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 |
|---|---|---|---|---|---|---|
374,700 | 31,359,980 | Memory efficient sort of massive numpy array in Python | <p>I need to sort a VERY large genomic dataset using numpy. I have an array of 2.6 billion floats, dimensions = <code>(868940742, 3)</code> which takes up about 20GB of memory on my machine once loaded and just sitting there. I have an early 2015 13' MacBook Pro with 16GB of RAM, 500GB solid state HD and an 3.1 GHz int... | <p>At the moment each call to <code>np.argsort</code> is generating a <code>(868940742, 1)</code> array of int64 indices, which will take up ~7 GB just by itself. Additionally, when you use these indices to sort the columns of <code>full_arr</code> you are generating another <code>(868940742, 1)</code> array of floats,... | python|performance|sorting|numpy|memory | 14 |
374,701 | 64,515,684 | CSV Date Parsing in Pandas | <p>I am trying to parse dates together from the following sample set of data</p>
<hr />
<pre><code>No,year,month,day,hour,pm2.5,DEWP,TEMP,PRES,cbwd,Iws,Is,Ir
1,2010,1,1,0,NA,-21,-11,1021,NW,1.79,0,0
2,2010,1,1,1,NA,-21,-12,1020,NW,4.92,0,0
3,2010,1,1,2,NA,-21,-11,1019,NW,6.71,0,0
4,2010,1,1,3,NA,-21,-14,1019,NW,9.84,0,... | <p>Try it passing as <code>dict</code> or list of list</p>
<pre class="lang-py prettyprint-override"><code>dataset = pd.read_csv("raw.csv", parse_dates={'date':['year', 'month', 'day',
'hour']}, index_col = 1, date_parser=dateparser)
</code></pre>
<p>Or</p>
<pre class="lang-py prettyprint-override"><code>data... | python|pandas|date|parsing | 1 |
374,702 | 64,355,794 | How to hold the output of a model at each training epoch in tensorflow 1.x? | <p>I am trying to implement a constraint on the output of a neural network using the output of the previous training epoch. I tried using tf.assign() to update the value of a variable that holds the output, but it turned out that it holds the initial value.</p> | <p>You must use callbacks.
It's my example for maximum scorу:</p>
<pre><code>checkpoint_precision = ModelCheckpoint(filepath='best-weights, precision_selu_pr.hdf5', monitor='val_precision', mode='max', verbose=1, save_best_only=True)
checkpoint_auc = ModelCheckpoint(filepath='best-weights-auc_selu_pr.hdf5', monitor='v... | python|tensorflow|machine-learning | 0 |
374,703 | 64,288,855 | Element Click Selenium Not Finding Button to Click | <p>For the url below, I am trying to click the "1-50" button (which has its own xpath) and then the "51-100," "101-150," etc. buttons (which all share a 2nd xpath), but my code does not seem to be able to click on the button. Anybody able to figure this out? Cheers!</p>
<pre><code>import p... | <p>Runs fine if you used the a tag.</p>
<pre><code>driver.find_element_by_xpath('//table[2]/tbody/tr/td/a').click()
time.sleep(2)
for i in range(1,3):
df = pd.read_html(driver.page_source)[0]
df_appended.append(df)
driver.find_element_by_xpath('//table[2]/tbody/tr/td/a[3]').click()
time.sleep(1)
</code... | python|pandas|selenium|selenium-webdriver|xpath | 0 |
374,704 | 64,438,066 | How can I fillna based on the columns from another dataframe? | <p>I'm trying to fill the null value in <code>job_industry_category</code> from a lookup dataframe. For example:</p>
<pre><code>df = pd.DataFrame()
df['job_title'] = ['Executive Secretary', 'Administrative Officer' , 'Recruiting Manager' , 'Senior Editor', 'Media Manager I']
df['job_industry_category'] = ['Health', 'Fi... | <pre><code>#Boolean select NaN
m=df.job_industry_category.isna()
#Mask the NaNs and map across values using a dict of lookup['job_title']:lookup['job_industry_category'] df.loc[m,'job_industry_category']=df.loc[m,'job_title'].map(dict(zip(lookup.job_title,lookup.job_industry_category)))
job_title job_... | python|pandas|dataframe | 0 |
374,705 | 64,190,008 | Renaming values in a column based on predefined ranges | <p>I have a list of years in a column (pandas)</p>
<pre><code>Year
2001
2002
2018
2002
2006
2010
2019
2010
</code></pre>
<p>I would like to visualise in a bar chart how many years are by 2012 and how many years there are after 2012, i.e. I should have in my column something like this:</p>
<pre><code>Year
<2012
<2... | <p>This looks like a job for <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html" rel="nofollow noreferrer"><code>pd.cut</code></a>:</p>
<pre><code>pd.cut(df['Year'], bins=[-np.inf, 2012, np.inf], labels=['<2012', '>2012'])
0 <2012
1 <2012
2 >2012
3 <2012
4... | python|pandas | 0 |
374,706 | 64,322,401 | How to use groupby in python for multiple columns? | <p>have a df with values :</p>
<pre><code>name numb exam marks
tom 2546 math 25
tom 2546 science 25
tom 2546 env 25
mark 2547 math 15
mark 2547 env 10
sam 2548 env 18
</code></p... | <p>Consider <code>pivot_table</code> with some column name manipulation due to hierarchy and aggreate name:</p>
<pre><code>pivot_df = df.pivot_table(index='name', columns='exam', values='marks', aggfunc=['count', 'sum'],
margins=True, margins_name='total')
pivot_df.columns = [i+'_'+j.replace... | python|pandas|group-by|pivot|aggregate | 0 |
374,707 | 64,247,915 | Modify column data before a specific character using Regex in pandas | <p>I'm trying to modify the Address column data by removing all the characters before the comma.</p>
<p>Sample data:</p>
<pre><code> **ADDRESS**
0 Ksfc Layout,Bangalore
1 Vishweshwara Nagar,Mysore
2 Jigani,Bangalore
3 Sector-1 ... | <p>Try this:</p>
<pre><code>data.ADDRESS = data.ADDRESS.str.split(',').str[-1]
</code></pre> | python|regex|pandas | 0 |
374,708 | 64,280,524 | How to multiply 2 different dataframe which have different shape but same header and row label in Python Pandas? | <p><strong>Dataframe - 1 (number of products by country)</strong></p>
<p><strong>Note:</strong> Use below code to generate example dataframe</p>
<pre><code>df1 = pd.DataFrame({'Devices':['Mobile','Mobile','Mobile','Mobile','Mobile','Laptop','Desktop'],'Sources':['India','India','India','India','UK','UK','US'],'Status':... | <p>Set <code>Devices</code> and <code>Sources</code> as the index and then multiply</p>
<pre><code>df1.set_index(['Devices', 'Sources', 'Status'])[df1.columns[3:]].mul(df2.set_index(['Devices', 'Sources'])[df2.columns[3:]]).reset_index()
Devices Sources Status 10/01/2020 10/02/2020 10/03/2020 10/04/2020
0 Desk... | python-3.x|pandas|dataframe | 1 |
374,709 | 64,484,576 | Why do I get an error trying to use keras package in R? | <p>I'm trying to run through an example for building a neural network in R. I tried following the instructions at <a href="https://opendatascience.com/using-keras-and-tensorflow-in-r/" rel="nofollow noreferrer">https://opendatascience.com/using-keras-and-tensorflow-in-r/</a>.
I've installed the keras and tensorflow pac... | <p>In case anyone has a similar problem, I just uninstalled Rstudio and installed again through a new python environment in anaconda navigator and installed the modules I needed in that environment. That seemed to work.</p> | python|r|tensorflow|keras | 0 |
374,710 | 64,329,250 | How to mask paddings in LSTM model for speech emotion recognition | <p>Given a few directories of .wav audio files, I have extracted their features in terms of a 3D array (batch, step, features).</p>
<p>For my case, the training dataset is (1883,100,136).
Basically, each audio has been analyzed 100 times (imagine that as 1fps) and each time, 136 features have been extracted. However, t... | <p><code>Embedding</code> layer is not for your case. You can consider instead <code>Masking</code> <a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Masking" rel="nofollow noreferrer">layer</a>. It is simply integrable in your model structure, as shown below.</p>
<p>I also remember you that the input... | tensorflow|machine-learning|keras|deep-learning|lstm | 1 |
374,711 | 64,362,016 | Error pip installing wheel file from github repository (to download pycocotools) | <p>I am installing Tensorflow (1.15.0) in order to perform some deep learning object detection, but am having trouble pip installing pycocotools. I am following <a href="https://www.youtube.com/watch?v=usR2LQuxhL4&t=134s" rel="nofollow noreferrer">this</a> tutorial, which is an updated tutorial originally from YouT... | <p>Try running it as follows:</p>
<pre><code>pip install pycocotools-windows
</code></pre>
<p>as suggested <a href="https://github.com/cocodataset/cocoapi/issues/169" rel="nofollow noreferrer">here</a>.</p> | tensorflow|github|pip|pycocotools | 1 |
374,712 | 64,311,465 | Testing a Random Image against a Python Keras/Tensorflow CNN | <p>I've created and CNN and I am trying to figure out how to test a random image against it. I am utilizing Keras and Tensorflow. Lets assume I wanted to test the image found here: <a href="https://i.ytimg.com/vi/7I8OeQs7cQA/maxresdefault.jpg" rel="nofollow noreferrer">https://i.ytimg.com/vi/7I8OeQs7cQA/maxresdefault.... | <p>Step 1: Save the model</p>
<pre><code>model.save('model.h5')
</code></pre>
<p>Step 2: Load the model</p>
<pre><code>loaded_model = tensorflow.keras.models.load_model('model.h5')
</code></pre>
<p>Step 3: Download the image via requests library(answer is taken from: <a href="https://stackoverflow.com/questions/3042757... | python|tensorflow|keras|conv-neural-network|image-classification | 0 |
374,713 | 64,555,580 | Change saved tensorflow model input shape at inference time | <p>I've searched everywhere but couldn't find anything. It looks so weird that nobody have already encountered the same problem as I... Let me explain:</p>
<p>I've trained a <strong>Tensorflow 2</strong> custom model. During the training I have used <code>set_shape((None, 320, 320, 14))</code> so that Tensorflow knows ... | <p>I answer my own question.
