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 |
|---|---|---|---|---|---|---|
360,500 | 42,331,848 | How to display dataframes? | <p>I am doing the Titanic problem in Kaggle and I have problems displaying the dataframe:</p>
<pre><code>import pandas as pd
import numpy as np
titanic = pd.read_csv("input/train.csv")
titanic.head()
</code></pre>
<p>This should display the <code>train.csv</code> but it doesn't. Do you know why?</p> | <p>Whether you are using the REPL in Sublime Text or just running the program, you can display a dataframe called <em>titanic</em> as:</p>
<pre><code># prints first 5 rows in dataframe format
print(titanic.head())
# prints all rows in dataframe format
print(titanic)
</code></pre>
<p>If you want to display the data f... | python|pandas|numpy|dataframe|kaggle | 1 |
360,501 | 42,523,086 | Grouping Equal Elements In An Array | <p>I’m writing a program in python, which needs to sort through four columns of data in a text file, and return the four numbers the row with largest number in the third column for each set of identical numbers in the first column. </p>
<p>For example:</p>
<p>I need: </p>
<pre><code>1.0 19.3 15.5 0.1
1.0 ... | <p>An approach could be to use a dict where the value is the row keyed by the first column item. This way you won't have to load the whole text file in memory at once. You can scan line by line and update the dict as you go.</p> | python|algorithm|numpy|grouping|slice | 0 |
360,502 | 42,568,800 | Finding the largest element in a array based on another array and delete it (python 3.x) | <p>I am using python-3.x
I have two arrays, and I want to delete two rows that in (x) and (e) based on the largest two numbers in the (e) list:</p>
<pre><code> index
0 x=[0 0 0 1 0] e=[ [12]
1 [1 1 1 1 0] [6 ]
2 [0 0 1 0 0] delete this row [20] --> the 1st large... | <p>You can use <code>argpartition</code> in this way, firstly negate the <code>e</code> array, so that the largest values' index will be partitioned at the head of the result, after the partition, remove the first two indices and sort the remaining indices:</p>
<pre><code>x[np.sort((-e.ravel()).argpartition(2)[2:]),:]... | python|arrays|python-3.x|numpy | 1 |
360,503 | 42,497,340 | How to convert one-hot encodings into integers? | <p>I have a numpy array data set with shape (100,10). Each row is a one-hot encoding. I want to transfer it into a nd-array with shape (100,) such that I transferred each vector row into a integer that denote the index of the nonzero index. Is there a quick way of doing this using numpy or tensorflow?</p> | <p>You can use <a href="https://web.archive.org/web/20170227233359/https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html" rel="noreferrer">numpy.argmax</a> or <a href="https://web.archive.org/web/20170227233407/https://www.tensorflow.org/api_docs/python/tf/argmax" rel="noreferrer">tf.argmax</a>. Exam... | python|numpy|tensorflow | 46 |
360,504 | 42,466,639 | Convert a dictionary to a pandas dataframe | <p>I'm trying to convert a dictionary that only has 1 record to a pandas dataframe. I've used the following code from other solutions:</p>
<pre><code>d = {'id': 'CS2_056', 'cost': 2, 'name': 'Tap'}
pd.DataFrame(d.items(), columns=['id', 'cost','name'])
</code></pre>
<p>But I get the following error:</p>
<pre><code... | <p>You dict has only one record use list:</p>
<pre><code>import pandas as pd
d = {'id': 'CS2_056', 'cost': 2, 'name': 'Tap'}
df = pd.DataFrame([d], columns=d.keys())
print df
</code></pre>
<p>Output:</p>
<pre><code> id cost name
0 CS2_056 2 Tap
</code></pre> | python|pandas|dictionary|dataframe | 53 |
360,505 | 42,267,128 | Extract multiple patterns from a text file and save it to a panda dataframe [python] | <p>my Text file looks like this</p>
<pre><code>Description: Text 1 follows <br/> blah blah blah Cause: Cause Text 1
follows here <br/>Description: Text 2 follows <br/> blah blah
blah Cause: Cause Text 2 follows here<br/>Description: Text 3 follows <br/>
blah blah blah Description: Text... | <p>Here's what I came up with.</p>
<pre><code>r"Description:(.*?)<br/>(?:(?!Cause)(?!Description).)*(?:Cause:(.*?)<br/>)?"
</code></pre>
<p>If you use this regex, which matches both a <code>Description</code> <em>and</em> an optional <code>Cause</code>, it will ensure the pairings of descriptions and caus... | python|regex|pandas | 0 |
360,506 | 42,354,675 | Where does Bazel store TenserFlow build? | <p>I'm trying to build TensorFlow from sources following this guide: <a href="https://www.tensorflow.org/install/install_sources" rel="nofollow noreferrer">Installing TensorFlow from Sources</a>. The build seems to have worked fine, but then there's the last step:</p>
<blockquote>
<p>Invoke pip install to install th... | <p>It sounds like you may have skipped a step. Bazel does not create this file. The program that Bazel builds does.</p>
<p>The prior step on <a href="https://www.tensorflow.org/install/install_sources" rel="nofollow noreferrer">https://www.tensorflow.org/install/install_sources</a> to the one that you mention is to ru... | macos|tensorflow|bazel | 3 |
360,507 | 42,234,672 | python: efficient way to make large-scale check | <p>I have an array <code>test</code> with many lines, and each line has 3 numbers. I need to get <code>True</code> for each line where all 3 numbers are positive, and <code>False</code> otherwise. Currently I use</p>
<pre><code>check = np.all((test[:] > 0), axis=1)
</code></pre>
<p><code>test</code> looks like thi... | <p>Use <code>numexpr</code>:</p>
<pre><code>import numexpr as ne
t0, t1, t2 = test[:,0], test[:,1], test[:,2]
check = ne.evaluate('(t0 > 0) & (t1 > 0) & (t2 > 0)')
</code></pre> | python|numpy | 4 |
360,508 | 42,539,872 | How to keep a dictionary of dictionaries (or something with similar functionality) in pandas? | <p>So I have a large dataframe with many columns. Let us say the two main columns I am interested are messages and names. Each message will be something like a personal status and will be accompanied by the person's name. Let's say I have a word bank of emotion/feeling words which is fairly huge but a condensed version... | <p>If you are looking for a very <em>easy</em> way of accomplishing what you are looking for, I would suggest using the pandas <a href="http://pandas.pydata.org/pandas-docs/stable/groupby.html" rel="nofollow noreferrer">groupby</a> functionality in combination with the <a href="https://docs.python.org/2/library/collect... | python|pandas|dictionary|dataframe | 3 |
360,509 | 42,413,209 | Is it possible to load large data directly into numpy int8 array using h5py? | <p>I have a very large data file (1000 by 1400000 array) that contains integers of 0, 1, 2 and 4. It takes a very long time to load this big data into a numpy array using h5py because my memory(4GB) cannot hold that much and the program uses the swap space. Since there are only 4 numbers in the data, I want to use a 8 ... | <p>From the dataset docs page</p>
<pre><code> astype(dtype)
Return a context manager allowing you to read data as a particular type.
