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 |
|---|---|---|---|---|---|---|
369,800 | 67,089,849 | AttributeError: 'GPT2TokenizerFast' object has no attribute 'max_len' | <p>I am just using the huggingface transformer library and get the following message when running run_lm_finetuning.py: AttributeError: 'GPT2TokenizerFast' object has no attribute 'max_len'. Anyone else with this problem or an idea how to fix it? Thanks!</p>
<p>My full experiment run:
mkdir experiments</p>
<p>for epoch... | <p>The <a href="https://github.com/huggingface/transformers/issues/8739" rel="noreferrer">"AttributeError: 'BertTokenizerFast' object has no attribute 'max_len'" Github issue</a> contains the fix:</p>
<blockquote>
<p>The <code>run_language_modeling.py</code> script is deprecated in favor of <code>language-mod... | tokenize|huggingface-transformers|transformer-model|huggingface-tokenizers|gpt-2 | 6 |
369,801 | 66,856,104 | Conditioning on NumPy array based on values from different arrays | <p>Suppose I have two (sample) arrays in the following manner:</p>
<pre><code>a = np.array([4, 5,-1, 2, -3, 3, -4])
b = np.array([0, 1, 0, 0, 1, 1, 0])
</code></pre>
<p>Now, I want to calculate the count of occurrences where (a > 0 and b == 0) and also where (a < 0 and b == 1). How can I condition an array based ... | <p>One can use the bitwise-and operator <code>&</code>. Take care to wrap the expressions in parentheses because <code>&</code> binds more tightly</p>
<pre class="lang-py prettyprint-override"><code>a[(a < 0) & (b == 1)]
</code></pre>
<p>One can achieve the same behavior with <a href="https://numpy.org/d... | python|numpy | 4 |
369,802 | 66,950,845 | ValueError: Cannot feed value of shape (64,) for Tensor 'TargetsData/Y:0', which has shape '(?, 1)' | <p>I have quite a bit of experience with Python programming in general, but am very new to neural networks and deep learning. After having gone through Tech With Tim's Mega AI course, I decided to create a neural network on my own. It's very simple - it takes in a name and outputs 0 or 1 depending on its gender - 0 = m... | <p>The error is self-explaining</p>
<pre><code>ValueError: Cannot feed value of shape (64,) for Tensor 'TargetsData/Y:0', which has shape '(?, 1)'
</code></pre>
<p>It means your passed shape of <code>y</code> which is <code>(64,)</code> doesn't match the expected shape <code>(?, 1)</code>. To fix it, you only need to c... | python|pandas|numpy|tensorflow|neural-network | 0 |
369,803 | 66,800,810 | How can I randomly set elements to zero in TF? | <p>The pure numpy solution is:</p>
<pre><code>import numpy as np
data = np.random.rand(5,5) #data is of shape (5,5) with floats
masking_prob = 0.5 #probability of an element to get masked
indices = np.random.choice(np.prod(data.shape), replace=False, size=int(np.prod(data.shape)*masking_prob))
data[np.unravel_index(in... | <p>Use <a href="https://www.tensorflow.org/api_docs/python/tf/nn/dropout" rel="nofollow noreferrer"><code>tf.nn.dropout</code></a>:</p>
<pre><code>import tensorflow as tf
import numpy as np
data = np.random.rand(5,5)
</code></pre>
<pre><code>array([[0.38658212, 0.6896139 , 0.92139911, 0.45646086, 0.23185075],
[... | python|numpy|tensorflow | 2 |
369,804 | 66,844,236 | Refer to or INNER JOIN a Pandas dataframe value in the WHERE clause of a SQL Server query | <p>I have a pandas dataframe from which I want to retrieve patient values from a SQL Server table based upon their matching patient id column called PatID</p>
<pre><code>query = "SELECT * FROM [Hospital].[dbo].[Patient] WHERE PatID= df.PatID"
df2 = pd.read_sql(query, sql_conn)
</code></pre>
<p>Is there any w... | <p>I think that generates a list of your ids and concatenate it in your sql query is the easiest way to solve this problem</p>
<pre><code>ids = ','.join(df['PatID'].unique())
query = f"SELECT * FROM [Hospital].[dbo].[Patient] WHERE PatID in ({ids})"
df2 = pd.read_sql(query, sql_conn)
</code></pre>
<p>Be care... | python|sql|sql-server|pandas|join | 1 |
369,805 | 66,991,331 | Replacing nested loops in Python using NumPy | <p>So I have these 6 nested loops, and their purpose is only to multiply and add arrays <code>X</code> and <code>Y</code> over different indices to get array <code>Z</code>.</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
dim_a = 5
dim_b = 9
Z = np.zeros((dim_a,dim_b,dim_b,dim_a))
X = np.arange(... | <p>You can certainly do with <a href="https://numpy.org/doc/stable/reference/generated/numpy.einsum.html" rel="nofollow noreferrer"><code>np.einsum</code></a>:</p>
<pre><code>Z[i,a,b,j] += X[m,e,b,j] * Y[m,e,a,i] * 2
</code></pre>
<p>translates to</p>
<pre><code>Z = np.einsum('mebj,meai->iabj', X,Y) * 2
</code></pre... | python|arrays|python-3.x|numpy | 2 |
369,806 | 66,849,863 | Tensorflow 2 :NotImplementedError: numpy() is only available when eager execution is enabled | <p>There is a question in this code, I delete <code>SeBlock</code> class and just run CNN class, then all is well. If I plug <code>SeBlock</code> to <code>CNN</code> class the error will occur, and display <code>NotImplementedError</code>. I don't know cause this problem, I try to solve this problem, but what method I ... | <p>This <a href="https://github.com/IBM/dl-learning-path-assets/blob/main/fundamentals-of-deeplearning/examples/Eager_Execution_in_TensorFlow_2.x_with_output.ipynb" rel="nofollow noreferrer">notebook</a> should help to upgrade, check, and enable. Good luck!</p> | python|artificial-intelligence|classification|tensorflow2 | -1 |
