tolerance. Instead of counting elements exactly equal in <code>y_true</code> and <code>y_pred</code>, this accuracy regards the two elements are consistent if their difference within a given tolerance value. For example, if the differences between predicted degrees and t... | <p>You can't make a list comprehension with a tensor. The operation you're looking for is <a href="https://www.tensorflow.org/api_docs/python/tf/where" rel="nofollow noreferrer"><code>tf.where</code></a> and you can use it as follows:</p>
<pre><code>def accuracy_with_tolerence(y_true, y_pred):
threshold = 5
dif... | python|tensorflow|keras|customization|metrics | 1 |
2,022 | 66,306,546 | Append array as column to dataframe (or create new dataframe according to other dataframe's date) | <p>First of all, I want to say that there's a lot of similar questions and I'm spending almost 2 days looking and try to solve my problem, using all of the functions but couldn't find what I need, even though I believe there's going to be a very simple solution.</p>
<p>Complete code</p>
<pre><code>import matplotlib.pyp... | <p>The easiest is to use <code>pd.concat</code> like this:</p>
<pre class="lang-py prettyprint-override"><code>mt = pd.Series(
[34.678714, 34.087302, 33.857141, 33.250000, 33.124999, 31.818181, 31.082676, 29.107807, 30.144405],
index=['2019-12-31', '2020-01-02', '2020-01-03', '2020-01-06', '2020-01-07', '2020-0... | python|pandas|dataframe|matplotlib|series | 0 |
2,023 | 52,665,131 | How we can create similarity matrix from dictionar? | <p>I have a dict as following:</p>
<pre><code>dic = {a1: [a,b,c], b1:[b,k,l]}.
</code></pre>
<p>I want to create a similarity matrix for each key's value list. for example, for key <code>a1</code>, I want to compute similarities between <code>(a,b), (a,c) and (b,c)</code> using suppose method <code>f</code>. <code>f... | <p>If <code>f</code> is expensive and not vectorizable, you could use <code>np.tri</code> and friends along the lines of</p>
<pre><code>>>> import numpy as np
>>> from operator import itemgetter as iget
>>>
# set up an example
>>> a1, b1 = 'a1', 'b1'
>>> a, b, c, k, l = np... | python|numpy|scipy | 1 |
2,024 | 46,353,749 | How to Union Intersecting Geometries in Same Geopandas Dataframe | <p>I have a dataframe with circles, some of which intersect others. I want to merge those intersecting regions to be new rows in the dataframe, adding the attributes from the intersecting regions. I only see how to use sjoin between two dataframes.</p> | <p><strong>Setup</strong> </p>
<pre><code>import geopandas as gpd, pandas as pd
from urbansim.maps import dframe_explorer
from shapely.geometry import Point
%matplotlib inline
c1 = Point(1, 0).buffer(1)
c2 = Point(.5, 0).buffer(1)
gdf = gpd.GeoDataFrame(dict(A=[1, 2], B=[3, 4]), geometry=[c1, c2])
gdf.plot()
</cod... | pandas|union|intersect|geopandas | 1 |
2,025 | 46,627,610 | Successfully installed SciPy, but "from scipy.misc import imread" gives ImportError: cannot import name 'imread' | <p>I have successfully installed scipy, numpy, and pillow, however I get error as below</p>
<blockquote>
<p>ImportError: cannot import name 'imread'</p>
</blockquote> | <p><code>imread</code> and <code>imsave</code> are deprecated in scipy.misc</p>
<p>Use <code>imageio.imread</code> instead after <code>import imageio</code>.</p>
<p>For saving -
Use <code>imageio.imsave</code> instead or use <code>imageio.write</code></p>
<p>For resizing use <code>skimage.transform.resize</code> ins... | tensorflow|scipy|ubuntu-16.04|python-import | 0 |
2,026 | 58,173,241 | text classification with machine learning | <p>I have a data set, with news headlines and the category of that news. I wish I could predict the category of the news by entering only its headline.
I need to be able to classify text.
Thank you</p> | <p>your question cannot be answered completely, but i can give you some starting points.
, you need to do some own research
this tutorial will is good for start. <a href="https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/" rel="nofollow noreferrer">link</a></p>
<p>For local development i w... | python|tensorflow|machine-learning | 0 |
2,027 | 69,038,101 | Pandas groupBy multiple columns and aggregation | <p>In dataframe have 4 columns col_A,col_B,col_C,col_D.Need to group the columns(col_A,col_B,col_C) and aggregate mean by col_D. Below is the code snippet I tried and it worked</p>
<p><code>df.groupby(['col_A','col_B','col_C']).agg({'col_D':'mean'}).reset_index()</code></p>
<p>But in addition to the above result, also ... | <p>Using <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#named-aggregation" rel="nofollow noreferrer">Named Aggregation</a>:</p>
<pre class="lang-py prettyprint-override"><code>result = (
df.groupby(['col_A', 'col_B', 'col_C'], as_index=False)
.agg(mean=('col_D', 'mean'), count=(... | python|pandas | 2 |
2,028 | 44,590,646 | Iterating through a dataframe to create PDF documents | <p>I have a worksheet that I have imported as a Pandas dataframe which looks something like this:</p>
<p>FileName FilePath Date Pagestart PageEnd</p>
<p>file1 path1 date1 5 10</p>
<p>file2 path2 date2 20 100</p>
<p>My goal... | <pre><code>import PyPDF2
import os
for row in df.itertuples():
page_start, page_end = row.PageStart, row.PageEnd
output_filename = generate_output_name
filename = os.path.join(row.FilePath, row.FileName)
with PdfFileMerger() as merger:
merger.append(filename, pages=(page_start, page_en))
... | python|python-3.x|pandas|pdf|dataframe | 1 |
2,029 | 44,434,416 | Plot basic example of neural network | <p>I am studying about neural network tutorial and made simple perceptron code like this below </p>
<p>The purpose is </p>
<ul>
<li>Spliting 20 points into two groups.</li>
</ul>
<p>perceptron.py</p>
<pre><code>import numpy as np
from pprint import pprint
import pandas as pd
import matplotlib
import matplotlib.py... | <p>You can plot using <code>scatter</code> for data and <code>contour</code> for boundary decision:</p>
<pre><code>xx = np.linspace(-2,10)
yy = np.linspace(-2,10)
[X1,X2] = np.meshgrid(xx,yy)
Y = [t(i) for i in range(len(x))]
Z = (w[0] * X1.ravel() + w[1] * X2.ravel()) + b
plt.scatter(x[:,0], x[:,1], s=20, c=Y, cma... | python|pandas|neural-network|deep-learning|artificial-intelligence | 0 |
2,030 | 61,113,087 | TypeError: 'DataFrame' object cannot be interpreted as an integer in python 3.7 | <p>I have a simple question, I am creating new column in a list of dataFrame within function. I got this error</p>
<pre><code>data['datenum'] = np.zeros((data))
TypeError: 'DataFrame' object cannot be interpreted as an integer
</code></pre> | <p>Your argument to np.zeros needs to be an integer. Right now you have data, which you say is a DataFrame. Perhaps you're looking for: </p>
<pre><code>data['datenum'] = np.zeros(data.shape[0])
</code></pre>
<p>If you have multiple dataframes, you can do the following: </p>
<pre><code>for data in dataframes:
da... | python|python-3.x|pandas|python-2.7 | 1 |
2,031 | 69,821,979 | Stop Tensorflow trying to load cuda temporarily | <p>I have this code to disable GPU usage:</p>
<pre><code>import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import tensorflow as tf
w = tf.Variable(
[
[1.],
[2.]