Conversion is handled by HDF5 directly, on the fly:
>>> dset = f.create_dataset("bigint", (1000,), dtype='int64')
>>> with dset.astype('int16'):
out = dset... | python|arrays|numpy|h5py | 2 |
360,510 | 42,151,460 | pandas DataFrame - calculate average for a column for each unique index without hardcoding each index label? | <p>Really liking pandas so far, here is something I can't solve though! </p>
<p>I'm showing a simplified dataframe here for some flight data. Carriers are the carriers like Am. Air. and Uni. Air. </p>
<pre><code>print (df)
Carrier | Num_Passengers
AA 40
AA 35
AA 64
UA 40
UA 25
UA ... | <p>You are looking to groupby your index and then get the mean number of passengers.</p>
<pre><code>df.groupby(level=0).mean()
Num_Passengers
Carrier
AA 46.333333
UA 40.333333
</code></pre> | python|pandas | 2 |
360,511 | 42,142,913 | Tensorflow not using GPU for one dataset, where it does for a very similar dataset | <p>I'm using TensorFlow to train a model using data originating from two sources. For both sources the training and validation data shape are almost identical and the dtypes throughout are np.float32.</p>
<p>The strange thing is, when I use the first data set the GPU on my machine is used, but when using the second da... | <p>The cause of the slowness was the memory layout of the ndarray backing the DataFrame. The s2 data was column-major meaning that each row of features and target was not contiguous.</p>
<p>This operation changes the memory layout:</p>
<pre><code>s2_train_data = s2_train_data.values.copy(order='C')
</code></pre>
<p>... | tensorflow|gpu | 1 |
360,512 | 42,471,523 | How can I generate a proper MNIST image? | <p>Hey guys so I've been working on a tensorflow project and I want to take a took at the test images from the MNIST database. Below is the gist of my code for converting the original data(ubyte?) into 2d numpy:</p>
<pre><code>from PIL import Image
from tensorflow.examples.tutorials.mnist import input_data
mnist = inp... | <p>Multiply the data by 255 and convert to np.uint8 <a href="http://pillow.readthedocs.io/en/3.4.x/handbook/concepts.html" rel="nofollow noreferrer">(uint8 for mode 'L')</a> have it work.</p>
<pre><code>def gen_image(arr):
two_d = (np.reshape(arr, (28, 28)) * 255).astype(np.uint8)
img = Image.fromarray(two_d, ... | python|numpy|tensorflow|mnist | 2 |
360,513 | 42,235,155 | Matrix inverse in numpy/python not giving correct matrix? | <p>I have a nxn matrix <code>C</code> and use <code>inv</code> from <code>numpy.linalg</code> to take the inverse to get <code>Cinverse</code>. My <code>C</code>matrix has elements of order <code>10**4</code> but my <code>Cinverse</code> matrix has elements of order <code>10**12</code> and higher (not sure if thats cor... | <p>The outer product of two vectors (be they the same or not) is <em>not</em> invertible. Since it is just a stack of scaled copies of the same vector its rank is one. Rank defective matrices <a href="https://en.wikipedia.org/wiki/Rank_(linear_algebra)#Properties" rel="nofollow noreferrer">cannot be inverted</a>.</p>
... | python|numpy|matrix|linear-algebra|matrix-inverse | 1 |
360,514 | 42,457,193 | How to reshape a big array (Memory Error) | <p>I have a very big 6D array as <code>(225, 97, 225, 32, 32, 32)</code>. I want to reshape it into 4D array like <code>(225*97*225, 32, 32, 32)</code>. I tried to used python 2.7 in ubuntu 14.04 with bellow code but I got the memory error. How could I solve it? Thanks</p>
<pre><code>import numpy as np
#input_6D shap... | <p>The strided expression works with <code>xstep,ystep,zstep=1,1,1</code>. </p>
<p>if input is <code>np.zeros((256,128,256),np.int8)</code> with strides <code>(32768, 256, 1)</code>, the output will have the given shape and strides <code>(32768, 256, 1, 32768, 256, 1)</code>.</p>
<p>I calculate that the strides for ... | python|python-2.7|numpy | 0 |
360,515 | 69,937,584 | Pandas.read_html() How to avoid putting rows with <th> elements into the header? | <p>I have a HTML table.</p>
<p><div class="snippet" data-lang="js" data-hide="false" data-console="false" data-babel="false">
<div class="snippet-code">
<pre class="snippet-code-html lang-html prettyprint-override"><code><table>
<thead>
<tr>
<th> header 1 </th>
<th>... | <p>You can simply use <code>numpy</code> <code>vstack</code></p>
<pre><code>df = pd.DataFrame(np.vstack([df.columns, df]))
</code></pre> | python|pandas | 0 |
360,516 | 69,876,688 | Loading a HuggingFace model into AllenNLP gives different predictions | <p>I have a custom classification model trained using <code>transformers</code> library based on a BERT model. The model classifies text into 7 different categories. It is persisted in a directory using:</p>
<pre class="lang-py prettyprint-override"><code>trainer.save_model(model_name)
tokenizer.save_pretrained(model_n... | <p>As discussed on GitHub: The problem is that you are constructing a 7-way classifier on top of BERT. Even though the BERT model will be identical, the 7-way classifier on top of it is randomly initialized every time.</p>
<p>BERT itself does not come with a classifier. That has to be fine-tuned for your data.</p> | python|pytorch|huggingface-transformers|allennlp | 1 |
360,517 | 69,962,843 | Python numpy : Sum an array selectively given information from a second array | <p>Let's say I have a N-dimensionnal array, for example:</p>
<pre><code>A = [ [ 1, 2] ,
[6, 10] ]
</code></pre>
<p>and another array B that defines an index associated with each value of A</p>
<pre><code>B = [[0, 1], [1, 0]]
</code></pre>
<p>And I want to obtain a 1D list or array that for each index contains the s... | <p>I think I found the best answer for now actually.</p>
<p>I can just use:</p>
<pre><code>np.histogram(B, weights = A)
</code></pre>
<p>This code provides the solution I want.</p> | python|arrays|numpy | 0 |
360,518 | 69,720,964 | filter out a dictionary within NOT isin | <p>I read a dataframe into a dictionary. Earlier, I was filtering out all the rows which are <code>.isin(['I1','I2','I3','I4'])</code>. This worked correctly</p>
<pre><code>df = {}
df['cgo_eafapo_t'] = pd.read_sql_table('cgo_eafapo_t', engine, schema_z2,
columns = cols)
df['cgo_eafap... | <p>Use <code>~</code> before condition:</p>
<pre><code>df['cgo_eafapo_t'] = df['cgo_eafapo_t'][~df['cgo_eafapo_t']['art_kennz'].isin(['I1','I2','I3','I4'])]
</code></pre>
<p>Better is select by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html" rel="nofollow noreferrer"><code>... | python|pandas|dataframe|numpy|dictionary | 0 |
360,519 | 69,785,637 | Bicubic block in Keras model | <p>While working with the code of <a href="https://github.com/deepak112/Keras-SRGAN/blob/master/Network.py" rel="nofollow noreferrer">SRGAN</a>, I wanted to replace <code>UpSampling2D</code> by <code>tf.image.resize_bicubic</code>. I used keras lambda layer for this function, as below</p>
<pre><code>def bicubic_lambda(... | <p>here is a one liner solution.</p>
<pre><code>model = Lambda(lambda image: tf.image.resize_images(image, (image.shape[1]*2, image.shape[2]*2), method = tf.image.ResizeMethod.BICUBIC))(model)
</code></pre> | tensorflow|keras | 0 |
360,520 | 69,968,835 | How can i print a specific value in matrix in python? (Using numpy) | <p>I got this code which prints a zero matrix given the size (m x n). But i wanna extract a specific value in a specific location of that matrix but i've tried everything i know and it's still not working. Here's my code:</p>
<pre><code>import numpy as np
class Matrix:
def __init__(self, m, n):
self.row = m
self... | <pre><code>import numpy as np
class Matrix:
def __init__(self, m, n):
self.row = m
self.column = n
def define_matrix(self):
return np.zeros((self.row, self.column), dtype=int)
def __str__(self):
return self.define_matrix()
def get(self, a, b):
try:
if not a in range(self.row+1):
print('El valo... | python|numpy|matrix | 2 |
360,521 | 69,920,078 | What does "save_graph" keyword in WandbCallback mean? | <p>I'm using Weights and Biases to track my deep learning models. To monitor everything I use the <code>WandbCallback</code> in <code>.fit</code>.
In the <a href="https://docs.wandb.ai/ref/python/integrations/keras/wandbcallback" rel="nofollow noreferrer">WandbCallback documentation</a> there is the keyword <code>save_... | <p>That is used to create log a <a href="https://docs.wandb.ai/ref/python/data-types/graph" rel="nofollow noreferrer">wandb.Graph</a> of the model. This class is typically used for saving and diplaying neural net models. It represents the graph as an array of nodes and edges. The nodes can have labels that can be visua... | tensorflow2.0|tf.keras|wandb | 1 |
360,522 | 69,688,196 | Keras/Tensorflow network inference performance | <p>I am using a Keras network which I am calling <code>predict()</code> many times on a single input. A rough calculation based on the layers gives ~3Mops. Running on my CPU should give ~1000 inferences per second, however in a test run which had 400 predicts it took 12 seconds => ~30 inferences per second. It only ... | <p>It can be also done by using a different toolkit for the inference e.g. <a href="https://docs.openvino.ai/latest/openvino_docs_install_guides_overview.html" rel="nofollow noreferrer">OpenVINO</a>. OpenVINO is optimized for Intel hardware but it should work with any CPU. It optimizes your model by converting to Inter... | tensorflow|keras | 1 |
360,523 | 69,964,138 | Getting the latest record available from a DataFrame | <p>Currently I have a DataFrame as below:</p>
<pre><code>import pandas as pd
import numpy as np
d = {'name': ['a', 'a','a','b','b','b','c','c','c'],
'Year': ['2000', '2010', '2020', '2000', '2010', '2020', '2000', '2010', '2020'],
'v1': [np.NaN, np.NaN, np.NaN, 41, 51, 61, 71, 81, 91],
'v2': [12, 22, 32... | <p>Simply use <code>groupby</code>+<code>last</code> and <code>as_index=False</code> as parameter for <code>groupby</code>:</p>
<pre><code>df.groupby('name', as_index=False).last()
</code></pre>
<p>Alternatively, if you know that the last year is <code>"2020"</code>:</p>
<pre><code>df.query('Year == "202... | python|pandas|dataframe | 3 |
360,524 | 69,769,999 | StandardScaler in Python | <p>I want to standardize 'x_train'.</p>
<p>The first 'x_train' in the picture is the original data set, and the next 'x_train' below the previous one is standardized.</p>
<p>I just want to standardize the first six columns, so I wrote x_train[:,0:6] during standardization.</p>
<p>However, the result of standardization ... | <p>Try -</p>
<pre><code>scaler = preprocessing.StandardScaler().fit(x_train.iloc[:, 0:6])
#returning the scaled values to a new variable
X_train_first_six = scaler.transform(x_train.iloc[:, 0:6])
X_test_first_six = scaler.transform(x_test.iloc[:, 0:6])
</code></pre>
<p>ref. <a href="https://pandas.pydata.org/docs/refe... | python|arrays|pandas|machine-learning|standardized | 0 |