369,807 | 67,013,645 | Appliyng data augmentation to all but one class in python | <p>I have a dataset with 8 categories and I'm currently performing data augmentation in all the classes with the following code:</p>
<pre><code>train_dataGen = ImageDataGenerator(rescale=None,horizontal_flip=True,rotation_range=90,
vertical_flip=True)
train_generator = train_dataGen.... | <p>Yes you need to use 2 generators and you can iterate with the help of chain method</p>
<p>following your example :</p>
<pre><code>from itertools import chain
train_others = ImageDataGenerator(rescale=None,horizontal_flip=True,rotation_range=90,
vertical_flip=True)
train_cats= Imag... | python|tensorflow|keras|data-augmentation | 1 |
369,808 | 66,873,185 | How to use a custom .csv dataset in TensorFlow Recommenders library? | <p>I'm new to tensorflow. I want to train a recommendation model on my dataset using the TensorFlow Recommenders library and the simple code provided at:</p>
<p><a href="https://github.com/tensorflow/recommenders" rel="noreferrer">https://github.com/tensorflow/recommenders</a></p>
<p>I want to know how can I use (load ... | <pre><code>import pandas as pd
DATA_URL = "D:/ratings.csv"
df = pd.read_csv(DATA_URL)
ratings = tf.data.Dataset.from_tensor_slices(dict(df)).map(lambda x: {
"user_id": str(x["user_id"]),
"item_id": str(x["item_id"]),
"rating": float(x["rating... | python|tensorflow|tensorflow-datasets|recommendation-engine|recommendation-system | 4 |
369,809 | 66,777,819 | Is there any better way to store unpacked values in python | <p>I'm having a function which unpack 6 values and I'm storing those values in 6 variables but is there any better approach for unpacking those values and storing unlike writing large line of code.</p>
<pre><code>var_value1,var_value2,var_value3,var_value4,var_value5,var_value6 = Some_function()
</code></pre>
<p>Note: ... | <ul>
<li><a href="https://www.python.org/dev/peps/pep-0008/#indentation" rel="nofollow noreferrer">Indent</a></li>
</ul>
<p>or</p>
<ul>
<li>Create a dictionary, unpack there, <br />
<br />
<code>var_val = Somefunction()._asdict()</code>
<br />
<br />
and navigate using keys</li>
</ul> | python-3.x|pandas | 1 |
369,810 | 67,107,580 | How to set all unmasked values to a certain value? | <p>In Python I have a masked array, <code>mask_array</code>, and I want to set all remaining (unmasked) values to 1. When I do <code>mask_array[(mask_array >= 0) & (mask_array < 0)= 1</code>, the cells keep their original values and do not change to 1. When I do <code>mask_array[mask_array>=0]=1</code>, al... | <p>The condition <code>(mask_array >= 0) & (mask_array < 0)</code> can't match any cell. No value inside <code>mask_array</code> can be <em>bigger or equal to 0</em> <strong>and</strong> <em>smaller than 0</em> at the same time, so nothing matches => no changes</p> | python|numpy | 1 |
369,811 | 67,027,649 | How to unlist a list with one value inside a pandas columns? | <p>I have a pandas data frame:</p>
<pre><code>Id Col1
1 ['string']
2 ['string2']
</code></pre>
<p>Is possible to convert the data frame into another data frame that look like this?</p>
<pre><code>Id Col1
1 string
2 string2
</code></pre>
<p>I tried with this way but only get the <code>[</code>.... | <blockquote>
<p>I tried with this way but only get the [.</p>
</blockquote>
<p>Then this means they are <code>str</code>ings, not <code>list</code>s. You can convert them to <code>list</code>s by <code>apply</code>ing <a href="https://docs.python.org/3/library/ast.html#ast.literal_eval" rel="noreferrer"><code>ast.liter... | python|pandas|dataframe | 5 |
369,812 | 67,059,958 | Invalid number of arguments during code refactoring scipy SLSQP | <p>I am trying to optimize an objective function using <code>scipy.optimize.minimize.</code></p>
<p>Initially, I kept getting the error</p>
<p><code>TypeError: numpy boolean subtract, the - operator, is deprecated, use the bitwise_xor, the ^ operator, or the logical_xor function instead.</code></p>
<p>After looking for... | <p>With a boolean dtype array:</p>
<pre><code>In [131]: x = np.ones(4, bool)
</code></pre>
<p>Your first error:</p>
<pre><code>In [132]: x-x
Traceback (most recent call last):
File "<ipython-input-132-966d70d4047a>", line 1, in <module>
x-x
TypeError: numpy boolean subtract, the `-` operator... | python|numpy|scipy-optimize|scipy-optimize-minimize | 0 |
369,813 | 66,922,933 | numpy arrays slicing using reshape | <p>I was learning how to reshape an array. I found several tutorials on the topic. I have confusion after reading all those stuff.</p>
<p>Suppose I declared an array using numpy</p>
<pre><code>arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
</code></pre>
<p>I reshaped it</p>
<pre><code>newarr = arr.reshape(4, 3... | <p>arr is unidimensional, then only arr.shape[0] is defined</p>
<p>Try printing arr.shape</p> | python|numpy|multidimensional-array|numpy-slicing | 0 |
369,814 | 66,951,778 | Pandas: If NaN show the NaN row as well as the row above | <p>I would like Python Pandas to display not only the row with NaN values but also the row above the respective row with NaN values. I need this because if there is a NaN value in a row I need to delete not only the one with NaN values but also the prior row.</p>