])
</code></pre>
<p>I get this output still, not sure why :</p>
<pre><code>E:\MyTFProject\ve... | <p>You can try to reinstall tensorflow with CPU-only version. The links are available here depending on your OS and your python version:
<a href="https://www.tensorflow.org/install/pip?hl=fr#windows_1" rel="nofollow noreferrer">https://www.tensorflow.org/install/pip?hl=fr#windows_1</a></p> | tensorflow | 0 |
2,032 | 69,778,354 | Pandas mistake when reading date from excel file | <p>Pandas error when reading date from excel file.
I am creating a dataframe using the following command.</p>
<pre><code>df = pd.read_excel("report_file.xls", parse_dates=['operation_date'])
df.dtypes
operation_date datetime64[ns]
</code></pre>
<p>Everything looks good. But when analyzing the dataframe, an e... | <p>Try passing the date format explicitly, something like this:</p>
<pre><code>pd.read_excel(
"report_file.xls",
parse_dates=['operation_date'],
date_parser=lambda x: pd.to_datetime(x, format='%Y-%m-%d %I:%M:%S')
)
</code></pre> | python|excel|pandas|datetime64 | 0 |
2,033 | 69,933,833 | Calculate de mean of a list inside of a Nested Dictionary | <p>I have a nested dictionary, that I transformed in a pickle file. The pickle file can be found <a href="https://github.com/joaodavidfreitas/sistemas_inteligentes/blob/main/map_results_LSTM_acoes_variandotest.pickle" rel="nofollow noreferrer">here</a>.
To open the pickle file is just like thar:</p>
<pre><code>import p... | <p>The problem is that <code>values</code> is a string, not a dictionary like you try to use it. <code>keys()</code> returns a list of strings. I suggest you use <code>items()</code> instead to get the key, value pairs from the dictionary you are iterating. This will also let you avoid the long indexing syntax from the... | python|numpy|dictionary|time-series|mean | 1 |
2,034 | 43,132,792 | matplotlib unexpected results polar plot | <p>I am trying to plot simple function r = 3*sin(2*theta) using matplotlib:</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
import matplotlib.pyplot as plt
theta = np.arange(0,2*np.pi,0.01)
r = 3.0*np.sin(2.0*theta)
ax = plt.subplot(111, projection='polar')
ax.plot(theta, r)
plt.show()
</code></... | <p>this patches the polar plot for neg r</p>
<pre><code>import numpy as np
import matplotlib.pyplot as plt
theta = np.arange(0,2*np.pi,0.01)
r = 3.0*np.sin(2.0*theta)
theta = theta + (1 - np.sign(r))*np.pi/2 # add pi to points with negative r values
r = np.abs(r) # make all r values postive to fake out matplotlib
ax =... | python|numpy|matplotlib | 1 |
2,035 | 72,256,427 | Determine if geopandas point is in generic polygon with holes | <p>This thread <a href="https://stackoverflow.com/questions/48097742/geopandas-point-in-polygon">here</a> gave a solution of how to determine if a <code>geopandas</code> <code>POINT</code> is in a solid <code>POLYGON</code>.</p>
<p>What would be a generic solution to determine this for a <code>POLYGON</code> with holes... | <p>Strictly the sample you have provided are polygons. Geometry contains a hole.</p>
<p>It's pretty straight forward to test, just use <strong>convex_hull</strong>. Code below does both tests.</p>
<pre><code>pnts.assign(
**{
**{key: pnts.within(geom) for key, geom in polys.items()},
**{key+"_... | python|pandas|geopandas|point-in-polygon | 0 |
2,036 | 72,360,913 | Is there a non-looping way to perform text searching in a data frame | <p>I have a huge list of ngrams to search. I want to know what frequency they have on my historic dataframe and the mean of a numeric variable that I have on my historic. I have a really really ugly way of doing it (that works), but as the list of ngrams is huge, it's really slow.</p>
<p>I am trying to avoid doing the ... | <p>Try DataFrame.apply()</p>
<pre class="lang-python prettyprint-override"><code>def func(x):
temp = pd.DataFrame(data={'ngram' : [i],
'count' : historic_df['text_variable'].str.contains(i, na=False).sum(),
'mean' : historic_df[historic_df['text_variable']... | python|pandas|n-gram | 0 |
2,037 | 72,146,783 | Groupby id and change values for all rows for the earliest date to NaN | <p>I have the following id, i would like to groupby id and then replace value <code>X</code> with <code>NaN</code>. My current df.</p>
<pre><code>
ID Date X other variables..
1 1/1/18 0.118758835
1 1/1/18 0.148103273
1 1/1/18 0.365541214
1 1/2/18 0.405002687
1 1/2/18 0.130580... | <p>You can call <code>min</code> in <code>groupby.transform</code> to get the earliest dates for each ID; then compare it with "Date" to get a boolean mask; finally use the mask to <code>mask</code> earliest "X"s:</p>
<pre class="lang-py prettyprint-override"><code>df['X'] = df['X'].mask(df.groupby(... | python|pandas|dataframe|pandas-groupby | 1 |
2,038 | 50,291,083 | how to parse selected values from nested json using pandas | <p>I am trying to parse only a selected elements from nested json.</p>
<p>below is my json file</p>
<p><div class="snippet" data-lang="js" data-hide="false" data-console="true" data-babel="false">
<div class="snippet-code">
<pre class="snippet-code-html lang-html prettyprint-override"><code>{
"creation-date": "Fri Ma... | <p>I'm not really sure why you want to get this into pandas. You only have two columns with one value for each of them.</p>
<p>Also, the pandas json loader isn't really designed to get data from your ad hoc JSON files, but to load more regular ones.</p>
<p>I would extract the data I wanted and load that into pandas i... | python|pandas | 0 |
2,039 | 50,457,074 | How to polyfit() an OpenCV calcHist() histogram? | <p>I have something like this:</p>
<pre><code>import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
import numpy.polynomial.polynomial as poly
img = cv.imread('SomeImage.jpg')
color = ('b','g','r')
for i,col in enumerate(color):
histr = cv.calcHist([img],[i],None,[32],[0,256])
plt.plot(hist... | <p>It looks like a minor syntax mistake when calling <code>np.linspace</code>. The correct syntax is</p>
<pre><code>x = np.linspace(interval_start, interval_end, number_of_points)
</code></pre>
<p>so in your case, that would be</p>
<pre><code>x = np.linspace(0, 1, histr.shape[0])
</code></pre> | python|numpy|opencv | 1 |
2,040 | 50,653,562 | Why does "tf.constant(tf.random_normal((10, 4)))" cause an error? | <p>In the following code, "a" works perfectly fine, and "c" also works. But "b" causes an error. Could someone explain the reason?</p>
<pre><code>#!/usr/bin/python
import tensorflow as tf
import numpy as np
a = tf.Variable(tf.random_normal((10, 4)))
b = tf.constant(tf.random_normal((10, 4)))
c = tf.constant(np.rando... | <p>I am also a new one who start using tensorflow. I believe that there is something wrong with your variable type. According to the tensorflow API, you should feed a constant or list of value to 'tf.constant()'. However, in you code, before you initialize the variables and run this session, 'tf.random_normal()' is som... | python|tensorflow|initialization | 1 |