360,525 | 69,951,249 | How can I use minibatches with a non-variational GPR in gpflow? | <p>I have tried to adapt the instructions in <a href="https://gpflow.readthedocs.io/en/master/notebooks/advanced/gps_for_big_data.html?highlight=minibatch#Minibatches-speed-up-computation" rel="nofollow noreferrer">this documentation</a> to use minibatches for a training a GPR model, but nothing I have tried works. I c... | <p>You can construct the <code>data</code> tuple to be two <code>tf.Variable</code> objects (if you want to be able to have different-length minibatches, you can pass a <code>shape=None</code> or <code>shape=(None, dim)</code> argument). Something like</p>
<pre class="lang-py prettyprint-override"><code>X = tf.Variable... | tensorflow|gpflow|gaussian-process|mini-batch | 0 |
360,526 | 69,885,909 | Python chat bot is not recognizing when I increase the content of the intents file | <p>The python bot code:</p>
<pre><code>import nltk
nltk.download('punkt')
from nltk.stem.lancaster import LancasterStemmer
stemmer=LancasterStemmer()
import numpy
import tflearn
import tensorflow
import random
import json
import pickle
with open("intents.json") as file:
data=json.load(file)
print... | <p>I realized that actually, the code works correctly, but
it is necessary to pay attention to if the content of the intent file changes, it is necessary to delete the ".pickle" file and run the code again.</p> | python-3.x|tensorflow|nltk|chatbot|tflearn | 0 |
360,527 | 69,963,949 | How can I fill a column with values that are computed between two dates in pandas? | <p>I have this dataframe:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>Date</th>
<th>Position</th>
<th>TrainerID</th>
<th>Win%</th>
</tr>
</thead>
<tbody>
<tr>
<td>2017-09-03</td>
<td>4</td>
<td>1788</td>
<td>0 (0 wins, 1 race)</td>
</tr>
<tr>
<td>2017-09-16</td>
<td>5</td>
<td>1788</td>... | <p>Create a indicator column to represent the win, then group the indicator column by <code>TrainerID</code> and apply the <code>rolling</code> <code>mean</code> to calculate the winning percentage, finally <code>merge</code> the calculated percentage column with the original dataframe</p>
<pre><code># Create indicator... | python|pandas|dataframe | 3 |
360,528 | 69,858,921 | `tf.svd` fails during GradientTape | <p>I'm trying to contract a network with multiple tensors and using singular value decomposition during contraction to simplify the contraction process. Whilst this works perfectly when I'm not taking any gradient, it fails once gradient tape starts to watch the tensors (I'm not sure why this is related). Below I wrote... | <p>I found a temporary solution that does not include all the aspects of the previous <code>svd</code> function but it works. TensorFlow requires object shapes to be set after slicing or manipulation (this might not be for every case but specific to mine). Thus I modified the <code>svd</code> function accordingly;</p>
... | python|tensorflow|gradient|gradient-descent|svd | 0 |
360,529 | 69,964,939 | Split string in columns in Python | <p>I have a list like this:</p>
<pre><code>[[{'contributionScore': 0.841473400592804, 'variable': 'series_2'},
{'contributionScore': 0.6113986968994141, 'variable': 'series_3'},
{'contributionScore': 0.5985525250434875, 'variable': 'series_1'},
{'contributionScore': 0.5641148686408997, 'variable': 'series_4'},
... | <p>I am a bit confused with the statement</p>
<blockquote>
<p>How can I obtain a dataframe with a column for each series?</p>
</blockquote>
<p>if you meant a single column, for all the series data with column "variable" then Celius Stingher's answer should be good enough.</p>
<p>If you meant as in each series... | python|pandas|list|dataframe|split | 1 |
360,530 | 70,001,353 | Building a quick GRU model for stock prediction | <p>I am beginner in <code>RNNs</code> and would like to build a running model gated recurrent unit <code>GRU</code> for stock prediction.</p>
<p>I have a numpy array for the training data with this shape:</p>
<pre><code>train_x.shape
(1122,20,320)
</code></pre>
<blockquote>
<pre><code>`1122` represents the total amount... | <p>So if you have 1122 data samples and each sample has 20 time steps and each time step has 320 features and you want to teach your model to make a binary decision between buying and selling, try something like this:</p>
<pre class="lang-py prettyprint-override"><code>import tensorflow as tf
tf.random.set_seed(1)
mod... | python|tensorflow|keras|recurrent-neural-network|gated-recurrent-unit | 1 |
360,531 | 69,927,457 | How to perform outer subtraction along an axis in numpy | <p>I used to perform an outer subtraction on two one-dimensional arrays as follows to receive a single two-dimensional arrays that contains all pairs of subtractions:</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
a = np.arange(5)
b = np.arange(3)
result = np.subtract.outer(a, b)
assert result.... | <p>You can expand/reshape <code>A</code> to (5, 1, 2) and <code>B</code> to (1, 3, 2) and let the broadcasting do the job:</p>
<pre><code>A[:, None, :] - B[None, :, :]
</code></pre> | python|python-3.x|numpy | 3 |
360,532 | 69,976,624 | RuntimeWarning: overflow encountered in reduce return | <p>I am testing this code using real data or a generated dataset from sklearn. In both cases, the code works without errors if the number of factors in the model is less than 6. With 7 factors, I get an error:</p>
<pre><code>RuntimeWarning: overflow encountered in square return np.mean((y_true - y_pred)**2)
</code></pr... | <p>I found the problem on my own. The code itself has no errors, but it uses gradient descent for optimization. I set the learning rate =1 and 2000 iterations. This, of course, is too much and retraining was taking place. The gradient continued to grow uncontrollably and I was getting huge values. The best solution is ... | python|numpy | 0 |
360,533 | 69,666,955 | Reducing the dimensions of a tensor in tensorflow | <p>I have a tensor with the following shape:</p>
<pre><code>> tf.Tensor: shape=(1, 1440)
</code></pre>
<p>How do I reduce such a shape so that I can get the following:</p>
<pre><code>> tf.Tensor: shape=(1440,)
</code></pre> | <p>Use <a href="https://www.tensorflow.org/api_docs/python/tf/squeeze" rel="nofollow noreferrer"><code>tf.squeeze</code></a>:</p>
<pre><code>import tensorflow as tf
tensor = tf.random.uniform((1, 1440))
print(tensor.shape
</code></pre>
<pre><code>TensorShape([1, 1440])
</code></pre>
<p>And now:</p>
<pre><code>squeezed... | python|tensorflow | 1 |
360,534 | 69,775,335 | Most efficient way to find polygon-coordinate intersections in large GeoJSON object | <p>I'm working on a project that requires coordinate mappings - determining whether a coordinate point exists in a series of polygons. The number of mappings is quite large - ~10 million coordinates across 100+ million polygons.</p>
<p>Before I continue, I've already looked at the questions <a href="https://stackoverfl... | <p>I would use a spatial join.</p>
<p>Given this fake data:</p>
<p><a href="https://i.stack.imgur.com/snmxH.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/snmxH.png" alt="enter image description here" /></a></p>
<p>I'd join it with the "within" predicate:</p>
<pre class="lang-py prettyprin... | python|python-3.x|computational-geometry|geopandas|shapely | 3 |
360,535 | 69,927,473 | a left join using pandas is populating the data twice for the same row | <p>I am trying to use pandas for a data analysis, used <code>merge</code> for performing a vlookup, the two data sets are as below,</p>
<pre><code>data1 =
acc_name tier content group gcode acc_ID
abc 3 55 b 111 R-DDD
def 4 45 c 222 X-TTT
xyz ... | <p>Remove duplicates in <code>df2</code> by column for join, here <code>Accountn</code>, so no duplicates in output:</p>
<pre><code>final = pd.merge(data1,data2[['Accountn','status']].drop_duplicates('Accountn'),
on=['Accountn'],
how='left')
</code></pre> | python|python-3.x|pandas | 1 |
360,536 | 69,916,421 | How to turn it an element of a list to a string? | <p>I have lists like this in a column of a data frame:</p>
<pre><code>list1 = [[Petitioner Jae Lee,his,he],[]]
list2 = [[lee],[federal officials]]
list3 = [[],[lawyer]]
</code></pre>
<p>But I want to turn into</p>
<pre><code>list1 = ['Petitioner Jae Lee' , 'his','he']
list2 = ['lee' , 'federal officials']]
list3 = ['... | <pre><code>list1 = [['Petitioner Jae Lee','his','he'],[]]
list2 = [['lee'],['federal officials']]
list3 = [[],['lawyer']]
flat_list1 = [item for sublist in list1 for item in sublist]
flat_list2 = [item for sublist in list2 for item in sublist]
flat_list3 = [item for sublist in list3 for item in sublist]
print(flat_li... | pandas|list|dataframe|for-loop | 0 |
360,537 | 69,858,044 | How to compare elements of one dataframe to another? | <p>I have a dataframe, called <code>PORResult</code>, of daily temperatures where rows are years and each column is a day (121 rows x 365 columns). I also have an array, called <code>Percentile_90</code>, of a threshold temperature for each day (length=365). For every day for every year in the <code>PORResult</code> da... | <p>(<em>Edited</em>:)
Depending on your data structure, I think</p>
<pre class="lang-py prettyprint-override"><code>CountResult = PORResult.gt(Percentile_90,axis=0).astype(int)
</code></pre>
<p>should do the trick. Generally, the toolset provided in <code>pandas</code> is sufficient that <code>for</code>-looping over a... | python|pandas|dataframe|numpy|weather | 0 |
360,538 | 69,709,776 | Python - issue with dimension of sequency | <p>I want to create in Python the following sequence of zero's and one's:</p>
<p><code> {0, 1,1,1,1, 0,0, 1,1,1, 0,0,0, 1,1, 0,0,0,0, 1}</code></p>
<p>So there is first 1 zero and 4 one's, then 2 zeros and 3 one's, then 3 zeros and 2 ones and finally 4 zeros and 1 one. The final array is supposed to have dimension 20x1... | <p>You can use <code>flatten</code>:</p>
<pre><code>import numpy as np
l = np.array([[0] * n + [1] * (5 - n) for n in range(1, 5)]).flatten()
print(l)
# >>> [0 1 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 0 1]
</code></pre> | python|numpy|sequence | 1 |
360,539 | 70,004,390 | ValueError: Exception encountered when calling layer "max_pooling2d_26" (type MaxPooling2D) | <p>I have the following code while building a CNN model with Keras. I have added three convolution layers and three pool layers. While compiling the model a value error arises from the pooling layer. I have added the code and error. please help</p>
<pre><code>model = Sequential()
model.add(Conv2D(filters = 32, kernel_s... | <p>The input to the third MaxPool layer is of size (1,1,64) on which you cannot run a pool of 2x2. You need to check the input dimensions for each layer. Sample:</p>
<pre><code>model = Sequential()
model.add(Conv2D(filters = 32, kernel_size = (4,4), input_shape = (28,28,1), activation = 'relu'))
model.add(MaxPool2D(poo... | tensorflow|keras|valueerror | 1 |
360,540 | 69,752,833 | ImageDataGenerator that outputs patches instead of full image | <p>I have a big dataset that I want to use to train a CNN with Keras (too big to load it in memory). I always train using <code>ImageDataGenerator.flow_from_dataframe</code>, as I have my images across different directories, as shown below.</p>
<pre><code>datagen = ImageDataGenerator(
rescale=1./255.
