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 |
|---|---|---|---|---|---|---|
2,500 | 56,818,822 | How can I weigh columns in data frame and add them up | <p>I have a data frame with 5 columns, I only want to add the second and the third, but each point in the third column has to be <strong>multiplied by 3</strong>,<br><br> so I need to add a new column called <br>
<code>"Total score" which is df['Second'] + 3* df['Third']</code></p>
<p>I have tried with sum but I don't... | <p>Make sure your columns is order correctly, then we can using <code>dot</code> </p>
<pre><code>df['Total Score'] = df.dot([0,1,3,0,0])
</code></pre>
<p>Or to be safe </p>
<pre><code>df['Total Score'] = df[['Second','Third']].dot([1,3])
</code></pre> | python|python-3.x|pandas|dataframe | 1 |
2,501 | 56,841,451 | Why is my neural net only predicting one class (binary classification)? | <p>I am having some trouble with my ANN. It is only predicting '0.' The dataset is imbalanced (10:1), ALTHOUGH, I undersampled the training dataset, so I am unsure of what is going on. I am getting 92-93% accuracy on the balanced training set, although on testing (on an unbalanced test set) it just predicts zeroes. Uns... | <p>This is not right:</p>
<pre><code>y_pred_bool = np.argmax(y_pred, axis=1)
</code></pre>
<p>Argmax is only used with categorical cross-entropy loss and softmax outputs. For binary cross-entropy and sigmoid outputs, you should round the outputs, which is equivalent to thresholding predictions > 0.5:</p>
<pre><code>... | python|tensorflow|keras|neural-network | 2 |
2,502 | 56,704,844 | How to exclude current date in a group by value rolling window execution in pandas? | <p>I have a dataframe containing IDs, a date and numerical values. I group the data for each ID, and then I calculate the cumulative amount of the previous rows, with a time window of 30 days. In the dataframe below this has been accomplished using the code below (the actual dataframe contains more than one ID and more... | <p>Because you have several values for a same day, I would say you should first <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.resample.html" rel="nofollow noreferrer"><code>resample</code></a> daily to get the <code>sum</code> per day and then <a href="https://pandas.pydata.org/pa... | python|pandas|dataframe | 4 |
2,503 | 25,464,589 | RcppCNPy not usable after Rcpp update | <p>I am trying to use some older code that relies on RcppCNPy, which used to work on my machine. At some point in the past few months I updated Rcpp and now when I try to attach the RcppCNPy library (<code>library() or require()</code>) I get the following:</p>
<pre><code>*** caught segfault ***
address 0x0, cause 'me... | <p>Recompilation ought to work. Check that you do not have an old version in your <code>.libPath()</code>.</p>
<p>CRAN does checks on packages on would alert the respective maintainer (me, in this case) if RcppCNPy were broken on the Mac. See</p>
<ul>
<li><a href="http://cran.rstudio.com/web/checks/check_results_Rcpp... | r|numpy|rcpp | 0 |
2,504 | 66,906,422 | Unable to create a version in Cloud AI Platform using custom containers for prediction | <p>Because of certain VPC restrictions I am forced to use custom containers for predictions for a model trained on Tensorflow. According to the <a href="https://cloud.google.com/ai-platform/prediction/docs/custom-container-requirements" rel="nofollow noreferrer">documentation</a> requirements I have created a HTTP serv... | <p>Answering this myself after working with the Google Cloud Support Team to figure out the error.</p>
<p>Turns out the port I was creating a <code>Version</code> on was conflicting with the Kubernetes deployment on Cloud AI Platform's side. So I changed the <code>Dockerfile</code> to the following and was able to succ... | docker|tensorflow|google-cloud-platform|tensorflow-serving|google-cloud-ml | 2 |
2,505 | 67,101,069 | Efficient Way to Repeatedly Split Large NumPy Array and Record Middle | <p>I have a large NumPy array <code>nodes = np.arange(100_000_000)</code> and I need to rearrange this array by:</p>
<ol>
<li>Recording and then removing the middle value in the array</li>
<li>Split the array into the <code>left</code> half and <code>right</code> half</li>
<li>Repeat Steps 1-2 for each half</li>
<li>St... | <p>Edit:
The question has been updated to have a much smaller input array so I leave the below for historical reasons. Basically it was likely a typo but we often get accustomed to computers working with insanely large numbers and when memory is involved they can be a real problem.</p>
<p>There is already a numpy based... | python|performance|numpy | 1 |
2,506 | 66,922,956 | How to convert list of dictionaries from CSV to dataframe? | <p>I have list of dictionaries from CSV with header test as follows:</p>
<pre><code>[{'points': 50, 'time': '5:00', 'year': 2010},
{'points': 25, 'time': '6:00', 'month': "february"},
{'points':90, 'time': '9:00', 'month': 'january'},
{'points_h1':20, 'month': 'june'}]
</code></pre>
<p>when I use pd.DataFr... | <p>The reason your code failed is that your input file is actually <strong>not</strong>
any CSV file. It is a <strong>string representation</strong> of your list of
dictionaries (not a list of dictionaries).</p>
<p>I assume that your input file contains what you put as the first sample.</p>
<p>To handle such an input f... | python|pandas|jupyter-notebook | 0 |
2,507 | 68,132,060 | Drop only Nan values from a row in a dataframe | <p>I have a dataframe which looks something like this:</p>
<pre><code>Df
lev1 lev2 lev3 lev4 lev5 description
RD21 Nan Nan Nan Nan Oil
Nan RD32 Nan Nan Nan Oil/Canola
Nan Nan RD33 Nan Nan Oil/Canola/Wheat
Nan Nan RD34 Nan Nan Oil/Canola/Flour
N... | <p>You can use stack and groupby like this to find the fist non null value,</p>
<pre><code>df['code'] = df[['lev1', 'lev2', 'lev3', 'lev4', 'lev5']].stack().groupby(level=0).first().reindex(df.index)
</code></pre>
<p>Now, you can select the code column and description column</p>
<pre><code>df[['code', 'description']]
... | python|pandas|dataframe | 1 |
2,508 | 59,309,566 | In a Pandas dataframe how do I calculate the median value for each decile within each month | <p>I have a dataframe with 50 data points per month. I'd like to calculate the median value for each decile within each month. In my groupby call I lead with the date, then qcut. But qcut calculates the bins over the whole dataset, not by month. Here's what I have so far:</p>
<pre><code>import numpy as np
import panda... | <p>First, <code>groupby</code> month to create the quantile labels within month. Then <code>groupby</code> month and quantile to find the median. </p>
<pre><code>df['q'] = df.groupby(df.index).Data.apply(lambda x: pd.qcut(x, 10, labels=False))
df.groupby([df.index, 'q']).median()
</code></pre>
<hr>
<pre><code> ... | python|pandas|group-by | 0 |
2,509 | 59,105,414 | Pandas to_html does not show the appended data | <p>When trying to export my pandas DataFrame to a html page, through the to_html() functionality, the output html page does not show the appended data-rows.</p>
<pre><code>import pandas as pd
df_test = pd.DataFrame(columns=['TEST1', 'TEST2'])
df_test.append({'TEST1':11, 'TEST2':22}, ignore_index=True)
df_test.append({... | <p>Because pandas <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.append.html" rel="nofollow noreferrer"><code>DataFrame.append</code></a> not working inplace is necessary assign output back:</p>
<pre><code>df_test = df_test.append({'TEST1':11, 'TEST2':22}, ignore_index=True)
df_tes... | python|pandas | 1 |
2,510 | 59,416,760 | How to write pandas dataframe into Databricks dbfs/FileStore? | <p><a href="https://i.stack.imgur.com/Waxvu.png" rel="noreferrer"><img src="https://i.stack.imgur.com/Waxvu.png" alt="enter image description here"></a><a href="https://i.stack.imgur.com/YSh53.png" rel="noreferrer"><img src="https://i.stack.imgur.com/YSh53.png" alt="enter image description here"></a>I'm new to the Data... | <p>Try with this in your notebook databricks:</p>
<pre><code>import pandas as pd
from io import StringIO
data = """
CODE,L,PS
5d8A,N,P60490
5d8b,H,P80377
5d8C,O,P60491
"""
df = pd.read_csv(StringIO(data), sep=',')
#print(df)
df.to_csv('/dbfs/FileStore/NJ/file1.txt')
pandas_df = pd.read_csv("/dbfs/FileStore/NJ/file1... | python|pandas|dataframe|amazon-s3|databricks | 9 |
2,511 | 57,170,571 | Python - GeoPandas Does Not Work After Opening .DXF With Adobe Illustrator | <p>I'm attempting to plot a CAD file (.dxf) using GeoPandas then save it as a KML file. When I attempt to do so - the CAD file ends up showing up in the wrong place (in the middle of the ocean - when it should be in Florida). The strange part is this only occurs after opening the .dxf then saving it with Adobe Illustra... | <p>The fix / solution to this issue is to use an Adobe Illustrator plugin which allows for the preservation of GIS / Geo-spatial data. We've decided to use: <a href="https://www.avenza.com/mapublisher/" rel="nofollow noreferrer">https://www.avenza.com/mapublisher/</a></p>
<p>Thank you to everyone who provided input re... | python|gis|geopandas|epsg | 0 |
2,512 | 35,568,605 | Why cumulative sum is not being carried over in the following numerical integration to calculate the area bewteen two curves? | <p>Description:</p>
<p>In the following python code, I am producing a Gaussian PDF, namely p(y). I am trying to find the area confined between the curve and any horizontal line in the range of [min_p, max_p] through the method of rectangular summation. My main problem is in the implementation of the function that is s... | <p>After making quite a few changes here is the code I think you are trying to produce:</p>
<pre><code>from scipy.stats import norm
import numpy as np
import pylab as p
%matplotlib inline
N = 10 # Number of sigmas away from central value
M, K = 2**10, 2**10 ... | python|arrays|for-loop|numpy|sympy | 0 |
2,513 | 28,505,008 | numpy.polyfit: How to get 1-sigma uncertainty around the estimated curve? | <p>I use numpy.polyfit to fit observations. polyfit gives me the estimated coefficients of the polynomial and can also provides me the error covariance matrix of the estimated coefficients. Fine. Now, I would like to know if there is a way to estimate the +/- 1sigma uncertainty around the estimated curve.</p>