2,041 | 45,374,905 | higher precision in python | <p>I am running some <code>python</code> v3.3.2 scripts that use <code>numpy</code> and <code>scipy</code> and <code>math</code>. I am suspecting that there is an issue of numerical precision in my computation, and I would like to increase the precision in some particular modules that I have written and see if it makes... | <p>When your code is based on numpy/scipy and co., you can only use the types supported by these libs. Here is the <a href="https://docs.scipy.org/doc/numpy/user/basics.types.html" rel="nofollow noreferrer">overview</a>.</p>
<p>The paragraph <a href="https://docs.scipy.org/doc/numpy/user/basics.types.html#extended-pre... | python-3.x|numpy|scipy|precision|scientific-computing | 2 |
2,042 | 62,605,998 | fill column with value of a column from another dataframe, depending on conditions | <p>I have a dataframe that looks like this (my input database on COVID cases)</p>
<p>data:</p>
<pre><code> date state cases
0 20200625 NY 300
1 20200625 CA 250
2 20200625 TX 200
3 20200625 FL 100
5 20200624 NY 290
6 20200624 CA 240
7 20200624 TX 100
8 20200624... | <p>You can do:</p>
<pre><code>df = df.set_index(['date', 'state']).unstack().reset_index()
# fix column names
df.columns = df.columns.get_level_values(1)
state CA FL NY TX
0 20200624 240.0 NaN 290.0 NaN
1 20200625 250.0 100.0 300.0 200.0
</code></pre>
<p>Later, to set i... | python|pandas|numpy | 4 |
2,043 | 62,488,554 | Pandas shows data in a wrong diagram | <p>I have two functions which both create a diagramm. But when I run those 2 functions, in the second one is the data which should be in the first one. Here are the diagramms:<a href="https://i.stack.imgur.com/u3oII.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/u3oII.jpg" alt="enter image descripti... | <p>You could try this. Matplotlib needs to know, if you want a <em>new figure</em> for each plot or not.</p>
<pre class="lang-py prettyprint-override"><code>from pandas import DataFrame
import sqlite3
import matplotlib.pyplot as plt
import pandas as pd
from datetime import date, datetime
datum = str(date.today())
da... | python|pandas|matplotlib | 1 |
2,044 | 54,291,617 | Vectorizing array access from indices matrix | <p>Consider the following:</p>
<pre><code>In [51]: arr = np.arange(6, 10)
In [52]: idx = np.random.randint(4, size=(3, 4))
In [53]: idx
Out[53]:
array([[0, 3, 3, 1],
[1, 3, 3, 2],
[1, 1, 1, 1]])
In [54]: result = np.empty_like(idx)
In [55]: for i in range(idx.shape[0]):
...: result[i] = arr[idx[i]]... | <p>As noted in the comments, you can simply index into the array <code>arr</code> using the <code>idx</code> array.</p>
<pre><code>In [47]: arr
Out[47]: array([6, 7, 8, 9])
In [48]: idx
Out[48]:
array([[3, 2, 2, 0],
[0, 3, 2, 3],
[3, 2, 2, 3]])
In [49]: arr[idx]
Out[49]:
array([[9, 8, 8, 6],
... | python|numpy|multidimensional-array|vectorization|matrix-indexing | 0 |
2,045 | 54,362,961 | Concatenating, sorting, and re-partitioning xyz data | <p>I have a situation where I have two lists of [x, y, z] data, I want to concatenate these lists, sort them, then extract a matrix for the z values, with x increasing along the columns, and y increasing along the rows. </p>
<p>To give an example:</p>
<pre><code>list1 = np.linspace(-2,2,3)
list2 = np.linspace(-1,1,3)... | <p>My approach would be </p>
<pre><code>result = []
_, occurences = np.unique(dat_sorted[:,0], return_inverse=True)
for i in range(np.max(occurences) + 1):
result.append(dat_sorted[occurences == i, 2])
</code></pre>
<p>This will give you a x value ordered list of y value ordered arrays of z values. This is not a... | python|numpy|sorting|multidimensional-array|data-structures | 2 |
2,046 | 54,413,499 | How to use the black/white image as the input to tensorflow | <p>When implementing the reinforcement learning with tensorflow, the inputs are black/white images. Each pixel can be represented as a bit 1/0.</p>
<p>Can I give the data directly to tensorflow, with each bit as a feature? Or I had to expand the bits to bytes before sending to tensorflow? I'm new to tensorflow, so som... | <p>You can directly load the Image data as you would normally do, the Image being binary will have no effect other that the input channel width becoming 1 for the input.</p>
<p>Whenever you put an Image through a convnet, each output filter generally learns features for all the channels, so in case of a binary image, ... | tensorflow | 0 |
2,047 | 73,638,057 | count number of elements in a list inside a dataframe | <p>Assume that we have a dataframe and inside the dataframe in a column we have lists. How can I count the number per list? For example</p>
<pre><code>A B
(1,2,3) (1,2,3,4)
(1) (1,2,3)
</code></pre>
<p>I would like to create 2 new columns wit... | <p>You can use the <a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.apply.html" rel="nofollow noreferrer"><code>.apply</code></a> method on the Series for the column <code>df['A']</code>.</p>
<pre><code>>>> import pandas
>>> import pandas as pd
>>> pd.DataFrame({"colum... | python|pandas | 2 |
2,048 | 73,776,270 | Try catch condition while combining CSV files in pandas | <p>I am combining multiple csv files into a single dataframe using this line -</p>
<pre><code>df = pd.concat(map(pd.read_csv, files), ignore_index=True)
</code></pre>
<p>I was earlier using a <code>for</code> loop where I combine two dataframes at time. This allowed me to use <code>try-catch</code> statements to catch ... | <p>try:</p>
<pre><code>files = ['file1.csv', 'file2.csv', 'file3.csv']
def readcsv(path):
try:
dff = pd.read_csv(path)
except pd.errors.EmptyDataError:
print('error')
dff = pd.DataFrame([]) #or anything else when error happen
#I put empty dataframe here so the concat don't fail,... | python-3.x|pandas | 1 |
2,049 | 73,743,698 | Pandas UDF with dictionary lookup and conditionals | <p>I want to use pandas_udf in Pyspark for certain transformations and calculations of column. And it seems that pandas udf can't be written exactly as normal UDFs.</p>
<p>An example function looks something like below:</p>
<pre><code>def modify_some_column(example_column_1, example_column_2):
lookup_dict = {'a' :... | <p>With this simple if/else logic, you don't have to use UDF. In fact you should avoid to use UDFs as much as possible.</p>
<p>Assuming you have the dataframe as follow</p>
<pre><code>df = spark.createDataFrame([
('a', 'something'),
('a', 'something else'),
('c', None),
('c', ''),
('c', 'something')... | apache-spark|pyspark|pyspark-pandas|pandas-udf | 1 |
2,050 | 71,371,204 | How can I use row index values as column for dataframe? | <p>So, I collected data from 21 participants with 16 EEG channels and I extracted the Gamma band. My current dataframe looks like this ([336 rows x 2 columns]):</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>Channels</th>
