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 |
|---|---|---|---|---|---|---|
350,900 | 55,281,499 | Pytorch RNN HTML Generation | <p>I’ m stuck for a couple of days trying to make and <strong>RNN network</strong> to <strong>learn</strong> a <strong>basic HTML template</strong>.
I tried different approaches and I even <strong>overfit</strong> on the following data:</p>
<pre><code><!DOCTYPE html>
<html>
<head>
<title>Page T... | <p>First of all for a GRU (RNN) to be efficient, you may need more data to train. </p>
<p>Second, it seems that you have a problem with the embedding. It looks like, the mapping vocabulary['id2letter'] does not work, otherwise you would obtain
sequences of tags like <code><head><title><title><tit... | python|html|nlp|pytorch|recurrent-neural-network | 0 |
350,901 | 55,213,452 | Tensorflow: stacking subarrays in a tensor | <p>I have a tensor that looks like:</p>
<pre><code>array([[[ 1, 2, 3],
[ 3, 4, 5]],
[[11, 22, 33],
[33, 44, 55]]], dtype=int32)
</code></pre>
<p>I would like to concatenate/stack the values at each index in the inner array so it looks like:</p>
<pre><code>array([[[1, 3], [2, 4], [3, ... | <p>You can use <code>tf.transpose()</code>:</p>
<pre><code># t
# array([[[ 1, 2, 3],
# [ 3, 4, 5]],
# [[11, 22, 33],
# [33, 44, 55]]])
tf.transpose(t, perm=[0, 2, 1])
# array([[[ 1, 3],
# [ 2, 4],
# [ 3, 5]],
# [[11, 33],
# [22, 44],
# [33, 55]]])... | python|tensorflow | 2 |
350,902 | 55,457,816 | How to check if a numpy array contain a list of numbers? | <p>I have a numpy array with another arrays inside and I want to know how I can check if all the values of another numpy array (Or list) are the same of the first one.</p>
<pre><code>array1 = np.array([[11,3,4,6,7,8,9,1,2], [6,7,2,1,9,5,3,4,8]])
array2 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
</code></pre>
<p>I tried ... | <p>you are currently checking to see if any of the arrays match.</p>
<p>if you want False and True, you need an elementwise comparison. done via a list comprehension:</p>
<pre><code>[all(array2 == arr) for arr in np.sort(array1)]
</code></pre>
<p>which gives <code>[False, True]</code></p>
<p>the <code>all()</code> ... | python|arrays|numpy|multidimensional-array | 3 |
350,903 | 55,157,832 | What am I trying to do here? train acc: 100%, test acc: 80% does this mean overfitting? | <pre><code>classifier.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
classifier.fit(X_train, y_train, epochs=50, batch_size=100)
Epoch 1/50
27455/27455 [==============================] - 3s 101us/step - loss: 2.9622 - acc: 0.5374
</code></pre>
<p>I know I'm compiling my model in firs... | <p>Ok, let's begin from the top,</p>
<p>First, <code>metrics = ['accuracy']</code>, The model can be evaluated on multiple parameters, accuracy is one of the metrics, other can be <code>binary_accuracy</code>, <code>categorical_accuracy</code>, <code>sparse_categorical_accuracy</code>, <code>top_k_categorical_accuracy... | python|tensorflow|machine-learning|keras|deep-learning | 2 |
350,904 | 55,399,080 | Returning specific elements with Dataset api | <p>i wrote a tfrecord file in which i have images and their labels.Then i can pick them up using</p>
<pre><code> def parserTrain(record):
keys_to_features = {
"image_raw": tf.FixedLenFeature((), tf.string, default_value=""),
"label": tf.FixedLenFeature((), tf.int64,
... | <p>Replying to my question as i found the answer.
the correct syntax for a filter function to a dataset of tuples is the following:</p>
<pre><code>def f(im, label):
return tf.equal(label, 1)
ds1 = dataset.filter(f)
</code></pre> | python-3.x|tensorflow|tensorflow-datasets|tfrecord | 0 |
350,905 | 55,259,255 | Change pandas column based on another column | <p>I have a pandas DataFrame that contains looks like this:</p>
<pre><code>A A_type
"Hello" String
15 Integer
"Hi" String
56.78 Float
</code></pre>
<p>I want to create a third column that reports the same value as A if A has the corresponding "A_type" element named "String", print "blank" ot... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.where.html" rel="nofollow noreferrer"><code>Series.where</code></a> or
<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.mask.html" rel="nofollow noreferrer"><code>Series.mask</code></a> with inverted... | python|pandas|for-loop|conditional|multiple-columns | 4 |
350,906 | 55,205,750 | Unable to Normalize Tensor in PyTorch | <p>I am trying to normalize the tensor outputted by my network but am getting an error in doing so. The code is as follows:</p>
<pre class="lang-py prettyprint-override"><code>device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_load_path = r'path\to\saved\model\file'
model.... | <p>In order to apply <code>transforms.Normalize</code> you have to convert the input to a tensor. For this you can use <a href="https://pytorch.org/docs/stable/torchvision/transforms.html#torchvision.transforms.ToTensor" rel="nofollow noreferrer"><code>transforms.ToTensor</code></a>.</p>
<pre class="lang-py prettyprint... | pytorch | 2 |
350,907 | 55,142,210 | Can't use loaded model in Keras with Tensorflow with multithreaded environment | <p>I have two threads, one handling the training, and the other one is handling the estimations. I have several entities and I would like to have a model for every entity, so I load and save models "on the fly" (I know this is quite slow). </p>
<p>If I load the model every time I want to call to the predict function, ... | <p>After investigating and more trial/error, I have found a solution.</p>
<p>About the Session Cancelled Error, the answer 1 for this <a href="https://stackoverflow.com/questions/50895110/what-do-i-need-k-clear-session-and-del-model-for-keras-with-tensorflow-gpu">question</a> could be useful:</p>
<blockquote>
<p>K.... | python|multithreading|tensorflow|keras | 0 |
350,908 | 55,537,857 | Faster alternative to execute a for loop on a data frame? | <p>I have a data frame, df that has 10 million rows. I am running the below loop that takes a lot of time to execute. Can there be a faster way to do the same task?</p>
<pre><code>for i in range(len(df)):
if df['col_1'][i] in ('a','b', 'c', 'd', 'e'):
df.at[i,'col_2']=1
else:
df... | <p>You can use <a href="https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.where.html" rel="nofollow noreferrer">numpy.where</a> to set values using boolean logic:</p>
<pre><code>import numpy as np
df["col2"] = np.where(df["col1"].isin(('a','b', 'c', 'd', 'e')), 1, 0)
</code></pre> | python|pandas | 2 |
350,909 | 55,332,669 | 'Sequential' object has no attribute 'loss' - When I used GridSearchCV to tuning my Keras model | <p>I am using <code>tensorflow</code> and <code>keras</code> to build a simple MNIST classification model, and I want to fine tune my model, so I choose <code>sklearn.model_selection.GridSearchCV</code>.</p>
<p>However, when I call the <code>fit</code> function, it said: </p>
<p><code>AttributeError: 'Sequential' obj... | <p>The <code>build_model</code> function above doesn't configure your <code>model</code> for training. You have added <code>loss</code> and other parameters.</p>
<p>You can compile the model by using keras sequential method <code>compile</code>. <a href="https://keras.io/models/sequential/" rel="nofollow noreferrer">h... | python|tensorflow|keras | 7 |
350,910 | 55,466,893 | Couldn't understand the role of vars() in the code | <p>So in the code, I am sharing multiple stocks data has been downloaded through a user-defined function and user defined function stores the data in CSV format
then the code we have to calculate some stats like hit ratio, daily returns, the total number of trades and some other stuff that's not the problem
the prob... | <p>Using <code>vars()[variable]</code> allows you to use a variable to name another variable.</p>
<p>Usually, a much superior method both in terms of readability and reliability is to use a dictionary.</p> | python|pandas|algorithmic-trading | 0 |
350,911 | 55,533,808 | How to fix 'No space left for this device' error in installing tensorflow using conda? | <p>I am trying to install tensorflow using conda in AWS EC2 instance for several times. But it is always giving below error.