<p>I kno... | <p>If you have enough data points, you can get with the parameter <code>cov=True</code> an estimated covariance matrix from <code>polyfit()</code>. Remember that you can write a polynomial <code>p[0]*t**n + p[1]*t**(n-1) + ... + p[n]</code> as the matrix product <code>np.dot(tt, p)</code> with <code>tt=[t**n, tt*n-1, .... | python|numpy | 8 |
2,514 | 28,571,741 | Retrieve approximate Hessian inverse from L-BFGS-B | <p>With the L-BFGS-B minimizer in scipy, is it possible to retrieve the approximate inverse Hessian that's calculated internally?</p>
<p>Having it in the implicit factored form, so that it's possible to compute arbitrary inverse Hessian matrix - vector products, would be fine.</p> | <p>It doesn't appear so. I'm not an expert on these algorithms but it seems that with L-BFGS specifically it is not possible. According to <a href="http://en.wikipedia.org/wiki/Limited-memory_BFGS" rel="nofollow">Wikipedia</a>:</p>
<blockquote>
<p>Instead of the inverse Hessian H_k, L-BFGS maintains a history of t... | python|numpy|scipy|mathematical-optimization|hessian-matrix | 2 |
2,515 | 50,674,011 | Replace the year in pandas.datetime column | <p>I have a dataframe with a date column converted using pd.to_datetime(). When I inspected the data I found few of these dates with year mentioned as 2216, which should have been 2016. Can you please help me change the year for these dates from 2216 to 2016</p>
<pre><code> Date
0 2216-12-21
1 2216-12-23
2 2... | <p>Use:</p>
<pre><code>df['Date'] = df['Date'].mask(df['Date'].dt.year == 2216,
df['Date'] + pd.offsets.DateOffset(year=2016))
print (df)
Date
0 2016-12-21
1 2016-12-23
2 2016-01-31
3 2016-12-23
4 2016-12-27
5 2016-12-25
6 2016-12-23
</code></pre>
<p>For better performance:</p>
... | python|pandas | 14 |
2,516 | 51,090,580 | Pandas dataframe adding zero-padding before the datetime | <p>I'm using Pandas dataframe. And I have a dataFrame <code>df</code> as the following:</p>
<pre><code>time id
-------------
5:13:40 1
16:20:59 2
...
</code></pre>
<p>For the first row, the time <code>5:13:40</code> has no zero padding before, and I want to convert it to <code>05:13:40</code>. So my expecte... | <p>Use <code>pd.to_timedelta</code>:</p>
<pre><code>df['time'] = pd.to_timedelta(df['time'])
</code></pre>
<p>Before:</p>
<pre><code>print(df)
time id
1 5:13:40 1.0
2 16:20:59 2.0
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 1 to 2
Data columns (total 2 columns):
time ... | python|pandas | 1 |
2,517 | 33,147,411 | Adding a pandas column without creating a list | <p>I have 2 datasets of more than 1million rows and I am analyzing it with pandas (therefore they both are <code>pd.Dataframe</code> and noted <code>df1</code> and <code>df2</code>). I need to do add a column to df1 depending on the value of df2. I used the python list, but it is incredibly slow. Any advice to be quick... | <p>It's not so much that you are creating a list, but that you have a nested loop, taking you over all combinations of <code>df1</code> and <code>df2</code>. Roughly</p>
<pre><code>for line in np.array(df1):
numObs.append([num for i,num,exp in df2 if i==line[0]][0])
</code></pre>
<p>expands to</p>
<pre><code>fo... | python|list|numpy|pandas | 0 |
2,518 | 66,685,526 | Function to select pandas dataframe rows based on list of tuples of columns and cutoffs? | <p>I´m trying to create a python function that takes 2 arguments: a pandas dataframe, and a list of tuples, where each tuple in the list have 3 elements, a column name, a min value and a max value. So each tuple represent a condition to be applied to a column in the dataframe. And then the function would return a sub d... | <h1>Dynamic Query function</h1>
<p>Since you want to check for all the conditions, these will be AND. So we can start filtering them one by one.</p>
<pre><code>import pandas as pd
def sub_df(dx,cuts):
for cx in cuts:
col = cx[0]
minval = cx[1]
maxval = cx[2]
dx = dx[(dx[col] >= ... | python|pandas | 2 |
2,519 | 16,207,023 | Python pandas read_csv like functionality from list to a DataFrame? | <p>I have a list with values like the following:</p>
<pre><code>[['2013-04-02 19:42:00.474', '1'],
['2013-04-02 19:42:00.529', '2'],
['2013-04-02 19:42:00.543', '3'],
['2013-04-02 19:42:00.592', '4'],
['2013-04-02 19:42:16.671', '5'],
['2013-04-02 19:42:16.686', '6'],
['2013-04-02 19:42:16.708', '7'],
['2013-04-02 19:... | <pre><code>In [40]: df.index = df.index.to_datetime()
In [41]: df.index
Out[41]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-04-02 19:42:00.474000, ..., 2013-04-02 19:42:58.225000]
Length: 13, Freq: None, Timezone: None
</code></pre> | python|datetime|csv|pandas | 3 |
2,520 | 16,329,218 | Face Recognition - How to return the correct image? | <p>I am trying to make hand gesture recognition (similar to face recognition) using Principal Component Analysis(PCA) in python. I have a Test image and I want to get its nearest match from a set of Training images.</p>
<p>Here is my code:</p>
<pre><code>import os, sys
import numpy as np
import PIL.Image as Image
d... | <p><code>dst.argmin()</code> will tell you the index of the element in <code>dst</code> which is smallest.</p>
<p>So the closest image would be</p>
<pre><code>idx = dst.argmin()
closest = a[idx]
</code></pre>
<p>since <code>a</code> is a list of arrays representing training faces.</p>
<p>To display the closest imag... | python|image-processing|numpy|face-recognition|pca | 3 |
2,521 | 57,662,437 | How to check if every item pandas column of lists is an int? | <p>I have a pandas column of lists. I need to check if every item in those lists are ints. </p>
<p>For a regular list, I can find if an item is an int using</p>
<pre><code>all(isinstance(x, int) for x in lst)
</code></pre>
<p>and for a regular pandas column, I can check if they're all ints using </p>
<pre><code>df.... | <p>You can use <code>apply</code> with your current list check:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import random
# create random df
x = [{'A': [random.randint(0,300) for i in range(10)]} for i in range(10)]
df = pd.DataFrame(x)
df.A.apply(lambda x: all(isinstance(y, int) for y in... | python|pandas | 2 |
2,522 | 24,183,101 | Pandas: Bar-Plot with two bars and two y-axis | <p>I have a DataFrame looking like this:</p>
<pre><code> amount price
age
A 40929 4066443
B 93904 9611272
C 188349 19360005
D 248438 24335536
E 205622 18888604
F 140173 12580900
G 76243 6751731
H 36859 3418329
I 29304 2758928
J 39768 3201269
K 30350 286... | <p>Using the new pandas release (0.14.0 or later) the below code will work. To create the two axis I have manually created two matplotlib axes objects (<code>ax</code> and <code>ax2</code>) which will serve for both bar plots.</p>
<p>When plotting a Dataframe you can choose the axes object using <code>ax=...</code>. A... | python|matplotlib|plot|pandas | 96 |
2,523 | 43,894,828 | Group data based on column name pandas | <p>In the example below, I want to first sort based on UID and then the TSTAMP for each TID.</p>
<p>In this context, here is a minimal working example I generated:</p>
<pre><code>df = pd.read_csv(dataset_path, names = ['TID','UID','TSTAMP'], delimiter=';')
df = df.sort_values(by=['TID'], ascending=[True])
print df
#p... | <p>It seems you need <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html" rel="nofollow noreferrer"><code>sort_values</code></a>:</p>
<pre><code>df = df.sort_values(['TID', 'TSTAMP', 'UID'], ascending=[True, False, True])
print (df)
TID UID TSTAMP
22267 7... | python|pandas | 2 |
2,524 | 43,879,875 | concat specific rows of two pandas dataframe using data in two columns as reqs | <p>I have two dataframes DF1 and DF2, where </p>
<p>both have subframes "data" and "metadata," and DF1 has substantially more rows than DF2</p>
<pre><code>DF1
DATA METADATA
0 1 2 3 4 5 attr1 attr2 .. attrN
11 1 1 1 1 1 1 000 apple
13 1 1 1 1 1 1 140 ora... | <p>It sounds like you want to do a merge on attr1, something like:</p>
<pre><code>df1.merge(df2, how='left')
</code></pre>
<p>For example (slightly tweaked):</p>
<pre><code>In [11]: df1
Out[11]:
DATA METADATA
0 1 2 3 4 5 attr1 attr2
11 1 1 1 1 1 1 0 bean
13 ... | python|python-3.x|pandas | 0 |
2,525 | 43,771,023 | Interpolate a curve on itself using NumPy | <p>I have the following curve as two arrays, of x and y positions. </p>
<p><img src="https://i.stack.imgur.com/yS0Bp.png" alt="curve"></p>
<p>Imagine if you were to draw vertical lines going through each point, and add points on the curve wherever these lines intersect the curve. This is what I want. </p>
<p>I tr... | <p>According to the <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.interp.html" rel="nofollow noreferrer">docs</a> the array of X values should be sorted (or periodic), otherwise "the result is nonsense". You can try to split your curve into sections, and then interpolate each part on the others. Y... | python|numpy|interpolation | 0 |
2,526 | 73,159,568 | Multi-channel, 2D mask weights using BCEWithLogitsLoss in Pytorch | <p>I have a set of 256x256 images that are each labeled with nine, binary 256x256 masks. I am trying to calculate the <code>pos_weight</code> in order to weight the <code>BCEWithLogitsLoss</code> using Pytorch.</p>
<p>The shape of my masks tensor is <code>tensor([1000, 9, 256, 256])</code> where 1000 is the number of t... | <p>TLDR; This is a broadcasting issue which is surprisingly not handled by PyTorch's <a href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html?highlight=bce#torch.nn.BCEWithLogitsLoss" rel="nofollow noreferrer"><code>nn.BCEWithLogitsLoss</code></a> namely <a href="https://github.com/pytorch/pyt... | python|deep-learning|pytorch|loss-function|weighted | 1 |
2,527 | 73,146,875 | How to select values out of many in pandas dataframe using conditions? | <p>I have a CSV with multiple values for a single value and I have to filter them out based on several conditions. Below is an example of my data.</p>
<pre><code>df1 = pd.DataFrame(
data=[['Afghanistan','2.7;2.7','27.0;26.7','','22.9;22.8'],
['Bahrain','6.3;6.3;6.4','13.0;13.0;13.0','16.8;17.0',''],
['Djibouti'... | <p>Use from apply method for each col</p>
<pre class="lang-py prettyprint-override"><code>def f(x):
a = x.split(';')
if cond1:
return ...