)
train_gen=da... | <p>You could try using a preprocessing function in your <code>ImageDataGenerator</code> combined with <code>tf.image.extract_patches</code>:</p>
<pre class="lang-py prettyprint-override"><code>import tensorflow as tf
import matplotlib.pyplot as plt
BATCH_SIZE = 32
def get_patches():
def _get_patches(image):
... | python|tensorflow|keras|deep-learning|conv-neural-network | 4 |
360,541 | 69,740,711 | How to create a NumPy array of booleans with k% True values? | <p>I know that we can create a NumPy array of boolean values with the following line of code:</p>
<pre><code>np.random.choice(a=[False, True], size=(N,))
</code></pre>
<p>But what if I want to specify that I want this random array to have around 60% (or more generally k%) <strong>True</strong> values?</p> | <p>Using <code>np.random.choice</code> can be really slow for large arrays. I recommend doing</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
N = 100_000
booleans = np.random.rand(N) < 0.6
</code></pre>
<p><code>np.random.rand</code> will produce uniform random numbers between <code>0.0</code>... | python|numpy|random | 3 |
360,542 | 69,726,577 | Python matrix row shifting(Also column) | <p>Is there any way that I can shift specific row in python? (It would be nice if numpy is used).
I want</p>
<pre><code>[[1,2],
[3,4]]
</code></pre>
<p>to be</p>
<pre><code>[[1,2],
[4,3]].
</code></pre>
<p>Also for the column would be nice!</p>
<pre><code>[[1,2],
[3,4]]
</code></pre>
<p>to be</p>
<pre><code>[[1,4],
[3,... | <p><code>np.roll</code> is your friend.</p>
<pre><code>>>> import numpy as np
>>> x = np.array([[1,2],[3,4]])
>>> x
array([[1, 2],
[3, 4]])
>>> x[1]
array([3, 4])
>>> np.roll(x[1],1)
array([4, 3])
>>> x[1] = np.roll(x[1],1)
>>> x
array([[1, 2],
... | python|numpy|matrix|shift | 1 |
360,543 | 69,944,447 | How to change the directory of mlflow logs? | <p>I am using MLflow to log the metrics but I want to change the default saving logs directory. So, instead of writing log files besides my main file, I want to store them to <code>/path/outputs/lg </code>. I don't know how to change it. I use it without in the <code>Model</code>.</p>
<pre class="lang-py prettyprint-ov... | <p>The solution is:</p>
<pre class="lang-py prettyprint-override"><code>mlflow.set_tracking_uri(uri=f'file://{hydra.utils.to_absolute_path("../output/mlruns")}')
exp = mlflow.get_experiment_by_name(name='Emegency_landing')
if not exp:
experiment_id = mlflow.create_experiment(name='Emegency_landing',
... | machine-learning|deep-learning|pytorch|mlflow | 0 |
360,544 | 69,921,886 | How to replace value while grouping with specific one? | <p>I have a dataframe:</p>
<pre><code>id type val
a1 q 100
a1 v 4
a1 l 17
b1 p 1
b1 j 700
b1 s 3
</code></pre>
<p>I want to group by id with keeping column type and summing values in column val. Value in column type must be one with highest va... | <p>You can try this:</p>
<pre><code>df.sort_values(by='val', ascending=False).groupby('id').agg({'type': 'first', 'val': 'sum'})
</code></pre>
<p>It gives:</p>
<pre><code> type val
id
a1 q 121
b1 j 704
</code></pre> | python|python-3.x|pandas|dataframe|group-by | 1 |
360,545 | 69,884,260 | Python: JSON with list of objects to dataframe | <p>I have a JSON example which I would like to flatten into a pandas DataFrame. I already used to apply some methods which I wrote myself, but I wondered if there is a better/shorter solution to this problem.</p>
<p><strong>JSON example:</strong></p>
<pre class="lang-json prettyprint-override"><code>{
"documentN... | <p>A more straightforward way is to use <code>json_normalize</code> but you lost the information about 'hope':</p>
<pre><code>import pandas as pd
import json
with open("data.json") as file:
data = json.load(file)
out = pd.json_normalize(data, ['data', 'scores'],
meta=['documentNa... | python|json|pandas|dataframe | 2 |
360,546 | 69,886,991 | Getting all diagonals from top right to bottom left in array | <p>I am trying to store all the diagonals in the matrix from the top right to the bottom left and store them in an array.</p>
<pre class="lang-py prettyprint-override"><code>matrix = array([[2, 0, 0, 2],
[3, 0, 0, 3],
[3, 0, 0, 2],
[0, 0, 0, 0]])
</code></pre>
<p><strong>... | <p>If you do not flip the matrix it works. Using:</p>
<pre class="lang-py prettyprint-override"><code>def get_diags_lower_left(matrix):
return [matrix.diagonal(i) for i in range(-3, 4)][::-1]
</code></pre>
<p>Will yield:</p>
<pre><code>[array([2]),
array([0, 3]),
array([0, 0, 2]),
array([2, 0, 0, 0]),
array([3,... | python|arrays|numpy|matrix | 2 |
360,547 | 69,893,620 | break values in pandas column into bins | <p>I have a following DataFrame:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>columns1</th>
<th>parametr_1</th>
<th>parametr_2</th>
<th>parametr_3</th>
</tr>
</thead>
<tbody>
<tr>
<td>val_1</td>
<td>1</td>
<td>2</td>
<td>1</td>
</tr>
<tr>
<td>val_2</td>
<td>1</td>
<td>2</td>
<td>5</td>
<... | <p>First is specified bins for each <code>parameter</code> column by dictioanry and call <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html" rel="nofollow noreferrer"><code>cut</code></a>, then count values by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Seri... | python|pandas | 2 |
360,548 | 69,689,910 | Pandas add column on condition: If value of cell is True set value of largest number in Period to true | <p>I have a pandas dataframe with lets say two columns, for example:</p>
<pre class="lang-py prettyprint-override"><code> value boolean
0 1 0
1 5 1
2 0 0
3 3 0
4 9 1
5 12 0
6 4 0
7 7 1
8 8 1... | <p>Compute the rolling max of the 'value' column</p>
<pre><code>>>> rolling_max_value = df.rolling(window=4, min_periods=1)['value'].max()
>>> rolling_max_value
0 1.0
1 5.0
2 5.0
3 5.0
4 9.0
5 12.0
6 12.0
7 12.0
8 12.0
9 8.0
10 17.0
11 17.0
12 1... | python|pandas | -1 |
360,549 | 69,753,155 | Pandas regex upcase after colon and space | <pre><code>import pandas as pd
import re
# Source data
df = pd.DataFrame( data={'A': ['abc: aaa 123: 111', 'edf: 111', 'ghi: a11 324: aaa', 'jkm: bn2 jsk: 1f4']})
df['A'] = df['A'].re.sub("(^|[:])\s*([a-zA-Z])", lambda p: p.group(0).upper(), s)
</code></pre>
<p>Result:</p>
<p>AttributeError: 'Series' objec... | <p>Try this:</p>
<pre><code>df['A'] = df.A.str.replace(r':\s+[a-zA-Z]', lambda p: p.group(0).upper())
# output
A
0 abc: Aaa 123: 111
1 edf: 111
2 ghi: A11 324: Aaa
3 jkm: Bn2 jsk: 1f4
</code></pre> | python-3.x|pandas|dataframe | 0 |
360,550 | 69,669,135 | How do I create a Pandas Dataframe from a dictionary containing a nested dictionary? | <p>I am working on a project where I am getting JSON data from a GraphQL API. After receiving the data, I am using json.loads() on the data and then accessing parts of the JSON I need, which is then stored in a dictionary containing another dictionary. The dictionary is:</p>
<pre><code>{'placement': 1, 'entrant': {'id'... | <p>You need to create proper dictionaries for pandas to create a dataframe. I'm assuming here you have a list of dicts called dictionaries.</p>
<pre class="lang-py prettyprint-override"><code>pd.DataFrame(
[
{"placement": d["placement"], "id": d["entrant"]["id&qu... | python|json|pandas|dataframe|dictionary | 1 |
360,551 | 69,771,753 | How do I strip data from a row in Pandas? | <p>I have a Pandas dataframe and I need to strip out components.schema.Person.properties and just call it id.</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th style="text-align: left;">column</th>
<th>data_type</th>
<th>data_description</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:... | <p>Like this?</p>
<pre class="lang-py prettyprint-override"><code>df['column'] = df['column'].apply(lambda x: x.split('.')[-1])
</code></pre>
<p>or more compact <a href="https://stackoverflow.com/questions/69771753/how-do-i-strip-data-from-a-row-in-pandas#comment123329869_69771753">solution by @Chris Adams</a>:</p>
<pr... | python|pandas | 1 |
360,552 | 69,997,820 | How can I delete a sequence of rows based on a condition? | <p>I have the following dataframe:</p>
<pre><code> id outcome
0 3 no
1 3 no
2 3 no
3 3 yes
4 3 no
5 5 no
6 5 no
7 5 yes
8 5 no
9 5 yes
10 6 no
11 6 no
12 6 yes
13 6 no
14 6 no
</code></pre>
<p>I want to rem... | <p>Use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html" rel="nofollow noreferrer"><code>groupby</code></a> with <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.cumsum.html" rel="nofollow noreferrer"><code>cumsum</code></a> to mark all 'no' at the ... | python|pandas|dataframe|numpy|time-series | 2 |