Unfortunately, my answer <strong>will not satisfy</strong> everybody. There are so many convoluted things happening in TF (Not to mention that when you search for help, most of it concern the 1st API... -_-").</p>
<p>Anyway, here is the "solution"</p>
<p>In my Neural Network,... | tensorflow|input|tensorflow2.0|shapes | 0 |
374,714 | 64,398,484 | How to manipulate client gradients in tensorflow federated sgd | <p>I'm following <a href="https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification" rel="nofollow noreferrer">this tutorial</a> to get started with tensorflow federated. My aim is to run federated sgd (not federated avg) with some manipulations on client gradient values before they are... | <p><code>build_federated_sgd_process</code> is fully-canned; it is really designed to serve as a reference implementation, not as a point of extensibility.</p>
<p>I believe what you are looking for is the function that <code>build_federated_sgd_process</code> calls under the hoos, <a href="https://www.tensorflow.org/fe... | python|tensorflow|tensorflow-federated|sgd | 1 |
374,715 | 64,371,445 | Read excel sheet in pandas with different sheet names in a pattern | <p>I am trying to read multiple excel files in a loop using read_excel :</p>
<p>Different excel files contain sheet names which contain the word "staff"
eg Staff_2013 , Staff_list etc</p>
<p>Is there a way to read all these files dynamically using some wild card concept ?</p>
<p>Something like the code below ... | <p>The <code>pandas.read_excel</code> can only read one sheet. The use you are suggesting would be problematic in case of multiple matches.</p>
<p>So you have to list the sheets and select the ones you want to read one by one.</p>
<p>For instance:</p>
<pre><code>xls_file = pd.ExcelFile('my_excel_file.xls')
staff_fnames... | python|pandas|dataframe | 3 |
374,716 | 64,233,099 | pyTorch gradient becomes none when dividing by scalar | <p>Consider the following code block:</p>
<pre><code>import torch as torch
n=10
x = torch.ones(n, requires_grad=True)/n
y = torch.rand(n)
z = torch.sum(x*y)
z.backward()
print(x.grad) # results in None
print(y)
</code></pre>
<p>As written, <code>x.grad</code> is None. However, if I change the definition of <code>x</co... | <p>When you set <code>x</code> to a tensor divided by some scalar, <code>x</code> is no longer what is called a "leaf" <code>Tensor</code> in PyTorch. A leaf <code>Tensor</code> is a tensor at the beginning of the computation graph (which is a DAG graph with nodes representing objects such as tensors, and edg... | python|pytorch | 1 |
374,717 | 64,601,301 | Pytorch input tensor size with wrong dimension Conv1D | <pre><code> def train(epoch):
model.train()
train_loss = 0
for batch_idx, (data, _) in enumerate(train_loader):
data = data[None, :, :]
print(data.size()) # something seems to change between here
data = data.to(device)
optimizer.zero_grad()
recon_batch, mu, logvar = model(data) # ... | <p>I just found my mistake when I call <code>forward()</code> I am doing <code>self.encode(x.view(-1,1998))</code> which is reshaping the tensor.</p> | python|pytorch|tensor | 0 |
374,718 | 64,585,328 | Why can R's read.csv() read a CSV from GitLab URL when pandas' read_csv() can't? | <p>I noticed that panda's <code>read_csv()</code> fails at reading a public CSV file hosted on GitLab:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
df = pd.read_csv("https://gitlab.com/stragu/DSH/-/raw/master/Python/pandas/spi.csv")
</code></pre>
<p>The error I get (truncated):</p>
... | <p>If you're looking for a workaround, I recommend making the GET request via <a href="https://requests.readthedocs.io/en/master/" rel="nofollow noreferrer"><strong><code>requests</code></strong></a> library:</p>
<pre><code>import requests
from io import StringIO
url = "https://gitlab.com/stragu/DSH/-/raw/master/... | python|r|pandas|read.csv | 4 |
374,719 | 64,300,420 | How to reshape array which doesn't have column? | <p>I have one array which shape is (6000,) and now I want to convert it to (6000,1) to use it further. How can I do it?</p>
<pre><code> print("TrainX", str(trainX.T.shape))
np.reshape(trainY, (1, trainY.shape[0]))
print("TrainY", str(trainY.shape))
</code></pre>
<p>Both giving same output (6... | <p>Something like this?</p>
<pre><code>import numpy as np
a = np.array((1,2,3))
b = np.array([a]).T
print(np.shape(a))
print(np.shape(b))
</code></pre>
<p>Output:</p>
<pre><code>(3,)
(3, 1)
</code></pre> | python|arrays|numpy | 0 |
374,720 | 64,453,512 | Map dataframe values in some columns according to the values in other columns | <p>I have a dataframe which looks like this:</p>
<pre><code> home_player_1 home_player_2 home_player_3 away_player_1 away_player_2 away_player_3 player_1 ~~~ player_2000
1 23 34 45 2 6 688 0 ~~~ 0
2 233 341 4 ... | <p>The following code should give you the desired DataFrame:</p>
<pre class="lang-py prettyprint-override"><code>for index, row in df.iterrows():
values = row[:6]
for value in values:
df.at[index, 'player_{}'.format(value)] = 1
</code></pre>
<h2><strong>Edit:</strong></h2>
<p>In case you want to avoid i... | python|pandas|dataframe|logic | 1 |
374,721 | 64,203,682 | How do I filter the data when there are many conditions in pandas? | <p>I have a question about python pandas.</p>
<p>For example, the dataset df has 100 rows and the column names are a1, a2, a3, ... , a20. If I want to find specific rows where a1=20, a2=1, a3=0, a4=1, a5=2,...., a20=1, how can I filter out the rows if such row exists?</p>
<p>If I use pandas filter, how should I set th... | <p>Try this:</p>
<pre><code>df = df[(df['a1'] == 20) & (df['a2'] == 1) & (df['a3'] == 0)]
</code></pre> | python|pandas|dataframe | 0 |
374,722 | 64,451,388 | Summing up the output of multiple functions in python | <p>I currently have three sine functions (y1, y2, y3) and would like to sum the output of the functions in a new function (ytotal) but only where the output of the sine functions are greater than 0.</p>
<pre><code>import numpy as np
import matplotlib.pyplot as plt
#%%
phi = np.linspace(-2*np.pi, 2*np.pi, 100)
y1 = 0.... | <p>Do you mean:</p>
<pre><code>plt.plot(phi, y1.clip(0)+y2.clip(0)+y3.clip(0), label='Total')
</code></pre>
<p>Output:</p>
<p><a href="https://i.stack.imgur.com/uj1ec.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/uj1ec.png" alt="enter image description here" /></a></p> | python|numpy|math|trigonometry | 0 |
374,723 | 64,238,018 | How to use standard scaler model on dataset having less features than original dataset in which it was initially trained | <p>I was using standard scalar model from sklearn.preprocessing. I fitted the standard scaler model on the dataset having 27 features in it. Is it possible to use same standard scalar model on a testing dataset having less than 27 features in it Code Snippet</p>
<pre><code>from sklearn.preprocessing import StandardScal... | <p>If want select all features without first <code>3</code> features use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html" rel="nofollow noreferrer"><code>DataFrame.iloc</code></a>:</p>
<pre><code>from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_trai... | python|pandas|machine-learning|scikit-learn | 2 |
374,724 | 64,450,286 | How to effeciently create conditional columns arrays using Numpy? | <p>The objective is to create an array but by fulfilling the condition of <code>(x=>y) and (y=>z)</code>.</p>
<p>One naive way but does the job is by using a nested <code>for loop</code> as shown below</p>
<pre><code>tot_length=200
steps=0.1
start_val=0.0
list_no =np.arange(start_val, tot_length, steps)
a=np.ze... | <pre><code>tot_length = 200
steps = 0.1
list_no = np.arange(0.0, tot_length, steps)
a = list()
for x in list_no:
for y in list_no:
if y > x:
break
for z in list_no:
if z > y:
break
a.append([x, y, z])
a = np.array(a)
# if needed, a.transp... | python|pandas|numpy | 2 |
374,725 | 64,531,446 | sum rows in dataframe based on different columns | <p>How can I merge rows in the given dataframe as shown below? For each account and currency, I have to sum the values, so that there's no division by sector.</p>
<p><a href="https://i.stack.imgur.com/e9ory.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/e9ory.png" alt="enter image description here" ... | <p>Try</p>
<pre><code>df.groupby(['acccount','currency'])['sum'].sum().reset_index()
</code></pre> | python|pandas|dataframe|merge | 4 |
374,726 | 64,445,546 | Fill in timestamp gaps every 11th of a second | <p>I have a textfile 'example.txt' which contains data sampled at 11 Hz (so every 11th of a second).</p>
<p>Here you can find my code to load the textfile and convert 'Date' and 'Time' into datetime format. In the end the dataframe has a size of (34,6):</p>
<pre><code>import glob
import os
import datetime
#Specify fil... | <p>If you have your timestamp as a timestamp, the missing time gap will be filled in by a line connecting the two data points on either side. We can call out the legit values by using a scatterplot over top of the line plot.</p>
<pre><code>import seaborn as sns
import matplotlib.pyplot as plt
df['Time'] = pd.to_dateti... | python|pandas|dataframe|timestamp | 0 |
374,727 | 64,430,001 | Invalid pointer error whily running python in C++ using pybind11 and pytorch | <p>While running the following python code in C++ using pybind11, pytorch 1.6.0, I get "Invalid Pointer" error. In python, the code runs successfully without any error. Whats the reason? How can I solve this problem?</p>
<pre><code>import torch
import torch.nn.functional as F
import numpy as np
import cv2
imp... | <p>This line is causing the error because it assumes <code>__dict__</code> has a <code>backbone_name</code> element:</p>
<pre><code>backbone = resnet.__dict__[backbone_name](pretrained=pretrained)
</code></pre>
<p>When that isn't the case, it basically tries to access invalid memory. Check <code>__dict__</code> first w... | python|c++|c++11|pytorch|pybind11 | 0 |
374,728 | 64,459,038 | How to read all csv files in multiple zip files? | <p>I have a folder with many zip files and within those zip files are multiple csv files.