CondaMultiError: [Errno 28] No space left on device</p>
<p>I check df</p>
<pre><code>(base) ubuntu@ip-172-31-23-129:~/anaconda2$ df
Filesystem 1K-blocks Used Available Use% Mounted on
ud... | <p>(I will assume that you actually have space in the filesystem that you're installing to. In my case, I had space but the temp folder got filled up quickly, even though df -h and df -i did not suggest a problem. Since some of the filesystems in your list above are full or near-full, that may not be a good assumption.... | tensorflow|installation|conda | 1 |
350,912 | 55,264,848 | Slicing an array in python, understanding the code | <p>What does this piece of code mean in python?</p>
<pre><code>b[:,2]
</code></pre>
<p>I am not sure what the ,2 part is saying.</p>
<p>Thanks.</p> | <p>It is not a valid slice syntax.. should never be a , inside the [] unless you are using a slice object. </p>
<p>example:</p>
<pre><code>sliceObj = slice(1, 3)
</code></pre> | python|python-3.x|numpy | 0 |
350,913 | 55,459,847 | How would I change the values (type is string) of a series to an int? | <p>So basically I have this dataframe and in this dataframe there is the series 'shape' with the unique values ['cylinder', 'circle', 'light', 'cigar', 'diamond', 'oval', ...] and I want to turn these shapes into numbers so I can use those to make a scatterplot for example.</p>
<p>Is there a way to make another series... | <p>Try <code>sklean</code> <code>LabelEncoder</code> to convert you <code>categorical</code> columns to <code>Numerical</code> , then you can Plot it </p>
<pre><code>import pandas as pd
df = pd.DataFrame(['cylinder', 'circle', 'light', 'cigar', 'diamond', 'oval'])
df.columns = ['shape']
from sklearn.preprocessing imp... | python|numpy|dataframe | 0 |
350,914 | 55,226,703 | Numpy remove duplicate columns with values greater than 0 | <p>I've the following array.</p>
<pre><code>array([[ 0, 0, 0, 0, 0, 3],
[ 4, 4, 0, 0, 0, 0],
[ 0, 0, 0, 23, 0, 0]])
</code></pre>
<p>I am looking to find the unique values column wise such that my result is.</p>
<pre><code>array([[ 0, 0, 0, 0, 3],
[ 4, 0, 0, 0, 0],
[... | <p>This will accomplish what you want:</p>
<pre><code>import numpy as np
import pandas as pd
x = np.array([[ 0, 0, 0, 0, 0, 3],
[ 4, 4, 0, 0, 0, 0],
[ 0, 0, 0, 23, 0, 0]])
df = pd.DataFrame(x.T)
row_sum = np.sum(df, axis=1)
df1 = df[row_sum != 0].drop_duplicates()
df0 = df... | python|numpy | 0 |
350,915 | 55,195,407 | Is there a more concise way of taking the mean of multiple variables based on a specific sub-string from a string | <p>I have variables associated with a name that i want to take the mean of, based on its MainName. Noting that i have more than two MainNames as opposed to the example below, and would look messy doing all of it. So i was wondering if anyone could make this more concise?
Thanks in advance!</p>
<pre><code>fullname = ['... | <p>You can use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.extract.html" rel="nofollow noreferrer"><code>str.extract</code></a> for get substrings with joined substrings from list joined by <code>|</code> for regex <code>OR</code> passed to <code>groupby</code> with aggregating ... | python|pandas|for-loop | 1 |
350,916 | 55,286,406 | python pandas difference between df_train["x"] and df_train[["x"]] | <p>I have the following dataset and reading it from csv file.</p>
<pre><code>x =[1,2,3,4,5]
</code></pre>
<p>with the pandas i can access the array</p>
<pre><code>df_train = pd.read_csv("train.csv")
x = df_train["x"]
</code></pre>
<p>And </p>
<pre><code>x = df_train[["x"]]
</code></pre>
<p>I could wonder since bo... | <p>In pandas, you can slice your data frame in different ways. On a high level, you can choose to select a single column out of a data frame, or many columns.</p>
<p>When you select many columns, you have to slice using a <code>list</code>, and the return is a pandas <code>DataFrame</code>. For example</p>
<pre><code... | python|pandas | 0 |
350,917 | 55,285,139 | Keras share weights between custom layers | <p>I am working with the keras-capsnet implementation of Capsule Networks, and am trying to apply the same layer to 30 images per sample.</p>
<p>The weights are initialized within the <strong>init</strong> and build arguments for the class, shown below. I have successfully shared the weights between the primary routin... | <p>The answer was simple. Set up a layer without calling it on input, and then use that built layer to call the data individually.</p> | python|tensorflow|keras|deep-learning | 1 |
350,918 | 55,254,420 | Future prediction using time series data set with Tensorflow | <p>I have a Time series data for almost 5 years. Using this data I want to forecast next 2 years. How to do this?</p>
<p>I referred many websites regarding this. I noticed that mostly predictions are done only with same set of data used for training they are not forecasting for future such as for next 30 days. If it p... | <p>for all machine learning problem you want to ask yourself the question "What do i want to predict and what data do i have ?"</p>
<p>In your case you want to predict values at an undefined time in the future, let's call that time <em>T</em>.</p>
<p>We suppose that your current data is labelled ie. for each sample/r... | python|tensorflow|time-series|prediction | 1 |
350,919 | 55,209,562 | Pandas: Join two dataframes if substring in df1 exists in string of df2 (if string contains substring) | <p>I have two dataframes, and I would Like to join df1 to df2 where df1 contains a url and df2 contains a list of urls.</p>
<p>The shape of df1 and df2 are different</p>
<p>Example:</p>
<pre><code>df1 = pd.DataFrame({'url': ['http://www.example.jp/pro/sanada16']})
df2 = pd.DataFrame({'urls': ['[https://www.example.j... | <p>Here is one way, if I understand correctly. You can iterate over the patterns you want to search for, and then store the matches using <code>df.at</code>.</p>
<pre><code>import pandas as pd
data_1 = pd.DataFrame(
{
'url': ['http://www.ex.jp', 'http://www.ex.com']
}
)
data_2 = pd.DataFrame(
{
... | python|pandas | 0 |
350,920 | 55,180,959 | Can't correctly convert Numpy array to c array for Python Extension (c++) | <p>I'm developing a Python extension in c++. I'm am really rusty in c++ however and don't have the necessary experience to figure this out it seems. I'm trying to read in numpy arrays, do the calculations I want to do and then return a numpy array. The problem I'm having is converting the numpy array to something of a ... | <p>Specifying the data type in your python deceleration for a,b,c as dtype=np.float64. Double in C parlance is 64 bit float. using np.array like the way you've used it usually returns np.int64. using np.array like so will return a np.float64</p>
<pre><code>a=np.array([1.,2.,3.])
</code></pre> | python|c++|arrays|numpy|python-extensions | 1 |
350,921 | 55,179,232 | How do I solve OOM error when using tf.data.Dataset? | <p>I am building a data pipeline using tf.data.Dataset API but got an OOM error. Assume I already have <code>features</code> and <code>labels</code> in hand, which are 4D numpy arrays in the order of [N,H,W,C]. Here is how I create my <code>dataset</code> object:</p>
<pre><code>batch_size = 100
num_samples = features.... | <p>In docs for <code>shuffle</code> it is written that <code>... fills a buffer with buffer_size elements ...</code> so in your case your dataset would take at least 54368*40*3*64*32*2 bits which is around 3.4GB. Just for the shuffle operation. Are you using 4GB gpu?</p>
<p>Another thing is that prefetch buffer_size s... | python|tensorflow | 0 |
350,922 | 55,318,944 | Python deepcopy() vs just initiating a numpy array in terms of run-time speed? | <p>I am curious if </p>
<pre><code>elevation_arr = numpy.zeros([900, 1600], numpy.float32)
climate_arr = copy.deepcopy(elevation_arr)
rainfall_arr = copy.deepcopy(elevation_arr)
</code></pre>
<p>is faster or slower to execute than </p>
<pre><code>elevation_arr = numpy.zeros([900, 1600], numpy.float32)
climate_arr = ... | <p><code>numpy_zeros</code> performs slightly better for smaller arrays and much better for larger arrays as shown below</p>
<pre><code>import copy
import numpy as np
def deep_copy():
elevation_arr = np.zeros([900, 1600], np.float32)
climate_arr = copy.deepcopy(elevation_arr)
rainfall_arr = copy.deepcopy(... | python|arrays|numpy|copy|deep-copy | 1 |
350,923 | 55,220,500 | How to Import Multiple excel file in PandasDataframe | <p>I cannot load multiple excel files from a directory in only one Dataframe.
I have tried two different ways and both do no work.</p>
<p>Gives me this error.</p>
<p>How can I solve the problem? It does find the files when creates the list, but than cannot open it in the Dataframe.