360,553 | 69,890,117 | How to free up memory in Lambda due to numpy.core._exceptions._ArrayMemoryError using pandas compare() | <p>When I run this code in a lambda function in which the memory allocation setting is set to max (10240):</p>
<pre><code>df_compare = first_less_dupes[compare_columns].compare(second_less_dupes[compare_columns])
</code></pre>
<p>I'm seeing this error:</p>
<pre><code>Unable to allocate 185. MiB for an array with shape ... | <p><code>del</code> does not properly delete objects, but simple drops the reference tied to the variable name being deleted. You must make sure that every other reference is properly dropped too.</p>
<p>Then you might still need to wait for garbage collection to happen. However with <code>pandas</code> and <code>numpy... | python|pandas|amazon-web-services|aws-lambda | 1 |
360,554 | 69,746,285 | How to convert a dictionary with lists to a different kind of dictionary for a column | <p>I have a dataframe like this:</p>
<pre><code>import pandas as pd
frame={'location': {(2, 'eng', 'US'): {"['sc']": 3, "['delhi']": 2, "['sonepat', 'delhi']": 1, "['new delhi']": 1}}}
df=pd.DataFrame(frame)
df.head()
</code></pre>
<p>Output</p>
<pre><code> ... | <p>You could try this:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
df = pd.DataFrame(
{
"location": {
(2, "eng", "US"): {
"['sc']": 3,
"['delhi']": 2,
"['sonepat',... | python|pandas|dataframe|dictionary | 2 |
360,555 | 69,999,180 | Equivalent of NumPy index arrays with standard Python lists or arrays | <p>I can use an array or a list to index into <code>numpy.array</code>, e.g.:</p>
<pre><code>a = np.array([1, 2, 3, 4])
print(a[[1, 3]])
</code></pre>
<p>will produce</p>
<pre><code>[2 4]
</code></pre>
<p>Is there an equivalent construct to index into a standard Python list or array?</p>
<p>Just to be more specific: in... | <pre><code>from operator import itemgetter
print(itemgetter(1,3)(a))
</code></pre>
<p>and to turn it into a list:</p>
<pre><code>print(list(itemgetter(1,3)(a)))
</code></pre> | python|arrays|list|numpy|matrix-indexing | 1 |
360,556 | 69,998,276 | How to fix the NumPy .dtype 'NoneType' error | <p>I am running the following Python code in PyCharm debug mode.</p>
<pre><code>import numpy as np, pandas as pd, numpy.polynomial.chebyshev as chebyshev
from pathlib import Path
home = str(Path.home())
directory = '/Downloads'
d = pd.read_csv(home+directory+'/data.csv')
np.random.seed(0)
nData = 4
data = np.random.... | <p>You'd need to downgrade to Python 3.9, or wait until this CPython/cython bug is fixed.</p>
<p>This is actually a real bug (as of 2022-04-14), and is showing up...:</p>
<ul>
<li>in cython: <a href="https://github.com/cython/cython/issues/4609" rel="nofollow noreferrer">#4609</a></li>
<li>in CPython: <a href="https://... | python-3.x|numpy | 5 |
360,557 | 70,019,622 | Python problem importing pandas dataframe | <p>I have a csv file with this structure:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>Path name</th>
<th>Path ID</th>
<th>Phenotype data</th>
</tr>
</thead>
<tbody>
<tr>
<td>path name 1</td>
<td>ID 1</td>
<td>Table 1</td>
</tr>
<tr>
<td>path name 2</td>
<td>ID 2</td>
<td>Table 2</td>
</... | <p>The <code>pheno_data</code> looks like a textual dump of a dataframe into a single cell. Due to the default formatting it has truncated the output, the <code>[</code> does not have a closing <code>]</code> and the last value shows <code>...</code>.</p>
<p>Your example is also missing the second half of the last row ... | python|pandas|string|dataframe|csv | 0 |
360,558 | 69,845,202 | why pandas read duplicates in .csv file then rename them? | <p>I am working with python and I read a file and I want to drop duplicates from the same questions but it keeps reading the duplicates with the name.1</p>
<p>for example: there is 2 of question1 it reads them question1 and question1.1</p>
<p>so when I use .drop_duplicates() it does not do anything, what is the problem... | <p>I think you should consider just specifying which columns <em>you know are duplicates</em> and specifically drop them in. I don't know Pandas, but I imagine you can specify the columns in a row, maybe something like the following to cut out the fourth column (if that's a duplicate):</p>
<pre class="lang-py prettypr... | python|pandas|csv|jupyter-notebook|duplicates | 0 |
360,559 | 43,172,606 | Getting weird values on predicting MNIST dataset | <p>I am using TF.LEARN with mnist data. I trained my neural network with 0.96 accuracy but now I am not really sure how to predict a value.</p>
<p>Here is my code..</p>
<pre><code>#getting mnist data to a zip in the computer.
mnist.SOURCE_URL = 'https://web.archive.org/web/20160117040036/http://yann.lecun.com/exdb/mn... | <p>You are getting a 9, which is quite similar to a 4.</p>
<p>What <code>model.predict</code> returns is <strong>score</strong> and while the 5-th value in the results array (the 5th value is 4 since it starts with a zero) gets a relatively high score (0.26-second high) - your model gives the last digit (9) the highes... | python|tensorflow|mnist|tflearn | 0 |
360,560 | 43,266,224 | Slicing Output Files | <p>I'm reading in a large file and I want to write a certain portion of it to a new file. </p>
<p>The pattern 'CARTESIAN COORDINATES' appears twice in this file and I want to omit everything before the second occurrence in the new file. So far I have:</p>
<pre><code>#!/usr/bin/env python
import string,sys
import nump... | <p>So I'm assuming the <code>101.out</code> file looks something like: </p>
<pre><code>not to be written
not to be written
CARTESIAN COORDINATES
not to be written
CARTESIAN COORDINATES
written
written
written
</code></pre>
<p>And you want all the stuff after the second <code>CARTESIAN COORDINATES</code> if I've und... | python|numpy|scientific-computing | 1 |
360,561 | 43,060,206 | what does `control_flow_ops.with_dependencies` mean for tensoflow? | <p>I am reading the code of tensorflow model:
<a href="https://github.com/tensorflow/models/blob/master/slim/train_image_classifier.py" rel="nofollow noreferrer">https://github.com/tensorflow/models/blob/master/slim/train_image_classifier.py</a></p>
<p>I am very confused with this code part:</p>
<pre><code>train_tens... | <p>The function has two arguments <code>control_flow_ops.with_dependencies(dependencies, output_tensor)</code>. The second argument <code>output_tensor</code>, which in your case is <code>total_loss</code> is evaluated only <em>after</em> all operations in <code>dependencies</code> are evaluated. As the name implies, <... | python|tensorflow|computer-vision|deep-learning|conv-neural-network | 5 |
360,562 | 43,463,111 | multi-numpy array from dataframe | <p>I have 5 Pandas DataFrames, its 5 classes of objects in 80,00 images and the coordinates of the objects. Basically, there is more than one class in each image</p>
<p>class 1:</p>
<pre><code> image_id x y
image_0 4835 106
image_0 2609 309
image_0 2891 412
image_0 1823 431
image_... | <p>Create a dictionary of dataframes to pass to <code>pd.concat</code></p>
<pre><code>clss = {'class 1': cls1, 'class 2': cls2}
catted = pd.concat(clss)
g = catted.groupby(['image_id', pd.Grouper(level=0)])[['x', 'y']]
g.apply(lambda x: list(zip(*x.values.T))).unstack()
</code></pre>
<p><a href="https://i.stack.imgu... | python|arrays|pandas|numpy | 1 |
360,563 | 43,456,244 | pandas - fetch series of column values and put them into a cell | <p>I have a dataframe that is multi-indexed by country and by date like so:</p>
<p><a href="https://i.stack.imgur.com/xrRHy.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/xrRHy.png" alt="enter image description here"></a></p>
<p>what I need is to create a table that is grouped by country and that ... | <p>Use <code>unstack</code> with <code>stack</code> to capture missing elements of a cartesian product.</p>
<pre><code>f = dict(
raws=dict(raws='sum', raws_sequence=lambda x: list(x)),
cost=dict(cost='sum')
)
d1 = df.unstack(fill_value=0).stack().groupby(level='country_name').agg(f)
d1.columns = d1.columns.dr... | pandas | 1 |
360,564 | 43,273,473 | Why does convolutional network use every 64 image for training? | <p>I'm looking a code from <a href="https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py#L15" rel="nofollow noreferrer">here</a> for Python 3.5 + TensorFlow + TFLearn:</p>
<pre><code># -*- coding: utf-8 -*-
""" Convolutional Neural Network for MNIST dataset classification task.