Is there any way to get all of the .csv files in one dataframe in python?
Or any way I can pass a list of zip files?</p>
<p>The code I am currently trying is:</p>
<pre><code>import glob
import zipfile
import pandas as pd
for zip_... | <p>The following code should satisfy your requirements (just edit <code>dir_name</code> according to what you need):</p>
<pre class="lang-py prettyprint-override"><code>import glob
import zipfile
import pandas as pd
dfs = []
for filename in os.listdir(dir_name):
if filename.endswith('.zip'):
zip_file = os.... | python|pandas|glob|zip | 0 |
374,729 | 64,354,164 | cuDNN Error Failed to get convolution algorithm. This is probably because cuDNN failed to initialize | <p>I have installed Anaconda with Python 3.8 and CUDA 10.1 with CUDNN 8.0.3 on my Window 10 with GPU GTX 1050. But Still I get the error <a href="https://i.stack.imgur.com/BARlk.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/BARlk.png" alt="Details of the Error" /></a></p> | <p>I've been having a similar issue with TF 2.3.1. However, right away I can tell you that your cudnn version is incompatible. Only cudnn 7.6 is supported with the latest TF which as of right now is 2.3.1. See compatibility link below.</p>
<p><a href="https://www.tensorflow.org/install/gpu#hardware_requirements" rel="n... | python|tensorflow|tensorflow2.0 | 1 |
374,730 | 64,302,315 | How to combine groupby, rolling and multiple columns' creation in Python | <p>I have an issue that is easily solved with dplyr in R, yet can't seem to find an easy way in Python.
I have a df with id(=customerid), s(=store), m(=month) and ttl(=total purchase) as columns.
I would like to calculate multiple new columns on id+s - for example last 3 months purchase and minimum purchase.</p>
<p>Exa... | <p>With python syntax and pandas the logic is nearly identical</p>
<pre><code>t = '''
id s m ttl
1 A 1/1/2020 7
1 A 2/1/2020 3
1 A 3/1/2020 7
1 A 4/1/2020 6
1 A 5/1/2020 7
1 A 6/1/2020 7
1 B 1/1/2020 6
1 B 2/1/2020 10
1 B 3/1/2020 8
1 ... | python|pandas|janitor | 1 |
374,731 | 64,275,440 | FIeld format conversion error in numpy array | <p>I am trying to create a numpy array consisting of an array of data, where each data point is another array of length 5. I am trying to do this by setting a first datum (i.e. array of five elements) and setting the name and formats of each data types using the following code:</p>
<pre><code>DTYPE = [('t_start', 'S32'... | <p>I can't reproduce your exact error message, but it looks like there are two issues: your inner list should be a tuple, and you should use <code>U</code> (Unicode string) in place of <code>S</code> in the format specifier (<a href="https://numpy.org/doc/stable/reference/arrays.dtypes.html" rel="nofollow noreferrer">d... | python|python-3.x|numpy | 0 |
374,732 | 64,396,124 | Selecting sentences from a data frame text column if only the sentences contain any of the keywords from a search list | <p>I have a dataframe where in one column, I have a full text with multiple very long sentences. I used <code>NLTK</code> to tokenize the text but now I need to make sure I only extract the sentences that contain any of the words from a given long list of full words. I wrote the following code but the problem with it i... | <p>The regex you are using is <em>almost</em> good. What you would need is to search for individual words which you can achieve by using <code>\b</code> special regex character which matches word boundaries (in regex sense).</p>
<p>Therefore, what would work is:</p>
<pre><code>symptoms = [long list of words ~ about 100... | python|python-3.x|regex|pandas|nltk | 0 |
374,733 | 64,362,318 | pandas: replicating an excel formula in pandas | <p>What i have is a dataframe like:</p>
<pre><code> total_sum pid
5 2
1 2
6 7
3 7
1 7
1 7
0 7
5 10
1 10
1 10
</code></pre>
<p>What I want is another column <code>pos</code> like:</p>
<pre><code> tot... | <p><strong>Explanation</strong>:</p>
<p>Doing <code>groupby</code> on <code>pid</code> to group the same <code>pid</code> into separate groups. On each group, apply these following operations:</p>
<p>_ Call <code>diff</code> on each group. <code>diff</code> returns integers or <code>NaN</code> indicate the differences ... | python|pandas|dataframe|sorting | 3 |
374,734 | 64,177,093 | Question Python Group by and Apply function | <p>I have a data set like below:</p>
<pre><code>idx start_date end_date flag
1. 6-17-20. 6-24-20. 1
2. 6-17-20. 6-24-20. 0
3. 6-17-20. 6-24-20. 1
4. 6-17-20. 6-24-20. 0
1. 6-25-20. 7-03-20. 1
2. 6-25-20. 7-03-20. 1
3. 6-25-20. 7-03-20. 1
4. 6-25-20. 7-03-20. 1
</code></pre>
<p>W... | <p>Your question could be clearer but I think I sort of understand what you are trying to achieve. Do comment if I am mistaken!</p>
<p>I'm presuming that you want the start and end dates as row entries and the columns of <code>idx</code>s indicating which IDs have those start and end dates. With that assumption in mind... | python|pandas-groupby|apply | 0 |
374,735 | 64,509,518 | Pandas - convert float to int when there are NaN values in the column | <p>I'm trying to convert float numbers to int in my df column with this one liner:</p>
<pre><code>df['id'] = df['id'].map(lambda x: int(x))
</code></pre>
<p>But some values are NaN an will throw:</p>
<pre><code>ValueError: cannot convert float NaN to integer
</code></pre>
<p>How do I fix this?</p> | <p><code>NaN</code> is itself float and can't be convert to usual <code>int</code>. You can use <code>pd.Int64Dtype()</code> for nullable integers:</p>
<pre><code># sample data:
df = pd.DataFrame({'id':[1, np.nan]})
df['id'] = df['id'].astype(pd.Int64Dtype())
</code></pre>
<p>Output:</p>
<pre><code> id
0 1
1 ... | pandas | 7 |
374,736 | 64,372,558 | Unable to convert full string from pandas series | <p>I have a pandas series where I save strings. But when I wish to convert this series to string, each cell is limited to a number of character. Does anyone know if I am doing something wrong or if theres a workaroud.</p>
<pre><code>data = np.array(['good day good sir how are you doing today? I sure hope you are well',... | <p><strong>Series.to_string()</strong> returns a string representation of the Series, not a reduced value, if you want to combine all string in a single one, replace <code>ser.to_string()</code> for <code>" ".join(ser)</code> and it will concat all string with a space.</p> | python|pandas|string|series | 0 |
374,737 | 64,589,892 | Tensorflow 2.3.1 IndexError: list index out of range | <p><strong>I got an error ,IndexError: list index out of range.</strong></p>
<p>it worked on a other machine but after i transferred it to a other machine it doesn't work anymore.</p>
<p>Python: 3.8.5</p>
<p>tensorflow: 2.3.1</p>
<p>Traceback says:</p>
<pre><code>tensorflow.python.autograph.impl.api.StagingError: in us... | <p>Define the detect_fn inside the get_model_detection_function function ,
something like this :</p>
<pre><code>def get_model_detection_function(model):
"""Get a tf.function for detection."""