Any hints ?</p>
<pre><code>import ... | <p>Try this:</p>
<pre><code>import os
import glob
path = '/Users/giovanni/Desktop/news media'
df = pd.DataFrame()
for file in glob.glob(os.path.join(path,'*.xlsx')):
data = pd.read_excel(file)
print(data)
df = df.append(data)
</code></pre> | python|excel|pandas|xlsx | 2 |
350,924 | 9,746,183 | Python: Producing a graph of a module which contains a feedback mechanism | <p>I'm fairly new to programming and I'm trying to produce a simple zero-dimensional energy balance model in Python 2.7 IDLE, to calculate surface temperatures of the Earth and have added a ice albedo feedback, i.e. if the temperature output of the model is higher than 280K the albedo stays at 0.3 (30% energy reflecte... | <p>The comment above is correct, and it's not clear what you want to do, but if you want to check if all elements in your array validate the condition then you could do:</p>
<pre><code>if tb.all() > 280.0:
</code></pre>
<p>If you are interested in if there exists a value in the array that fullfills it you could do... | python|numpy|matplotlib|feedback-loop | 1 |
350,925 | 10,126,125 | Fixing phase unwrapping errors in Numpy | <p>I have a series of unwrapped phases, with some unwrapping errors that consist of a jump of +/- a multiple of Pi:</p>
<pre><code>import numpy
a = numpy.array([0.5, 1.3, 2.4, 10.3, 10.8, 10.2, 7.6, 3.2, 2.9])
</code></pre>
<p>In this example there is a first jump of 2 cycles between 2.4 and 10.3, and a jump of -1 cy... | <p>NumPy offers the function <code>numpy.unwrap()</code> for phase unwrapping. With the default parameter values, it will correct an array of phases modulo 2π such that all jumps are less than or equal to π:</p>
<pre><code>>>> a = numpy.array([0.5, 1.3, 2.4, 10.3, 10.8, 10.2, 7.6, 3.2, 2.9])
>>> num... | python|numpy|phase | 8 |
350,926 | 9,964,809 | numpy vs. multiprocessing and mmap | <p>I am using Python's <code>multiprocessing</code> module to process large numpy arrays in parallel. The arrays are memory-mapped using <code>numpy.load(mmap_mode='r')</code> in the master process. After that, <code>multiprocessing.Pool()</code> forks the process (I presume).</p>
<p>Everything seems to work fine, exc... | <p>My usual approach (if you can live with extra memory copies) is to do all IO in one process and then send things out to a pool of worker threads. To load a slice of a memmapped array into memory just do <code>x = np.array(data[yourslice])</code> (<code>data[yourslice].copy()</code> doesn't actually do this, which c... | python|numpy|multiprocessing|mmap | 26 |
350,927 | 7,697,169 | padding arrays using numpy | <p>in my program i have a numpy array and do some convolution filtering on it. i am looking for some way to make array padding (and then unpad for output) easily using numpy to avoid boundary checking. i know that scipy can do convolution, but i have reasons to make it by myself. gnuplot.py is used for output.</p>
<pr... | <p>There's a <code>pad</code> module scheduled for inclusion in Numpy 1.7.0 – see <a href="http://projects.scipy.org/numpy/ticket/655" rel="nofollow">this ticket</a>. For now, just download it and use its <code>with_constant</code> function.</p>
<p>Unpadding is as simple as <code>field[1:-1, 1:-1]</code>.</p> | python|numpy | 6 |
350,928 | 56,804,384 | tf.keras.Model.save throws Not JSON Serializable when dtype of Input is uint8 | <p>TensorFlow: 1.14.0</p>
<p>I am trying to modify the MobileNetV2 implementation from tf.keras.applications to accept a uint8 input rather than float32. I then add the cast to float32 and rescaling to [-1,1] as the first few layers of the model. The idea is to have the conversion be part of the inference graph rathe... | <p>Try to use <code>.numpy()</code> function for <code>tf.Variable</code> methods when feeding into loss functions or other relevant places.</p>
<p>This might work for ex:
<code>x = backend.cast(img_input, dtype="float32").numpy()</code></p>
<p>I was encountering a similar problem when changing from <code>Ten... | python|tensorflow|keras | 1 |
350,929 | 56,534,903 | Python Pandas: Label data with condition on rows | <p>I have a dataframe such as below:</p>
<pre><code> ID Label
1 1
2 NaN
3 3
4 NaN
5 1
6 NaN
7 NaN
8 3
</code></pre>
<p>What I want to do is at the label column if the row is between label 1 and 3, I want to label it as 2.
Example output:</p>
<pre><code> ID Label
1 ... | <p>First we make three masks (read: we mark rows with <code>True</code> and <code>False</code>)</p>
<ol>
<li>All the rows which are <code>NaN</code> with <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.isna.html" rel="nofollow noreferrer"><code>isna</code></a></li>
<li>Rows which are ... | python|pandas | 1 |
350,930 | 56,772,319 | Initialize multiple columns in a dataframe using mutiple operations | <p>I am trying to initialize multiple columns in pandas df using apply function.
I have a dataframe df as :</p>
<pre><code>A
dog
cat12
rat_1 wow
</code></pre>
<p>what I want is </p>
<pre><code>A length alphabet digit
dog 3 3 0
cat12 5 3 2
rat_1 wow 9 6... | <p>How about <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.len.html" rel="nofollow noreferrer"><code>Series.str.len</code></a> and <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.count.html" rel="nofollow noreferrer"><code>Series.str.count</c... | python|python-3.x|pandas | 1 |
350,931 | 56,655,226 | Reordering groups of values per data frame row? | <p>Lets say I have a data frame with columns like:</p>
<pre><code>x_0, x_1, y_0, y_1, z_0, z_1
</code></pre>
<p>Right now, that values can be anything, across x,y,z, there is no pattern.
For each row, I want to reassign the three letters (x,y,z) their value pairs (0, 1) from smallest "1" to largest "1".</p>
<p>Examp... | <p>You can transform your row in a 2D-array, sort it by the second column and reshape it back to a single row.</p>
<pre><code>#This is your dataFrame:
columns = ["x_0", "x_1", "y_0", "y_1", "z_0", "z_1"]
data = np.array([5,6,4,2,6,1]).reshape(-1,6))
df = pd.DataFrame(data=data,columns=columns)
#Output
x_0 x_1... | python-3.x|pandas|dataframe | 0 |
350,932 | 56,802,845 | Add column with difference between dates pandas DataFrame | <p>I have this kind of DataFrame:</p>
<pre><code>season Date Holiday_Name
12-13 11/1/12 NaN
12-13 11/2/12 Nan
12-13 3/31/13 Easter
12-13 4/5/13 NaN
13-14 11/1/13 NaN.
13-14 4/1... | <p>It's easily solved using to use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html" rel="nofollow noreferrer">groupby</a>.</p>
<pre><code>ddf = df.groupby('season').apply(lambda x : x['Date'] - x.loc[x['Holiday_Name'] == 'Easter']['Date'].iloc[0]).reset_index()
df['dif... | python|pandas|difference | 0 |
350,933 | 56,746,552 | Created index based on different dates | <p>I have the following data which retrieve the 2 last events based on <code>time</code> from an influxdb. Now I would like to add index with last event = 1 and previous event = 2. The index would apply when <code>account_entity</code>, <code>base_ccy</code>, <code>source</code> are the same but the date is different. ... | <p>Try the below code (not sure if right logic but works):</p>
<pre><code>print(df.groupby(['account_entity', 'base_ccy']).cumcount().sub(1).abs().add(1))
</code></pre>
<p>Output:</p>
<pre><code>0 2
1 1
2 2
3 1
10 2
11 1
24 2
25 1
dtype: int64
</code></pre> | python|pandas|indexing | 2 |
350,934 | 56,534,452 | creating a column based on missing value in pandas | <p>I have a data-frame for which want to create a column that represents missing value patterns in data-frame.For example :</p>
<p>for example for the CSV file,</p>
<pre><code>A,B,C,D
1,NaN,NaN,NaN
Nan,2,3,NaN
3,2,2,3
3,2,NaN,3
3,2,1,NaN
</code></pre>
<p>I want to create a column E,which has value in following way:
... | <p>use <code>isnull</code> in combination with <code>sum(axis=1)</code></p>
<p>Example:</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'A': [1, None, 3, 3, 3],
'B':[ None, None, 1, 1, 1]})
df['C'] = df.isnull().sum(axis=1)
</code></pre> | python|pandas | 3 |
350,935 | 56,636,880 | Panda dataframe Creation a new column by comparing all other row | <p>I have the following example:</p>
<pre><code>import pandas as pd
import numpy as np
import time
def function(value,df):
return len(df[(df['A']<value)])
df= pd.DataFrame(np.random.randint(0,100,size=(30000, 1)), columns=['A'])
start=time.time()
df['B']=pd.Series([len(df[df['A']<value]) for value in df['... | <p>Usually I am using <code>numpy</code> broadcast for this type task </p>
<pre><code>%timeit df['B']=pd.Series([len(df[df['A']<value]) for value in df['A']])
1 loop, best of 3: 25.4 s per loop
%timeit df['B']=(df.A.values<df.A.values[:,None]).sum(1)
1 loop, best of 3: 1.74 s per loop
#df= pd.DataFrame(np.rand... | python|pandas | 1 |
350,936 | 56,467,434 | Making a Prediction Sagemaker Pytorch | <p>I have trained and deployed a model in Pytorch with Sagemaker. I am able to call the endpoint and get a prediction. I am using the default input_fn() function (i.e. not defined in my serve.py).</p>
<pre><code>model = PyTorchModel(model_data=trained_model_location,
role=role,
... | <p>Yes you are on the right track. You can send csv-serialized input to the endpoint without using the <code>predictor</code> from the SageMaker SDK, and using other SDKs such as <code>boto3</code> which is installed in lambda:</p>
<pre><code>import boto3
runtime = boto3.client('sagemaker-runtime')
payload = '0.12787... | python|pytorch|amazon-sagemaker | 2 |
350,937 | 56,858,595 | Separate String from and create a dataframe column | <p>I am working on a below problem :</p>
<pre><code>df_temp = pd.DataFrame()
df_temp.insert(0, 'Label', ["A|B|C","A|C","C|B","A","B"])
df_temp.insert(1, 'ID', [1,2,3,4,5])