<p>Thanks</p> | <p>Take index of all the rows with NaN values in a list. Then create a new index list by subtracting 1 from it. Then delete those indexes.</p>
<pre><code>nan_indexes = df[df.row.isnull()].index.tolist()
row_above_nan = [x-1 for x in nan_indexes]
final_list = nan_indexes + row_above_nan
df = df[~df.index.isin(final_lis... | python|pandas|dataframe|nan | 0 |
369,815 | 67,022,237 | KeyError with using get_group in python pandas | <p>I have time series as shown below:</p>
<pre><code> date_time system_load date month_year year month day hour load_group
0 2013-01-01 00:00:00 17.2 2013-01-01 2013-01 2013 1 1 0 (15, 20]
1 2013-01-01 01:00:00 16 2013-01-01 2013-01 2013 ... | <p>You must convert the group names to the exact object types which they were stored. Note the <code>dtypes</code> in your <code>df0.info()</code>:</p>
<ul>
<li><code>month_year</code> is <code>period[M]</code> instead of <code>str</code>.</li>
<li><code>load_group</code> is a category column containing <code>pd.Interv... | python-3.x|pandas | 1 |
369,816 | 66,829,994 | Can't open Anaconda Prompt(anaconda3) due to white spaces in user name | <p>Recently, I upgraded to Windows 2004 and since then getting the following message on opening Anaconda Prompt.</p>
<blockquote>
<p><a href="https://i.stack.imgur.com/eCgsE.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/eCgsE.jpg" alt="enter image description here" /></a></p>
</blockquote>
<p>Tried... | <p>This is still a problem without a patch resulting from the <code>Windows 8.3</code> filename convention Where <code>Windows</code> creates a short name of each <code>path</code> after being created, and this short name often has a space which anaconda refusses to pick up. Before you can do any of this you must reins... | python-3.x|windows|tensorflow|keras|anaconda | 0 |
369,817 | 67,180,564 | How to create column in dataframe with list of headings affected by condition, apply a cap and then exclude not respecting condition headings | <p>I'm struggling to solve this issue. Help would be very much appreciated.</p>
<p>Note: <strong>bold</strong> in the text refers to the columns i need to create.</p>
<p>I have a data set in which I count the values of the row that are different than nan, and it's represented in column [count]. In column <strong>[incl_... | <p>This should work:</p>
<pre><code>import pandas as pd
import numpy as np
non_value_columns = ["index", "incl_count", "excl", "lim", "count"]
max_lim = 3
entries = []
df = pd.read_excel('your.xlsx')
for entry in df:
if entry not in non_value_columns:
print... | python|pandas|dataframe|conditional-statements | 0 |
369,818 | 66,799,610 | Write dataframe to an existing excelfile without destrying it | <p>ive got a problem.</p>
<p>I want to write a dataframe to an existing Excel-List which contains formulas.
When i Open a workbook and use a writer with pandas, it always says there is unreadable content in it and i need to repair it when i open the Excel-List.</p>
<p>Do you know how to resolve this?</p>
<p>Here is my ... | <p>have a look at this: <a href="https://stackoverflow.com/a/38075046/14367973">https://stackoverflow.com/a/38075046/14367973</a></p>
<p>If i understood your question, you want to append more rows to a <code>.xlsx</code> file.
The new rows are from a dataFrame that have the same number of columns than the excel file.
I... | python|excel|pandas|dataframe | 0 |
369,819 | 66,837,752 | ffill with limit in numpy | <p>For performance reasons I'd like to use Numpy to do the same kind of forward fill I can get with Pandas like so:</p>
<pre class="lang-py prettyprint-override"><code>s = pd.Series([1, 2, nan, nan, nan, 7, 8, nan, nan, nan, nan, nan, nan, nan, nan])
s.ffill(limit=7)
</code></pre>
<p>which results in:</p>
<pre><code>ar... | <p>Basically combined solutions from these two sources:</p>
<ul>
<li><a href="https://stackoverflow.com/questions/41190852/most-efficient-way-to-forward-fill-nan-values-in-numpy-array">Most efficient way to forward-fill NaN values in numpy array</a></li>
<li><a href="https://stackoverflow.com/questions/24885092/finding... | python|numpy | 1 |
369,820 | 66,871,116 | Converting multiple lists into DataFrame | <p>I created those lists to store things i generate over a for loop which is shown a bit further down.</p>
<pre><code>neutralScore = []
lightPosScore = []
middlePosScore = []
heavyPosScore = []
lightNegScore = []
middleNegScore = []
heavyNegScore = []
</code></pre>
<p>Here comes the loop</p>
<pre><code>score = float
co... | <p>It is not entirely clear from question how you want your final dataframe to look like. But I would do something like:</p>
<pre class="lang-py prettyprint-override"><code>>>> import numpy as np
>>> import pandas as pd
>>> data = np.random.normal(0, 1, size=10)
>>> df = pd.DataF... | python|pandas|list | 0 |
369,821 | 66,952,988 | Look up in DataFrame | <p>I have a data frame as below:</p>
<pre><code>index name col1 col2 count
"there is some values"
col1 : is the index of a value in df
col2 : is the index of a value in df
</code></pre>
<p>I want find the name related to index in col 1 and col 2 and put them in the match 1 and match 2.