@tf.function
def detect_fn(image):
"""Detect objects in image.&quo... | python|tensorflow|tensorflow2.0|index-error|tensorflow2.x | 3 |
374,738 | 64,344,884 | Pandas DataFrame : Create a column based on values from different rows | <p>I have a pandas dataframe which looks like this :</p>
<pre><code> Ref Value
1 SKU1 A
2 SKU2 A
3 SKU3 B
4 SKU2 A
5 SKU1 B
6 SKU3 C
</code></pre>
<p>I would like to create a new column, conditioned on whether the values for a given Ref matc... | <p>Let's try <code>groupby().nunique()</code> to check the number of values within a ref:</p>
<pre><code>df['NewCol'] = np.where(df.groupby('Ref')['Value'].transform('nunique')==1,
'good', 'bad')
</code></pre>
<p>Output:</p>
<pre><code> Ref Value NewCol
1 SKU1 A bad
2 SKU2 A g... | python|pandas|dataframe | 3 |
374,739 | 64,431,313 | split multiple columns in pandas dataframe by delimiter | <p>I have survey data which annoying has returned multiple choice questions in the following way. It's in an excel sheet There is about 60 columns with responses from single to multiple that are split by /. This is what I have so far, is there any way to do this quicker without having to do this for each individual co... | <p>We can use list comprehension with <code>add_prefix</code>, then we use <code>pd.concat</code> to concatenate everything to your final df:</p>
<pre><code>splits = [df[col].str.split(pat='/', expand=True).add_prefix(col) for col in df.columns]
clean_df = pd.concat(splits, axis=1)
</code></pre>
<pre><code> q10 q2... | python|pandas|split | 6 |
374,740 | 64,318,011 | How can I find cosine similarity between input array and pandas dataframe and return the row in dataframe which is most similar? | <p>I have a data set as shown below and I want to find the cosine similarity between input array and reach row in dataframe in order to identify the row which is most similar or duplicate.
The data shown below is a sample and has multiple features. I want to find the cosine similarity between input row and each row in ... | <p>There are <a href="https://stackoverflow.com/questions/18424228">various ways of computing cosine similarity</a>. Here I give a brief summary on how each of them applies to a dataframe.</p>
<h2>Data</h2>
<pre><code>import pandas as pd
import numpy as np
# Please don't make people do this. You should have enough rep... | python|pandas|scikit-learn|scipy|cosine-similarity | 3 |
374,741 | 64,273,234 | Python Issue - Correlated Random Samples and Cholesky Decomposition | <pre><code>import numpy as np
from scipy.linalg import cholesky
X1_temp = np.random.normal(0, 1, (40, 10)) # with mean 0 and std. dev. 1
C = cholesky(C0, lower = False)
X1 = X1_temp @ np.transpose(C)
</code></pre>
<p>Thanks very much in advance. This is just a very simple task. I am given a desired covariance matrix C... | <p>Don't transpose <code>C</code> when you compute <code>X1</code>.</p>
<pre><code>In [33]: C0
Out[33]:
array([[0.00119545, 0.00055428, 0.00094478, 0.00057466, 0.00039038,
0.0004846 , 0.00047505, 0.00039403, 0.00041767, 0.00053985],
[0.00055428, 0.00055869, 0.0005272 , 0.00046757, 0.0002733 ,
0.... | python|numpy|random|statistics|covariance | 0 |
374,742 | 64,378,573 | Compare sums of multiple pandas dataframes in an effective way | <p>I have multiple pandas dataframes (5) with some common names index. They have different size. I need sum at least 5 different common <code>colum names</code> (25 in total) from each dataframe and then compare the sums.</p>
<pre><code>Data:
df_files = [df1, df2, df3, df4, df5]
df_files
out:
[ z n... | <p>If I understand your problem correctly, you need to bring the names of data frames and respective columns in one place to compare the sums. In that case I usually use a dictionary to keep the name of variable, something like this:</p>
<pre><code>df_files = {'df1':df1, 'df2':df2, 'df3':df3, 'df4':df4, 'df5':df5}
summ... | python|pandas|list|if-statement | 1 |
374,743 | 64,609,003 | Numpy vectorization instead of for loop | <p>I wrote a function which is too time consuming when used with for loops. It appends numpy vectors (10,0) as rows in each iteration. How can I use a vectorized numpy solution for the iterations to speed this up?</p>
<p>Any hint why the vstack-array solution below is even slower than the append-list solution?</p>
<p>T... | <p>In general you don't want to append to <code>numpy</code> arrays. Re-allocating space for them is too time consuming. If you know <code>n_iterations</code>, you can allocate up-front like this:</p>
<pre><code>result_array = np.empty([n_iterations, n_cols])
for i in range(n_iterations):
result_array[i] = samp... | python|numpy|vectorization | 1 |
374,744 | 64,377,601 | Counting from a column in a Pandas Dataframe | <p>I am attempting to count the number of instances of an element in a column of a Pandas Dataframe based on a set of criteria. I am running into difficulty in a few places.</p>
<p>Here is what I have up to this point. It effectively reads the CSV, drops the duplicates, and sorts df2. I am performing all of these s... | <p>Try pandas <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.value_counts.html" rel="nofollow noreferrer"><code>value_counts</code></a> function</p> | python|python-3.x|pandas|dataframe | -1 |
374,745 | 64,176,002 | fast fourier transform of csv data | <p>I have a csv file that contains time and torque data. <a href="https://pastebin.com/MAT2rG3U" rel="nofollow noreferrer">https://pastebin.com/MAT2rG3U</a> This data set is truncated because size limit.</p>
<p>I am trying to find the FFT of the data to find the frequency of a vibration.</p>
<p>Here is my code (here ... | <p>What you've posted works for me, but your data isn't valid for an FFT because the timesteps aren't consistent. That is, you don't have a well defined sample rate.</p>
<pre><code>data = pd.read_csv('torque_data.txt',index_col=0)
data = data['Torque'].astype(float).values
print(data)
N = data.shape[0] #number of ele... | python|pandas|numpy|matplotlib|fft | 1 |
374,746 | 64,402,771 | How to load numpy array with certain columns as specific type | <p>I tried following:</p>
<pre><code>>>> arr2 = [[0, 0, 0, -0.9, 0.3], [0, 0, 1, 0.9, 0.6], [0, 1, 0, -0.2, 0.6], [0, 1, 1, 0.8, 0.3], [1, 0, 1, 0.2, 1.0], [1, 1, 0, -0.8, 1.0]]
>>> narr2 = np.array(arr2)
>>> narr2
array([[ 0. , 0. , 0. , -0.9, 0.3],
[ 0. , 0. , 1. , 0.9, 0.6],
... | <p>I propose that you convert lists to tuples, and then assign the data types. Here's my solution:</p>
<pre><code>import numpy as np
arr2 = [[0, 0, 0, -0.9, 0.3], [0, 0, 1, 0.9, 0.6],
[0, 1, 0, -0.2, 0.6], [0, 1, 1, 0.8, 0.3], [1, 0, 1, 0.2, 1.0], [1, 1, 0, -0.8, 1.0]]
tupp2 = [tuple(l) for l in arr2]
datatype = [(... | python|arrays|numpy | 0 |
374,747 | 64,255,616 | How can I fill data frames with NAN with same values of previous data frames from the same list | <p>I have a list of data frames <code>A</code> most of them are NAN data frames some of them are not, I would like to fill all NAN data frames with same values of the previous data frames (that do not contain NAN) in the list.