df_temp
Label ID
0 A|B|C 1
1 A|C 2
2 C|B 3
3 A 4
4 B 5
</code></pre>
<p>I want to convert this dataf... | <p>Try this:</p>
<pre><code>(df_temp.set_index('ID')['Label']
.str.split('|', expand=True)
.reset_index()
.melt('ID')
.drop('variable', axis=1)
.dropna()
.sort_values('ID'))
</code></pre>
<p>Output:</p>
<pre><code> ID value
0 1 A
5 1 B
10 1 C
1 ... | python|pandas | 2 |
350,938 | 56,457,678 | Extract a table out of data frame in pandas based on a condition | <p>Here are the column names of the pandas dataframe</p>
<pre><code>result.columns.values
</code></pre>
<blockquote>
<p>['A' 'B' 'C' 'D' 'E' 'F' 'G' 'H' 'I' 'J']</p>
</blockquote>
<p>When I try to filter out using</p>
<pre><code>filtered = result[(result['A'] <result['C']<result['D']) and (result['F'] <r... | <p>I believe you will need to structure like:</p>
<pre><code>filtered = result[((result['A'] < result['C']) & (result['C'] < result['D'])) &
((result['F'] < result['G']) & (result['G'] < result['I']))]
</code></pre>
<h3>Example</h3>
<pre><code>import numpy as np
columns = [... | python-3.x|pandas | 1 |
350,939 | 56,812,782 | Concatenating results to a list during loop iteration | <p>I have a bunch of a files in a directory. I want to run a loop on all the files and then concatenate the results to 3 individual lists and then convert them to a dataframe. </p>
<pre><code>data= pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
'Product':['Umbrella', 'Um... | <p>You need to rewrite all of your loops like this:</p>
<pre class="lang-py prettyprint-override"><code>Total_Uncalibr = []
for i in files:
df= pd.read_csv(i,sep="|", low_memory = False)
Total_Uncalibrated = df[Last_Price].sum()
Total_Uncalibr.append(Total_Uncalibrated)
</code></pre>
<p>You should read u... | python|pandas | 0 |
350,940 | 56,685,995 | Resnet50 image preprocessing | <p>I am using <code>https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/3</code> to extract image feature vectors. However, I'm confused when it comes to how to preprocess the images prior to passing them through the module.</p>
<p>Based on the related <a href="https://github.com/tensorflow/hub/blob/master/do... | <p>The image modules on TensorFlow Hub all expect pixel values in range [0,1], like you get in your code snippet above. This makes it easy and safe to switch between modules.</p>
<p>Inside the module, the input values are scaled to the range that the network was trained for. The module <a href="https://tfhub.dev/googl... | tensorflow|feature-extraction|resnet|tensorflow-hub | 1 |
350,941 | 56,490,171 | Saving a 2d numpy array of strings with single array of different size to a csv file | <p>I have a 2D numpy array of strings. I am trying to save it to a CSV file. So, the issue comes when the sizes of 1D arrays are different. </p>
<p>For Example:</p>
<pre><code>b = [['a','b'] #size of single array = 2
['c','d']] #size of single array = 2
</code></pre>
<p>So, now if I try to save it using:</... | <p>You can transform it to pandas dataframe first, than save to csv:</p>
<pre><code>import pandas as pd
b = [['a','b'], ['c']]
df = pd.DataFrame(b)
df.fillna('', inplace=True)
df.to_csv(path)
</code></pre>
<p>But you asked about numpy array. If you have numpy array of lists, than you can transform it to list of list... | python|csv|numpy | 1 |
350,942 | 56,589,528 | How to read a CSV file every other row | <p>how do I take from a CSV file data every 2 rows?</p>
<p>For example if I have a file that looks this</p>
<pre><code> 0 1
0 23 34
1 45 45
2 78 16
3 110 78
4 48 14
5 76 23
6 55 33
7 12 13
8 18 76
</code></pre>
<p>how can iterate and extract every 2nd row to get something like this and append in a new da... | <p>Use the <code>skiprows</code> parameter of <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html" rel="noreferrer"><code>read_csv</code></a>:</p>
<p>To keep even rows:</p>
<pre><code>pd.read_csv('file.csv', skiprows=lambda x: (x != 0) and not x % 2)
</code></pre>
<p>To keep odd ... | python|pandas|numpy|data-science | 6 |
350,943 | 56,515,513 | Implementing self attention | <p>I am trying to implement self attention in Pytorch.
I need to calculate the following expressions.</p>
<p>Similarity function S (2 dimensional), P(2 dimensional), C'</p>
<p>S[i][j] = W1 * inp[i] + W2 * inp[j] + W3 * x1[i] * inp[j] </p>
<p>P[i][j] = e^(S[i][j]) / Sum for all j( e ^ (S[i]))</p>
<p>basically, P is ... | <p>Here is an example of Self Attention I had implemented in <a href="https://github.com/ucalyptus/BS-Nets-Implementation-Pytorch/blob/master/BSNets_with_Dual_Attention.ipynb" rel="nofollow noreferrer">Dual Attention for HSI Imagery</a></p>
<pre><code>class PAM_Module(Module):
""" Position attention module https://gi... | pytorch|attention-model | 1 |
350,944 | 56,796,261 | How to collect tensor elements by a given segment? | <p>I am trying to implement a "segment_collect" (very much like segment_max, but collect into a tensor instead of taking max).</p>
<pre><code>t = tf.constant(["a", "b", "c", "d"])
s = tf.constant([0, 1, 1, 0])
r = tf.segment_collect(t, s) # r == [["a", "d"], ["b", "c"]]
</code></pre>
<p>A naive implementation would ... | <p>If anybody is here looking for the answer, tensorflow has you covered:</p>
<pre><code>t = tf.constant(["a", "b", "c", "d"]) # input tensor
s = tf.constant([0, 1, 1, 0]) # segment ids
n = tf.unique(s).y.shape[0] # number of segments
r = tf.dynamic_partition(... | python|tensorflow | 0 |
350,945 | 56,629,986 | Split TFLite model into two submodels | <p>I have a trained TF model which has the following architecture:</p>
<p>Inputs:<br>
<code>word_a</code>, one-hot representation, vocab-size: <code>50000</code><br>
<code>word_b</code>, one-hot representation, vocab-size: <code>50</code></p>
<p>Output:<br>
<code>probs</code>, size: <code>1x10000</code></p>
<p>The n... | <p>I think what you described should work. </p>
<p>Is it easy to reproduce the problem that you're seeing? If you can isolate the reproducible steps and you believe there's a bug, could you <a href="https://github.com/tensorflow/tensorflow/issues" rel="nofollow noreferrer">file a bug on github</a>? Thanks!</p> | python|tensorflow|tensorflow-lite | 0 |
350,946 | 56,595,375 | Combining different date and time columns to form a datetime value | <p>I'm trying to create a YYYY-MM-DD HH:MM:SS AM/PM format (for e.g. 2017-01-01 12:00:00 AM) from
a. A date column that is of the DDMMMYYYY format (for e.g. 01JAN2017); and
b. A time column that is of the HH:MM:SS AM/PM (for e.g. 12:00:00 AM) format. </p>
<p>The AM/PM in (b) appears to be the biggest problem.</p>
<p>... | <p>Use <code>to_datetime</code> with <code>%I</code> for parse hour in 12H format with <code>%p</code> for parse <code>AM/PM</code>. Last if need in output <code>AM/PM</code> is necessary convert to strings by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.strftime.html" rel="nofoll... | python-3.x|pandas|datetime | 1 |
350,947 | 56,495,668 | How to apply a function using two Series as input with the output being a DataFrame of the function result of each combination of arguments? | <p>I have two Series, each containing variables that I want to use in a function. I want to apply the function for each combination of variables with the resulting output being a DataFrame of the calculated values, the index will be the index from one Series and the columns will be the index of the other Series.</p>
<... | <p>You could use <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.outer.html" rel="nofollow noreferrer"><code>numpy.outer</code></a> for this:</p>
<pre><code>import numpy as np
import pandas as pd
bands = pd.Series({'A': 5, 'B': 17, 'C': 9, 'D': 34}, name='band')
values = pd.Series({'Jan': 1, 'Feb'... | python|pandas|apply | 1 |
350,948 | 56,573,008 | Compare the previous N rows to the current row in a pandas column | <p>I am trying to populate a new column in my pandas dataframe by considering the values of the previous n rows. If the current value is not equal to any of the past n values in that column, it should populate "N", else "Y".</p>
<p>Please let me know what would be a good way to achieve this.</p>
<p>Here's my input da... | <p>Here is my way </p>
<pre><code>n=2
l=[False]*n+[df.iloc[x,0] in df.iloc[x-n:x,0].tolist() for x in np.arange(n,len(df))]
df['New']=l
df
col1 New
0 car False
1 car False
2 car True
3 bus False
4 bus True
5 bus True
6 car False
</code></pre> | python|pandas|dataframe | 5 |
350,949 | 56,598,250 | I can't create a Dataset.from_generator() with generator that uses pandas Dataframes as arguments | <p>I want to create a dataframe pipeline from a generator that uses pandas dataframes to find image paths on disk and load them into the pipeline. Tensorflow won't allow me to do this, poping a <code>Can't convert non-rectangular Python sequence to Tensor.</code> message.</p>
<p>I tryied to use <code>.values</code> in ... | <p>Found an answer. Instead of passing the pandas dataframes arguments for the generator function in the <code>args</code> parameter in the <code>tf.data.Dataset.from_generator</code> method, I used <code>lambda</code> to pass them in the generator function itself:</p>
<p><code>train_dataset = tf.data.Dataset.from_gen... | python|tensorflow|machine-learning|tensorflow-datasets | 2 |
350,950 | 56,691,159 | Pandas - Merge rows based on similar contents of two cells | <p>I have a pandas dataframe which resembles the following. I am trying to merge all the rows which contains identical pair of ID and CountryCode values.</p>
<pre><code>records = [ (1, 'IN', 'yes' , '', '' , '', '') ,
(1, 'MY', '' , 'yes', '' , '', '' ) ,
(1, 'MY', '' , '', 'yes', '', '' ) ,
... | <h3><code>max</code></h3>
<p>Because <code>'yes'</code> is greater than <code>''</code></p>