if cond2:
return ...
if cond3:
return ...
df['2019']=df['2019'].apply(f)
...
</code></pre>
<p>For your many cols you can do:</p>
<pre class="... | python|pandas|dataframe|data-cleaning | 1 |
2,528 | 72,866,905 | Create line from list of points while ignoring outliers | <p>I have a list of points that almost create a straight line (but they are not perfectly align on that line). I want to create a line that best describes those points.</p>
<p>For example, for points:</p>
<pre><code>points = [(150, 250),(180, 220), (200, 195), (225, 180), (250, 150), (275, 115), (300, 100)]
</code></pr... | <p>Thanks for <code>@Christoph Rackwitz</code>'s answer, I followed sklearn's doc for <a href="https://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html" rel="nofollow noreferrer">RANSAC</a>, and created simple script to calculate the <code>RANSAC</code> (of course that it's need to be polished):</p>
... | python-3.x|numpy|opencv|outliers | 1 |
2,529 | 73,159,673 | Using PANDAS to conditionally manipulate specific cells based on another cell and getting it to change original df | <pre><code>andrew_ramirez[andrew_ramirez['Datacenter'].isin(['ATL2','ACT1'])]['payout']*= 0.25
</code></pre>
<p>This is not changing my dataframe (named andrew_ramirez) based on the criteria. Am i missing something?</p> | <p>You can use from this code:</p>
<pre class="lang-py prettyprint-override"><code>df['payout']=np.select([df.Datecenter.isin(['ATL2', 'ACT1']), [df.payout*0.25], df.payout)
</code></pre> | pandas | 0 |
2,530 | 72,903,643 | Filter a string value from a column in Python? | <p>Need to extract Diabetes value from column name chronic from a df in python.</p>
<p>Can anyone pls help to retrieve this in python?</p>
<pre class="lang-none prettyprint-override"><code>Patients Chronic
1 Diabetes
2 Diabetes
3 Hypertension
4 Hypertension
5 Diabetes
</... | <p>If your <code>df</code> is:</p>
<pre class="lang-py prettyprint-override"><code> Patients Chronic
0 1 Diabetes
1 2 Diabetes
2 3 Hypertension
3 4 Hypertension
4 5 Diabetes type 1
</code></pre>
<p>Then:</p>
<pre class="lang-py prettyprint-over... | python|regex|pandas | -1 |
2,531 | 10,817,360 | Array order in pytables | <p>With <a href="http://www.pytables.org/moin" rel="nofollow">pytables</a>'s <a href="http://pytables.github.com/usersguide/libref.html#carrayclassdescr" rel="nofollow"><code>CArray</code></a>, is there a way to specify the order in which the data is stored on disk (Fortran/C)?</p>
<p>I am looking for something simila... | <p>You can use the <code>chunkshape</code> parameter that in effect specifies the data order:</p>
<p><a href="http://pytables.github.com/usersguide/libref.html#tables.File.createCArray" rel="nofollow">http://pytables.github.com/usersguide/libref.html#tables.File.createCArray</a></p>
<p>For instance, for 2-D data, <co... | python|numpy|pytables | 2 |
2,532 | 70,733,261 | Joining dataframes using rust polars in Python | <p>I am experimenting with <code>polars</code> and would like to understand why using <code>polars</code> is slower than using <code>pandas</code> on a particular example:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import polars as pl
n=10_000_000
df1 = pd.DataFrame(range(n), columns=['a']... | <p>A pandas <code>join</code> uses the indexes, which are cached.</p>
<p>A comparison where they do the same:</p>
<pre class="lang-py prettyprint-override"><code># pandas
# CPU times: user 1.64 s, sys: 867 ms, total: 2.5 s
# Wall time: 2.52 s
df1.merge(df2, left_on="a", right_on="b")
# polars
# CP... | python|pandas|dataframe|python-polars|rust-polars | 4 |
2,533 | 70,514,988 | taking out specific indexes from array | <p>I have and array which I am trying to slice/split, small part of the array is as follow:</p>
<pre><code>[(2008, b'2-room', 82000, 107000) (2008, b'3-room', 135000, 211000)
(2008, b'4-room', 223000, 327000) (2008, b'5-room', 305000, 428000)
(2008, b'3-room', 142000, 160000) (2008, b'4-room', 211000, 253000)
......... | <p>As suggested in the comment to the question by @Tim Roberts, using fancy indexing can help:</p>
<pre class="lang-py prettyprint-override"><code>mask = dataprice['financial_year']==2019
list_2019 = dataprice[mask]
list_rest = dataprice[~mask]
</code></pre> | python|numpy | -1 |
2,534 | 42,875,356 | Selection with pandas multiIndexed dataframe | <p>I have a multiIndexed dataframe that looks like this:</p>
<pre><code>df.head():
</code></pre>
<p><a href="https://i.stack.imgur.com/y2yUo.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/y2yUo.png" alt="enter image description here"></a></p>
<p>How can I select all of the rows where the first in... | <p>You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.query.html" rel="nofollow noreferrer"><code>query</code></a>:</p>
<pre><code>print (df.query('ilevel_0 == "School Name" and Month == "Jan"'))
</code></pre>
<p>Sample:</p>
<pre><code>df = pd.DataFrame({'A':['School Name','A... | python-3.x|pandas|dataframe|indexing|multi-index | 4 |
2,535 | 42,650,230 | Pandas pivot on column | <p>my CSV looks like:</p>
<pre><code>"a","b","c","d"
1, "x", 1, 1
1, "y", 2, 2
</code></pre>
<p>and I want to convert it based on column "b" to</p>
<pre><code>"a", "x_c", "y_c", "x_d", "y_d"
1, 1, 2, 1, 2
</code></pre>
<p>I've tried it with pivot and unstack. Is there a shortcome in pandas ?</p>
<p>EDIT: I have mu... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html" rel="noreferrer"><code>pivot_table</code></a>:</p>
<pre><code>df = df.pivot_table(index='a',columns='b', values=['c', 'd'], aggfunc=np.mean)
#Multiindex to columns
df.columns = df.columns.map(lambda x: '{}_{}'.format(x[1], x... | python|csv|pandas | 5 |
2,536 | 25,057,977 | Defining a function with a loop in Theano | <p>I want to define the following function of two variables in Theano and compute its Jacobian:</p>
<pre><code>f(x1,x2) = sum((2 + 2k - exp(k*x1) - exp(k*x2))^2, k = 1..10)
</code></pre>
<p>How do I make a Theano function for the above expression - and eventually minimize it using its Jacobian?</p> | <p>Since your function is scalar, the Jacobian reduces to the gradient. Assuming your two variables <code>x1, x2</code> are scalar (looks like it from the formula, easily generalizable to other objects), you can write</p>
<pre><code>import theano
import theano.tensor as T
x1 = T.fscalar('x1')
x2 = T.fscalar('x2')
k ... | python|numpy|scipy|theano | 3 |
2,537 | 25,260,000 | scikit-learn's GridSearchCV stops working when n_jobs>1 | <p>I have previously asked <a href="https://stackoverflow.com/questions/25249212/scikit-grid-search-for-knn-regression-valueerror-array-contains-nan-or-infinity">here</a> come up with following lines of code:</p>
<pre><code>parameters = [{'weights': ['uniform'], 'n_neighbors': [5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 1... | <p><code>libdispatch.dylib</code> from Grand Central Dispatch is used internally by OSX's builtin implementation of BLAS called Accelerate when you do a <code>numpy.dot</code> calls. The GCD runtime does not work when programs call the POSIX <code>fork</code> syscall without using an <code>exec</code> syscall afterward... | python|numpy|scikit-learn | 4 |
2,538 | 30,471,509 | How can I create an array of 1-element arrays from an array? | <p>I would like to be able to convert arrays, such as </p>
<pre><code>a = np.array([[1,2], [3,4]])
</code></pre>
<p>into the same array BUT each element as a 1-element array instead of a number.