<th>Gamma</th>
</tr>
</thead>
<tbody>
<tr>
<td>Fp1</td>
<td>0.345908... | <p>One approach is to group by the Channels and then set these groups as columns of your new dataframe. Assuming following dataframe:</p>
<pre><code> Channels Gamma
0 Fp1 0.345908
1 Fp2 0.121232
2 Fp1 0.455908
3 Fp2 0.213212
</code></pre>
<p>Then apply this code to the dataframe:</p>
<pre><... | python|pandas|dataframe|transpose|melt | 2 |
2,051 | 52,296,757 | Pandas DataFrame equivalent of laravel's 'pluck' on collections | <p>I am using pandas on python 3.6.5, I desire to achieve similar result on a DataFrame instance as the Collection's "pluck" method in Laravel. For example:</p>
<p>DataFrame</p>
<pre><code> one two
0 beer wine
1 beer tomato
</code></pre>
<p>PHP Laravel code:</p>
<pre><code>$plucked = $collection->plu... | <p>You can select ell entries in a DataFrame column by simply doing:</p>
<pre><code>storage_variable = df['Column Name']
</code></pre>
<p>So, in your case that would be:</p>
<pre><code>plucked = df['two']
</code></pre> | php|python|laravel|pandas|dataframe | 0 |
2,052 | 60,665,717 | module 'tensorflow_core._api.v2.data' has no attribute 'Iterator' | <p>Can't figure out what to use instead of Iterator</p>
<p>I tried tf.compat.v1.data.Iterator instead but got another error - <code>AttributeError: 'PrefetchDataset' object has no attribute 'output_types'</code></p>
<p>code:</p>
<pre><code>train_ds = prepare_for_train(labeled_ds)
val_ds = tf.data.Dataset.from_tenso... | <p>I was able to reproduce your error. Here is how you can fix it in <code>Tensorflow Version 2.x</code>.</p>
<p>You need to define <code>iter</code> as below -</p>
<pre><code>iter = tf.compat.v1.data.Iterator.from_structure(tf.compat.v1.data.get_output_types(train_dataset),
... | tensorflow2.0|tensorflow-datasets | 2 |
2,053 | 60,380,852 | How to find a string match in df col based on list of strings? | <p>I have a list of 1000 corporate companies and a df of all previous transactions for the year. For every match, I would like to create a new row value (True) in the new column (df$Covered).</p>
<p>I am not sure why I keep getting the errors below. I tried researching these questions but no luck so far.</p>
<p><a ... | <p>Thanks everyone, it has to do with my Customer_List having special characters so I needed to use map(re.escape</p>
<p>This link helped me below
<a href="https://stackoverflow.com/questions/28539253/python-regex-bad-character-range">Python regex bad character range.</a></p> | python|pandas | 0 |
2,054 | 72,744,383 | How can I input space separated integers in pyhton numpy array. (Like the list(map(int,input().spli(" ")) function does for a list.) | <p>I have tried to find alternatives but only available for list not for numpy arrays</p>
<p>I tried this but didnt work:</p>
<pre><code>5
1 2 3 4 5
Traceback (most recent call last):
File "<string>", line 6, in <module>
File "/usr/local/lib/python3.8/dist-packages/numpy/core/numeric.py&q... | <p>You can just convert to a numpy array:</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
numbers = input('Enter some numbers: ').split()
x = np.array(list(map(int, numbers)))
print(x)
</code></pre>
<p>Output:</p>
<pre><code>Enter some numbers: 1 2 3 4 5
[1 2 3 4 5]
</code></pre> | python|arrays|numpy|dictionary|input | 1 |
2,055 | 72,559,010 | In Pandas, how can I perform the .diff() method to numerical values only in a column that also contains NaNs? | <p>I have a Pandas dataset and I would like to calculate the difference of a column element compared with another element of the same column. In order to do so, the most intuitive method to apply is <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.diff.html" rel="nofollow noreferrer">.diff()</a></... | <p>You'll need to <code>dropna</code> and set up a temporary variable, and <code>reindex</code> like this:</p>
<pre><code>import numpy as np
df = pd.DataFrame({"col": [1, np.nan, 3, 4, np.nan, np.nan, 10, np.nan, 13]})
idx = df.index # create index from original data
tmp = df.dropna() # drop nan rows
tmp.d... | python|pandas|dataframe|nan | 2 |
2,056 | 72,776,999 | How to apply a point transformation to many points? | <p>I have a gridded temperature dataset and a list of weather stations across the country and their latitudes and longitudes. I want to find the grid points that are nearest to the weather stations. My gridded data has coordinates x,y which latitude and longitude are a function of. <a href="https://i.stack.imgur.com/Cl... | <p>There is no need to use geopandas in here... just use <code>crs.transform_points()</code> instead of <code>crs.transform_point()</code> and pass the coordinates as arrays!</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
import cartopy.crs as ccrs
data_crs = ccrs.LambertConformal(central_longi... | python|pandas|numpy|python-xarray|cartopy | 0 |
2,057 | 72,808,258 | Jupyter not showing visual representation | <p>I am researching to make a visual representation of clusters, and I have found the following source: <a href="https://plotly.com/python/v3/3d-point-clustering/#3d-clustering-with-alpha-shapes" rel="nofollow noreferrer">https://plotly.com/python/v3/3d-point-clustering/#3d-clustering-with-alpha-shapes</a></p>
<p>In it... | <p>I got it to display in Jupyter notebook. The error was due to plotly authenticiation error, so import iplot from offline.</p>
<pre><code>from plotly.offline import iplot
iplot(fig, filename='3d point clustering')
</code></pre>
<p>Here is the complete code:</p>
<pre><code>import chart_studio.plotly as py
from plotly.... | python|pandas|plotly|cluster-analysis | 0 |
2,058 | 72,601,457 | change string into datetime in pandas | <p>How can I change the following string datetime into datetime in python.Here's my dataframe</p>
<pre><code>IN OUT
2022/6/10 10:20:30.00000000000000000000000000 2022/6/17 13:25:30
2022/6/5 12:48:10.0 2022/6/11 10:15
2022/6/9 08:25:30 2022/6/13 10:25:30
2022-06-08 17:18:37.00000000000000000000 0
0 0
2022-06-0... | <p>Letting pandas infer the format should get you started. You can parse to datetime data type like</p>
<pre class="lang-py prettyprint-override"><code>df['IN'] = pd.to_datetime(df['IN'], errors='coerce')
df['IN']
0 2022-06-10 10:20:30
1 2022-06-05 12:48:10
2 2022-06-09 08:25:30
3 2022-06-08 17:18:37
4 ... | python|pandas|string|date|datetime | 0 |
2,059 | 59,553,742 | Error while custom periods based resampling using resample('W'),sum() in python | <p>I have the data frame ( frame_combined_DF) which looks like this. I need to do custom resampling based on Time_W weeks provided for each SKU.</p>
<pre><code>frame_combined_DF
SKU Qty Time Time_W
WY
2011-10-17 ABC 12.0 11.0 2
2012-01-16 ABC 20.0 11.0 ... | <p>From your sample data I see that <em>WY</em> is the index column.</p>
<p>But check whether this column is of <em>datetime</em> type (not <em>string</em>).