<pre><code>dfRecords.groupby(['ID', 'CountryCode'], as_index=False).max()
ID CountryCode Address MobileNo HomeNo OfficeNo TacNo
0 1 IN yes
1 1 MY yes ... | python|pandas | 2 |
350,951 | 56,865,751 | Confusion on the 'linear' activation in tf.keras.layers.Dense() | <p>In the <a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense" rel="nofollow noreferrer">explanation on activation under Arguments</a>, it says </p>
<blockquote>
<p>"linear" activation: a(x) = x.</p>
</blockquote>
<p>It's confusing. Should not linear activation act like <code>wx+b</code> (if u... | <p>No, the <code>Dense</code> layer itself computes <code>y = a(wx + b)</code>, and what the <code>activation</code> parameter does is change the function <code>a</code> in this computation in order to have different non-linear behavior, but if you need linear behavior, the only way to "cancel out" the <code>a</code> i... | tensorflow|tf.keras | 2 |
350,952 | 56,584,545 | How to use Pandas group-by and sum | <p>this is my dataframe:</p>
<pre><code> RefactoringType Detail
0 Move Method com.onegravity.colorpicker.ColorPickerDialog
1 Move Source Folder NaN
2 Move Attribute com.onegravity.colorpicker.ColorPickerDialog
3 Move Attribute com.onegravity.colorpicker.ColorPickerDialog
4 Move Att... | <p>Try:</p>
<pre><code>df.groupby(['Detail', 'RefactoringType']).size()\
.unstack().fillna(0, downcast='infer')
</code></pre>
<p>For the following test DataFrame:</p>
<pre><code>df = pd.DataFrame(data=[
[ 'Move Attribute', 'ColorPickerDialog' ],
[ 'Move Method', 'ColorPickerDialog' ],
[ 'Rename Me... | pandas|dataframe|group-by | 0 |
350,953 | 56,542,448 | "ValueError: The truth value of an array with more than one element is ambiguous." when using scipy.integrate.dblquad | <p>I'm trying to perform a double integration on this function </p>
<pre class="lang-py prettyprint-override"><code>def prob(x,y):
ch = np.sqrt((3-y)**2 + x**2)
hb = np.sqrt((4-x)**2 + y**2)
if np.isclose(4 * y, 12 - 3 * x):
# Without the if statement, any x, y values that satisfy the condition wil... | <p>Perhaps this is a typographical error.</p>
<p>From the edit I made it looks like you were calling:</p>
<pre><code>integ.dblquad(prob(x, y), 0, 4, lambda x: 0, lambda x: 3)
</code></pre>
<p>When in fact you should be calling:</p>
<pre><code>integ.dblquad(prob, 0, 4, lambda x: 0, lambda x: 3)
</code></pre>
<p>I b... | python|numpy|scipy | 1 |
350,954 | 56,660,453 | Looping through multiple arrays with np.where | <p>I have 2 lists distance_boundary and distance</p>
<pre><code>distance_boundary = [100,200,300]
distance = [125,255,285,140,160,180]
</code></pre>
<p>Now I want to create a new variable <strong>floor</strong> and I want to assign the value for floor based on the distance value and it is defined by distance_boundary... | <p><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.searchsorted.html" rel="nofollow noreferrer"><code>np.searchsorted</code></a> seems to be what you are looking for:</p>
<pre><code>np.searchsorted(distance_boundary, distance)
# array([1, 2, 2, 1, 1, 1])
</code></pre> | python|loops|numpy | 3 |
350,955 | 56,501,560 | Predict function for multiple linear regression | <p>I am trying to make a predict function for a homework problem where it takes the dot products of a <code>matrix(x)</code> and a <code>vector(y)</code> and inserts them into a numpy array</p>
<pre class="lang-py prettyprint-override"><code>def predict(x, y):
y_hat = np.empty
for j in range(len(y)):
y... | <p>There are two errors in the code:</p>
<ol>
<li><code>numpy.empty()</code> is a method which get arguments for the shape. Here, you must define it as <code>np.empty([len(y), len(x)])</code> (if <code>x</code> is matrix and <code>y</code> is a vector,<code>np.dot(x, y)</code> results a vector with length <code>len(x)... | python|numpy | 2 |
350,956 | 56,683,001 | >AttributeError: 'list' object has no attribute 'lower' (in a lowercase dataframe) | <p>I don't understand this error... I've already turned df into lowercase before turning it into a list</p>
<p>dataframe:</p>
<pre><code> all_cols
0 who is your hero and why
1 what do you do to relax
2 this is a hero
4 how many hours of sleep do you get a night
5 describe the last time you were relax
</c... | <p>Convert it into pandas data frame and then do the operation you are doing above. It will work.
I have still pasted the snippet and you can try yourself.</p>
<pre><code>import pandas as pd
col = pd.Series(["who is your hero and why", "what do you do to relax", "this is a hero", "how many hours of sleep do you get a... | python|pandas|cluster-analysis|lowercase | 3 |
350,957 | 56,668,248 | How to import trained model without initializing weights | <p>I converted a EfficientNet model that was pretrained on ImageNet to tensorflow-js using the tensorflowjs-converter. When I try to load the model into my script, it tries to initialize the weights with initializers, that are not implemented in tfjs. However, it is not necessary to initialize the weights, as the model... | <p>According to the documentation:</p>
<blockquote>
<p>TensorFlow.js Layers currently only supports Keras models using standard Keras constructs. Models using unsupported ops or layers—e.g. custom layers, Lambda layers, custom losses, or custom metrics—cannot be automatically imported, because they depend on Python ... | javascript|tensorflow|tensorflowjs-converter|tensorflowjs | 0 |
350,958 | 56,491,762 | Join several dataframes on an empty dataframe with fixed index, merging columns or appending those | <p>I have a dataframe with a range index and no data, in real data the index is a time range.</p>
<p>E.g.</p>
<pre><code>df_main = pd.DataFrame(index = pd.RangeIndex(0,15,1))
</code></pre>
<p>See Fig1</p>
<p>And I have several dataframes which varying columns and indexes, I just want to join those on the main dataf... | <p>You could <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html" rel="nofollow noreferrer"><code>groupby</code></a> the index and aggregate with <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.first.html" rel="nofollow noreferrer"><code... | python|pandas|dataframe|join | 3 |
350,959 | 56,779,489 | How do I tell pandas to group the same months across multiple years? | <p>I've got a dataFrame comprised of datetimes in the format <code>21-JAN-2016</code> which I hit with <code>pd.to_datetime(df[0])</code>. I've trying to group my data such that the same month, across the span of several years, is plotted side-by-side. For example, the # occurrences in January for 2015, 2016, 2017, etc... | <p>You are currently grouping by month and year. You just need to unstack the result into a table.</p>
<pre><code>by_year_per_month.unstack()
</code></pre>
<p>You should then be able to plot your data.</p>
<pre><code>dates = pd.DatetimeIndex(start='2016-01-01', freq='d', periods=356 * 4)
df = pd.DataFrame({'date': ... | python|pandas | 1 |
350,960 | 56,541,683 | Output of a CNN doesn't change much with the input | <p>I have been trying to implement Actor Critic with a convolutional neural network. There are two different images as the state for the reinforcement learning agent. The output(actions for Actor) of the CNN is the (almost)same for different inputs after (random)initialisation. Due to this even after training the agent... | <p>The problem with this was that the weight initialization was not appropriate. I used Gaussian initialization with twice the standard deviation as of the default. This helped in giving different outputs for different inputs. Although after a few episodes of training the actor starts giving the same value once again, ... | python|deep-learning|conv-neural-network|pytorch|reinforcement-learning | 0 |
350,961 | 56,728,128 | Filtering data in Pandas returns error 'method' object is not iterable | <p>I have a dataset as follows:
I am going to filter rows where the counts value equals 1.</p>
<pre><code>index count
1 4
2 5
3 1
4 1
</code></pre>
<p>This is my code:</p>
<pre><code>booleans =[]
for number in df1.count:
if number ==1:
booleans.append (True)... | <p>In your code the problem is with the this part <code>df1.count</code>. Actually, pandas is having a method <code>count()</code> which is used to count the no. of non-NA/null observations across the given axis.</p>
<p>And in your code it returns something like this,</p>
<pre><code><bound method DataFrame.count o... | python|pandas | 2 |
350,962 | 56,725,183 | Using multiprocessing module to runs parallel processes where one is fed (dependent) by the other for Viterbi Algorithm | <p>I have recently played around with Python's multiprocessing module to speed up the forward-backward algorithm for Hidden Markov Models as forward filtering and backward filtering can run independently. Seeing the run-time halve was awe-inspiring stuff. </p>
<p>I now attempt to include some multiprocessing in my ite... | <p>I have managed to get my code working thanks to @SıddıkAçıl. The producer-consumer pattern is what does the trick. I also realised that the processes can run successfully but if one does not store the final results in a "result queue" of sorts then it vanishes. By this I mean, that I filled in values in my numpy arr... | python|numpy|parallel-processing|multiprocessing|viterbi | 1 |
350,963 | 56,453,268 | Create Spark DataFrame from Pandas DataFrames inside RDD | <p>I'm trying to convert a Pandas DataFrame on each worker node (an RDD where each element is a Pandas DataFrame) into a Spark DataFrame across all worker nodes.</p>
<p>Example:</p>
<pre><code>def read_file_and_process_with_pandas(filename):
data = pd.read(filename)
"""
some additional operations using pa... | <p>Pandas dataframes can not direct convert to rdd.