The desired output would be: </p>
<pre><code>np.array([[np.array([1]), np.array([2])], [np.array([3]), np.array([4])]])
... | <p>The operation you describe is very rarely useful. More likely, it would be a better idea to add an extra dimension of length 1 to the end of your array:</p>
<pre><code>a = a[..., np.newaxis]
# or
a = a.reshape(a.shape + (1,))
</code></pre>
<p>Then <code>a[0, 1]</code> will be a 1D array, but all the nice NumPy fea... | python|arrays|numpy | 0 |
2,539 | 13,116,394 | pandas: flatten df with delimiter | <p>My goal is to load a dataframe into a DB using a stdin pipe to a load statement executed at the command line (e.g. cat {file_loc} | /path/to/sql --command "COPY table FROM STDIN WITH DELIMITER ',';"). I'm aware that this approach is suboptimal; it's a workaround due to pyodbc issues ;)</p>
<p>What's the most effici... | <p>Could you describe the pyodbc issues?</p>
<p>I created an issue here. To get the ultimate perf you'd want to drop down into C or Cython and build the raw byte string yourself using C string functions. Not very satisfying, I know. At some point we should build a better-performing to_csv for pandas, too:</p>
<p><a h... | python|numpy|pandas | 0 |
2,540 | 28,946,964 | How to be a faster Panda with groupbys | <p>I have a Pandas dataframe with 150 million rows. Within that there are about 1 million groups I'd like to do some very simple calculations on. For example, I'd like to take some existing column <code>'A'</code> and make a new column, <code>'A_Percentile'</code> that expresses the values of '<code>A'</code> as percen... | <p>As you are probably aware, the speed of groupby operations can vary tremendously -- especially as the number of groups gets high. Here's a really simple alternate approach that is quite a bit faster on some test datasets I tried (anywhere from 2x to 40x faster). Usually it is faster if you can avoid user-written f... | python|performance|pandas|bigdata|dataframe | 2 |
2,541 | 23,705,113 | How to make random Beta in python like normal between two value ? | <p>I want to make random beta in python like normal between two extreme values (ex : 800 / 1000 ). I use this code with numpy random.beta. My problem, I don't have min and max value with normalize and I want keep shape of value. </p>
<pre><code>#!/usr/bin/env python
# -*- coding: iso-8859-1 -*-
import numpy as np
impo... | <p><a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.beta.html" rel="nofollow"><code>numpy</code>'s <code>random.beta</code></a> will give a value between zero and one, so to apply the same distribution between <code>x</code> and <code>y</code> you simply do:</p>
<pre><code>z = x + (np.random.b... | python|numpy|random | 2 |
2,542 | 15,149,265 | pandas Timedelta error | <p>I'm getting errors when running the code samples from the pandas documentation. </p>
<p>I suspect it might be related to the version of pandas I'm using, but I haven't been able to confirm that. </p>
<pre><code>pandas VERSION 0.10.1
numpy VERSION 1.7.0
scipy VERSION 0.12.0.dev-14b1e07
</code></pre>
<p>The... | <p>If you look at the title of the page (top of your browser window) you are linking to, you can see that it's the development version of pandas:
<a href="http://pandas.pydata.org/pandas-docs/dev/timeseries.html#time-deltas" rel="nofollow">http://pandas.pydata.org/pandas-docs/dev/timeseries.html#time-deltas</a></p>
<p... | datetime|pandas|time-series|series|timedelta | 1 |
2,543 | 62,143,149 | Plotting sorted data | <p>I would need to plot accounts through time, by sorting opening account. </p>
<p>I have the following two columns, one for the Accounts and one for OpenTime (it is datetime):</p>
<pre><code>Account Name OpenTime
ABC 2002/05/20
BAB 2012/07/24
CMN 2012/07/24
GK... | <p>First we need convert the date to datetime , then <code>sort_values</code></p>
<pre><code>df.OpenTime=pd.to_datetime(df.OpenTime)
df=df.sort_values('OpenTime')
print(df['Account Name'].tolist())
</code></pre> | python|pandas|matplotlib|seaborn | 1 |
2,544 | 62,083,446 | Make date_range of hourly frequency over multiple years for a selected month | <p>I understand how to make a date_range in pandas using the freq option. However, I do not know how to use it to do two frequencies at once (or do I need a loop for this)?</p>
<p>I am trying to make an hourly date range for only july for a span over some years.</p>
<p>I have tried:</p>
<pre><code>In: pd.date_rang... | <p>You can create hours frequency with start and end <code>year</code> and then filter only <code>july</code>s:</p>
<pre><code>d = pd.date_range('1951-07-01','1955-07-01',freq='H')
d = d[d.month == 7]
print (d)
DatetimeIndex(['1951-07-01 00:00:00', '1951-07-01 01:00:00',
'1951-07-01 02:00:00', '1951-07... | python|pandas|datetime|date-range | 2 |
2,545 | 62,246,851 | Differential Privacy decreases the model performance significantly | <p><strong>Background Information</strong></p>
<p>I trained a classifier to predict three labels: COVID/Pneumonia/Healthy based on chest X-Ray images. It's a PyTorch implementation of <a href="https://github.com/lindawangg/COVID-Net" rel="nofollow noreferrer">COVID-Net</a>. I use a training set to train on, validation... | <p>It seems the PyTorch Differential Privacy library from Facebook Research is built on the concept of Renyi differential privacy guarantee that is well-suited for expressing guarantees of privacy-preserving algorithms and for composition of heterogeneous mechanisms. We need to have a good estimation of the heterogenit... | python|machine-learning|pytorch|privacy|confusion-matrix | 1 |
2,546 | 62,204,867 | Pandas Create New Column Based Off of Condition and Value in Other Column | <p>I have a data set like the following:</p>
<pre><code>ID Type
1 a
2 a
3 b
4 b
5 c
</code></pre>
<p>And I'm trying to create the column URL as shown by specifying a different URL based on the "Type" and appending the "ID".</p>
<pre><code>ID Type URL
1 a http://example.com/examplea/id=1
2 a ht... | <p>You should alter the command a bit:</p>
<pre><code>df.loc[df['Type'] == 'a', 'URL']= 'http://example.com/examplea/id='+df['ID'].astype(str)
df.loc[df['Type'] == 'b', 'URL']= 'http://example.com/bbb/id='+df['ID'].astype(str)
</code></pre>
<p>Or you can use <code>map</code> like this:</p>
<pre><code>url_dict = {
... | python|pandas|pandas-loc | 2 |
2,547 | 62,184,063 | Too many values to unpack using apply() | <p>Here is the code I have:</p>
<pre><code>def f(row):
if row['CountInBedDate'] == 1 and row['CountOutBedDate'] == 1:
SleepDate = row['DateInBed']
InBedTimeFinal = row['InBedTime']
OutBedTimeFinal = row['OutBedTime']
else:
SleepDate = -1
InBedTimeFinal = -1
OutBedTimeFinal = -1
return Sle... | <p>if <code>f</code> is your real function, then you should consider using <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.where.html" rel="nofollow noreferrer"><code>where</code></a> instead of apply, it will be way faster.</p>
<pre><code>s1[['SleepDate', 'InBedTimeFinal', 'OutBed... | python|pandas | 2 |
2,548 | 51,475,435 | Python find most common value in array | <pre><code>import numpy as np
x = ([1,2,3,3])
y = ([1,2,3])
z = ([6,6,1,2,9,9])
</code></pre>
<p>(only positive values)
In each array i need to return the most common value, or, if values come up the same amount of times - return the minimum.