If it is not, run <code>frame_combined_DF.index = pd.to_datetime(frame_combined_DF.index)</code>.</p>
<p>Another point to note is that <em>newdf</em> is a <stro... | python|pandas|numpy | 1 |
2,060 | 59,697,708 | Get date from list with `numpy.datetime64`-objects | <p>I have a list with quite some dates. Unfortunately they are all appear as <code>numpy.datetime64</code>-object. Does anyone has an idea of how I could extract the actual date?
The list looks like this: </p>
<pre><code>[numpy.datetime64('2016-01-04T00:00:00.000000000'),
numpy.datetime64('2016-01-14T00:00:00.0000000... | <p>Here's a way to do using <code>.astype</code>:</p>
<pre><code>dates = [str(x.astype('datetime64[D]')) for x in dates_list]
['2016-01-04', '2016-01-14', '2016-01-17', '2016-01-24']
</code></pre> | python|pandas|numpy|datetime|type-conversion | 2 |
2,061 | 59,503,069 | Need to add dataframes in a excel in iterative format | <p>I have created a pandas dataframe from dictionary and i need to copy the unique column data to a excel in the same sheet But its just writing one dataframe and doesnt write anything after that Help!
Below is the code:</p>
<pre><code>import pandas
import csv
import os
act_dict = {'bmc': [], 'adc': [], 'volume': []... | <pre><code>l1 = bmc_data.bmc.unique()
print(l1)
startcol=startrow = 0
file_name='/home/laxmi/Documents/volume_analyser_project/idmc.xlsx'
writer = pandas.ExcelWriter('idmc.xlsx', engine='xlsxwriter')
count1=(len(l1))
print(count1)
for i in range(count1):
df_i=bmc_data[bmc_data.bmc==l1[i]]
df_i = df_i.sort_value... | python|pandas | 0 |
2,062 | 32,325,410 | Label regions with unique combinations of values in two numpy arrays? | <p>I have two labelled 2D numpy arrays <code>a</code> and <code>b</code> with identical shapes. I would like to re-label the array <code>b</code> by something similar to a <a href="http://resources.arcgis.com/EN/HELP/MAIN/10.1/index.html#//00080000000s000000" rel="nofollow noreferrer">GIS geometric union</a> of the two... | <p>If I understood the circumstances correctly, you are looking to have unique pairings from <code>a</code> and <code>b</code>. So, <code>1</code> from <code>a</code> and <code>1</code> from <code>b</code> would have one unique tag in the output; <code>1</code> from <code>a</code> and <code>3</code> from <code>b</code>... | python|arrays|numpy|scipy|python-2.6 | 5 |
2,063 | 40,600,308 | Python: track job progress using tqdm | <p>I am using the following code to track a job progress:</p>
<pre><code>from tqdm import tqdm, tqdm_pandas
tqdm.pandas(tqdm())
my_df['target'] = my_df.progress_apply(lambda x: my_fun(x), axis = 1)
</code></pre>
<p>Then the code provide progress tracking like below:</p>
<pre><code> 0%| | 0/5 [00:00<?, ... | <p>Yes, just use the mininterval argument:</p>
<pre><code>tqdm.pandas(tqdm, mininterval=5)
</code></pre> | python|pandas|progress | 1 |
2,064 | 18,700,620 | printing sub-array in numpy as Matlab does | <p>How can you print sub-arrays in numpy the same way Matlab does? I have a 3 by 10000 array and I want to view the first 20 columns. In Matlab you can write</p>
<pre><code>a=zeros(3,10000);
a(:,1:20)
Columns 1 through 15
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 ... | <p>Does this give what you want?</p>
<pre><code>>>> for item in a[:,0:20].T:
print '\t'.join(map(str,item.tolist()))
</code></pre>
<p>Or this?</p>
<pre><code>>>> for item in a[:,0:20]:
print '\t'.join(map(str,item.tolist()))
</code></pre> | python|matlab|numpy | 1 |
2,065 | 61,949,905 | Getting Invalid argument: shape of all inputs must match:values[0].shape = [401408] != values[1].shape = [24485888] when using IoU metric in keras | <p>I'm using UNet to train on the TACO dataset, which is in COCO format. I tried training my model with the accuracy metric, only to end up with validation accuracy and accuracy reaching 1.000, which is honestly too good to be true. I was told that accuracy isn't exactly a fitting metric for segmentation problems, whic... | <p>I was facing a similar issue. the problem was with the output layer where the number of filter I had was 1 but the mask was a 3D image hence the filter number was suppose to be 3. maybe for you too, the number of filters in output layers doesn't match the mask dimensions. try changing it</p> | python|tensorflow|machine-learning|keras | 0 |
2,066 | 61,861,814 | list reshape as similar to dictionary type | <p>I'm dealing with patent data with pandas and numpy.
the steps that I've done and data I've got from the raw data is below.</p>
<p>code</p>
<pre><code>title = df['title'].tolist()
cpc = df_cpu['cpc'].tolist()
z = zip(title, cpc)
</code></pre>
<p>result</p>
<pre><code> ('(real-time information transmission syste... | <p>If I understood the end goal correctly, you want to use <code>split()</code> to split the cpc codes string, using <code>','</code> as the separator. This will generate a list, which you can then iterate through to create a new list/tuple.</p>
<p>Here is a snippet that I think accomplishes what you want:</p>
<pre><... | python|pandas|numpy | 1 |
2,067 | 58,150,686 | How to find the eigenvalues in Python with a matrix different to the identity matrix | <p>I am trying to find the eigenvalues of a characteristic equation in Python, the problem is that in the equation |A-lambda I|=0, the matrix that multiplies lambda isn't the identity matrix, but I have to make clear that this matrix different to the identity matrix is a diagonal matrix.</p> | <p>The problem you're facing is known as the generalized eigenvalue problem. An example solution with <code>numpy</code> is given in <a href="https://stackoverflow.com/questions/24752393/solve-generalized-eigenvalue-problem-in-numpy">this</a> question.</p> | python|numpy|matrix|linear-algebra|eigenvalue | 0 |
2,068 | 58,142,567 | Streaming NumPy data as input to Tensorflow | <p>I was reading <a href="https://www.tensorflow.org/guide/datasets" rel="nofollow noreferrer">https://www.tensorflow.org/guide/datasets</a> to look for a solution to stream NumPy arrays stored in npz files, which may be too large to fit in memory. This snippet is provided in the documentation:</p>
<pre><code># Load t... | <p>The utilities for <code>.npy</code> files indeed allocate the whole array into memory. </p>
<p>If all of your input data fit in memory, the simplest way to create a Dataset from them is to convert them to <code>tf.Tensor</code> objects and use <code>Dataset.from_tensor_slices()</code> like you are doing above. </... | python|numpy|tensorflow | 1 |
2,069 | 58,066,558 | Pandas Group-By and Sum not creating a new Data Frame | <p>I have a dataframe - </p>
<pre><code> TransactionDT TransactionAmt TransactionHour
0 86400 68.5 0
1 86401 29.0 1
2 86469 59.0 1
3 86499 50.0 2
4 86506 50.0 ... | <pre><code>sliced_data2 = data.groupby('TransactionHour',as_index = False).agg({"TransactionAmt" : "sum"})
</code></pre> | python|pandas | 1 |
2,070 | 34,095,310 | Pandas: How to get the column name where a row contain the date? | <p>I have a dataframe named <code>DateUnique</code> made of all unique dates (format datetime or string) that are present in my other dataframe named <code>A</code>.</p>
<pre><code>>>> print(A)
'dateLivraisonDemande' 'abscisse' 'BaseASDébut' 'BaseATDébut' 0 2015-05-27 2004-01-10 ... | <p>You can make a function that returns the appropriate column. Use the vectorized <code>isin</code> function, and then check if <code>any</code> value is <code>True</code>.</p>
<pre><code>df = pd.DataFrame({'dateLivraisonDemande': ['2015-05-27']*7 + ['2015-05-27', '2015-05-29', '2015-04-10'],
'ab... | python|pandas | 1 |
2,071 | 37,006,897 | scipy optimize SLSQP only takes last ineq constrant into account | <p>Let's say I have a portfolio with weights, sum = 1.<br>
Then I want to define pockets (0, 1, 2) with some assets included in those pockets, and sum(weights_pocket_assets) < pocket_max_weight<br>
On my UI, I have a 3 columns for each pocket, filled with 1 if asset is in pocket, 0 otherwise (this array is called 'p... | <p>As often, it was a problem with variable scope (odd stuff since variables were still defined, but overwritten. I did not investigate more to fully understand). The following code works as expected (all pockets constraints taken into account):</p>
<pre><code>constr += ({'type': 'ineq',
'fun': lambda x, m... | python|python-3.x|numpy|scipy | 0 |
2,072 | 36,940,253 | TensorFlow: change tanh of BasicRNNCell to another op? | <p>I want try some other transfer functions besides the default <code>tanh</code> in TensorFlow's <code>BasicRNNCell</code>.</p>
<p>The original implementation is like:</p>
<pre><code>class BasicRNNCell(RNNCell):
(...)