You can create a Spark DataFrame from Pandas</p>
<pre><code>spark_df = context.createDataFrame(pandas_df)
</code></pre>
<p>Reference: <a href="https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html" rel="nofollow n... | pandas|apache-spark|pyspark | 2 |
350,964 | 56,852,338 | How to construct a table (matrix) with two lists and arrays of data? | <p>I want to create a matrix/table that I can later retrieve. The two dimensions are: Croptypes and FixedInputs. </p>
<pre><code>Croptypes = ["barley", "rapeseed", "wheat"]
FixedInputs = ["land", "labor", "capital"]
Beta = [[0.3, 0.2, 0.3], [0.1, 0.1, 0.1], [0.3, 0.2, 0.2]]
</code></pre>
<p>The table/matrix should lo... | <p>Why not:</p>
<pre><code>print(pd.DataFrame(Beta, FixedInputs, Croptypes))
</code></pre> | python|arrays|pandas|numpy|pyomo | 1 |
350,965 | 56,657,323 | TypeError: ufunc add cannot use operands with types dtype('<M8[ns]') and dtype('<M8[ns]') | <p>I am trying to set an ARIMA model to some data, for this, I used 'autocorrelation_plot()' with my time series. It's generates however the error in the title.</p>
<p>I have an attribute table composed, among others, of a Date and time fiels.
I extracted them (after transforming the attribute table into a numpy ta... | <p>I found a solution, it can look barbaric, but it works!</p>
<p>I've just "recreated" <strong>pd.Series()</strong> with the <em>pd.Series</em> I had:</p>
<pre><code>data2 = pd.Series(O, A)
autocorrelation_plot(pd.Series(data2))
plt.show()
</code></pre> | pandas|numpy|matplotlib|arcpy | 1 |
350,966 | 56,676,986 | Does Python come with numpy library as default | <p>Does python come with numpy library as default or do you have to install it after installing python?</p> | <p>Python doesn't come with numpy installed. However it could have been installed as requirement while installing other packages.
You could install it via pip:</p>
<pre><code>pip install numpy
</code></pre>
<p>Or, you could check the list of the installed packages and their version via pip:</p>
<pre><code>pip list
... | python|numpy | 3 |
350,967 | 56,858,257 | How do I divide elements in a single column of a python dataframe? | <p>I need to divide every element in a specific column in a Pandas DataFrame by 100.</p>
<p>By default, the .div() function in Pandas divides <em>all</em> elements across all columns, and attempting to specify columns to divide leaves me with only those columns.</p>
<pre class="lang-py prettyprint-override"><code>d =... | <pre><code>data['ASSETS'] = data['ASSETS'].div(100)
</code></pre>
<p>You are overwriting your entire dataframe by assigning it back to data</p> | python|pandas|division | 4 |
350,968 | 56,652,237 | What does "Gathers values along an axis specified by dim." mean? | <p>I want to understand what does "Gathers values along an axis specified by dim." mean in the below code. How to structure the operation of function on data in my head. What is this function doing to data and how ?</p>
<p>Please refer this link <a href="https://pytorch.org/docs/stable/torch.html#torch.gather" rel="no... | <p>Yes, it goes through the given dim (dimension) of the tensor, and collects into a new tensor the values specified by the index provided. So if I had a 1D tensor (is that allowed?) as </p>
<pre><code>MyValues = torch.tensor([0,2,4,6,8])
</code></pre>
<p>and did </p>
<pre><code>torch.gather(MyValues, 0, torch.tenso... | pytorch | 1 |
350,969 | 56,442,669 | Tab seperated CSV file exported in only one column | <p>I'm new to python and want to import some csv data with pandas. The data is seperated with tabs ('\t').
The CSV file has 35339 rows and 23 columns.
The data read in over pandas works as expected but if I try to visualize the data it says that the read in data has only 35339 rows and 1 column. Even if the data seem... | <p>Try <code>skiprows</code>, it looks like the first row is a comment about the separator.</p>
<pre><code>sensor_df = pd.read_csv(filename, sep='\t', low_memory=False, skiprows=1)
</code></pre> | python|pandas|csv | 1 |
350,970 | 56,526,707 | Extract rows of clusters in hierarchical clustering using seaborn clustermap | <p>I am using hierarchical clustering from seaborn.clustermap to cluster my data. This works fine to nicely visualize the clusters in a heatmap. However, now I would like to extract all row values that are assigned to the different clusters. </p>
<p>This is what my data looks like:</p>
<pre><code>import pandas as pd
... | <p>Using scipy.cluster.hierarchy module with fcluster allows cluster retrieval:</p>
<pre><code>import pandas as pd
import seaborn as sns
import scipy.cluster.hierarchy as sch
df = pd.read_csv('expression_data.txt', sep='\t', index_col=0)
# retrieve clusters using fcluster
d = sch.distance.pdist(df)
L = sch.linkage(... | python|pandas|seaborn|hierarchical-clustering | 5 |
350,971 | 56,739,320 | Pandas - check if a value exists in multiple columns for each row | <p>I have the following Pandas dataframe: </p>
<pre><code>Index Name ID1 ID2 ID3
1 A Y Y Y
2 B Y Y
3 B Y
4 C Y
</code></pre>
<p>I wish to add a new column 'Multiple' to indicate those rows where there is a value Y in more than one of t... | <p>using numpy to sum by row to occurrences of Y should do it:</p>
<pre><code>df['multi'] = ['Y' if x > 1 else 'N' for x in np.sum(df.values == 'Y', 1)]
</code></pre>
<p>output:</p>
<pre><code> Name ID1 ID2 ID3 multi
Index
1 A Y Y Y Y
2 B Y Y ... | python|pandas|conditional-statements | 3 |
350,972 | 56,835,463 | Use multiple conditions on a column to assign values of new column | <p>I'm trying to assign one of 8 labels to my data based on the strings in an existing column. However, with the method I'm using I get this error: </p>
<blockquote>
<p><em>ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().</em></p>
</blockquote>
<p>I have 144... | <p>There's no need for <code>itterrows</code> here, which is <a href="https://stackoverflow.com/a/55557758/9081267">bad practice</a> and considered slow.</p>
<h3>Method 1 <a href="https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.cut.html" rel="nofollow noreferrer"><code>pd.cut</code></a></h3>
<pr... | python|python-3.x|pandas|any | 3 |
350,973 | 56,698,637 | Changes in Pandas DataFrames don't preserved after end of for loop | <p>I have a list of Pandas DataFrames and I want to perform some operations on them. To be more precise, I want to clean their names and add new column. So I have written the following code:</p>
<pre><code>import numpy as np
import pandas as pd
from janitor import clean_names
rng = np.random.RandomState(2019)
dataset ... | <p>This is what is happening:</p>
<pre><code>for df in dataset:
</code></pre>
<p>This makes <code>df</code> to refer to an item in the list in each iteration.</p>
<pre><code>df = df.clean_names()
</code></pre>
<p><code>df.clean_names()</code> returns a new object, different from <code>df</code> itself. The assignme... | python|pandas | 2 |
350,974 | 56,638,264 | Pandas acting weird when using dataframe.shift() | <p>I am reading in some data which looks like this:</p>
<p><a href="https://i.stack.imgur.com/Tu5my.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Tu5my.png" alt="Original data"></a></p>
<p>In this dataset, a number of rows have <code>null</code> in column 16. I need to shift the values in such ro... | <p>This is a bug in pandas, and more details can be found <a href="https://github.com/pandas-dev/pandas/issues/26929" rel="nofollow noreferrer">here</a> .</p>
<p>it seems that shifting object columns will automatically shift to the next column that has an object dtype.</p>
<p>In order to work around this issue, I sel... | python|pandas|dataframe | 1 |
350,975 | 56,741,046 | Not able to change column order(with condition) with .loc | <p>I have a DataFrame with lots of columns that I want to reorganize the order of some columns(not all of them), depending on the value of some other.</p>