References:... | <p>It is not only using every 64th image, it is loading batches of 64 image. That is why you see the iter increase by 64 each time, because it has processed 64 images per training step.
Take a look at the documentation for the regression layer <a href="http://tflearn.org/layers/estimator/" rel="nofollow noreferrer">htt... | python|python-3.x|tensorflow|tflearn | 0 |
360,565 | 43,090,069 | Pandas/matplotlib plot with date-axis shows correct day/month but wrong weekday/year | <p>I'm loading CSV data using pandas, where one of the columns takes the form of
a date in the format '%a %d.%m.%Y' (e.g. 'Mon 06.02.2017'), and then trying to
make some plots where the x-axis is labeled according to the date.</p>
<p>Something goes wrong during the plotting, because the date labels are wrong;
e.g. wha... | <p>Although I couldn't really figure out why it's not working, it seems it has something to do with plotting with pandas vs. solely with matplotlib and maybe the <code>mdates.DateFormatter</code>...</p>
<p>When I comment out the formatting lines, it seems to start working:</p>
<pre><code># ax1.xaxis.set_minor_locator... | python|pandas|matplotlib | 4 |
360,566 | 43,270,955 | Can some one explain logging device placement in tensorflow tutorial ? | <p><a href="https://www.tensorflow.org/tutorials/using_gpu" rel="nofollow noreferrer">Link for the tf tutorial</a></p>
<pre><code># Creates a graph.
with tf.device('/cpu:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='... | <p>That seems like a bug in the documentation, the MatMul operation will be placed on CPU in this case.</p>
<p>Indeed, running the code sample does show this:</p>
<pre><code>import tensorflow as tf
# Creates a graph.
with tf.device('/cpu:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
... | tensorflow | 2 |
360,567 | 43,241,728 | TFSlim ValueError Can not squeeze dim[1], expected a dimension of 1, got 3 for 'vgg_16/fc8/squeezed' (op: 'Squeeze') with input shapes: [3,3,3,2] | <p>Trying to fine-tune the Tensorflow Slim VGG16 net on a different set of class labels ( 2 ) excluding the fc8. On execution I am getting this error.</p>
<h1>Error</h1>
<pre><code>logits, _ = vgg.vgg_16(images, num_classes=NUM_CLASSES, is_training=True)
/models/slim/nets/vgg.py", line 178, in vgg_16
net = tf.squeeze... | <p>Can you try just defining the batch directly:</p>
<pre><code>with tf.Graph().as_default():
tf.logging.set_verbosity(tf.logging.INFO)
images = tf.randon_uniform([BATCH_SIZE, 224, 224, 3])
labels = tf.randon_uniform([BATCH_SIZE], max_value=NUM_CLASES)
with slim.arg_scope(vgg.vgg_arg_scope()):
... | python|tensorflow | 0 |
360,568 | 43,270,827 | Shifting values in datetimeindex of pandas dataframe | <p>I have a df with a DateTimeIndex of 30 minute intervals over a long period (> 1 year), so >17520 rows. For reasons related to daylight savings, two of the index values are repeated in the index and two values are missing. So the duplicated values are:</p>
<pre><code>In[1]: df[df.index.duplicated('first')]
Out[2]: ... | <p>I think you need map <code>duplicated index</code> with <code>rename</code> by <code>dict</code>:</p>
<pre><code>print (df)
a b c
timestamp
2013-10-06 01:00:00 1 NaN NaN
2013-10-06 01:30:00 2 NaN NaN
2013-10-06 01:00:00 3 NaN NaN
2013-10-06 01:30:00 4 NaN NaN
2012-1... | pandas|dataframe|datetimeindex | 0 |
360,569 | 43,247,065 | How to combine summary statistics from 100s of *csv files into one *csv with pandas? | <p>I have several hundreds *csv files, which when imported into a pandas data frame look as follows:</p>
<pre><code>import pandas as pd
df = pd.read_csv("filename1.csv")
df
column1 column2 column3 column4
0 10 A 1 ID1
1 15 A 1 ID1
2 19 B... | <p>Repeated appending to a pandas <code>DataFrame</code> is highly inefficient as it copies the DataFrame.<br>
Instead you could write the max values found to the resultant file directly.</p>
<pre><code>files = glob.glob("*.csv")
with open("totalfile.csv", "w") as fout:
for f in files:
df = pd.read_csv(f)... | python|csv|pandas|numpy|dataframe | 1 |
360,570 | 43,066,064 | Tensorflow tuples with different shapes | <p>I have a problem with returning a tuple of two variable <code>v</code> , <code>wt</code> where <code>v</code> has <code>shape=(20,20)</code> and <code>wt</code> has <code>shape=(1,)</code>. <code>wt</code> is a variable that is a weight value. I want to return the tuple (v,wt) inside a <code>map_fn</code></p>
<p>m... | <p>You are returning the results of a <code>tf.while</code> loop here. the <code>tf.while</code> loop returns a tuple of multiple values, in your case we can see that your while loop returned a value of interest and a counter value as a tuple.</p>
<pre><code>(<tf.Tensor 'map_2/while/while/Exit_1:0' shape=(20,) dtyp... | python|tensorflow|factorization | 1 |
360,571 | 43,412,007 | Numpy: Subtract 2 numpy arrays row wise | <p>I have 2 numpy arrays a and b as below:</p>
<pre><code>a = np.random.randint(0,10,(3,2))
Out[124]:
array([[0, 2],
[6, 8],
[0, 4]])
b = np.random.randint(0,10,(2,2))
Out[125]:
array([[5, 9],
[2, 4]])
</code></pre>
<p>I want to subtract each row in b from each row in a and the desired output i... | <p>Just use <code>np.newaxis</code> (which is just an alias for None) to add a singleton dimension to a, and let broadcasting do the rest:</p>
<pre><code>In [45]: a[:, np.newaxis] - b
Out[45]:
array([[[-5, -7],
[-2, -2]],
[[ 1, -1],
[ 4, 4]],
[[-5, -5],
[-2, 0]]])
</code></pr... | python|numpy | 5 |
360,572 | 43,234,346 | How to rename unnamed columns in Pandas? | <p>I have a pdf with a table in it, and trying to get that table into Pandas. Extracting pdf tables is notoriously difficult to get right, but I have found tabula works best. It is far and away the best I have seen, though still not perfect. I have this pdf table:</p>
<p><a href="https://i.stack.imgur.com/WvLxs.png" r... | <p>This is a pure pandas solution - assuming the dataframe is read exactly as pasted below.</p>
<pre><code>df.columns = df.columns.str.replace('Unnamed.*', '') + \
df.iloc[0].fillna('') + \
df.iloc[1].fillna('')
df.drop([0,1], inplace=True)
1 Asset Type Name ... | python|pandas|dataframe|tabula | 3 |
360,573 | 43,384,268 | Box plot using pandas | <p>Trying to plot a box plot for a pandas dataframe but the x-axis column names don't appear to be clear.</p>
<pre><code>import matplotlib.pyplot as plt
pd.set_option('display.mpl_style', 'default')
fig, ax1 = plt.subplots()
%matplotlib inline
df.boxplot(column = ['avg_dist','avg_rating_by_driver','avg_rating_of_drive... | <p>I think you need parameter <code>rot</code>:</p>
<pre><code>cols = ['avg_dist','avg_rating_by_driver','avg_rating_of_driver',
'avg_surge','surge_pct','trips_in_first_30_days','weekday_pct']
df.boxplot(column=cols, rot=90)
</code></pre>
<p>Sample:</p>
<pre><code>np.random.seed(100)
cols = ['avg_dist','avg... | python|pandas|matplotlib|boxplot | 2 |
360,574 | 43,115,112 | how to delete row for a given date index value | <p>I tried below but was not able to delete the row against the given date...</p>
<p>I tried the below syntax (line 2 below) but it still does not delete the row for index value = '2016-01-25"</p>
<pre><code>norm_data = normalize_data(data, '^NSEI')
norm_data.drop(pd.to_datetime('2016-01-25'))
print norm_data.head(50... | <p>This: </p>
<pre><code>norm_data.drop(pd.to_datetime('2016-01-25'))
</code></pre>
<p>Returns a copy of norm_data. It does not alter the norm_data df. To change it, you can use:</p>
<pre><code># Setting inplace flag to True