def __call__(self, inputs, state, scope=None):
"""Most basic RNN: output = new_state = tanh(W *... | <p>You do not need to change the code of the tensor flow implementation for this.</p>
<p>The BasicRNNCell has a parameter called activation function. You can just simply change that from tf.tanh to whatever activation function you want.</p> | python|inheritance|neural-network|tensorflow|recurrent-neural-network | 2 |
2,073 | 54,747,845 | Bokeh graph doesn't plot properly | <p>The following code doesn't generate a graph:</p>
<pre><code>import pandas
import numpy as np
from bokeh.plotting import figure, show, output_file
from bokeh.io import output_notebook
from datetime import datetime
output_notebook()
TOOLS="hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,\
... | <p>OK, as far as I can tell, this is what you want (using some project sample data, since you did not provide anything to run your code with):</p>
<pre><code>from bokeh.plotting import figure, show
from bokeh.sampledata.commits import data
p = figure(x_axis_type="datetime", y_axis_type="datetime")
p.circle(x=data.ind... | python|pandas|bokeh | 1 |
2,074 | 49,710,174 | Is there a way to recognise an object in an image? | <p>I am looking for some pre-trained deep learning model which can recognise an object in an image. Usually the images are of type used in shopping websites for products. I want to recognise what is the product in the image. I have come across some pre-trained models like VGG, Inception but they seems to be trained on ... | <p>I think the best way to do this is to build your own training set with the labels that you need to predict, then take an existing pre-trained model like VGG, remove the last fully connected layers and train the mode with your data, the process called transfer learning. Some more info <a href="https://www.tensorflow.... | tensorflow|deep-learning|keras|image-recognition | 0 |
2,075 | 49,556,135 | Load one-line-json formatted data into Pandas DataFrame | <p>I have a json doc with 7 columns and only 1 row.I am not able to load this Json into a DataFrame with read_json.</p>
<pre><code>url_global = 'https://api.coinmarketcap.com/v1/global/'
df_global = pd.read_json(url_global)
ValueError: If using all scalar values, you must pass an index
</code></pre> | <p>The params in this function is somehow complicated and un-orthogonal. I find it helpful to use</p>
<pre><code>pds.read_json("https://api.coinmarketcap.com/v1/global/", typ='series')
</code></pre>
<p>output would be (the type is 'pandas.core.series.Series')</p>
<pre><code>active_assets 6.7700... | python|pandas|dataframe | 0 |
2,076 | 27,913,806 | How to keep rows where at least one column satisfy a condition in Pandas | <p>I have the following DF:</p>
<pre><code>In [1]: import pandas as pd
In [2]: mydict = {'foo':[0, 0.3,5], 'bar':[1,0.55,0.1], 'qux': [0.3,4.1,4]}
In [3]: df = pd.DataFrame.from_dict(mydict, orient='index')
In [4]: df
Out[4]:
0 1 2
qux 0.3 4.10 4.0
foo 0.0 0.30 5.0
bar 1.0 0.55 0.1
</code></p... | <pre><code>In [201]: df.loc[(df > 2).any(axis=1)]
Out[201]:
0 1 2
qux 0.3 4.1 4
foo 0.0 0.3 5
</code></pre> | python|pandas | 10 |
2,077 | 73,294,933 | Pytorch: How to generate random vectors with length in a certain range? | <p>I want a <code>k</code> by <code>3</code> by <code>n</code> tensor representing <code>k</code> batches of <code>n</code> random 3d vectors, each vector has a magnitude (Euclidean norm) between <code>a</code> and <code>b</code>. Other than rescaling the entries of a random <code>kx3xn</code> tensor to <code>n</code> ... | <p>Assuming <code>a < b</code>, you now have a constraint on the 3rd random number due to the norm. i.e <code> sqrt(a^2 - x^2 - y^2) < z < sqrt(b^2 - x^2 - y^2)</code></p>
<p>Now <code>a^2 - x^2 - y^2 > 0</code> which implies that <code>x^2 + y^2 < a^2</code></p>
<p>We need two sets of generate numbers s... | python|numpy|random|pytorch | 2 |
2,078 | 73,354,949 | Reformatting a dataframe to replace repeating similar rows with a new column | <p>Input.</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>Name</th>
<th>Phrase number</th>
<th>Words said</th>
</tr>
</thead>
<tbody>
<tr>
<td>John</td>
<td>Phrase 1</td>
<td>Hi!</td>
</tr>
<tr>
<td>John</td>
<td>Phrase 2</td>
<td>How are you?</td>
</tr>
<tr>
<td>John</td>
<td>Phrase 3</td>... | <p>you can use <code>pivot</code> but have to use a few other methods to clean up the index and columns names (in order to exactly match the desired output):</p>
<pre><code>df = (df.pivot(index='Name', columns='Phrase number')
.droplevel(0, axis=1)
.reset_index()
.rename_axis('', axis=1))
df
Out[1]:
... | python|pandas | 0 |
2,079 | 73,320,055 | How to apply a low pass filter to a dicom image in python? | <p>I am trying to apply some blur using a low pass filter to a dicom image, however my resulting dicom image is not correct (see image below)
(all data below is publicly available)</p>
<pre><code>from scipy import fftpack
import numpy as np
import imageio
from PIL import Image, ImageDraw
import numpy as np
import pydic... | <p>I fixed the code using the GaussianBlur of cv2 library</p>
<pre><code>dicom = pydicom.dcmread("./CT000000.dcm")
dicom.PixelData = cv2.GaussianBlur(dicom.pixel_array, (7, 7), 0)
#save the image
dicom.save_as(r"./result.dcm")
</code></pre> | python|numpy|opencv|image-processing|pydicom | 0 |
2,080 | 34,917,727 | Stacked bar plot by grouped data with pandas | <p>Let's assume I have <code>pandas</code> dataframe which has many features and I am interested in two. I'll call them <code>feature1</code> and <code>feature2</code>.</p>
<p><code>feature1</code> can have three possible values.