I want... | <p>If I understood the question correctly, I believe this will get you there:</p>
<pre><code>df = pd.DataFrame([
['x1', 5, 3, 2, 'x5', 'x3'],
['x2', 4, 6, 3, 'x4', 'x6'],
['x3', np.nan, np.nan, 7, ],
['x4', np.nan, np.nan, 1, ],
['x5', np.n... | python|pandas|dataframe | 1 |
369,822 | 66,871,925 | Generating a probability distribution P(y) from another probability distribution P(x) such that highest probability in P(x) is least likely in P(y) | <p>So the problem at hand is that I have some values in a dictionary with counters, let's say</p>
<pre><code>dict = {"cats":0, "dogs":0, "lions":0}
</code></pre>
<p>I want to randomly select the keys from this dictionary and increment the counters as I select the particular keys.</p>
<p>... | <p>There are <strong>many</strong> ways of solving this, but as an alternative I'd be tempted to calculate the probabilities as:</p>
<pre><code>def iweight(k, *, alpha=1):
p = 1/(alpha + np.array(k))
return p / np.sum(p)
</code></pre>
<p>which could be used as:</p>
<pre><code>counts = [0, 0, 0, 20]
for _ in ran... | python|random|probability|probability-distribution|numpy-random | 2 |
369,823 | 66,804,489 | age calculation in pandas data frame | <p>My data frame looks like -</p>
<pre><code>id dob
1 13/01/1978
2 03/08/1957
3 22/12/1977
</code></pre>
<p>I want to calculate age based on 'dob' column.</p>
<pre><code>id dob age
1 13/01/1978 43
2 03/08/1957 64
3 22/12/1976 44
</c... | <p>Here is a solution:</p>
<pre><code>d = { 'Id': [1,2,3],
'dob': ['13/01/1978', '03/08/1957', '22/12/1977'] }
df = pd.DataFrame(d)
df['dob']= pd.to_datetime(df['dob'])
now = datetime.datetime.now()
df['age'] = df['dob'].apply(lambda x: now.year - x.year)
#Output:
Id dob age
0 1 1978-01-13 43... | python|pandas|scikit-learn | 1 |
369,824 | 67,043,448 | Local TFJS Model (Transfer Learning MobileNet) returns wrong predictions | <p>I use transfer learning with MobileNet to solve an image problem. I loaded the images with ImageDataGenerator <code>(rescale=1./127.5)</code>.</p>
<p>After training I converted it with:</p>
<pre><code>tensorflowjs_converter --input_format=tf_saved_model --weight_shard_size_bytes 10000000000 model tmp
</code></pre>
<... | <p>Solved it with this normalization:</p>
<pre><code>const normalized = imageTensor.toFloat().sub(127).div(128);
</code></pre> | tensorflow|expo|tensorflow.js | 0 |
369,825 | 66,890,904 | Python calculated Timedelta 50 years in future, should be same day | <p>This is a follow up to <a href="https://stackoverflow.com/questions/66683405/calculating-new-column-value-in-dataframe-based-on-next-rows-column-value">Calculating new column value in dataframe based on next rows column value</a></p>
<p>The solution in the previous question worked for a column holding hh:mm:ss value... | <p>The solution to this was easy, and staring me in the face.</p>
<p>The offending code:</p>
<pre><code>s = pd.to_timedelta(df.start_time).shift(-1).sub(pd.offsets.Second(1))
</code></pre>
<p>The <strong>correct</strong> way to create an end_time off of a timestamp type series/column:</p>
<pre><code>s = pd.to_timestamp... | python|pandas|timestamp|timedelta | 0 |
369,826 | 67,029,716 | Turn dictionary into dataframe | <p>I'm new to Python. I have this kind of dictionary, from a geodesic output and i wonder if i can turn this into DataFrame or matrix?