norm_data.drop(pd.to_datetime('2016-01-25'), inplace=True)
</code></pre>
<p>Or the less ... | pandas|delete-row | 0 |
360,575 | 43,170,242 | tensorflow modify variables in py_func (and its grad func) | <p>In tensorflow, we can define our own op and its gradient by:
<a href="https://gist.github.com/harpone/3453185b41d8d985356cbe5e57d67342" rel="nofollow noreferrer">https://gist.github.com/harpone/3453185b41d8d985356cbe5e57d67342</a></p>
<p>However, can we modify any variable in the computational graph in these python... | <p>You can modify the variables in the computational graph in these python functions. Your example code with <code>tmp = var*10</code> will work and does not convert anything to numpy. </p>
<p>In fact you should try to avoid converting to numpy as much as possible since it will slow down the computation.</p>
<p><stro... | tensorflow | 2 |
360,576 | 43,045,017 | Installing NumPy using Pip on Windows | <p>I downloaded Python 3.6.1 and it came with Pip preinstalled. I wrote this command to install numpy </p>
<pre><code>C:\Python36-32>python -m pip install numpy
</code></pre>
<p>To which I got this as the output: </p>
<blockquote>
<p>Collecting numpy Could not fetch URL
<a href="https://pypi.python.org/... | <p>I solved this problem using the following command: </p>
<pre><code>pip install numpy --index-url=http://pypi.python.org/simple/ --trusted-host pypi.python.org
</code></pre>
<p><a href="https://stackoverflow.com/a/29751768/7529378">This</a> answer helped me figure it out.</p> | python-3.x|numpy|ssl|pip | 1 |
360,577 | 43,451,249 | How does numpy.dot function work? | <pre><code>import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([5, 6])
a.dot(b)
b.dot(a)
</code></pre>
<p>What happens for <em>a.dot(b)</em> and <em>b.dot(a)</em>? For matrix mutiplication, <em>a.dot(b)</em> should be illegal.</p> | <p>In this setup, <code>b.dot(a)</code> is equivalent to <code>b.T.dot(a)</code>; indeed, <code>b</code> and <code>b.T</code> happen to have the same shape, so even though the notation makes it looks like <code>b</code> is a row vector, it really isn't. We can, however, redefine it to behave explicitly as a row vector,... | numpy | 3 |
360,578 | 43,048,218 | Color conditional data on a plot with matplotlib threw a loop | <p>I have a the following dataframe</p>
<pre><code>import pandas as pd
import matplotlib.pyplot as plt
datas = [['RAC1','CD0287',1.52,9.88], ['RAC1','CD0695',2.08,10.05],['RAC1','CD0845',2.01,10.2], ['RAC3','CD0258',1.91,9.8], ['RAC3','CD471',1.66,9.6], ['RAC8','CD0558',1.32,9.3], ['RAC8','CD0968',2.89,10.01]]
labels... | <p>You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html" rel="nofollow noreferrer"><code>groupby</code></a> here in conjunction with <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.difference.html" rel="nofollow noreferrer"><code>difference... | python|pandas|matplotlib | 4 |
360,579 | 43,151,403 | Using numpy reshape and the "array must be unchanged" error | <p>I am receiving the error:</p>
<pre><code>ValueError: total size of new array must be unchanged
</code></pre>
<p>And I cannot seem to figure out how my array is being changed to throw this error, and pointing to the last line:</p>
<pre><code> 1 data = hourlyElectricityForVisualization.values
----> 2 data =... | <p>You have to delete some data points to make it possible to fit into new matrix of size N-to-24*7. You can do the following:</p>
<p><code>
data = data[:(np.shape(data)[0] - np.shape(data)[0]%(24*7))]
data = np.reshape(data, (len(data)/24/7, 24*7))
</code></p>
<p>This code will delete last 121 points to make result ... | python|numpy|reshape | 1 |
360,580 | 43,202,644 | how to update tensorflow to 0.9 instead of 1.0? | <p>I want to run code on a Linux server with tensorflow <strong>0.8</strong>,but my code is written in tf <strong>0.9</strong>.I want to update it to 0.9 <strong>instead of 1.0</strong>,but the official site doesn't support version under 1.0.How can I get my ideal version? If I can't,is it different to fix my code to f... | <p>pip install the whl file of your version, for example </p>
<pre><code> pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0rc0-cp27-none-linux_x86_64.whl
</code></pre> | tensorflow | 1 |
360,581 | 43,104,309 | Is there an equivalent of the python built in function `all` in pandas? | <p>I'm quyering a pandas dataframe <code>df</code> like this.</p>
<pre><code>df = df[
(df.value1 >= threshold1) &
(df.value2 >= threshold2) &
(df.value3.isin(list3))
]
</code></pre>
<p>Python has the built in function <a href="https://docs.python.org/3/library/functions.html#all" rel="no... | <p><a href="https://stackoverflow.com/questions/43104309/is-there-an-equivalent-of-the-python-built-in-function-all-in-pandas#comment73290345_43104309">@juanpa.arrivillaga</a> already gave you a very good explanation about boolean indexing in Pandas.</p>
<p>I'd like to give you a bit nicer alternative - <a href="http:... | python|pandas|conditional-statements|subset|built-in | 4 |
360,582 | 72,376,173 | Is there a pandas function to give me the ordering positions of a series? | <p>I want a function that would give me the index of an element in a pandas Series object if the Series was sorted. I made the following implementation:</p>
<pre><code>def series_ordering(series):
positions = series.reset_index(drop=True, inplace=False).sort_values().index
return pd.Series(positions, index=seri... | <p>Use <a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.argsort.html" rel="nofollow noreferrer"><code>argsort</code></a> to get the sorting order:</p>
<p>Example:</p>
<pre><code>pd.Series([2,3,1,4,0]).argsort()
</code></pre>
<p>output:</p>
<pre><code>0 4
1 2
2 0
3 1
4 3
dtype: int64
</... | python|pandas|sorting | 0 |
360,583 | 72,157,236 | Why is my loss function tending to inifnity, however it is working appropiately when the x and y co-ordinates are swapped | <p>I have a cookie-cutter Linear Regression PyTorch model. To calculate the expected years of experience, dependent on the individuals' salary. A visualisation of the dataset can be viewed below</p>
<p><a href="https://i.stack.imgur.com/CFu5R.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/CFu5R.png... | <p>The issue arose due to the lost function exploding gradient issue since I was using MSEloss and my dataset was using large numbers. the Mean Squared error was initially high, So my iterations on stochastic gradient descent Iterated the loss to a higher value as expected, but these values were too large so when squar... | python-3.x|pytorch|linear-regression|gradient-descent | 0 |
360,584 | 72,152,650 | Built group of two within a dataframe | <p>I would like to create a group of two within a dataframe based on the ID. So basically connect the text inside the second column using a space. Using <code>groupby()</code> will only join the entire text. I would like to set the group size per ID myself. If the group size does not add up, then none should be added f... | <p>Create a sequential counter with <code>cumcount</code> then divide this by <code>2 (desired group size)</code> to create partitions, then group the dataframe by <code>ID</code> along with the partitions and aggregate <code>col2</code> with <code>join</code></p>
<pre><code>i = df.groupby('ID').cumcount() // 2
df.grou... | python|pandas|dataframe|pandas-groupby | 1 |
360,585 | 72,157,607 | Null values are not filling | <p>I have a dataset with null values inside the data. The null value is in the gender column.