This is home assignment and I can't use anything but numpy.</p>
<p>outputs... | <p>for a numpy exclusive solution something like this will work:</p>
<pre><code>occurances = np.bincount(x)
print (np.argmax(occurances))
</code></pre>
<p>The above mentioned method won't work if there is a negative number in the list. So in order to account for such an occurrence kindly use:</p>
<pre><code>not_requ... | python|numpy | 2 |
2,549 | 48,081,743 | Python utilizing file paths | <pre><code>sound_file_paths =[
"/Users/ferhatkaygun/Desktop/UrbanSound8K/audio/fold1/57320-0-0-7.wav",
"/Users/ferhatkaygun/Desktop/UrbanSound8K/audio/fold1/24074-1-0-3.wav",
"/Users/ferhatkaygun/Desktop/UrbanSound8K/audio/fold1/15564-2-0-1.wav",
"/Users/ferhatkaygun/Desktop/UrbanSound8K/audio/fold1/313... | <p>you are getting the error that soundfiles are not defined because the program cannot find the file. Most likely the paths you are using are not from your machine?</p>
<p>you need to put the files to a directory, ex <code>/Users/me/files/</code> on your machine and then replace the file paths in your script to point... | python|audio|tensorflow|filepath|librosa | 0 |
2,550 | 48,325,478 | Excel export using While loop | <p>I am new to Python. I am working on a large analytic program, and this is a snippet of it. Right now, this snippet exports multiple excel files. Is it possible to save what is done per loop on a sheet within a single excel document? So basically right now, it exports 5 files, rather than exporting 5 separate files, ... | <p>EDIT (to account for OP using <code>pandas</code> and <code>ExcelWriter</code>):</p>
<p>You need to define your target file with <code>ExcelWriter</code> and then write to it with variable sheet names. Also offering some Python clean-up for your iteration:</p>
<pre><code>#breaks up df into systems
#exports excel f... | python|excel|pandas|while-loop|export-to-excel | 1 |
2,551 | 48,235,916 | Cropping a minibatch of images in Pytorch -- each image differently | <p>I have a tensor named <code>input</code> with dimensions 64x21x21. It is a minibatch of 64 images, each 21x21 pixels. I'd like to crop each image down to 11x11 pixels. So the output tensor I want would have dimensions 64x11x11.</p>
<p>I'd like to crop each image around a different "center pixel." The center pixels ... | <p>I raised the question over on the pytorch forums, and got an answer there from smth. The <code>grid_sample</code> function should totally solve the problem.</p>
<p><a href="https://discuss.pytorch.org/t/cropping-a-minibatch-of-images-each-image-a-bit-differently/12247" rel="nofollow noreferrer">https://discuss.pyto... | pytorch | 2 |
2,552 | 48,192,177 | keras with tensorflow runs fine, until I add callbacks | <p>I'm running a model using Keras and TensorFlow backend. Everything works perfect:</p>
<pre><code>model = Sequential()
model.add(Dense(dim, input_dim=dim, activation='relu'))
model.add(Dense(200, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse', optimizer='Adam', metrics=['mae']... | <p>A <code>tensorboard</code> callback uses <code>tf.summary.merge_all</code> function in order to collect all tensors for histogram computations. Because of that - your summary is collecting tensors from previous models not cleared from previous model runs. In order to clear these previous models try:</p>
<pre><code>... | tensorflow|machine-learning|neural-network|keras|tensorboard | 2 |
2,553 | 48,578,272 | Why the model size is in huge different between different optimizer? | <p>With TensorFlow, my model size(model.ckpt.data) is 88M when optimizer is <code>tf.train.GradientDescentOptimizer</code>, but it turned to 220M when the optimizer changed to <code>tf.train.AdamOptimizer</code>.</p>
<p>Why is there so huge a difference?</p> | <p>ADAM adds two running means (for gradient and square of gradient) as additional non-trainable parameters for each trainable parameter, meaning it increases the number of total parameters to three times. These non-trainable parameters are also saved as they are required to restart the learning process. That's why the... | tensorflow|neural-network|deep-learning | 2 |
2,554 | 48,770,411 | how to convert columns to numeric while keep those failed intact in pandas | <p>I read my text file into pandas dataframe. All columns are object datatype. What I need to do is convert all those columns that appears 'numeric' to numeric columns.
If there are ust a few columns, it's very easy. But my real dataframe has over two hundred columns. I wonder if there is anyway to convert those colum... | <p>Op1. I usually using <code>to_numeric</code> then <code>fillna</code> (The reason : I usually have some mixed dtype within one column )</p>
<pre><code>df=df[['a', 'b', 'c', 'd']].apply(pd.to_numeric,errors='coerce').fillna(df)
df.dtypes
Out[605]:
a int64
b object
c object
d int64
dtype: object
</code... | python|pandas | 4 |
2,555 | 70,883,944 | Print multiple columns from a matrix | <p>I have a list of column vectors and I want to print only those column vectors from a matrix.
Note: the list can be of random length, and the indices can also be random.</p>
<p>For instance, the following does what I want:</p>
<pre><code>import numpy as np
column_list = [2,3]
a = np.array([[1,2,6,1],[4,5,8,2],[8,3,5... | <p>Yes, there's a shorter way. You can pass a list (or numpy array) to an array's indexer. Therefore, you can pass <code>column_list</code> to the columns indexer of <code>a</code>:</p>
<pre><code>>>> a[:, column_list]
array([[6, 1],
[8, 2],
[5, 3],
[4, 4],
[8, 8]])
# This i... | python-3.x|numpy | 1 |
2,556 | 70,887,198 | Pandas assign - passing column in a user defined function | <p>Given an input dataframe and string:</p>
<pre><code>df = pd.DataFrame({"A" : [10, 20, 30], "B" : [0, 1, 8]})
colour = "green" #or "red", "blue" etc.
</code></pre>
<p>I want to add a new column <code>df["C"]</code> conditional on the values in <code>df["... | <p>Applying a function like that can be inefficient, especially when dealing with dataframes with many rows. Here is a one-liner:</p>
<pre><code>colour = "green" #or "red", "blue" etc.
df['C'] = ((colour == 'red') & df['B'].lt(5)) | ((colour == 'blue') & df['B'].lt(5)) | ((colour ... | python|pandas|dataframe | 4 |
2,557 | 70,910,193 | How can I add CSV logging mechanism in case of Multivariable Linear Regression using TensorFlow? | <p>Suppose, the following is my Multivariable Linear Regression source code in Python:</p>
<pre><code>import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import sys, random
import time
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from te... | <p>Just use the <code>tf.keras.callbacks.CSVLogger</code> and any regression metric you want to log during training:</p>
<pre class="lang-py prettyprint-override"><code>import tensorflow as tf
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1, input_dim=40))
model.add(tf.keras.layers.Dense(128))
model.ad... | python|tensorflow|keras|logging|deep-learning | 2 |
2,558 | 70,824,180 | Get array with another array indexing with NumPy | <pre><code>arr_1 = np.array([5, 1, 6, 3, 3, 10, 3, 6, 12])
arr_2 = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90])
arr_idx_num_3 = np.where(arr_1 == 3)[0]
print(arr_idx_num_3) ## [3 4 6]
</code></pre>
<p>#how to i get this array Numpy with "arr_idx_num_3"</p>
<pre><code>arr_2 = [40 50 70]
</code></pre> | <p>Just use it like:</p>
<pre><code>print(arr_2[arr_idx_num_3])
</code></pre>
<p>output:</p>
<pre><code>>>> [40 50 70]
</code></pre> | python|numpy | 1 |
2,559 | 70,786,121 | Why my prediction function is giving error? ValueError: not enough values to unpack (expected 2, got 1) | <p>I'm trying to make prediction using the pre-trained model for binary segmentation using UNET and pytorch. Here is my code:
model.eval() # Set model to evaluate mode</p>
<pre><code>class SimDataset(Dataset):
def __init__(self, path, transform=None, isMask=False):
self.m = ("test&qu... | <p>Your code expects <em>two</em> outputs from the data loader:</p>
<pre class="lang-py prettyprint-override"><code>inputs, labels = next(iter(test_loader))
</code></pre>
<p>However, your <code>__getitem__</code> method in your dataset, returns only a <em>single</em> output:</p>
<pre class="lang-py prettyprint-override... | python|testing|pytorch|image-segmentation | 0 |
2,560 | 51,999,924 | Tensorflow Object Detection API - showing loss for training and validation on one graph | <p>I am playing with <a href="https://github.com/tensorflow/models/tree/master/research/object_detection" rel="nofollow noreferrer">Tensorflow Object Detection API</a> and training the Faster R-CNN network on my own dataset. I am checking the progress of learning at Tensorbord. All metrics are there, but is there a way... | <p>The underlying data for the plots is saved under different tag names (<code>loss</code> vs <code>loss_1</code>). I believe TensorBoard does not natively support displaying different tags in one plot. There might be third-party extensions to do this.</p>
<p>If different models used the same tag, the graphs would be ... | tensorflow|tensorboard | 1 |
2,561 | 51,577,885 | Converting list numpy array to normal array for CNN-Keras | <p>I have some images separated by folders. So I imported them and converted to them array of pixels. When I type in:</p>
<pre><code>In [9]: X_train.shape
out [9]: (7467,60,80,3)
</code></pre>
<p>I wanted to append this with the no. of classes, create a dataset and save as <code>.json</code> file and import in a fres... | <p>Your <code>np arrays</code> are converted to lists when storing the dataframe as a <code>.json</code>. To feed them to your Keras model, you need to have them in one <code>array</code> of shape <code>(images, height, width, channels)</code>:</p>
<pre><code>X_train = np.array(train['images'].tolist())
</code></pre> | python|numpy|keras|deep-learning|conv-neural-network | 0 |
2,562 | 51,657,913 | Tensorflow building error | <p>I got this error while building Tensorflow 1.1.0</p>
<pre><code>Starting local Bazel server and connecting to it...