here's an example data, but what i'm working right now has more than 8000 data</p>
<pre><code>{(0, 0): 0.0,
(0, 1): 1.3128088339744233,
(1, 0): 1.3128088339744233,
(1, 1): 0.0}
</cod... | <p>Try this -</p>
<blockquote>
<p>I have added additional entries to show how this approach scales to more rows and column indexes, and handles missing row, column indexes as well.</p>
</blockquote>
<pre><code>d = {(0, 0): 0.0,
(0, 1): 1.3128088339744233,
(1, 0): 1.3128088339744233,
(1, 1): 0.0}
df = pd... | python|pandas | 3 |
369,827 | 66,831,480 | "Importing tensorflow module not found" Only on jupyter notebook but not jupyter lab or terminal | <p>I launch the powershell anaconda prompt and activate an environment for a new project. Then I install tensorflow using the command provided by the tensorflow website <code>pip install tensorflow</code>.</p>
<p>To validate that the installation was successful, I open python from within the terminal and import tensorf... | <p>Follow these steps install Tenosrflow on Virtual environment with PIP</p>
<pre><code>#Install virtualenv
sudo pip3 install virtualenv
#Create virtual environment name: venv
virtualenv venv
#Activate venv
source venv/bin/activate
#Install tensorflow
venv$ pip3 install tensorflow
#Install Jupyter notebook
venv$ pip3 i... | python|tensorflow|jupyter-notebook|anaconda | 0 |
369,828 | 66,977,408 | PIL: How to draw shapes given a set of unordered outline dimensions | <p>I am using Python version 3.8.7 and the PIL library.</p>
<p>I have a DataFrame full of dimensions of various elements of a blueprint. The two main elements are lines (composing the outline), and the labels (colored rectangles). Using PIL I was able to draw the below image.</p>
<p><a href="https://i.stack.imgur.com/x... | <p>If this is the expected result:</p>
<p><a href="https://i.stack.imgur.com/ByPXo.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ByPXo.jpg" alt="enter image description here" /></a></p>
<p>You can use this pseudo-code:</p>
<pre><code>for thisColour in list of blob colours (1)
generate list of ne... | python-3.x|pandas|dataframe|python-imaging-library|shapes | 1 |
369,829 | 66,886,187 | Python dictionary, how can I create a key with a string and the actual key combined? | <p>I hope this is a quite easy question, but for me without a lot of python background I can't find an answer.</p>
<pre><code>df = pd.DataFrame(
{'Messung': ['10bar','10bar','10bar','20bar','20bar'],
'Zahl': [1, 2, 3, 4, 5],
'Buchstabe': ['a','b','c','d','e']})
</code></pre>
<p>There is a DataFrame ... | <p>While trying your solution I noticed, I can even delete the line with key=item[0:2] and directly build my key with 'RP_' and the item[0:2]</p>
<pre><code>d={}
for row, item in enumerate(df['Messung']):
key = "RP_"+item[0:2]
d.setdefault(key, []).append(df.iloc[row])
</code></pre> | python|pandas|dictionary|key|notin | 0 |
369,830 | 47,418,871 | how can I normalize n different set of data using a provided method which can only normalize a set of data | <p>I have n set of changing data, and I want to normalize every set of data using the <a href="https://stats.stackexchange.com/questions/43159/how-to-calculate-pooled-variance-of-two-groups-given-known-group-variances-mean">running mean method</a>, since every set has its own mean and std, I have to keep n different mo... | <p>Create an array of Scalar instances. If you have different <code>obs_dim</code> for each dataset, you can do <code>[Scalar(obs_dim) for obs_dim in obs_dims]</code>. If you have one <code>obs_dim</code>, use <code>[Scalar(obs_dim) for i in range(N)]</code> where <code>N</code> is the number of datasets. You can then ... | python|numpy|machine-learning|deep-learning | 2 |
369,831 | 47,272,523 | Update inventory stock csv file with python | <p>I have a stock inventory file in csv format like this:</p>
<pre><code>sku nome prezzo qty codice
1 uno 10 1 11111
2 due 10 1 22222
3 tre 10 1 33333
4 quattro 10 1 44444
5 cinque 10 1 55555
10 dieci 10 1 101010
</code></pre>
<p>The only column... | <p>IIUC:</p>
<pre><code>In [52]: r = b.set_index('sku') \
...: .reindex(pd.Index(a['sku']).union(pd.Index(b['sku']))) \
...: .combine_first(a.set_index('sku').assign(qty=0, prezzo=0)) \
...: .reset_index()
...:
In [53]: r[['prezzo','qty','codice']] = r[['prezzo','qty','codice']].asty... | python|pandas|csv|python-3.6 | 1 |
369,832 | 47,491,865 | i want to install tensor flow in anaconda but it shows error : | <p>i tried many ways to install tensor flow on my windows system with anaconda 5.0 and python 3.6</p>
<p>pip3 install --upgrade <a href="https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.0-py3-none-any.whl" rel="nofollow noreferrer">https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.0-py3-... | <p>Finally , Solved my problem by reinstalling the anaconda and python 3.5. and after the re-installation i install tensor-flow by anaconda navigator and it worked for me.</p> | python-3.x|tensorflow|installation|anaconda | 0 |
369,833 | 47,344,577 | How to query a pandas DataFrame using an array of tuples ? | <p>I have a pandas DataFrame with the following structure:</p>
<p><a href="https://i.stack.imgur.com/XQk6i.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/XQk6i.png" alt="sample data frame image"></a></p>
<p>And I have an array of tuples</p>
<p><code>arr_tuples = [(0,3),(1,1),(1,3),(2,1)]</code></... | <p>You can use <code>pd.DataFrame.lookup</code> with some <code>zip</code> and unpacking trickery</p>
<pre><code>df.lookup(*zip(*arr_tuples))
array([ 4, 5, 7, 12])
</code></pre>
<hr>
<ul>
<li><p><code>list(zip(*arr_tuples))</code> creates two tuples out of the list of tuples</p>
<pre><code>[(0, 1, 1, 2), (3, 1, ... | python|pandas|numpy|dataframe|indexing | 8 |
369,834 | 47,319,291 | How to parse String output of a tensorflow model | <p>Created a model using the code here : <a href="https://gist.github.com/gaganmalhotra/1424bd3d0617e784976b29d5846b16b1" rel="nofollow noreferrer">https://gist.github.com/gaganmalhotra/1424bd3d0617e784976b29d5846b16b1</a></p>