When I try to replace it with mode of that column using the code,</p>
<pre><code>name age income gender department grade performance_score
0 Allen Smith 45.0 NaN NaN Operations G3 723
1 S Kumar NaN 16000.0 F ... | <p>Try this,</p>
<pre><code>df["gender"] = df["gender"].fillna(df["gender"].mode()[0])
df.head()
</code></pre>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th style="text-align: center;"></th>
<th style="text-align: center;">name</th>
<th style="text-align: center;... | python|pandas|dataframe | 0 |
360,586 | 72,380,691 | np.where resulted in length of values does not match | <p>I have used column to filter and create new column</p>
<pre><code>df['brought_profit_to_company'] = np.where([(df['product_id'] == '7.99_7free') & (df['trial'] == True)], 0, 5)
df.head()
</code></pre>
<p>But It has resulted in error:</p>
<pre><code>/usr/local/lib/python3.7/dist-packages/pandas/core/common.py in... | <p>Remove the [] from the where function, it should be like this instead:</p>
<pre><code>df['brought_profit_to_company'] = np.where((df['product_id'] == '7.99_7free') & (df['trial'] == True), 0, 5)
</code></pre> | pandas|numpy | 0 |
360,587 | 72,196,029 | Batch plotting in PyTorch using Matplotlib isn't working | <p>I am trying to plot a batch of image (batch size 128) in pytorch using make_grid function in my local machine. But when I call the function its like the calling block never compile. I have waited for long but the asteric sign of compiling never be gone.<a href="https://i.stack.imgur.com/RJldo.jpg" rel="nofollow nore... | <p>Please try adding <code>plt.show()</code> after <code>ax.imshow()</code> (<a href="https://stackoverflow.com/questions/54422714/when-is-plt-show-required-to-show-a-plot-and-when-is-it-not">this answer will be helpful</a>), or replace <code>break</code> to <code>time.sleep(10)</code></p> | matplotlib|pytorch|tensor|image-classification | 0 |
360,588 | 72,241,521 | Python pandas - series to dataframe | <p>.</p>
<p>How do I print out only the country names that exist in the dataframe among series with country names as index?</p> | <p>The following will filter for rows with an index value that is also in the index of <code>df2</code> using <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.isin.html" rel="nofollow noreferrer"><code>.isin()</code></a>.</p>
<pre><code>df1.loc[df1.index.isin(df2.index)]
</code></pre... | python|pandas|dataframe|series | 1 |
360,589 | 72,171,808 | Unable to assign nn.Module class to self.encoder in another nn.Module class | <p>In the code shown below, I would like to create an <code>Encoder</code> class and then assign it into <code>self.encoder</code> in <code>ComponentEmbedding</code> class. However, when i try to access <code>ComponentEmbedding().encoder</code>, the value is still <code>None</code>. Anyone help?</p>
<p>Note: I need to ... | <p>You are initializing <code>encoder</code> as a class attribute with value <code>None</code> whereas it should be an instance attribute:</p>
<pre><code>class Encoder(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
pass
class ComponentEmbedding(nn.Module):
def... | python|pytorch | 0 |
360,590 | 72,273,520 | Increment numbers in a Pandas column, based on existing highest value | <p>I'm trying to concat two dataframes. dfA has incremental numbers in column 1, dfB does not. Starting from highest value in dfA, I would like to increment the numbers in the rows from dfB when they are concatenated in dfC. So it should be:</p>
<p>dfA:</p>
<div class="s-table-container">
<table class="s-table">
<th... | <p>You could modify <code>dfB</code> like this -</p>
<pre><code>dfB['Column 1'] = pd.Series(list(range(dfA['Column 1'].max(), dfA['Column 1'].max() + len(dfB.index))))
</code></pre>
<p>Then you could simply concatenate <code>dfA</code> and the updated version of <code>dfB</code>.</p> | python|pandas | 0 |
360,591 | 72,220,449 | How to devide each raw of data into 3 matrixes in python? | <p>I have data with 1034 columns, I want to divide each raw of it into 3 matrixes of 49*7. It remains 5 columns delete them. How can I do this in python?</p>
<p>First, I removed the last 5 columns from the data.</p>
<pre><code>rawData = pd.read_csv('../input/smartgrid/data/data.csv')#import the data
#remove t... | <p>Here's a way to do what you're asking:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import numpy as np
#rawData = pd.read_csv('../input/smartgrid/data/data.csv')#import the data
rawData = pd.DataFrame([[x * 5 + i for x in range(1034)] for i in range(2)], columns=range(1034))
numRowsPerMat... | python|pandas|dataframe|matrix|conv-neural-network | 2 |
360,592 | 72,297,056 | Python pandas update row value from another row | <p>I have a dataframe like the one given below.</p>
<pre><code>C1 SIZE M COLOR
C1 PRIZE L COLOR
C1 COLOR Nan BLUE
C2 SIZE L COLOR
C2 PRIZE S COLOR
C2 COLOR Nan YELLOW
</code></pre>
<p>I am looking for ways to transform it to the one given below.</p>
<pre><code>C1 SIZE M BLUE
C1 PRIZE L BLUE
C1 COLOR Nan ... | <p>This should work</p>
<pre><code>df = pd.DataFrame({'Col1': ['C1', 'C1', 'C1', 'C2', 'C2', 'C2'],
'Col2': ['SIZE', 'PRIZE', 'COLOR', 'SIZE', 'PRIZE', 'COLOR'],
'Col3': ['M', 'L', 'Nan', 'L', 'S', 'Nan'],
'Col4': ['COLOR', 'COLOR', 'BLUE', 'COLOR', 'COLOR', 'YEL... | python|python-3.x|pandas|dataframe | 1 |
360,593 | 72,463,796 | Train multiple connected neural networks with a single optimizer | <p>How can I jointly optimize the parameters of a model comprising two distinct neural networks with a single optimizer? What I've tried is the following, after having initialized an optimizer:</p>
<pre><code>optim_global = optim.Adam(zip(model1.parameters(), model2.parameters()))
</code></pre>
<p>but I get this error<... | <p>These are generator you can control either with the <a href="https://stackoverflow.com/questions/10967819/python-when-can-i-unpack-a-generator">unpacking operator</a> <code>*</code>:</p>
<pre><code>>>> optim.Adam([*model1.parameters(), *model2.parameters()])
</code></pre>
<p>Or using <a href="https://docs.p... | pytorch | 1 |
360,594 | 72,401,330 | How to convert a Pandas DataFrame into a valid MLserver Predict V2-encoded payload? | <p>I recently found the KServe and MLserver projects which are open source tools for serving ML models. These are great. What's not so great is that these both use a (new to me) and novel formatting for inference inputs, documented here: <a href="https://kserve.github.io/website/modelserving/inference_api/" rel="nofoll... | <p>The V2 Inference Protocol can be thought of as a lower-level spec. It doesn't try to define how to encode higher-level data types (e.g. a Pandas Dataframe) and leaves this to the inference servers themselves.</p>
<p>Based on this, MLServer introduces its own conventions which, if followed, ensure that the payload ge... | python|pandas|seldon | 1 |
360,595 | 72,333,706 | How to pull a key from a dict (pandas series) to its own row? | <p>Here is my example data with two fields where the last one [outbreak] is a pandas series.</p>
<p>Start:
<a href="https://i.stack.imgur.com/nfUOg.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/nfUOg.png" alt="Start" /></a></p>
<p>Goal (Excel mock-up):
<a href="https://i.stack.imgur.com/z7PML.png" ... | <p>You can try with <code>ast</code> convert to <code>dict</code> format , then we do conversion</p>
<pre><code>import ast
out = df.pop('outbreak').map(ast.literal_eval).apply(pd.Series).stack().reset_index(level=1).join(df)
out.columns = ['outbreak_id','outbreak_value','report_id']
Out[157]:
level_1 ... | python|pandas|dataframe|dictionary|series | 2 |
360,596 | 72,319,999 | Convert combinations of row+column as Column headers | <p>I have a dataframe as follows:</p>
<pre><code>Machine Time Part PowerA PowerB
1 20:30 1 0.1 0.4
1 20:30 2 0.9 0.7
1 20:31 1 0.3 0.1
1 20:31 2 0.2 0.3
2 20:30 1 0.2 0.5
2 ... | <p>Let us do <code>pivot_table</code> then <code>swaplevel</code></p>
<pre><code>s = df.pivot_table(index= ['Machine','Time'],
columns = df.Part.astype(str).radd('Part'),
values=['PowerA','PowerB'],
fill_value=-1).swaplevel(1,0, axis=1).sort_index(level=0, axis=... | python|pandas|dataframe | 2 |
360,597 | 72,240,264 | Convert date format from a 'yfinance' download | <p>I have a <em>yfinance</em> download that is working fine, but I want the Date column to be in YYYY/MM/DD format when I write to disk.</p>
<p>The Date column is the Index, so I first remove the index. Then I have tried using Pandas' "to_datetime" and also ".str.replace" to get the column data to ... | <p>Change the format of the date after resetting the index:</p>
<pre><code>df.reset_index(inplace=True)
df['Date'] = df['Date'].dt.strftime('%Y/%m/%d')
</code></pre>
<p>As noted in <em><a href="https://stackoverflow.com/questions/52027033/convert-datetime-to-another-format-without-changing-dtype/52027599#52027599">Con... | python|pandas|dataframe|yfinance | 0 |
360,598 | 72,236,704 | Highlight element based on boolean pandas df | <p>I have 2 data frames with identical indices/columns:</p>
<pre><code>df = pd.DataFrame({'A':[5.5, 3, 0, 3, 1],
'B':[2, 1, 0.2, 4, 5],
'C':[3, 1, 3.5, 6, 0]})
df_bool = pd.DataFrame({'A':[0, 1, 0, 0, 1],
'B':[0, 0, 1, 0, 0],
... | <p>You can also use <a href="https://numpy.org/doc/stable/reference/generated/numpy.where.html" rel="nofollow noreferrer">np.where</a> to convert <code>df_bool</code> into a DataFrame of styles based on the locations of <code>1</code> values (<code>df_bool.eq(1)</code>).</p>
<p>By setting <code>axis=None</code> to <a h... | python|pandas|dataframe|pandas-styles | 1 |
360,599 | 72,355,444 | Why does pandas.concat() add (), to column name | <p>I am trying to work out why the column names for pandas.concat() are in brackets.</p>
<p>There is a similar question <a href="https://stackoverflow.com/questions/60897820/when-using-pd-concat-the-resulting-dataframe-column-names-appear-in-parenthese">here</a> - but in my context I don't understand how this can be ha... | <p>The categories are stored in a list of arrays. When you make them column names, each name becomes a one-element tuple. Change this line:</p>
<pre><code>month_df.columns = ohe.categories_
</code></pre>
<p>to:</p>
<pre><code>month_df.columns = ohe.categories_[0]
</code></pre> | pandas|dataframe | 2 |
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