ERROR: /home/bishal/.cache/bazel/_bazel_bishal/798d6395d959361055d9b5ddcd7dcd45/external/io_bazel_rules_closure/closure/testing/phantomjs_test.bzl:31:10: name 'set' is not defined
ERROR: /home/bishal/... | <p>You'll need to use <a href="https://github.com/bazelbuild/bazel/releases/tag/0.5.4" rel="nofollow noreferrer">Bazel 0.5.4</a> to build Tensorflow 1.1.0. Please note that 0.5.4 is very old -- it's 0.16.0 as of time of writing this answer.</p>
<p>Do you need to specifically build Tensorflow 1.1.0?</p> | tensorflow|bazel | 2 |
2,563 | 64,568,948 | Generating a dictionary of column names based on a condition among columns of a dataframe | <p>I have the following data frame :</p>
<pre><code> a_11 b_14 c_13 d_12
AC True False False False
BA True False False True
AA False False False False
</code></pre>
<p>I want a dictionar... | <p>Use dictioanry comprehension if performance is important with transpose DataFrame and convert columns names to list:</p>
<pre><code>d = {k: v.index[v].tolist() for k, v in df.T.items()}
print (d)
{'AC': ['a_11'], 'BA': ['a_11', 'd_12'], 'AA': []}
</code></pre>
<p>Another idea with <code>zip</code> and convert values... | python|pandas|dataframe|dictionary | 1 |
2,564 | 64,603,437 | Handyspark Dataframe works on driver or executor | <p>Handyspark dataframe in Pyspark is a bridge between pyspark dataframe and pandas dataframe ,So does it reside on executor node or driver node?</p> | <p>HandySpark isn't a "bridge" - it's a wrapper round a Spark DataFrame which gives it a pandas-like API. Therefore it executes on the executors; there would be little point in the project if it executed on the driver as you could always just to <code>toPandas</code> on your DataFrame to pull it back to the d... | pandas|dataframe|pyspark | 0 |
2,565 | 64,425,696 | Equivalent of np.resize in TensorFlow | <p>I have a 1D array <code>x</code> and want to reshape it to the requested shape in the same way that <a href="https://numpy.org/doc/stable/reference/generated/numpy.resize.html" rel="nofollow noreferrer">np.resize</a> is doing, i.e. if there is too many elements in <code>x</code> they are dropped, if it is too few, t... | <p>I am not sure that this is doable in single operation in TF, but one can write a function using crops or <code>tf.tile</code> and then reshaping the result.</p> | tensorflow|tensor | 1 |
2,566 | 64,401,900 | Python, x-axis title is overlapping the tick labels in matplotlib | <p>I'm plotting a graph and the x-axis label is not visible in the graph.</p>
<p><a href="https://i.stack.imgur.com/WDuWN.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/WDuWN.png" alt="enter image description here" /></a></p>
<p>I have tried to solve it by adding the</p>
<pre><code>ax.xaxis.labelpad... | <p>You could use "<a href="https://matplotlib.org/tutorials/intermediate/tight_layout_guide.html" rel="nofollow noreferrer">Tight Layout</a>" function in matplotlib to solve the issue.</p>
<p>Add the line before you plot the graph, where <code>h_pad</code> will adjust the height, <code>w_pad</code> will adjus... | python|pandas|matplotlib|graph|adjustment | 3 |
2,567 | 64,212,463 | Combine series by date | <p>The following 2 series of stocks in a single excel file:</p>
<p><a href="https://i.stack.imgur.com/nY0bj.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/nY0bj.png" alt="enter image description here" /></a></p>
<p>Can be combined using the date as index?</p>
<p>The result should be like this:</p>
<... | <p>I am trying this:</p>
<pre><code>df3 = pd.concat([df1, df2]).sort_values('Date').reset_index(drop=True)
</code></pre>
<p>or</p>
<pre><code>df3 = df1.append(df2).sort_values('Date').reset_index(drop=True)
</code></pre> | python|pandas|dataframe|indexing | 1 |
2,568 | 47,718,865 | How to apply a function to mulitple columns of a pandas DataFrame in parallel | <p>I have a pandas DataFrame with hundreds of thousands of rows, and I want to apply a time-consuming function on multiple columns of that DataFrame in parallel.</p>
<p>I know how to apply the function serially. For example:</p>
<pre><code>import hashlib
import pandas as pd
df = pd.DataFrame(
{'col1': range(10... | <p>The easiest way to do this is using <a href="https://docs.python.org/3/library/concurrent.futures.html" rel="nofollow noreferrer"><code>concurrent.futures</code></a>.</p>
<pre><code>import concurrent.futures
with concurrent.futures.ProcessPoolExecutor(16) as pool:
df['md5'] = list(pool.map(foo, df['col1'], df[... | python|pandas|concurrent.futures | 2 |
2,569 | 47,898,147 | Tensorflow Module Import error: AttributeError: module 'tensorflow.python.ops.nn' has no attribute 'rnn_cell' | <p>When attempting to pass my RNN call, I call tf.nn.rnn_cell and I receive the following error: </p>
<pre><code>AttributeError: module 'tensorflow.python.ops.nn' has no attribute 'rnn_cell'
</code></pre>
<p>Which is odd, because I'm sure I imported everything correctly: </p>
<pre><code>from __future__ import print_... | <p>Replace <code>tf.nn.rnn_cell</code> with <code>tf.contrib.rnn</code></p>
<p>Since version 1.0, <code>rnn</code> implemented as part of the contrib module.</p>
<p>More information can be found here
<a href="https://www.tensorflow.org/api_guides/python/contrib.rnn" rel="nofollow noreferrer">https://www.tensorflow.or... | python|tensorflow|python-import|attributeerror|rnn | 3 |
2,570 | 48,899,041 | Separate letters and digits using regex with pandas | <p>I have a column called 'value' from a pandas dataframe, df, that has a mixture of numbers and words. It looks something like this:</p>
<pre><code> VALUE
0 done
1 Yes
2 3.45
3 2bc
</code></pre>
<p>I want to split the column up to 2 columns where the left one only has letters and the right one only numbers... | <p>Fix your pattern, and use <code>str.extractall</code>:</p>
<pre><code>(df.VALUE.str.extractall('(\d+(?:\.\d+)?)|([^\d.]+)')
.unstack()
.groupby(level=0, axis=1)
.first())
0 1
0 NaN done
1 NaN Yes
2 3.45 NaN
3 2 bc
</code></pre> | python|regex|string|pandas | 3 |
2,571 | 58,845,305 | Pandas - date range with monthly rollover, weekmask and list of holidays | <p>I was looking for similar problem but I could not have find an answer for my issue. I try to generate date range in Pandas with monthly or quarterly rollover in respect to a weekmask and a list of holidays. So far I managed to make a range but with daily frequency. Is there any way I could make this dates rolling mo... | <p>I think I found a nice solution provided by @MaxU at <a href="https://stackoverflow.com/questions/48454189/pandas-date-range-for-six-monthly-values">Pandas date_range for six-monthly values</a>
However it does not behave as expected because it skips start_date in 1) and 2) solution while it returns an error in 3) so... | python|pandas|time-series | 0 |
2,572 | 58,912,108 | Continuously calculating averages over past intervals w/ Pandas DataFrame | <p>I believe that my problem is really straightforward and there must be a really easy way to solve this issue, however as I am don't feel really confident on working with timestamps so I could not sort that problem by my own.</p>
<p>I made the following example, which represents a simple case of what I have been work... | <p><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.window.Rolling.mean.html#pandas.core.window.Rolling.mean" rel="nofollow noreferrer">https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.window.Rolling.mean.html#pandas.core.window.Rolling.mean</a></p>
<pre><code>data[... | python|pandas|dataframe | 1 |
2,573 | 58,647,340 | Finding intersection of pandas data frame index in groupby | <p>I am using Python and have a data frame with a datetime index, a grouping variable (gvar) and a value variable (x).