Here's a small example:</p>
<pre><code>A=[]
data = {'set_of_numbers': [1,2,3,4,4,5,9]}
df1 =... | <p>If I understand correctly:</p>
<pre><code>for i, df in enumerate(A):
df[df.isnull()] = A[i-1]
</code></pre>
<p>or if you wish to change the dtype of previously non-nan df:</p>
<pre><code>for i, df in enumerate(A):
if df.isnull().all().all():
A[i] = A[i-1].copy()
</code></pre>
<p>per OP's <strong>EDIT</strong... | python|pandas|numpy|dataframe | 1 |
374,748 | 47,703,374 | 2 groupby in the same dataframe, it is possible? | <p>I want to the following df, to make a <code>df = df.groupby(['id','quarter'])['jobs].mean()</code> but at the same time that dataframe must have the mean of jobs by id and year in another column. </p>
<pre><code> id year quarter month jobs
1 2007 1 1 10
1 2007 1 ... | <p>Using <code>transform</code> then <code>drop_duplicates</code></p>
<pre><code>df['jobs1']=df.groupby(['id','quarter'])['jobs'].transform('mean')
df['jobs_year']=df.groupby(['id','year'])['jobs'].transform('mean')
df=df.drop_duplicates(['id','year','quarter'])
df
Out[305]:
id year quarter month jobs jo... | python|pandas|numpy | 3 |
374,749 | 47,585,653 | returning rows within range in pandas MultiIndex | <p>I have a dataframe that looks like:</p>
<pre><code> count
year person
a.smith 1
2008 b.johns 2
c.gilles 3
a.smith 4
2009 b.johns 3
c.gilles 2
</code></pre>
<p>in which both <code>year</code> and <code>person</code> are part of the index. I'd like to return all ... | <p>Using <code>pd.IndexSlice</code></p>
<pre><code>idx = pd.IndexSlice
df.loc[idx[:,'a.smith'],:]
Out[200]:
count
year person
2008 a.smith 1
2009 a.smith 4
</code></pre>
<p>Data Input </p>
<pre><code>df
Out[211]:
count
year person
2008 a.smith 1
b.... | python|pandas | 0 |
374,750 | 47,796,207 | subsetting affects .view(np.float64) behaviour | <p>I'm trying to use some sklearn estimators for classifications on the coefficients of some fast fourier transform (technically Discrete Fourier Transform). I obtain a numpy array X_c as output of np.fft.fft(X) and I want to transform it into a real numpy array X_r, with each (complex) column of the original X_c trans... | <p>I get a warning:</p>
<pre><code>In [5]: X_c2 = X_c[:,range(3)]
In [6]: X_c2
Out[6]:
array([[ 0.+0.j, 1.+0.j, 2.+0.j],
[ 4.+0.j, 5.+0.j, 6.+0.j]])
In [7]: X_c2.view(np.float64)
/usr/local/bin/ipython3:1: DeprecationWarning: Changing the shape of non-C contiguous array by
descriptor assignment is deprecat... | python|arrays|numpy|casting|subset | 0 |
374,751 | 47,600,891 | Slice dataframe for each unique pair of columns | <p>I want to split a pandas dataframe according to unique pairs taken from two columns, then select the rows relative to that pair and project the remaining columns.</p>
<pre><code>df: Col1 Col2 Col3 Col4
1 1 a 100
1 2 b 200
1 2 c 300
1 2 d 400
... | <p>Using <code>groupby</code></p>
<pre><code>for x,y in df.groupby(['Col1','Col2']):
print(x)
print(y)
(1, 1)
Col1 Col2 Col3 Col4
0 1 1 a 100
(1, 2)
Col1 Col2 Col3 Col4
1 1 2 b 200
2 1 2 c 300
3 1 2 d 400
(3, 4)
Col1 Col2 Col3 Col4
4 3 ... | python|pandas | 1 |
374,752 | 47,883,487 | pandas: applying function over each row of Dataframe | <p>I have a pandas DataFrame which contains 3 columns:</p>
<pre><code>| val1 | val2 | val3 |
|--------------------------|
| Nike | NaN | NaN |
| Men | Adidas | NaN |
| Puma | Red | Women |
</code></pre>
<p>and 3 lists:</p>
<pre><code>Brands = ['Adidas', 'Nike', 'Puma']
Gender = ['Men'... | <p>You can use:</p>
<pre><code>df['brand'] = df[df.isin(Brands)].ffill(axis=1).iloc[:, -1]
df['gender'] = df[df.isin(Gender)].ffill(axis=1).iloc[:, -1]
df['color'] = df[df.isin(Color)].ffill(axis=1).iloc[:, -1]
print (df)
val1 val2 val3 brand gender color
0 Nike NaN NaN Nike NaN NaN
1 Men ... | python|python-2.7|pandas|dataframe|apply | 0 |
374,753 | 47,784,388 | How to prepare data.frame for keras using tensoflow CNN in r | <p>I have been trying to prepare a train data set for CNN in keras, but i can't find the way to properly set the data. </p>
<p>In the keras <a href="https://keras.rstudio.com/articles/examples/mnist_cnn.html" rel="nofollow noreferrer">CNN example</a>, they use the MNIST data set, in which:</p>
<pre><code>library(kera... | <p>This might help:</p>
<pre><code># Create an empty array the size you want
x_array <- array(NA, dim = c(3, 3, length(x)))
# Loop in each object of your list into the array
for (i in length(x)) {
x_array[,, i] <- x[[i]]
}
</code></pre> | r|tensorflow|keras | 0 |
374,754 | 47,972,667 | Importing pandas.io.data | <p>I am following along with this tutorial: <a href="https://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/" rel="nofollow noreferrer">https://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/</a></p>
<p>He suggests the following import:</p>
<pre><code>import pandas.io... | <p><strong>Update:</strong> </p>
<p>As mentioned by wilkas, now you might need to do </p>
<pre><code>import pandas_datareader.data as web
</code></pre>
<hr>
<p>I am assuming that you are using the latest version of the package. Check out the newest documentation at <a href="https://pandas-datareader.readthedocs.io/... | python|python-3.x|pandas | 3 |
374,755 | 47,587,595 | Google Cloud ML: Use Nightly TF Import Error No Module tensorflow | <p>I want to train the NMT model from Google on Google Cloud ML.
<a href="https://github.com/tensorflow/nmt" rel="nofollow noreferrer">NMT Model</a></p>
<p>Now I put all input data in a bucket and downloaded the git repository.
The model needs the nightly version of tensorflow so I defined it in setup.py and when I use... | <p>The TensorFlow 1.5 might need newer version of CUDA (i.e., CUDA 9), and but the version CloudML Engine installed is CUDA 8. Can you please try to use TensorFlow 1.4 instead, which works on CUDA 8? Please tell us if 1.4 works for you here or send us an email via cloudml-feedback@google.com</p> | tensorflow|google-cloud-ml | 0 |
374,756 | 47,802,612 | weird behavior of numpy.histogram / random numbers in numpy? | <p>I stumbled upon some peculiar behavior of random numbers in Python , specifically I use the module numpy.random.</p>
<p>Consider the following expression:</p>
<pre><code>n = 50
N = 1000
np.histogram(np.sum(np.random.randint(0, 2, size=(n, N)), axis=0), bins=n+1)[0]
</code></pre>
<p>In the limit of large <code>N</... | <p>You're using <code>histogram</code> wrong. The bins aren't where you think they are. They don't go from 0 to 50; they go from the minimum input value to the maximum input value. The 0s represent bins that lie entirely between two integers.</p>
<p>Try it with <code>numpy.bincount</code>:</p>
<pre><code>In [31]: n =... | python|numpy|random|statistics | 5 |
374,757 | 47,836,295 | Tensorflow. ValueError: The two structures don't have the same number of elements | <p>My current code for implementing encoder lstm using <code>raw_rnn</code>. This question is also related to another question I asked before (<a href="https://stackoverflow.com/questions/47835350/tensorflow-raw-rnn-retrieve-tensor-of-shape-batch-x-dim-from-embedding-matrix">Tensorflow raw_rnn retrieve tensor of shape ... | <p>Resolved the problem by changing initial state and input:</p>
<blockquote>
<p>init_input = tf.zeros([batch_size, input_embedding_size], dtype=tf.float32)</p>
<p>init_cell_state = cell.zero_state(batch_size, tf.float32)</p>
</blockquote>
<pre><code>def loop_fn_initial():
init_elements_finished = (0 >=... | python|machine-learning|tensorflow|nlp|lstm | 2 |
374,758 | 47,901,313 | tensorflow.python.framework.errors_impl.NotFoundError: Op type not registered 'FertileStatsResourceHandleOp' | <p>I trying to run some example for tensorflow but it not works since such exception. I have no idea how to solve it?</p>
<p>Probably I need install some packages or this example is not compatible with Tensorflow 1.4. I am using Windows 10, no cuda, Python 3.6.</p>
<p>Is it require other version Tensorflow or it is n... | <p>I ran into the same issue today.
I resolved it by updating my installed version of Tensorflow (or Tensorflow-GPU)
In my case, I was on 1.8 and updating to 1.9 fixed it.</p>
<p>All things aside, check your Tensorflow version and adjust as necessary
Steve</p> | python|tensorflow | 0 |
374,759 | 47,620,346 | Python Pandas: How can I count the number of times a value appears in a column based upon another column? | <p><a href="https://i.stack.imgur.com/iB5HO.jpg" rel="nofollow noreferrer">This is my pandas dataframe I am referring to.</a></p>
<p>Basically I would like to be able to display a count on <code>'crime type'</code> based on <code>'council'</code>. So for example, where <code>'council == Fermanagh and omagh'</code> cou... | <p>I believe you need <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html" rel="nofollow noreferrer"><code>groupby</code></a> with <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.size.html" rel="nofollow noreferrer"><code>size</code></a... | python|pandas|dataframe|count | 3 |
374,760 | 47,698,020 | How to populate Pandas dataframe as function of index and columns | <p>I have a dataframe where the index and column are both numbers--i.e.</p>
<pre><code>rng = np.arange(2,51)
box = pd.DataFrame(index = rng, columns = rng)
</code></pre>
<p>I want the values of the dataframe to be a function of the index and column--so for instance box[2][2] should equal 4.</p>
<p>Currently I have i... | <p>Here's what I think you should be doing with numpy broadcasting:</p>
<pre><code>import numpy
import pandas
rng = numpy.arange(2, 51)
box = pandas.DataFrame(index=rng, columns=rng, data=rng*rng[:, None])
</code></pre>
<p>In the case that knowing all of the value ahead of time isn't feasible, you could assign them ... | python|pandas|dataframe | 3 |
374,761 | 47,736,468 | how to use custom calculation based on two dataframes in python | <p>I have 2 dataframes as below,</p>
<p>df1</p>
<pre><code>index X1 X2 X3 X4
0 6 10 6 7
1 8 9 11 13
2 12 13 15 11
3 8 11 7 6
4 11 7 6 6
5 13 14 11 10
</code></pre>
<p>df2</p>
<pre><code>index Y
0 20
1 14
2 17
3 14
4 15
5 20
</code></pre>
<p>I want to get 3rd... | <p>You can use numpy for vectorized solution i.e </p>
<pre><code>df[df.columns] = (df2.values - df2.values/df.values)
X1 X2 X3 X4
index
0 16.666667 18.000000 16.666667 17.142857
1 12.250000 12.444444 12.727273 12.923077
2 ... | python|pandas|numpy|dataframe | 1 |
374,762 | 47,612,822 | How to create pandas dataframe from Twitter Search API? | <p>I am working with the Twitter Search API which returns a dictionary of dictionaries. My goal is to create a dataframe from a list of keys in the response dictionary.</p>
<p>Example of API response here: <a href="https://developer.twitter.com/en/docs/tweets/search/api-reference/get-search-tweets.html" rel="nofollow ... | <p>I will share a more generic solution that I came up with, as I was working with the Twitter API. Let's say you have the ID's of tweets that you want to fetch in a list called <code>my_ids</code> :</p>
<pre><code># Fetch tweets from the twitter API using the following loop:
list_of_tweets = []
# Tweets that can't be... | python|api|pandas|twitter | 1 |
374,763 | 47,612,069 | Tensorflow Serving: Large model, protobuf error | <p>I am trying to make serve a big (1.2 GB in size) model with Tensorflow Serving, but I am getting a:</p>
<pre><code>2017-12-02 21:55:57.711317: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:236] Loading SavedModel from: ...