<p>To get the predictions of the probabilites in java it can be done using below code:</p>
... | <p>The <code>DT_STRING</code> typed TensorFlow tensors contain <a href="https://www.tensorflow.org/api_docs/java/reference/org/tensorflow/DataType" rel="nofollow noreferrer">arbitrary byte sequences</a> as elements, not Java <code>String</code>s (sequence of characters).</p>
<p>Thus, what you want is something like th... | java|python|tensorflow|tensorflow-serving | 2 |
369,835 | 47,395,119 | Center datetimes of resampled time series | <p>When I resample a Pandas time series to reduce the number of data points, the timestamp of each resulting datapoint is at the start of each resampling bin. When overplotting graphs with different resampling rates, this causes an apparent shift of the data. How can I "center" the timestamp of the resampled data in it... | <p>I don't know how to use the midpoint in general. There is the <code>label</code>-parameter, but that only has the options <code>right</code> and <code>left</code>. However, in a concrete case as this you can explicitly offset the resampled timestamp with the <code>loffset</code>-parameter:</p>
<pre><code>d.resample... | python|pandas | 3 |
369,836 | 47,214,979 | concatenate two matrices with interleaved columns | <p>I have two 2-D arrays and I would like to concatenate them interleaving the columns </p>
<p>Initial arrays (with shape (3, 8) each):</p>
<pre><code>array([[ 107, 115, 132, 138, 128, 117, 121,135],
[ 149, 152, 151, 143, 146, 149, 149,148],
[ 152, 142, 146 , 141, 143, 148, 149, 153]])... | <p>You can column stack the two arrays, then reshape:</p>
<pre><code>np.column_stack((a, b)).reshape(-1, a.shape[1])
#array([[107, 115, 132, 138, 128, 117, 121, 135],
# [ 25, 28, 28, 25, 23, 21, 20, 18],
# [149, 152, 151, 143, 146, 149, 149, 148],
# [ 3, 3, 2, 2, 10, 12, 12, 1],
#... | python|numpy|matrix | 3 |
369,837 | 47,356,011 | how do i make labels list manually for my imported images in tensorflow | <pre><code>fq=glob.glob("*.jpg") # ['0.jpg','1.jpg','2.jpg','3.jpg','4.jpg'],labels for images=[1,1,1,0,0]
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot))
</code></pre>
<p>assuming that there is just <strong>2 classes</strong> and <strong>batch size is 2</strong> and <... | <p>If your input array is a NumPy array, you can use <code>np.eye</code>:</p>
<pre><code>label_array = np.array([1, 1, 1, 0, 0])
onehot_array = np.eye(2)[label_array]
</code></pre>
<p>The result is: [[ 0. 1.], [ 0. 1.], [ 0. 1.], [ 1. 0.], [ 1. 0.]]</p> | tensorflow | 0 |
369,838 | 47,325,413 | Pandas calling partial row data | <p>I have the following data in a column:</p>
<pre><code>Company Name
Company Name\Cortana Place\rBaton Rouge, LA 70815
Some Product Company\r1Highway 21\rMadis
df = pd.read_csv(csv_cropped_tabula, encoding = "ISO-8859-1")
</code></pre>
<p>when I call <code>df['Company Name'][0]</code> or <code>df['Company Name'][1... | <p>It's parsing the embedded commas as separators, it looks like you only have a single column so you can tell it to only load that column and pass <code>lineterminator='\n'</code>:</p>
<pre><code>In[86]:
t="""Company Name
Company Name\Cortana Place\rBaton Rouge, LA 70815 Some Product
Company\r1Highway 21\rMadi"""
df ... | python|python-3.x|pandas|python-3.6 | 0 |
369,839 | 47,196,719 | Pandas: Convert fractional months to datetime | <p>I have some monthly data with a date column in the format: YYYY.fractional month. For example:</p>
<pre><code>0 1960.500
1 1960.583
2 1960.667
3 1960.750
4 1960.833
5 1960.917
</code></pre>
<p>Where the first index is June, 1960 (6/12=.5), the second is July, 1960 (7/12=.583) and so on.</p>
<p>... | <p>I think you need a bit maths:</p>
<pre><code>a = df['date'].astype(int)
print (a)
0 1960
1 1960
2 1960
3 1960
4 1960
5 1960
Name: date, dtype: int32
b = df['date'].sub(a).add(1/12).mul(12).round(0).astype(int)
print (b)
0 7
1 8
2 9
3 10
4 11
5 12
Name: date, dtype: int32
c =... | python|pandas|datetime | 3 |
369,840 | 47,316,783 | Python Dataframe: Remove duplicate words in the same cell within a column in Python | <p>Below shows a column with data I have and another column with the de-duplicated data I want. </p>
<p><a href="https://i.stack.imgur.com/bHyD1.png" rel="noreferrer"><img src="https://i.stack.imgur.com/bHyD1.png" alt="enter image description here"></a></p>
<p>I honestly don't even know how to start doing this in Py... | <p>If you're looking to get rid of consecutive duplicates <em>only</em>, this should suffice:</p>
<pre><code>df['Desired'] = df['Current'].str.replace(r'\b(\w+)(\s+\1)+\b', r'\1')
df
Current Desired
0 Racoon Dog Racoon Dog
1 Cat Cat Cat
2 Dog Dog Dog Dog ... | python|string|pandas|dataframe | 21 |
369,841 | 47,341,519 | json_normalize for dicts within dicts | <p>I have been trying to <code>normalize</code> a very nested json file I will later analyze. What I am struggling with is how to go more than one level deep to normalize.</p>
<p>I went through the <a href="http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.io.json.json_normalize.html" rel="nofollow ... | <pre><code>In [23]: lst = [l for l in raw['hits']['hits'] if l['_source'].get('authors')]
In [24]: json_normalize(lst,
[['_source', 'authors']],
['_id', ['_source', 'journal'], ['_source', 'title']])
Out[24]:
affiliations author_id author_name ... | python|json|pandas | 2 |
369,842 | 47,417,537 | Pandas: Cascading Partition over multiple keys | <p>I have a dataframe that looks something like this:</p>
<pre><code>nodename ip <otherfields>
amelia 192.168.23.8 <...>
boris 10.8.45.3 <...>
boris 192.168.67.4 <...>
clyde 192.168.45.3 <...>
darwin 192.168.67.4 <...>
el... | <p>For posterity:</p>
<p>I ended up looping through my dataset using the following algorithm:</p>
<pre><code>1. Select first unmatched row currently in the dataset and use that to initialise sets of search keys.