<p>I tried to change the order with .loc, when I run the code, it shows what I want, but when I try to set the values with it, it doesn't.</p>
<p>Example, I want t... | <p>This is what you're looking for:</p>
<pre><code>data.loc[data.Type_Math == 'A', [1,2,3]] = data.loc[data.Type_Math == 'A', [3,2,1]].values
</code></pre>
<p>Output:</p>
<pre><code> Type_Bio Type_Math Ans_Bio Ans_Math 1 2 3
0 A B AEC AEC A E C
1 A B ABC ABC A ... | python|pandas | 0 |
350,976 | 25,694,668 | How to interpolate a series in numpy | <p>I have a problem in the interpolation of a series. I want to interpolate a series for example,<code>[1.0,2.0,3.0,4.0]</code>, and I want to get the series like <code>[1.0,1.5,2.0,2.5,3.0,3.5,4.0,4.5]</code>, is there an easy way to get this in <code>numpy</code>?</p> | <p>Use <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html" rel="nofollow">np.arange(start, end, step)</a>:</p>
<pre><code>>>> import numpy as np
>>> np.arange(1, 5, 0.5)
array([ 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])
</code></pre>
<p>To incorporate it with your s... | numpy | 1 |
350,977 | 25,866,506 | create a 3D array of square matrices in numpy | <p>I want to vectorize the creation of a set of 2x2 arrays,
so I've written the following code</p>
<pre><code>import numpy as np
# an array of parameters
a = np.array(( 1.0, 10.0, 100.0))
# create a set of 2x2 matrices
b = np.array((( 1*a, 2*a), ( 3*a, 4*a)))
# to access the 2x2 matrix, I can do as follows
for i in r... | <p>see <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.transpose.html" rel="nofollow"><code>numpy.transpose</code></a>; for your example:</p>
<pre><code>>>> b.transpose(2, 0, 1)
array([[[ 1., 2.],
[ 3., 4.]],
[[ 10., 20.],
[ 30., 40.]],
... | python|numpy|multidimensional-array | 0 |
350,978 | 25,801,246 | SublimeCodeIntel Autocomplete Fails with Pandas and Numpy | <p>I'm trying and failing to get autocomplete working with python in Sublime Text 3. Sublimecodeintel is recommended on multiple blogs and 'set up' guides. When it works, it's great, but I can't get it to work with numpy or pandas, the two packages I use most.</p>
<p>Set up:
Mac OS X 10.9.4
I have installed python 2... | <p>I used to recommend SublimeCodeIntel, despite random hiccups like this, until I discovered <a href="https://sublime.wbond.net/packages/Anaconda" rel="nofollow"><code>Anaconda</code></a>. Once you set it up (a very brief process), it just works. There's no database to initialize or get corrupted, it automatically dis... | python|autocomplete|pandas|sublimetext3|sublimecodeintel | 4 |
350,979 | 25,772,198 | Inconsistent behavior of `dataframe.groupby(allcolumns).agg(len)` | <p>For demonstration purposes, first, I define a couple of simple dataframes, <code>df0</code> and <code>df1</code>:</p>
<pre><code>>>> import pandas as pd
>>> import collections as co
>>> data = [['a', 1],
... ['b', 2],
... ['a', 3],
... ['b', 1],
... ['a... | <p>See the note at the bottom of the aggrgation section: <a href="http://pandas.pydata.org/pandas-docs/stable/groupby.html#aggregation" rel="nofollow">http://pandas.pydata.org/pandas-docs/stable/groupby.html#aggregation</a>. Pandas 'eats' the aggregator column, so you are left with nothing to aggregate.</p>
<p>You ess... | pandas | 2 |
350,980 | 25,473,153 | Python Pandas iterrows() with previous values | <p>I have a pandas Dataframe in the form:</p>
<pre><code> A B K S
2012-03-31 NaN NaN NaN 10
2012-04-30 62.74449 15.2 71.64 0
2012-05-31 2029.487 168.8 71.64 0
2012-06-30 170.7191 30.4 71.64 0
</code></pre>
<p>I trying to create a function that... | <p>It looks like your initial answer is pretty close.</p>
<p>The following should work:</p>
<pre><code>for index, row in df.iterrows():
if df.loc[index, 'S'] != 0:
df.loc[index, 'S'] = df.loc[str(int(index) - 1), 'S']
</code></pre>
<p>Essentially, for all but the first index, i.e. 0, change the value in ... | python|for-loop|pandas|dataframe | 9 |
350,981 | 25,476,880 | Using DataFrame.ix with a tuple index in Pandas | <p>I have a bunch of Pandas code that uses tuples as indices. I've recently come across the need to access an individual element of a DataFrame with <code>DataFrame.ix</code>, which is getting confused by the tuples. It seems to think my tuple is a sequence of keys I want to access, not a single keys (which happens to ... | <p>You can wrap the tuple in a list to make this work.</p>
<pre><code>In [17]: bar.ix[[bar.iloc[0].name]]
Out[17]:
col1 col2
(a, b, c) 0.216689 0.262511
</code></pre> | python|pandas|dataframe | 6 |
350,982 | 25,923,397 | Broadcasting a function over two vectors to get a 2d numpy array | <p>I want to broadcast a function f over a vectors so that the result is a matrix P where P[i,j] = f(v[i], v[j]).
I know that I can do it simply:</p>
<pre><code>P = zeros( (v.shape[0], v.shape[0]) )
for i in range(P.shape[0]):
for j in range(P.shape[0]):
P[i, j] = f(v[i,:], v[j,:])
</code></pre>
<p>or mor... | <p>I believe what you search for is <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.vectorize.html" rel="nofollow">numpy.vectorize</a>. Use it like so:</p>
<pre><code>def f(x, y):
return x + y
v = numpy.array([1,2,3])
# vectorize the function
vf = numpy.vectorize(f)
# "transposing" the vector by... | python|numpy|matrix|scipy|array-broadcasting | 1 |
350,983 | 26,006,432 | Create bokeh timeseries graph using database info | <p><em>Note from maintainers: this question is about the obsolete <code>bokeh.charts</code> API removed several years ago. For an example of timeseries charts in modern Bokeh, see here:</em></p>
<p><a href="https://docs.bokeh.org/en/latest/docs/gallery/range_tool.html" rel="nofollow noreferrer">https://docs.bokeh.org/... | <p>You don't have to use Pandas, you simply need to supply a sequence of x-values and a sequence of y-values. These can be plain Python lists of numbers, or NumPy arrays, or Pandas Series. Here is another time series example that uses just NumPy arrays:</p>
<p><a href="http://docs.bokeh.org/en/latest/docs/gallery/colo... | python|charts|pandas|time-series|bokeh | 4 |
350,984 | 26,153,539 | Deep copy of a pandas panel? | <p>When I try to copy a pandas panel object using the <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Panel.copy.html" rel="nofollow">instructions provided in the online documentation</a>, I do not get the expected bahavior. </p>
<p>Maybe this will illustrate the problem: </p>
<pre><code>import ... | <p>You didn't make a Panel, just a dict of DataFrames. Add this line to convert it to a Panel object, and it should work as you expect.</p>
<pre><code>pnl = pd.Panel(pnl)
</code></pre> | pandas|deep-copy | 1 |
350,985 | 26,046,208 | Normalize DataFrame by group | <p>Let's say that I have some data generated as follows:</p>
<pre><code>N = 20
m = 3
data = np.random.normal(size=(N,m)) + np.random.normal(size=(N,m))**3
</code></pre>
<p>and then I create some categorization variable:</p>
<pre><code>indx = np.random.randint(0,3,size=N).astype(np.int32)
</code></pre>
<p>and genera... | <pre><code>In [10]: df.groupby('indx').transform(lambda x: (x - x.mean()) / x.std())
</code></pre>
<p>should do it.</p> | python|pandas | 80 |
350,986 | 25,965,270 | removing bad data pairs in numpy array | <p>I am working with a large array of data, but every so often I wind up with a nan instead of a value. I need to remove these somehow. Here is an example of my dataset</p>
<pre><code>1 2
3 4
nan 5
6 7
8 nan
9 10
</code></pre>
<p>and I would to remove the bad data to become:</p>
<pre><code> 1 2
3 4
6 7
9 10
</cod... | <p>If you're just using numpy, use logical indexing:</p>
<pre><code>import numpy as np
x = np.array([[ 1., 2.],
[ 3., 4.],
[ np.nan, 5.],
[ 6., 7.],
[ 8., np.nan],
[ 9., 10.]])