[libprotobuf ERROR external/protobuf_archive/src/google/protobuf/io/coded_str... | <p>Hope it helps someone, but I "found" a solution.</p>
<p>The major problem was obvious; his is an NLP model, thus it has a big vocabulary that goes along with it. Leaving the vocabulary in the graph definition bloats the metagraphdef, and protobuf is giving an error when faced with such a big protocol. </p>
<p>The ... | c++|machine-learning|tensorflow|deep-learning|tensorflow-serving | 2 |
374,764 | 47,985,750 | Extracting data/string from Pandas DF column | <p>Im trying to extract currency pairs from the poloniex API using Python pandas.</p>
<p>I believe the data returned is all just a single column name:</p>
<pre><code>Columns: [{"BTC_BCN":{"BTC":"479.74697466", "BCN":"1087153595.32266165"}, "BTC_BELA":{"BTC":"32.92293515", "BELA":"1807337.13247948"}, "BTC_BLK":{"BTC":... | <p>You don't need <code>BeautifulSoup</code> here at all. <em>The contents of the webpage is JSON</em> - parse it with <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_json.html" rel="nofollow noreferrer"><code>.read_json()</code></a> directly:</p>
<pre><code>df = pd.read_json('https://polon... | python|pandas|beautifulsoup | 0 |
374,765 | 47,736,531 | Vectorized matrix manhattan distance in numpy | <p>I'm trying to implement an efficient vectorized <code>numpy</code> to make a Manhattan distance matrix. I'm familiar with the construct used to create an efficient Euclidean distance matrix using dot products as follows:</p>
<pre><code>A = [[1, 2]
[2, 1]]
B = [[1, 1],
[2, 2],
[1, 3],
[1, 4]]... | <p>I don't think we can leverage BLAS based matrix-multiplication here, as there's no element-wise multiplication involved here. But, we have few alternatives.</p>
<p><strong>Approach #1</strong></p>
<p>We can use <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html" rel="no... | python|numpy|vectorization | 18 |
374,766 | 47,583,428 | Pandas duplicates when grouped | <pre><code>x = df.groupby(["Customer ID", "Category"]).sum().sort_values(by="VALUE", ascending=False)
</code></pre>
<p>I want to group by Customer ID but when I use above code, it duplicates customers...</p>
<p>Here is the result:</p>
<p><a href="https://i.stack.imgur.com/y4UDn.png" rel="nofollow noreferrer"><img sr... | <p>I think you are looking for something like this:</p>
<pre><code>df_out = df.groupby(['Customer ID','Category']).sum()
df_out.reindex(df_out.sum(level=0).sort_values('Value', ascending=False).index,level=0)
</code></pre>
<p>Output:</p>
<pre><code> Value
Customer ID Category
B ... | pandas|pandas-groupby | 2 |
374,767 | 47,766,805 | pandas only shows date and drops time when I select data from database | <p>how are you?</p>
<p>I'm new to pandas and I have faced a problem where I'm using <code>read_sql</code>.</p>
<pre><code>df = pd.read_sql("select TIME, col_1, col_2 from TABLE", connection)
</code></pre>
<p>A real database has TIME data which looks like below.</p>
<pre><code> TIME
2017-12-08 00:00:00
2017-... | <p>Have you tried accessing the data manually to see if there is a seconds data? i.e.</p>
<pre><code>print(df['TIME'][2].second)
</code></pre>
<p>If this data exists, then what you see is just the <code>__str__</code> representation of <code>M8[ns]</code> objects when your print your dataframe.</p> | python|pandas | 0 |
374,768 | 47,965,026 | Why is my date axis formatting broken when plotting with Pandas' built-in plot calls as opposed to via Matplotlib? | <p>I am plotting aggregated data in Python, using Pandas and Matlplotlib.
My axis customization commands are failing as a function of which of two similar functions I'm calling to make bar plots. The working case is e.g.:</p>
<pre><code>import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot a... | <p>Bar plots in pandas are designed to compare categories rather than to display time-series or other types of continuous variables, as stated <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.bar.html" rel="nofollow noreferrer">in the docstring</a>:</p>
<blockquote>
<p>A bar plo... | python|pandas|matplotlib | 1 |
374,769 | 47,721,663 | How to use tf.nn.crelu in tensorflow? | <p>I am trying different activation functions in my simple neural network. </p>
<p>It does not matter using <code>tf.nn.relu</code>, <code>tf.nn.sigmoid</code>,... the network does what it should do. </p>
<p>But if I am using <code>tf.nn.crelu</code>, I have a dimension error.</p>
<p>It returns something like <code>... | <p>You're right, if you're building the network manually, you need to adjust the dimensions of the following layer to match <code>tf.nn.crelu</code> output. In this sense, <code>tf.nn.crelu</code> is <em>not</em> interchangeable with <code>tf.nn.relu</code>, <code>tf.nn.elu</code>, etc.</p>
<p>The situation is simpler... | machine-learning|tensorflow|computer-vision|activation-function | 1 |
374,770 | 47,775,927 | Pandas Transform Position/Rank in Group | <p>I have the following <code>DataFrame</code> with two groups of animals and how much food they eat each day,</p>
<pre><code>df = pd.DataFrame({'animals': ['cat', 'cat', 'dog', 'dog', 'rat',
'cat', 'rat', 'rat', 'dog', 'cat'],
'food': [1, 2, 2, 5, 3, 1, 4, 0, 6, 5]},... | <p>Use double <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.transform.html" rel="nofollow noreferrer"><code>transform</code></a>:</p>
<pre><code>df['daily_meal'] = df.groupby(['animals', 'groups'])['food'].transform('mean')
df['group_rank'] = df.groupby('groups')['daily_mea... | python|pandas|pandas-groupby | 6 |
374,771 | 47,860,633 | TensorFlow pip install not working on Windows 10 | <p>I have spent a lot of time tying to install tensorflow for windows. I keep getting errors like "not supported."</p>
<p>I have tried the commands:</p>
<pre><code>pip install tensorflow
</code></pre>
<p>and</p>
<pre><code>pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.0... | <p>Note that TensorFlow needs x64 Windows and that Python 3.5 and higher go with <code>pip3</code> instead of <code>pip</code>. Still, there's a glitch with the installation script; I ran into the same problem and resolved it by using <a href="https://www.anaconda.com/distribution/#download-section" rel="nofollow noref... | python|tensorflow|pip | 0 |
374,772 | 47,714,805 | Predicting the future with pandas and statsmodels | <p>What i need to do is plot future temperature with these "requirments" : " assume that temperature is roughly a linear function of CO2 emission,
estimating the coefficients of the linear function from recent data points (using
the past 2 is fine, as is using the past 10 or so if you want to be more thorough).