2. Iteratively select all rows matching the keys, and rebuild the sets of search keys.
3. When the sets ... | python|pandas | 0 |
369,843 | 47,502,981 | tf.decode_raw and tf.reshape using different image size | <p>I'am using the following code to generate tfrecords file.</p>
<pre><code> def generate_tfrecords(data_path, labels, name):
"""Converts a dataset to tfrecords."""
filename = os.path.join(args.tfrecords_path, name + '.tfrecords')
writer = tf.python_io.TFRecordWriter(filename)
for index, data in enu... | <p>Use <a href="https://www.tensorflow.org/api_docs/python/tf/image/decode_jpeg" rel="noreferrer">decode_jpeg</a> instead of decode_raw</p> | tensorflow|tensorflow-datasets | 7 |
369,844 | 47,108,856 | g2p-seq2seq error: 'module' object has no attribute 'core_rnn_cell' | <p>
I'm trying to get g2p-seq2seq to work as per github instructions: <a href="https://github.com/cmusphinx/g2p-seq2seq" rel="nofollow noreferrer">https://github.com/cmusphinx/g2p-seq2seq</a></p>
<p>However I get the following error when I try to validate the installation with <code>$ g2p-seq2seq --interactive --model... | <p>I had to downgrade the tensorflow version to 1.0 to make this work.</p> | tensorflow|cmusphinx | 1 |
369,845 | 47,145,311 | Transform 1-D numpy array into 3D RGB array | <p>What is the best way to transform an 1D array that contains rgb data into a 3D RGB array ?</p>
<p>If the array was in this order, it would be easy, (a single reshape)</p>
<blockquote>
<p>RGB RGB RGB RGB...</p>
</blockquote>
<p>However my array is in the form,</p>
<blockquote>
<p>RRRR...GGGG....BBBB</p>
</blo... | <p>Reshape to <code>2D</code>, transpose and then reshape back to <code>3D</code> for <code>RRRR...GGGG....BBBB</code> form -</p>
<pre><code>a1D.reshape(3,-1).T.reshape(height,-1,3) # assuming height is given
</code></pre>
<p>Or use reshape with <code>Fortran</code> order and then swap axes -</p>
<pre><code>a1D.resh... | python|arrays|numpy|rgb | 3 |
369,846 | 47,243,021 | Numpy: subtract matrix from all elements of another matrix without loop | <p>I have two matrices X,Y of size (m x d) and (n x d) respectively. Now i want to subtract the whole matrix Y from each element of the matrix X to get a third matrix Z of size (m x n x d). Using loops it would look this:</p>
<pre><code>Z = [(Y-x) for x in X]
</code></pre>
<p>but i want to avoid loops and use numpy o... | <p>If i understand correctly, here is a small demo:</p>
<pre><code>In [81]: X = np.arange(6).reshape(2,3)
In [82]: Y = np.arange(12).reshape(4,3)
In [83]: X
Out[83]:
array([[0, 1, 2],
[3, 4, 5]])
In [84]: Y
Out[84]:
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
In [85]... | python|numpy|matrix | 2 |
369,847 | 47,359,743 | How to make Min-plus matrix multiplication in python faster? | <p>So I have two matrices, A and B, and I want to compute the min-plus product as given here: <a href="https://en.wikipedia.org/wiki/Min-plus_matrix_multiplication" rel="nofollow noreferrer">Min-plus matrix multiplication</a>. For that I've implemented the following:</p>
<pre><code>def min_plus_product(A,B):
B = n... | <p>Here is an algo that saves a bit if the middle dimension is large enough and entries are uniformly distributed. It exploits the fact that the smallest sum typically will be from two small terms.</p>
<pre><code>import numpy as np
def min_plus_product(A,B):
B = np.transpose(B)
Y = np.zeros((len(B),len(A)))
... | python|performance|numpy|matrix | 4 |
369,848 | 47,166,372 | Jupyter Pandas DataFrame - reading column values | <p>I have a snippet of python code that reads the values for SQL columns for a given row. The snippet below simply iterates thru columns within a DataFrame context and appends the numeric values to an array.</p>
<p>If i print out the value of each column, the output looks correct. However, if I print out the final arr... | <p>I think you need convert values to <code>numpy array</code>, transpose and convert to <code>list</code>:</p>
<pre><code>df = pd.DataFrame({
'A': ['a','e','g'],
'B': list(range(3))
})
print (df)
A B
0 a 0
1 e 1
2 g 2
L = df.values.T.tolist()
print (L)
[['a', 'e', 'g'], [0, 1, 2]]
</code></pre>
<p... | python|pandas|dataframe | 2 |
369,849 | 47,167,670 | Mixed precision not enabled with TF1.4 on Tesla V100 | <p>I was interested in testing my neural net (an Autoencoder that serves as a generator + a CNN as a discriminator) that uses 3dconv/deconv layers with the new Volta architecture and benefit from the Mixed-Precision training. I compiled the most recent source code of Tensorflow 1.4 with CUDA 9 and CudNN 7.0 and cast al... | <p>Based on <a href="http://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow" rel="nofollow noreferrer">NVIDIA documentation</a> I run benchmark with FP16 (TensorCore). For that I modyfied <code>alexnet_benchmark</code> delivered by tensorflow:
<a href="https://gist.github.com/melgor/946b... | tensorflow|tesla | 3 |
369,850 | 47,492,387 | Python Linear Regression input values | <p>I have a Excel sheet with 2 colums and 1000 rows.