<code>feature2</code> can have two possible values.</p>
<p>I need bar plot grouped by <c... | <p>Also, I have found another way to do this (with pandas):</p>
<p><code>df.groupby(['feature1', 'feature2']).size().unstack().plot(kind='bar', stacked=True)</code></p>
<p>Source:
<a href="https://stackoverflow.com/questions/26683654/making-a-stacked-barchart-in-pandas">making a stacked barchart in pandas</a></p> | python|pandas|plot | 24 |
2,081 | 67,495,100 | Problem with freezing pytorch model - requires_grad is always true | <p>I have tried to freeze part of my model but it does not work. Gradient computation is still enabled for each layer. Is that some sort of bug or am I doing something wrong? :)</p>
<pre class="lang-py prettyprint-override"><code>model = models.resnet18(pretrained=True)
# To freeze the residual layers
for param in mod... | <p>This is just a typo (<code>require_grad</code> must be <code>requires_grad</code>):</p>
<pre class="lang-py prettyprint-override"><code># To freeze the residual layers
for param in model.parameters():
param.requires_grad = False # it was require_grad
for param in model.fc.parameters():
param.requires_grad ... | python|pytorch | 2 |
2,082 | 67,286,133 | Creating new column based on other column values with condition | <p>I have a column with values:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th style="text-align: left;">brand</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">Brand1</td>
</tr>
<tr>
<td style="text-align: left;">Brand2</td>
</tr>
<tr>
<td style="text-align: left;"></td>
</tr... | <p>This answer just focusses on <em>Why did the first iteration not work</em></p>
<p>In your code when you replace the <code>data.brand</code> with the <code>regex</code>, you replace with <code>np.nan</code> which is not <code>nan</code>, hence the first init cannot identify the condition in the next line : <code>np.w... | python|pandas|dataframe|numpy | 2 |
2,083 | 34,670,464 | How can I add coordinate system / reference frame information to my data to avoid errors? | <p>Often times when dealing with vectors, reference frames are implicitly enforced through documentation, comments, or worse, (human) memory. For example, I want to compute the torque acting on a body moving with a given velocity from a plane due to drag (using a simple drag model):</p>
<pre><code>torque = velocity.do... | <p>If I understand correctly you are looking to implement what Sympy provides in the vector module. Have a <a href="http://docs.sympy.org/0.7.2/modules/physics/mechanics/vectors.html" rel="nofollow noreferrer">look</a> at the <code>ReferenceFrame</code> class. </p>
<pre><code>from sympy.physics.vector import Reference... | python|math|numpy|coordinates|physics | 2 |
2,084 | 60,068,277 | free up the memory allocation cuda pytorch? | <blockquote>
<p>RuntimeError: CUDA out of memory. Tried to allocate 12.00 MiB (GPU 1;
11.91 GiB total capacity; 10.12 GiB already allocated; 21.75 MiB free; 56.79 MiB cached)</p>
</blockquote>
<p>I encountered the preceding error during pytorch training. <br/>
I'm using pytorch on jupyter notebook. Is there a way ... | <p>I had the same issue sometime back.
There are generally two way I go about.</p>
<ol>
<li>Decrease the batch size</li>
</ol>
<p>Sometimes, even when I had decrease the batch size to '1', this issue persists. Then I changed my approach as follows.</p>
<ol start="2">
<li>Decrease the image size ( or patch size, dependi... | gpu|pytorch | 2 |
2,085 | 59,965,978 | Making computation more efficient using a binary file | <p>I am solving N coupled differential equations (u1(t),v1(t),u2(t),v2(t),...) iteratively. I have a ring of N oscillators, and each oscillator is connected to P neighbours. I am trying to improve the efficiency by not saving all of my iteration steps into lists, but instead by exporting my results for every 10th time ... | <p>In your second computation, in the first line, you are allocating the <code>u</code> and <code>v</code> arrays to the same memory location. That is, when you assign to <code>u[j]</code> and <code>v[j]</code>, you assign to the same place, overwriting the previous content. This will give a completely different comput... | python|python-3.x|numpy|binary|differential-equations | 1 |
2,086 | 63,798,869 | Replace dot product for loop Numpy | <p>I am trying to replace the dot product for loop using something faster like NumPy</p>
<p>I did research on dot product and kind of understand and can get it working with toy data in a few ways in but not 100% when it comes to implementing it for actual use with a data frame.</p>
<p>I looked at these and other SO thr... | <p>Evidently <code>unit_vectors</code> is a dictionary, from which you extract to 2 values, <code>u1</code> and <code>u2</code>.</p>
<p>But what are those? Evidently dicts as well (this iteration would not make sense with a list):</p>
<pre><code>for dimension in u1:
if dimension in u2:
dot_product += u1[di... | python|python-3.x|numpy|nlp|self | 1 |
2,087 | 63,845,441 | Key/Value Pairs in Pandas Dataframe | <p>I have a dataframe that I created by merging multiple MATLAB <code>.mat</code> files and then loading the merged list of dictionaries to pandas.</p>
<pre><code> KEY_COLUMN VALUE_COLUMN
0 [[[KEY1]], [[KEY2]], [[KEY3]], [[KEY4]]] [[VALUE], [VALUE], [VALUE], [VALUE]]
1 [[[KEY2... | <p>Let's create a new dataframe by mapping key value pairs inside a list comprehension and using <code>np.squeeze</code> to remove the single dimensions:</p>
<pre><code>df1 = pd.DataFrame([dict(zip(*map(np.squeeze, v))) for v in df.to_numpy()])
</code></pre>
<p>Result:</p>
<pre><code># for sample data
KEY1 KEY2 ... | python|pandas|matlab|dataframe | 0 |
2,088 | 46,716,472 | Getting a " A nested call to gcloud failed" error when trying to create a datalab in gcloud | <p>Just starting to use Google Cloud Platform. Trying to familiarize myself with tensorflow and am following the Stack Skills tutorial Machine Learning and TensorFlow on the Google Cloud. I am using the gcloud console on firefox and following the tutorial I use the commands</p>
<ul>
<li>gcloud config set core/project ... | <p>It is likely that "my-first-project" does not exist as a project that your account has access to. You need to create the project first either through the console, or via the command line:</p>
<pre><code>gcloud projects create my-first-project
</code></pre> | tensorflow|google-cloud-platform|google-cloud-datalab | 0 |
2,089 | 46,849,831 | Using the OR operator seems to only take the first of two conditions when used with np.where filter | <p>Here is a small sampling of my dataset:</p>
<pre><code>Search_Term Exit_Page Unique_Searches Exit_Pages_actual
nitrile gloves /store/catalog/product.jsp? 10 /store/catalog/product.jsp?