I would like to find all the common datetimes between the groups.</p>
<p>I already have a solution using functools, but I am seeking a way to do it using pandas functionalities only (if possible).</p>... | <p>This should do it:</p>
<pre><code>>>> df.reset_index().loc[df['gvar'].reset_index().drop_duplicates().duplicated('index'),'index'].tolist()
</code></pre>
<p>Returning:</p>
<pre><code>[Timestamp('2018-01-03 00:00:00')]
</code></pre>
<p>And if you need the corresponding groups or values:</p>
<pre><code>&... | python|pandas | 1 |
2,574 | 59,034,759 | Count how many times value A exists in dataframe rows, how many times value B and how many times value A and B | <p>I have a dataframe "dfTags" with 140.000 rows (all lowercase), number of comma separated values in column "tags" can range from 71 to 1. But column tags is one single string, Pandas does not know arrays or lists:</p>
<pre><code>index tags
0 a, b, c, aa, bb, 2019
1 a, d, 18, gb
2 aa, a... | <p>This is not a complete answer, but it will give you, for every tagTupples(<code>tt</code>) how many times the first element of the <code>tt</code> appears and how many times both of them appear and then you can do your calculations</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'tags': [['a', 'b', 'c', 'aa']... | string|pandas|dataframe|count|csv | 0 |
2,575 | 58,808,798 | pandas dataframe, regrouping | <p>I have the following sample dataset:</p>
<pre><code>import pandas as pd
data = {'Sentences':['Sentence1', 'Sentence2', 'Sentence3', 'Sentences4', 'Sentences5', 'Sentences6','Sentences7', 'Sentences8'],\
'Start_Time':[10,15,77,120,150,160,176,188],\
'End_Time': [12,17,88,128,158,168,182,190],... | <p>Use:</p>
<pre><code>mean_time=df[['Start_Time','End_Time']].mean(axis=1).rename('Interval Time')
labels = ["{0}-{1}".format(time_list[i], time_list[i+1]) for i in range(len(time_list)-1)]
new_df= ( df.groupby(pd.cut(mean_time,bins=time_list, labels=labels,include_lowest=True))
.Sentences
.a... | python-3.x|pandas | 2 |
2,576 | 58,662,187 | Pandas promotes int to float when filtering | <p>Pandas seems to be promoting an <code>int</code> to a <code>float</code> when filtering. I've provided a simple snippet below but I've got a much more complex example which I believe this promotion leads to incorrect filtering because it compares <code>floats</code>. Is there a way around this? I read that this is a... | <p>There is no float comparison happening here. <code>isin</code> is returning <code>NaN</code>'s for missing data, and since you are using <code>numpy</code>'s <code>int64</code>, the result is getting cast to <code>float64</code>.</p>
<p>In 0.24, pandas added a <a href="https://pandas.pydata.org/pandas-docs/stable/... | python|pandas|numpy | 1 |
2,577 | 58,878,953 | Convert Mysql.connector dtypes objects to numeric/ string | <p>I have an SQL query with mysql.connector in python 3. I m converting the result of fetchall to a pandas Dataframe.</p>
<pre><code>mycursor.execute(sql_query)
m_table = pd.DataFrame(mycursor.fetchall())
m_table.columns = [i[0] for i in mycursor.description]
</code></pre>
<p>Getting dtypes gives me :</p>
<pre><c... | <p>This is an easy way to apply this conversion to all columns in case you <strong>are sure</strong> you need them <strong>all</strong> to be transformed into floats except the ones that can't (because they contain strings):</p>
<pre><code>import numpy as np
import pandas as pd
data = {'a':[1,2,3,4],'b':['a','b','aa',... | python|pandas|numpy | 1 |
2,578 | 58,903,566 | Grouping and adding values based on row string with pandas? | <p>I have the following pandas data set:</p>
<pre><code>date, pair, value, fruit
2019-11-15 09:35:33,EUR,10,BANANA
2019-11-15 09:35:32,EUR,12,BANANA
2019-11-15 09:35:31,EUR,21,APPLE
2019-11-15 09:35:30,EUR,17,ORANGE
2019-11-15 09:35:28,EUR,19,BANANA
2019-11-14 09:58:05,EUR,37,APPLE
2019-11-14 09:23:42,EUR,41,ORANGE
20... | <p>I think this might help </p>
<pre><code>a=your_df.groupby(["fruit"]).sum()["value"]
</code></pre> | python|pandas|numpy|data-science | 0 |
2,579 | 70,330,526 | Operations on specific elements of a dataframe in Python | <p>I'm trying to convert kilometer values in one column of a dataframe to mile values. I've tried various things and this is what I have now:</p>
<pre><code>def km_dist(column, dist):
length = len(column)
for dist in zip(range(length), column):
if (column == data["dist"] and dist in data.loc[(... | <p>Instead your solution filter rows with mask and divide column <code>dist</code> by <code>5820</code>:</p>
<pre><code>data.loc[data["dist"] > 25, 'dist'] /= 5820
</code></pre>
<p>Working same like:</p>
<pre><code>data.loc[data["dist"] > 25, 'dist'] = data.loc[data["dist"] > 25, ... | python|pandas | 1 |
2,580 | 70,356,417 | Tensorflowjs - Reshape/slice 4d tensor into image | <p>I am trying to apply style transfer to a webcam capture. I am reading a frozen model I've previously trained in python and converted for TFjs. The output tensor's shape and rank is as follows:
<a href="https://i.stack.imgur.com/KIB00.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/KIB00.png" alt="... | <p>given your tensor is <code>[1, 15, 20, 512]</code><br />
you can remove any dims with value of 1 (same dim you've added by running <code>expandDims</code>) by running</p>
<pre><code>const squeezed = tf.squeeze(tensor)
</code></pre>
<p>that will give you <strong>shape</strong> of <code>[15, 20, 512]</code></p>
<p>but... | tensorflow|deep-learning|tensorflow.js | 1 |
2,581 | 70,156,578 | Color Formating from pandas to excel | <p>I have a pandas dataframe with values and a condition according to previous filtering. I would like to print my dataframe in an excel and color the cell according to the filtering result (if <em>passed</em>: <strong>green</strong> and if <em>not_passed</em>: <strong>red</strong>). Here is an example code and how I w... | <p>Use a for loop to check if the <code>filter value == 'passed'</code><br>
If it is, you can apply the green format to this cell, and Vice versa for Red using worksheet.write(row_index,column_index,value,format).</p>
<p><em>Note that Pandas data frames use a different indexing method than Excel. Notably, Pandas starti... | python|excel|pandas|dataframe | 2 |
2,582 | 56,386,719 | Keras Tensorflow fails to learn simple linear relationship | <p>I am fairly new to Tensorflow/Keras and am trying to set up an LSTM model. I have successfully run my code already, but my results have failed to give me meaningful results. I, therefore - as a test - let my LSTM network learn one of the features I am inputting. I am aware that the LSTM and relu use nonlinear relati... | <p>A few issues:</p>
<p>Typically, LSTM layers go at the start, followed by a few dense layers. </p>
<p>Also, the LSTM layer before the dense layer needs to have return_sequence set to False. </p>
<p>However, I'm not sure that they are the reason to cause this problem, I'm just pointing out the problems. I think it ... | tensorflow|machine-learning|keras|lstm | 0 |
2,583 | 55,632,558 | Number of days between two successive rows in pandas with timestamp ERROR: dtype('<m8[D]') | <p>i have a pandas dataframe like follows:</p>
<pre><code>device_id date
101 2018-10-30 10:42:32
101 2018-12-20 14:14:14
102 2018-09-26 14:21:33
102 2018-10-24 09:12:35
102 2018-11-12 04:52:21
</code></pre>
<p>My expected output is</p>
<pre><code>device_id date ... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.floor.html" rel="nofollow noreferrer"><code>Series.dt.floor</code></a> for datetimes without times, then <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html" rel="nofollow noreferr... | python|pandas|pandas-groupby | 2 |
2,584 | 55,664,514 | Pandas fillna() not working on DataFrame slices | <p>Pandas <code>fillna</code> is not working on DataFrame slices, here is an example</p>
<pre><code>df = pd.DataFrame([[np.nan, 2, np.nan, 0],
[3, 4, np.nan, 1],
[np.nan, np.nan, np.nan, 5],
[np.nan, 3, np.nan, 4]],
columns=list('ABCD'))
df[["A", 'B']].fi... | <p>If we look at the <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html" rel="nofollow noreferrer"><code>pandas documentation</code></a> it says you should use the following to <code>fillna</code> on slices:</p>
<pre><code>values = {'A':0, 'B':0}
df.fillna(value=values, in... | python|pandas|dataframe|fillna | 3 |
2,585 | 55,953,800 | Change case for columns in list | <p>How do I change the case for data frame columns that are in a list? I know how to make all columns upper case but I don't know how to only make specific columns upper case. </p>
<pre><code>d = {'name':['bob','john','sue'],'id':[545,689,143],'fte':[1,.5,.75]}
df = pd.DataFrame(d)
# list of columns I want to make upp... | <p>It won't work the way you're trying to do it, the reason being that indices <em>do not</em> support <strong>mutable operations</strong>. So one thing you could do is to use a list comprehension to generate a new list of column names an reassign it to <code>df.columns</code>:</p>
<pre><code>df.columns = [i.upper() i... | python|pandas | 5 |
2,586 | 64,964,813 | replace 2 selected row values based on others | <p>I have a df that looks like this:</p>
<pre><code>Id Class Label
0 APPS Item
1 MODEL Item
2 PRICE Money
</code></pre>