Furthe... | <p>The method <a href="http://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLS.predict.html#statsmodels.regression.linear_model.OLS.predict" rel="nofollow noreferrer"><code>OLS.predict</code></a> do not take <code>x</code> as arguments but the model parameters (and eventually exogenous data). B... | python|pandas|statsmodels | 4 |
374,773 | 47,708,345 | Pandas substring search for filter | <p>I have a use case where I need to validate each row in the df and mark if it is correct or not. Validation rules are in another df.</p>
<pre><code>Main
col1 col2
0 1 take me home
1 2 country roads
2 2 country roads take
3 4 me home
Rules
col3 col4
0 1 take
1 2 home
2 ... | <p>Merge and then apply the condtion using np.where i.e </p>
<pre><code>temp = main.merge(rules,left_on='col1',right_on='col3')
temp['results'] = temp.apply(lambda x : np.where(x['col4'] in x['col2'],'Pass','Fail'),1)
no_dupe_df = temp.drop_duplicates('col2',keep='last').drop(['col3','col4'],1)
col1 ... | python|pandas|dataframe | 1 |
374,774 | 49,222,772 | Pandas: frequencies per date grouped by column in a form of a list | <p>I would like to obtain frequencies of technologies per date from pandas data frame. A reproducible example:</p>
<pre><code>data = pd.DataFrame(
{'dates': ['2017-01-31', '2017-02-28', '2017-02-28'],
'tech': [['c++', 'python'], ['c++', 'c', 'java'], ['java']]}
)
</code></pre>
<p>The end resul... | <p>Seems like you need <code>get_dummies</code></p>
<pre><code>pd.get_dummies(data.set_index('dates').tech.apply(pd.Series).stack()).sum(level=0)
Out[193]:
c c++ java python
dates
2017-01-31 0 1 0 1
2017-02-28 1 1 2 0
</code></pre>
<p>Or <code>skl... | python|string|list|pandas|pandas-groupby | 2 |
374,775 | 49,214,286 | Find common values between 3 DataFrames? | <p>I have 3 dataframes: df1, df2, and df3. </p>
<pre><code>df1 = 'num' 'type'
23 a
34 b
89 a
90 c
df2 = 'num' 'type'
23 a
34 b
56 a
90 c
df3 = 'num' 'type'
56 a
34 s
71 a
90 ... | <p>et voila my friend</p>
<pre><code>df_full = pd.concat([df1,df2,df3], axis = 0)
df_agg = df_full.groupby('num').agg({'type': 'count'})
df_agg = df_agg.loc[df_agg['type'] >= 2]
</code></pre> | python|pandas|dataframe|counter | 4 |
374,776 | 49,144,138 | How to subtract numbers in 1 column of a pandas dataframe? | <p>Currently, I am using Pandas and created a dataframe that has two columns: </p>
<pre><code>Price Current Value
1350.00 0
1.75 0
3.50 0
5.50 0
</code></pre>
<p>How Do I subtract the first value, and then subtract the sum of the previous two values, continuously (Similar to... | <p>This will achieve what you need , <code>cumsum</code></p>
<pre><code>1350*2-df.Price.cumsum()
Out[304]:
0 1350.00
1 1348.25
2 1344.75
3 1339.25
Name: Price, dtype: float64
</code></pre>
<p>After assign it back </p>
<pre><code>df.Current=1350*2-df.Price.cumsum()
df
Out[308]:
Price Current
0 13... | python|pandas|dataframe | 2 |
374,777 | 48,924,041 | Remove a row in 3d array | <p>given the index of a row, l would like to remove that row.</p>
<p>l tried the following :</p>
<pre><code>a.shape
Out[128]: (60, 3)
</code></pre>
<p>when l try to remove the row number 14 from my 3D array <strong>a</strong> as follow :</p>
<pre><code>np.delete(a,14,axis=0)
a.shape
Out[130]: (60, 3)
</code></pre>
... | <p>Assignment will solve it as apparently <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.delete.html" rel="nofollow noreferrer">delete</a> returns an array instead of working inplace:</p>
<pre><code>a = np.delete(a,14,axis=0)
</code></pre> | python-3.x|numpy | 1 |
374,778 | 49,316,668 | Can we combine conditional statements using column indexes in Python? | <p><strong>For the following dataframe:</strong></p>
<hr>
<p><strong>Headers:</strong> Name P1 P2 P3</p>
<hr>
<p><strong>L1:</strong> A 1 0 2</p>
<hr>
<p><strong>L2:</strong> B 1 1 1</p>
<hr>
<p><strong>L3:</strong> C 0 5 6</p>
<p>I want to get yes where all P1, P2 and P3 are greater than... | <p>I think need <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.all.html" rel="nofollow noreferrer"><code>DataFrame.all</code></a> for check all <code>True</code>s per rows:</p>
<pre><code>cols = ['P1','P2','P3']
df['Check']= np.where((df[cols] > 0).all(axis=1),'Yes','No')
print (df)... | python|pandas|numpy | 0 |
374,779 | 48,919,302 | Join in near index python | <p>Due to the lack of power at the station I use the meteorological data, I do not have the schedules and I need to create these schedules with <code>nan</code>. I can create the times normally (times where they are of frequency of <code>10 Hz</code>). But when the station comes back to work the rounding of the date th... | <p>Solved using </p>
<pre><code>df.index = df.index.round('0.1S')
</code></pre> | python|pandas | 0 |
374,780 | 49,097,905 | Referring to previous groups in Pandas SeriesGroupBy | <p>I'm writing a Python script which compares the maximum values of each group. I think there must be more beautiful ways using methods offered by <code>pandas</code> or not using global variables such as <code>previous_max</code> in the following code snippet. Please tell me how to do it.</p>
<pre><code>import pandas... | <p>Use:</p>
<ul>
<li><a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.transform.html" rel="nofollow noreferrer"><code>transform</code></a> by <code>max</code>, get difference by <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.diff.html" rel="nofoll... | python|pandas|pandas-groupby | 0 |
374,781 | 49,200,989 | calculate value based on value from previous value | <p>I have the following dataset given, describing transactions for purchasing items in a frame (frameNo). the frame encompasses events that happened in a single minute. therefore, the "currentGold" value only depicts the value of gold a player has had entering the frame. </p>
<p>What I am trying to do, is to calculate... | <p>Is it possible that you have an error in your "desired" output in the following rows?</p>
<pre><code>948881246 BR1 12.0 701438 1051 850 400 650
948881246 BR1 12.0 701796 1042 850 300 350
948881246 BR1 12.0 703291 2010 ... | python-3.x|pandas|pandas-groupby|cumulative-sum | 1 |
374,782 | 49,273,684 | output is in NAN Wrong distance not be calculated | <pre><code>Data input:
cell_id Lat_Long Lat Long
15327 28.46852_76.99512 28.46852 76.99512
52695 28.46852_76.99512 28.46852 76.99512
52692 28.46852_76.99512 28.46852 76.99512
29907 28.46852_76.99512 28.46852 76.99512
29905 28.46852_76.99512 28.46852 76.99512
<... | <p><code>Geo.Inverse</code> returns a dictionary not a single value. Check the <a href="https://geographiclib.sourceforge.io/html/python/code.html" rel="nofollow noreferrer">documentation</a>.</p>
<p>The distance is returned with the key <code>s12 – the distance from the first point to the second in meters</code></p>
... | python|pandas|geodesic-sphere | 0 |
374,783 | 48,952,625 | Split every row containing long text into multiple rows in pandas | <p>I have a DataFrame which has a string column such as below:</p>
<pre><code>id text label
1 this is long string with many words 1
2 this is a middle string 0
3 short string 1
</code></pr... | <p>You could</p>
<pre><code>In [1257]: n = 3
In [1279]: df.set_index(['label', 'id'])['text'].str.split().apply(
lambda x: pd.Series([' '.join(x[i:i+n]) for i in range(0, len(x), n)])
).stack().reset_index().drop('level_2', 1)
Out[1279]:
label id 0
0 1 1 this... | python|pandas | 1 |
374,784 | 49,127,947 | how to change a object-form array to a normal one | <p>Here is a <code>.txt</code> data file,where the first 2 lines are some headers: </p>
<pre><code> REC OBS REPORT TIME STATION LATI- LONGI- ELEV STN PR STN DSLP ALTIM AIR.T DEWPT R.HUM WIND WIND HOR 3H PR 24H PR | ... | <p>Maybe you should call <code>delim_whitespace=True</code> and manually rewrite your <code>columns</code>.</p>
<pre><code>obs = pd.read_table('sample.tex', header=1,delim_whitespace=True).drop('|',axis=1)
obs.columns = ['REC_TYPE','OBS_TYPE','REPORT_TIME','STATION_BBSSS','LATITUDE','LONGITUDE','ELEV','STN_PR',
... | python|pandas|numpy | 1 |
374,785 | 49,122,116 | Error importing keras backend - cannot import name has_arg | <p>i attempt to import keras backend to get_session as follows, but i encounter an error:
<a href="https://i.stack.imgur.com/CQHYI.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/CQHYI.png" alt="enter image description here"></a></p> | <p>There should be no need to import the tensorflow_backend explicitly.</p>
<p>Look at the first lines of an example from the <a href="https://keras.io/backend/" rel="nofollow noreferrer">Keras documentation</a>:</p>
<pre><code># TensorFlow example
>>> from keras import backend as K
>>> tf_session =... | tensorflow|keras | 1 |
374,786 | 49,141,730 | JSON Normalize On Extremely Nested JSON Data Structure | <p>I have the following JSON data structure. I am trying to get it into a Pandas DataFrame. </p>
<p>The pandas.io.json json_normalize works OK, except for the 'tunnels-in' and 'tunnels-out' sections. These are lists with some nested dictionaries inside of them. I have tried almost every format of the json_normaliz... | <p>I'm struggling with this right now. I got around that selection by index <code>[0]</code> issue by pulling it out into another pandas.dataframe. As such:</p>