zytek gloves /store/product/KT781010 20 /store/pro
</code></pre>
<p>So th... | <p>@tw-uxtli51nus in the comments is basically correct.</p>
<p>We can accomplish what you want by wrapping logical conditions with ()
and using '|' in place of 'or'.</p>
<p>So np.where would look like:</p>
<pre><code>df['new_col'] = np.where(
(
(df['Exit_Page'].str[:10]=='/store/cat')
|
(df['Exit_Pag... | python|python-3.x|pandas | 1 |
2,090 | 38,643,151 | Importing structured data into python | <p>I have a text file with a set of arrays in it that looks like this:</p>
<pre><code>[(0,1,3),(0,4,5),...(1,9,0)]
[(9,8,7),(0,4,5),...(1,9,0)]
</code></pre>
<p>where the rows are not the same length. </p>
<p>This is essentially a list of paths, where each set of points is a path, ie:</p>
<pre><code>(0,1,3),(0,4,5... | <p>The following code reads the data in (assuming one path per line, and no extra whitespace) into a list of numpy arrays, then demonstrates how to compute the distance between two points.</p>
<pre><code>import numpy as np
import numpy.linalg as la
#replace with your datafile
datafile = "../data/point_path.txt"
paths... | python|arrays|numpy|import | 0 |
2,091 | 63,111,918 | Unable to replace NaN value with a date in pandas | <p>Trying to replace a <code>NaN</code> in a <code>datetime</code> column with another <code>datetime</code> object from the same pandas dataframe. I have tried set_value, at, <code>loc</code>. They all result in <code>nan</code> being saved instead of the actual date.<br />
Here is the most recent code I tried, seein... | <p>For example, to fill empty column <code>column_to_fill</code> with values from the same dataframe <code>df</code> with values from column <code>column_from</code> use:</p>
<pre><code>df['column_to_fill'] = df['column_to_fill'].fillna(df['column_from'])
</code></pre> | python|pandas|datetime|nan | 0 |
2,092 | 63,214,018 | How to prevent/avoid duplicate row insert in dataframe? | <p>here's my code snippet:</p>
<pre><code>insert os
insert sys
insert pandas as pd
data=[['2019-04-04',1105],['2019-04-05',1145],['2019-04-06',1125],['2019-04-07',1130],['2019-04-08',1122],
['2019-04-09',1105],['2019-04-10',1145],['2019-04-11',1125],['2019-04-12',1130],['2019-04-13',1122],
['2019-05-04',11... | <pre><code>date_str = '2019-05-14'
price = 1200
if any(pp['Date'] == date_str):
print('Date already exists.')
else:
pp.loc[len(pp)] = [date_str, price]
print('New date added to dataframe.')
print(pp)
</code></pre> | python|pandas | 0 |
2,093 | 62,986,296 | TypeError: '<=' not supported between instances of 'str' and 'int' Duplicate | <p>I am using Python3 and I'm working on several files where some of my data (AYield & BYield) is missing which is considered a NaN, however, when I'm running the last line of the code, I get an error. Both Ask and Bid data frames contain the same rows and columns. Thank you</p>
<pre><code>Askyield = pd.read_excel(... | <p>I can reproduce this error with this example:</p>
<pre><code>import pandas as pd
df = pd.DataFrame(dict(x=["5", "10"],
y=[1, 4]))
df.dtypes
# x object
# y int64
# dtype: object
df[df.x > df.y]
# TypeError: '>' not supported between instances of 'str' and '... | python|pandas|dataframe|nan | 2 |
2,094 | 63,285,923 | Why is i not incrementing in for loop? | <p>I'm new to programming may I know why my <code>i</code> is not incrementing in the for loop. I want to update the plot name for each subplot. Thank you.<br />
<img src="https://i.stack.imgur.com/5Vbwd.png" alt="Code screenshot" /></p>
<pre><code>from matplotlib import pyplot as plt
fig= plt.figure()
fig,axes = plt.s... | <p>This is because your axes array is like shown below</p>
<pre><code>[[<matplotlib.axes._subplots.AxesSubplot object at 0x000001DCA32BB2E0>
<matplotlib.axes._subplots.AxesSubplot object at 0x000001DCA54476A0>
<matplotlib.axes._subplots.AxesSubplot object at 0x000001DCA547D250>]]
</code></pre>
<p>... | python|numpy|for-loop|matplotlib|subplot | 3 |
2,095 | 63,319,129 | Index Error: Unable to print cost_function | <p>I am trying to run a for loop to print the cost functions for three different slopes and bias = 0 by defining a function. The dataset has 5 rows and cost function is to predict marks based on attendance.
I am able to print cost function if I define three separate functions for each value of slope. Here is my code:</... | <p>You aren't accessing the elements of your slope df correctly. <code>slope.shape</code> returns <code>(3, 1)</code> so you want to iterate through the row number, not the column number.</p>
<p><code>sum_of_squared_error += (y - (slope.iloc[0, i]*x + bias)) ** 2</code> should be: <code>sum_of_squared_error += (y - (sl... | python|pandas | 1 |
2,096 | 67,636,342 | python how to choose just special elements from df string | <p>Please, help.
I need to choose just 'yellow', 'green', 'black' or combinatons of these elements, if there are several of them in the string.
df:</p>
<pre><code>0 ['blue','green','white','yellow','orange','pink','black']
1 ['green','yellow','orange','pink','pink']
2 ['white','orange','black']
3 ['green','white','yell... | <p>Your dataframe <code>df</code>:</p>
<pre><code> val
0 ['blue','green','white','yellow','orange','pin...
1 ['green','yellow','orange','pink','pink']
2 ['white','orange','black']
3 ['green','white','yellow','orange']
4 ['green']
</code></pre>
<p>Try with <code>apply()</code> and list comprehension:</p>
<p... | python|pandas | 1 |
2,097 | 67,644,891 | How do I create embeddings for every sentence in a list and not for the list as a whole? | <p>I need to generate embeddings for documents in lists, calculate the Cosine Similarity between every sentence of corpus 1 with every sentence of corpus2, rank them and give out the best fit:</p>
<pre><code>embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
embeddings1 = ["I'd li... | <p>As I mentioned in the comment, you should write the for loop as follows:</p>
<pre><code>for sentence in embeddings1:
print(sentence, embed([sentence]))
</code></pre>
<p>the reason is simply that embed is expecting a list of strings as an input. No more detailed explanation than that.</p> | python|tensorflow|nlp|cosine-similarity|sentence-similarity | 0 |
2,098 | 31,795,045 | How can I make pandas.to_excel() include the index but NOT on a separate line? | <p>When I save a Pandas DataFrame to Excel (with the index option left as it's default: True), the resulting Excel file has a line beneath the row of headers. Said row contains the index name. How can I avoid that extra line and just have the index name(s) show up in the same row as the rest of the column headers?</p>
... | <p>It appears that setting merge_cells (which is by default True) to False accomplishes the objective, but it's not immediately clear to me why that's the case.</p>
<pre><code>df[field_list].to_excel(path, merge_cells=False)
</code></pre> | python|excel|pandas | 0 |
2,099 | 41,560,796 | Numpy not found after installation | <p>I just installed numpy on my PC (running windows 10, running python 3.5.2) using WinPython, but when i try to import it in IDLE with: <code>import numpy</code> I get the ImportError: <code>Traceback (most recent call last):
File "C:\Users\MY_USERNAME\Desktop\DATA\dataScience1.py", line 1, in <module>
i... | <p>In Linux and Mac OS systems we can install modules directly by mentioning</p>
<pre><code>pip install modulename (or) sudo pip install modulename
</code></pre>
<p>in terminal or command prompt.</p>
<p>But in windows we should mention location of python folder in c directory like c:\python3 and later we should use<... | python|numpy|python-3.5 | 1 |
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