# find which rows contain... | python|numpy | 5 |
350,987 | 26,388,500 | How to plot in CCDF with a list? | <p>I can very well plot CDF and CCDF when the data is in one column. But I am a little clueless how to plot a CDF or CCDF when the data is in the below given format. The pairs in round brackets <code>()</code> are the node pairs. The values in square brackets <code>[]</code> are the occurrence value and the number in b... | <p>You can do this by simply finding the index of the <code>[</code> and <code>]</code>, slicing out the data line by line and parsing it to a list using <code>ast.literal_eval</code> and appending it to the main list.</p>
<pre><code>import ast
import numpy as np
from pylab import *
file_data = """('4503', '656') 7 [... | python|arrays|list|numpy|matplotlib | 0 |
350,988 | 25,939,853 | Create new colum on pandas getting previous row | <p>I have a pandas DataFrame with tax per month.</p>
<pre><code>>> df
2014-08-01 0.25
2014-07-01 0.01
2014-06-01 0.40
2014-05-01 0.46
2014-04-01 0.67
2014-03-01 0.92
2014-02-01 0.69
2014-01-01 0.55
2013-12-01 0.92
2013-11-01 0.54
2013-10-01 0.57
2013-09-01 0.35
2013-08-01 0... | <p>There is a built in method <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.rolling_sum.html#pandas.rolling_sum" rel="nofollow"><code>rolling_sum</code></a>, I also call <code>shift</code> at the end to align it the way you want:</p>
<pre><code>In [14]:
df['rolling_sum']= pd.rolling_sum(df['va... | python|pandas|data-analysis | 2 |
350,989 | 26,175,570 | Convert structured array with various numeric data types to regular array | <p>Suppose I have a NumPy structured array with various numeric datatypes. As a basic example,</p>
<pre><code>my_data = np.array( [(17, 182.1), (19, 175.6)], dtype='i2,f4')
</code></pre>
<p>How can I cast this into a regular NumPy array of floats? </p>
<p>From <a href="https://stackoverflow.com/a/5957455/2223706... | <p>You can do it easily with Pandas:</p>
<pre><code>>>> import pandas as pd
>>> pd.DataFrame(my_data).values
array([[ 17. , 182.1000061],
[ 19. , 175.6000061]], dtype=float32)
</code></pre> | python|arrays|numpy|structured-array | 3 |
350,990 | 26,387,986 | Strip time from an object date in pandas | <p>I am having trouble with some dates from zipped xlsx files. These files are loaded into a sqlite database then exported as .csv. Each file is about 40,000 rows per day. The issue I run into is that <code>pd.to_datetime</code> does not seem to work on these objects (dates from Excel format is causing the issue I thin... | <p>You can apply a post-processing step that first converts the string to a datetime and then applies a lambda to keep just the date portion:</p>
<pre><code>In [29]:
df['Created Date'] = pd.to_datetime(df['Created Date']).apply(lambda x: x.date())
df['Repeat Date'] = pd.to_datetime(df['Repeat Date']).apply(lambda x: ... | python|pandas | 11 |
350,991 | 26,385,482 | Python: How to generate a vector with nonzero entries at random positions? | <p>I want to generate a vector using Numpy that is k-sparse, i.e. it has <em>n</em> entries of which <em>k</em> are nonzero. The positions of the nonzero entries are chosen randomly, and the entries themselves are chosen from a Gaussian distribution with zero mean and unit variance. The test vector is small (256 entr... | <p>How about this:</p>
<pre><code>In [41]: import numpy as np
In [42]: x = np.zeros(10)
In [43]: positions = np.random.choice(np.arange(10), 3, replace=False)
In [44]: x[positions] = np.random.normal(0,1,3)
In [45]: x
Out[45]:
array([ 0. , 0.11197222, 0. , 0.09540939, -0.04488175,
0. ... | python|numpy | 5 |
350,992 | 26,321,416 | Explanation of this code: Determining if a point is on which side of a line | <p>Source: <a href="http://datasciencelab.wordpress.com/2014/01/10/machine-learning-classics-the-perceptron/" rel="nofollow">http://datasciencelab.wordpress.com/2014/01/10/machine-learning-classics-the-perceptron/</a></p>
<p>"The general equation of a line given two points in it, <code>(x1,y2)</code> and <code>(x2,y2)... | <h2>Why is B determined by yb - ya? (or rather y2 - y1 for our example)</h2>
<p>The text assumes the line equation to be: <code>A + B*x + C*y = 0</code></p>
<p>Let's say we have two points from that line, <code>P1(x1, y1)</code> and <code>P2(x2, y2)</code></p>
<p>Using the <a href="http://en.wikipedia.org/wiki/Linea... | python|numpy|vector|linear-algebra|perceptron | 0 |
350,993 | 26,152,556 | Drop columns that aren't common between two dataframes? | <p>I have two dataframes that have many columns in column but a few that do not exist in both. I would like to create a dataframe that only has the columns that are in common between both dataframes. So for example:</p>
<pre><code>list(df1)
['Survived', 'Age', 'Title_Mr', 'Title_Mrs', 'Title_Captain']
list(df2)
['Su... | <p>Using the comments of @linuxfan and @PadraicCunningham we can get a list of common columns:</p>
<pre><code>common_cols = list(set(df1.columns).intersection(df2.columns))
</code></pre>
<p>Edit: @AdamHughes' answer made me consider preserving the column order. If that is important you could do this instead:</p>
<pr... | python|pandas|dataframe | 4 |
350,994 | 26,251,997 | Can I vectorise this python code? | <p>I have written this python code to get neighbours of a label (a set of pixels sharing some common properties). The neighbours for a label are defined as the other labels that lie on the other side of the boundary (the neighbouring labels share a boundary). So, the code I wrote works but is extremely slow:</p>
<pre>... | <p>Here you go:</p>
<pre><code>def get_boundaries2(segments, i):
x, y = np.where(segments == i) # where i is
right = x + 1
rightMask = right < segments.shape[0] # keep in bounds
down = y + 1
downMask = down < segments.shape[1]
rightNeighbors = segments[right[rightMask], y[rightMask]]
... | python|optimization|numpy | 5 |
350,995 | 26,215,098 | Working with a big csv in python | <p>I've got a big table in a csv file, which has 5 million rows and 4 columns.
My objective is to take each row from the first 500k and to compare it with all the following rows (i.e. 5kk - n) based on certain condition. The condition is something like</p>
<p>row(n).column1 == row(n+1).column1 AND row(n).column2 == ro... | <p>I ended up using following things to improve performance.</p>
<ol>
<li>Separated loop in a function (about 20% gain)</li>
<li>Improved logical operations.</li>
<li>Used PyPy interpreter (300+% increase)</li>
</ol> | python|csv|numpy|pandas | 0 |
350,996 | 66,888,593 | Summing values of (dropped) duplicate rows Pandas DataFrame | <p>For a time series analysis, I have to drop instances that occur on the same date. However, keep some of the 'deleted' information and add it to the remaining 'duplicate' instance. Below a short example of part of my dataset.</p>
<pre><code>z = pd.DataFrame({'lat':[49.125,49.125], 'lon':[-114.125 ,-114.125 ], 'time':... | <p>If those are all the columns in your dataframe, you can get your result using a <code>groupby</code>on your time column, and passing in your aggregations for each column.</p>
<p>More specifically, you can <em>drop the (duplicate) instance which has the lowest 'duration'</em> by keeping the <code>max()</code> duratio... | python|pandas|dataframe|drop-duplicates | 2 |
350,997 | 67,110,989 | How to Match multiple columns with given single column name and get its value in new column? | <p>I have previously asked a question
<a href="https://stackoverflow.com/questions/67043612/how-to-match-multiple-columns-with-given-single-column-and-get-its-name-in-new-c">How to Match multiple columns with given single column and get its name in new column?</a></p>
<p>But now i want to get data from column name :</p... | <p>Try with , notice we used to have lookup, but pandas will no longer support this function sad ..</p>
<pre><code>df['value'] = df.values[df.index,df.columns.get_indexer(df.Near)]
df
Out[27]:
mtc C1 C2 C3 C4 Near value
0 1 0.5 1.0 1.0 4.1 C2 1
1 2 2.0 3.0 3.0 6.0 C1 2
2 3 3.5 ... | python|pandas | 0 |
350,998 | 67,043,246 | how to specify Int in dataframe.to_sql? | <p>I am trying to fetch data from my database and trying to write it into another table but for some reason the dataframe.to_sql throws an error:</p>
<pre><code>> engine = create_engine("mysql+pymysql://{user}:{pw}@localhost/{db}"
.format(user="root",
... | <p>Make sure the datatypes match. the table column type and the dtype passed in your df.to_sql should match:</p>
<blockquote>
<p>count_duplicate():
```engine = create_engine("mysql+pymysql://{user}:{pw}@localhost/{db}"
.format(user="root",
pw="dhanusha",
db="postmanassignment"),p... | python|dataframe|pandas-to-sql | 0 |
350,999 | 66,846,983 | Crop sides of a numpy array by w elements (where w may be zero) | <p>I'd like to generalize the following to allow the number <code>w</code> of cropped elements to possibly be zero:</p>
<pre><code>a = np.arange(42).reshape(6, 7)
w = 1 # Number of elements to crop on each side.
print(a[w:-w, w:-w])
</code></pre>
<p>And also in this generalization to arbitrary dimensions:</p>
<pre><co... | <p>It may be necessary to write a custom function:</p>
<pre><code> def crop_array(array, width) -> np.ndarray:
array = np.asarray(array)
width = np.broadcast_to(width, array.ndim)
assert np.all(width >= 0)
return array[tuple((slice(None) if w == 0 else slice(w, -w)) for w in width)]
</... | numpy|numpy-ndarray|numpy-slicing | 0 |
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