<pre><code>df = json_normalize(pd.DataFrame(list(json_dict['data']['viptela-oper-vpn']['dpi']['flows']))['tunnels-in'])
</code></pre> | python|json|pandas | 0 |
374,787 | 48,920,430 | Resampling hourly data to 6 hours | <pre><code> Timestamp Value
0 2017-11-22 09:00:00 12.356965
1 2017-11-22 10:00:00 26.698426
2 2017-11-22 11:00:00 13.153104
3 2017-11-22 12:00:00 15.425182
4 2017-11-22 13:00:00 15.161085
5 2017-11-... | <p>You can use <code>rolling.mean</code>:</p>
<pre><code>df.set_index('Timestamp').rolling('6h').mean()
Value
Timestamp
2017-11-22 09:00:00 12.356965
2017-11-22 10:00:00 19.527696
2017-11-22 11:00:00 17.402832
2017-11-22 12:00:00 16.908419
2017-11-22 13:00:00 16.5589... | python|pandas | 3 |
374,788 | 49,108,757 | Error installing pandas and quandl via pip - windows | <p>Goodmorning, I'm trying installing pandas and quandl. I used <code>pip install quandl</code> and <code>pip install pandas</code> but the feedback is for both:</p>
<pre><code>Command "C:\Python34\python.exe -u -c "import setuptools, tokenize;__file__='C:\\Users\\tomsa\\AppData\\Local\\Temp\\pip-build-m2kos9k2\\panda... | <p>Solved installing them manually from <a href="http://www.lfd.uci.edu/~gohlke/pythonlibs/#pandas" rel="nofollow noreferrer">here</a> via </p>
<pre><code>pip install pandas‑0.20.3‑cp34‑cp34m‑win_amd64.whl
</code></pre>
<p>in the directory (normally X:\Users\Download in windows if the wheel has not been moved somewhe... | python-3.x|pandas|pip|quandl | 1 |
374,789 | 49,204,671 | Pandas : Boolean indexing on multiple columns | <p>I have a data frame as below.</p>
<pre><code>In [23]: data2 = [{'a': 'x', 'b': 'y','c':'q'}, {'a': 'x', 'b': 'p', 'c': 'q'}, {'a':'p', 'b':'q'},{'a':'q', 'b':'y','c':'q'}]
In [26]: df = pd.DataFrame(data2)
In [27]: df
Out[27]:
a b c
0 x y q
1 x p q
2 p q NaN
3 q y q
</code></pre>
<p>I wan... | <p>You can compare your df with 'x' and 'y' and then do a logical or to find rows with either 'x' or 'y'. Then use the boolean array as index to select those rows.</p>
<pre><code>df.loc[(df.eq('x') | df.eq('y')).any(1)]
Out[68]:
a b c
0 x y q
1 x p q
3 q y q
</code></pre> | python|pandas | 2 |
374,790 | 49,165,672 | Using pandas to find the max value for specific rows | <p>I've got a csv that looks like this (there are more years):</p>
<pre><code>year,title_field,value
2009,Total Housing Units,39499
2009,Vacant Housing Units,3583
2009,Occupied Housing Units,35916
2008,Total Housing Units,41194
2008,Vacant Housing Units,4483
2008,Occupied Housing Units,36711
2009,Owner Occupied,18057
... | <p>I think you need <code>idxmax</code></p>
<pre><code>df.loc[[df.groupby(['title_field'])['value'].idxmax().loc['Vacant Housing Units']]]
Out[92]:
year title_field value
4 2008 Vacant Housing Units 4483
</code></pre> | python|pandas|csv | 1 |
374,791 | 49,277,640 | How to extract values from a Pandas DataFrame, rather than a Series (without referencing the index)? | <p>I am trying to return a specific item from a Pandas DataFrame via conditional selection (and do not want to have to reference the index to do so).</p>
<p>Here is an example:</p>
<p>I have the following dataframe:</p>
<pre><code> Code Colour Fruit
0 1 red apple
1 2 orange orange
2 3 yellow ban... | <p>Let's try this:</p>
<pre><code>df.loc[df['Fruit'] == 'blueberry','Code'].values[0]
</code></pre>
<p>Output:</p>
<pre><code>5
</code></pre>
<p>First, use <code>.loc</code> to access the values in your dataframe using the boolean indexing for row selection and index label for column selection. The convert that re... | python|pandas|dataframe | 5 |
374,792 | 49,143,496 | compare two pandas columns of mixed data datatypes | <p>i have a dataframe as follows, column A and Refer has values of types str,float and int. i have to compare and create a new column if both values same then pass or else fail. it is very simple if all values string datatype but before compare any numeric value in column A must be rounded of, if its a decimal and ends... | <p>IIUC, there are lot of build in function in pandas </p>
<p>Update <code>to_numeric</code></p>
<pre><code>df.apply(pd.to_numeric,errors='ignore',axis=1).nunique(1).eq(1).map({True:'Pass',False:'Fail'})
Out[272]:
0 Pass
1 Pass
2 Fail
3 Pass
4 Pass
5 Fail
6 Fail
7 Pass
dtype: object
</code></... | python|pandas | 1 |
374,793 | 49,232,854 | Feature Selection using MRMR | <p>I found two ways to implement MRMR for feature selection in python. The source of the paper that contains the method is: </p>
<p><a href="https://www.dropbox.com/s/tr7wjpc2ik5xpxs/doc.pdf?dl=0" rel="noreferrer">https://www.dropbox.com/s/tr7wjpc2ik5xpxs/doc.pdf?dl=0</a></p>
<p>This is my code for the dataset.</p>
... | <p>You'll probably need to contact either the authors of the original paper and/or the owner of the Github repo for a final answer, but most likely the differences here come from the fact that you are comparing 3 different algorithms (despite the name).</p>
<p><a href="https://en.wikipedia.org/wiki/Minimum_redundancy_... | python|pandas|numpy | 2 |
374,794 | 49,207,577 | DataFrame to List format | <p>Im converting DataFrame into List using following code</p>
<pre><code>resultDataFrame = []
resultDataFrame = pd.DataFrame(resultDataFrame, columns=('day', 'hour', 'minute'))
</code></pre>
<p>and appending values ...</p>
<pre><code>resultDataFrame.to_dict('l')
{'day': [6], 'hour': [12], 'minute': [54]}
</code></pr... | <p>You can select first row for <code>Series</code> e.g. by <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.iloc.html" rel="nofollow noreferrer"><code>iloc</code></a> or <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.loc.html" rel="nofollow noreferrer"><c... | python|pandas | 2 |
374,795 | 49,150,462 | getting the top 2 values of a list | <p>In the below dataframe I have a list of values, how can we get the top 2 repeated from a list ?</p>
<p>DataFrame:</p>
<pre><code>user pro
A [AA,AA,AA,BB,CC,AA,AA,CC,CC,BB]
B [AA, BB, EE,BB,BB,EE,AA,CC,BB,EE]
C [EE,EE,EE,CC,CC,CC,CC,DD,DD,AA]
D [DD,AA,AA,AA,AA,AA,BB,BB,BB]
</code></pre>
<p>Expected outp... | <p>Use <a href="https://docs.python.org/3/library/collections.html#collections.Counter.most_common" rel="nofollow noreferrer"><code>collections.Counter.most_common</code></a>:</p>
<pre><code>from collections import Counter
df['new'] = df['pro'].apply(lambda x: [k for k, v in Counter(x).most_common(2)])
print (df)
... | python|pandas | 4 |
374,796 | 49,038,659 | How to substitute NaNs in a numpy array with elements in another list | <p>I'm facing an issue with a basic substitution. I have two arrays, one of them contains numbers and NaN, and the other one numbers that are supposed to replace the NaN, obviously ordered as I wish. As an example:
<code>x1 = [NaN, 2, 3, 4, 5, NaN, 7, 8, NaN, 10]</code> and
<code>fill = [1, 6, 9]</code> and I want to o... | <p>Does this work for you?</p>
<pre><code>train = np.array([2, 4, 4, 8, 32, np.NaN, 12, np.NaN])
fill = [1,3]
train[np.isnan(train)] = fill
print(train)
</code></pre>
<p>Output:</p>
<pre><code>[ 2. 4. 4. 8. 32. 1. 12. 3.]
</code></pre> | python|python-3.x|numpy | 4 |
374,797 | 49,158,505 | Visualizing spherical harmonics in Python | <p>I am trying to draw a spherical harmonics for my college project. The following formula I want to depict,</p>
<pre><code>Y = cos(theta)
</code></pre>
<p>for that, I wrote this code</p>
<pre><code>import numpy as np
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
def sph2cart(r, phi, tta):... | <p>The picture in the Wikipedia article <a href="https://en.wikipedia.org/wiki/Spherical_harmonics" rel="nofollow noreferrer">Spherical harmonics</a> is obtained by using the <em>absolute value</em> of a spherical harmonic as the r coordinate, and then coloring the surface according to the sign of the harmonic. Here is... | python|python-3.x|numpy | 4 |
374,798 | 49,082,287 | Read text file data to pandas DataFrame | <p>I have specific file format from CNC (<em>work center</em>) data. saved like .txt .
I want read this table to pandas dataframe but i never seen this format before.</p>
<pre><code>_MASCHINENNUMMER : >0-251-11-0950/51< SACHBEARB.: >BSTWIN32<
_PRODUKTSCHLUESSEL : >BST 500< DATUM ... | <p>Yes, it is possible, but really data dependent:</p>
<ul>
<li>first <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html" rel="nofollow noreferrer"><code>read_csv</code></a> with omit first <code>3</code> rows and omit first whitespaces</li>
<li>omit trailing whitespaces in columns by ... | python|pandas | 6 |
374,799 | 48,923,565 | math.fsum for arrays of multiple dimensions | <p>I have a numpy array of dimension <code>(i, j)</code> in which I would like to add up the first dimension to receive a array of shape <code>(j,)</code>. Normally, I'd use NumPy's own <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html" rel="nofollow noreferrer"><code>sum</code></a></p>
<p... | <p>This one works fast enough for me.</p>
<pre><code>import numpy
import math
a = numpy.random.rand(100, 77)
a = numpy.swapaxes(a, 0, 1)
a = numpy.array([math.fsum(row) for row in a])
</code></pre>
<p>Hopefully it's the axis you are looking for (returns 77 sums).</p> | python|arrays|numpy | 2 |
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