<p>I want to check all <code>Class</code>entries where the Label is <code>Item</code>. Among these classes, I want to replac... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html" rel="nofollow noreferrer"><code>DataFrame.loc</code></a> with chain 2 conditions by <code>&</code> for bitwise <code>AND</code> - here is assign list with 2 values because selected 2 columns <code>['Class', 'Label']... | python|python-3.x|pandas|dataframe|data-analysis | 0 |
2,587 | 64,869,905 | Serving tensorflow models on GCP? | <p>Recently I've been trying to host a custom image classification tensorflow saved model on GCP and use a REST API to send prediction requests. I've hosted this model on Google's <a href="https://cloud.google.com/ai-platform/prediction/docs/reference/rest/v1/projects/predict" rel="nofollow noreferrer">AI Platform API<... | <p>Firstly, I don't recommend you to publicly open billable resources like this, because you are exposed to attack and huge consumption.</p>
<p>But, if you really want to achieve this, you can allow <code>allUsers</code> on your deployed models</p>
<pre><code>gcloud ai-platform models add-iam-policy-binding <MY_MODE... | tensorflow|machine-learning|google-cloud-platform|google-ai-platform | 1 |
2,588 | 64,850,973 | remove points located within a specific area - python | <p>I'm trying to remove points that are located within a specific area. Using below, I'm hoping to remove points that are located within the blue box. Ideally, I'd map out a polygon that followed the contour of the circle more closely. This is just a rough description.</p>
<p>I'm currently applying a crude subset to th... | <p>I'd suggest to store your polygon (the <code><Line2D object></code>) in a variable like this:</p>
<pre><code>line = plt.plot(x,y)
</code></pre>
<p>Which enables you to utilise the <a href="https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D.get_path" rel="nofollow nore... | python|pandas | 1 |
2,589 | 40,064,587 | Image display error after changing dtype of image matrix | <p>I'm using opencv + python to process fundus(retinal images). There is a problem that im facing while converting a float64 image to uint8 image.</p>
<p><strong>Following is the python code:</strong></p>
<pre><code>import cv2
import matplotlib.pyplot as plt
import numpy as np
from tkFileDialog import askopenfilename... | <p>Look I executed your code and there are the <a href="http://postimg.org/gallery/1qi7dozn0" rel="nofollow">results</a></p>
<p>They seem pretty normal to me... this is the exact <a href="https://pastebin.com/6jwgKi9r" rel="nofollow">code</a> I used</p>
<p>Ar is different from the others because when you <code>imShow... | python|opencv|numpy | 1 |
2,590 | 43,953,594 | calculate row difference groupwise in pandas | <p>I need to calculate the difference between two rows groupwise using pandas.</p>
<pre><code>| Group | Value | ID |
----------------------
| M1 | 10 | F1 |
----------------------
| M1 | 11 | F2 |
----------------------
| M1 | 12 | F3 |
----------------------
| M1 | 15 | F4 |
------------------... | <p>I think you need custom function with <a href="http://pandas.pydata.org/pandas-docs/stable/groupby.html#flexible-apply" rel="nofollow noreferrer">apply</a> which return <code>DataFrame</code> for each group, for select by position is used <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.i... | python|pandas|numpy | 1 |
2,591 | 40,847,809 | Pandas aggregation subtraction based on column value | <p>Suppose I have DataFrame</p>
<pre><code>'name' 'quantity' 'day'
'A' 1 'Monday'
'A' 10 'Sunday'
'A' 5 'Friday'
'B' 2 'Monday'
'B' 30 'Sunday'
'B' 5 'Thursday'
</code></pre>
<p>What I need to build is anot... | <p><strong><em>setup</em></strong> </p>
<pre><code>import pandas as pd
from io import StringIO
txt = """name quantity day
A 1 Monday
A 10 Sunday
A 5 Friday
B 2 Monday
B 30 Sunday
B 5 Thursday"""
df = pd.r... | python|pandas|group-by | 4 |
2,592 | 40,895,730 | Python DataFrame from a list | <p>So, I have to create a dataframe. I do not mind my source to be a list of dicts or a dict.</p>
<pre><code>List of Dict:
[{'A': 'First', 'C': 300, 'B': 200},
{'A': 'Second', 'C': 310, 'B': 210},
{'A': 'Third', 'C': 330, 'B': 230},
{'A': 'Fourth', 'C': 340, 'B': 240},
{'A': 'Fifth', 'C': 350, 'B': 250}]
</code></pre>... | <p>Also you can use <code>pd.DataFrame.from_records()</code> where you can set a specific column to be index:</p>
<pre><code>pd.DataFrame.from_records([{'A': 'First', 'C': 300, 'B': 200},
{'A': 'Second', 'C': 310, 'B': 210},
{'A': 'Third', 'C': 330, 'B': 230},
{'A': 'Fourth', 'C': 340, 'B': 240},
{'A': 'Fifth', 'C'... | python|pandas|dataframe | 3 |
2,593 | 53,957,213 | Tensorflow feed_dict dimension miss match with the neural network input and training input | <p>I have two classes of diseases <code>A</code> and <code>B</code>. My training data has <code>28</code> images including both classes.
I have created resize function using opencv.</p>
<pre><code>def resize_cv(x,width,height):
new_image=cv.resize(x,(width,height))
return new_image
</code></pre>
<p><code>X</... | <p>The answer was simple the reason the Conversion of <strong>(196,196,3)</strong> happened due to the extra for loop in the scaling function. </p>
<p>Instead of using this code </p>
<pre><code>def scaling (X):
new=[]
for i in X:
for j in i:
new.append(j/255)
break
return n... | python-3.x|tensorflow|multidimensional-array | 0 |
2,594 | 53,967,271 | Detecting a list type in pandas | <p>Is there a way to see if a field is an array in <code>pandas</code>? For example:</p>
<pre><code>>>> data=[{'name':'tom','colors':[1,2,3]}]
>>> df = pd.DataFrame(data)
colors name
0 [1, 2, 3] tom
>>> df['colors']['dtype']
Name: colors, dtype: object
</code></pre>
<p>Is there a wa... | <p>If the data in the columns is consistent that is lists then use:</p>
<pre><code>type(df.loc[0,'colors'])
list
</code></pre> | python|pandas | 0 |
2,595 | 53,926,627 | Does keras use gpu automatically? | <p>It seems like it uses gpu automatically, but I do not know why.</p>
<p>First, I declared as below</p>
<pre><code>tf_config = tf.ConfigProto( allow_soft_placement=True )
tf_config.gpu_options.allow_growth = True
sess = tf.Session(config=tf_config)
keras.backend.set_session(sess)
</code></pre>
<p>Then I defined so... | <p>According to the <a href="https://www.tensorflow.org/guide/using_gpu" rel="noreferrer">documentation</a> TensorFlow will use GPU by default if it exist:</p>
<blockquote>
<p>If a TensorFlow operation has both CPU and GPU implementations, <strong>the GPU devices will be given priority</strong> when the operation is... | tensorflow|model|keras|gpu | 9 |
2,596 | 66,158,638 | First 'Group by' then plot/save as png from pandas | <p>first I need to filter data then plot each group separately and save files to directory</p>
<pre><code>for id in df["set"].unique():
df2= df.loc[df["set"] == id]
outpath = "path/of/your/folder/"
sns.set_style("whitegrid", {'grid.linestyle': '-'})
plt.figure(figs... | <p>This worked for me but it is very slow</p>
<pre><code>groups = df.groupby("set")
for name, group in groups:
sns.set_style("whitegrid", {'grid.linestyle': '-'})
plt.figure(figsize=(12,8))
ax1=sns.scatterplot(data=group, x="x", y="y", hue="result&... | pandas|matplotlib|plot | 0 |
2,597 | 66,101,687 | Reformatting a numpy array | <p>I have come across some code (which may answer <a href="https://stackoverflow.com/questions/65936033/assigning-a-label-to-its-corresponding-grid-cell">this</a> question of mine). Here is the code (from Vivek Maskara's solution to my issue):</p>
<pre><code>import cv2 as cv
import numpy as np
def read(image_path, lab... | <p>I will first create and explain a simplified example, and then explain the part you pointed.</p>
<p>First, we create the ndarray named <code>label_matrix</code>:</p>
<pre><code>import numpy as np
label_matrix = np.ones([2, 3, 4])
print(label_matrix)
</code></pre>
<p>This code means that you wil get an array containi... | python|python-3.x|numpy|numpy-ndarray | 1 |
2,598 | 66,181,278 | targeting jth-kth element in a matirx using python | <p><a href="https://i.stack.imgur.com/l092C.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/l092C.jpg" alt="enter image description here" /></a></p>
<p>I would like to implement a matrix that satisfies the conditions shown in the image:</p>
<ol>
<li>The matrix is an <code>m * n</code> matrix</li>
<li... | <p>You're overthinking this. Start with</p>
<pre><code>A = np.zeros((m, n))
</code></pre>
<p>The condition <code>k = j + 1</code> is just the first diagonal above the main one. You can use <a href="https://numpy.org/doc/stable/reference/generated/numpy.fill_diagonal.html" rel="nofollow noreferrer"><code>np.fill_diagona... | python|numpy|matrix | 2 |
2,599 | 66,296,162 | Numpy ravel takes too long after a slight change to a ndarray | <p>I am working with a flatten image (1920x1080x4), in which I need to reshape (e.g. <code>arr.reshape((1920,1080,4))</code>), remove the last channel (e.g. <code>arr[:,:,:3]</code>), convert from BGR to RGB (e.g. <code>arr[:,:,::-1]</code>) and finally flatten again (e.g. <code>arr.ravel()</code>). The problem is with... | <p>This is because all your operations above are producing views for the same data, but the last ravel is required to make a copy.</p>
<p>An array in numpy array has an underlying memory, and shape & strides determining where each element lies.</p>
<p>Reshaping a contiguous array may be performed by simply changing... | python|arrays|numpy|memory|flatten | 2 |
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