I want to give this as inputs to my Linear Regression Fit command using the sklearn.
/
when I want to create a dataframe using panda how can I give the inputs?
like <code>df_x=pd.dataFrame(...)</code></p>
<p>I used without dataframe sucessfully as:</p>
<pre><code>n... | <p>I think you can convert a pandas dataframe to a numpy array by <code>np.array()</code></p>
<p>This is discussed here: <a href="https://www.quora.com/How-does-python-pandas-go-along-with-scikit-learn-library-Has-anyone-doing-data-analysis-using-pandas-and-then-then-fit-models-using-scikit-learn" rel="nofollow norefe... | python|pandas | 0 |
369,851 | 47,272,971 | Pytorch: how to convert data into tensor | <p>I am a beginner for Pytorch.
I was trying to write CNN code referring Pytorch tutorial.
Below is a part of the code, but it shows error "RuntimeError: Variable data has to be a tensor, but got list". I tried to cast input data to tensor but didn't work well. If anybody know the solution, please help me out...</p>
<... | <p>If my guess is correct, you are probably getting error in the following line.</p>
<pre><code># wrap them in Variable
images_batch, labels_batch = Variable(images_batch), Variable(labels_batch)
</code></pre>
<p>It means, <code>images_batch</code> and/or <code>labels_batch</code> are lists. You can simple convert th... | python|machine-learning|deep-learning|pytorch | 10 |
369,852 | 11,249,427 | Display a live numpy array | <p>In APL, you can trivially bring up a window that shows the contents of a variable whilst your program is running and watch it update live.</p>
<p>This is illustrated by the classic <a href="http://www.youtube.com/watch?v=a9xAKttWgP4" rel="nofollow">Game of Life</a> video at 5 minutes in.</p>
<p>Is there any simila... | <p>Have you looked at the python debugger? (<a href="http://docs.python.org/library/pdb.html" rel="nofollow">pdb</a>)</p>
<p>It's not graphical but it is completely portable. Depending what IDE you're using, there might be a visual debugger built in.</p> | python|numpy|debugging|inspector | 1 |
369,853 | 11,286,864 | Is there a way to check if NumPy arrays share the same data? | <p>My impression is that in NumPy, two arrays can share the same memory. Take the following example:</p>
<pre><code>import numpy as np
a=np.arange(27)
b=a.reshape((3,3,3))
a[0]=5000
print (b[0,0,0]) #5000
#Some tests:
a.data is b.data #False
a.data == b.data #True
c=np.arange(27)
c[0]=5000
a.data == c.data #True ( S... | <p>You can use the <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.base.html" rel="nofollow noreferrer">base</a> attribute to check if an array shares the memory with another array:</p>
<pre><code>>>> import numpy as np
>>> a = np.arange(27)
>>> b = a.reshape((3,3,3))
... | python|numpy | 38 |
369,854 | 68,383,634 | CUDA error: CUBLAS_STATUS_INVALID_VALUE error when training BERT model using HuggingFace | <p>I am working on sentiment analysis on steam reviews dataset using BERT model where I have 2 labels: positive and negative. I have fine-tuned the model with 2 Linear layers and the code for that is as below.</p>
<pre><code> bert = BertForSequenceClassification.from_pretrained("bert-base-uncased",
... | <p>I suggest trying out couple of things that can possibly solve the error.</p>
<p>As shown in this <a href="https://discuss.pytorch.org/t/runtimeerror-cuda-error-cublas-status-invalid-value-when-calling-cublassgemm-handle-opa-opb-m-n-k-alpha-a-lda-b-ldb-beta-c-ldc/124544" rel="nofollow noreferrer">forum</a>, one possi... | python|pytorch|sentiment-analysis|bert-language-model | 2 |
369,855 | 68,447,264 | Pytorch: mat1 and mat2 shapes cannot be multiplied | <p>I have set up a toy example for my first pytorch model:</p>
<pre><code>x = torch.from_numpy(np.linspace(1,100,num=100))
y = torch.from_numpy(np.dot(2,x))
</code></pre>
<p>I have built the model as follows:</p>
<pre><code>class Net(nn.Module):
def __init__(self):
super(Net,self).__init__()
self.fc... | <p>There are four issues here:</p>
<ol>
<li><p>Looking at the model's first layer, I assume your batch size is 100. In that case, the correct input shape should be <code>(100, 1)</code>, not <code>(100,)</code>. To fix this you could use <a href="https://pytorch.org/docs/stable/generated/torch.unsqueeze.html" rel="nofo... | python|pytorch | 2 |
369,856 | 68,039,160 | Join Table in python Pandas (like Vlookup based on two columns value similarity) | <p>it should be easy but I don't find the solution.
I want to join two data frames using pandas, joined like V loop Up style when comparing values of two columns from two data frames.
See example</p>
<pre><code> df1_test = pd.DataFrame({'X_mm': [1,2,3,4,5],
'Y_mm': [2,5,6,7,9],
... | <p>Try:</p>
<pre class="lang-py prettyprint-override"><code>print(df1_test.merge(df2_test, on=["X_mm", "Y_mm"]))
</code></pre>
<p>Prints:</p>
<pre class="lang-none prettyprint-override"><code> X_mm Y_mm Measurement_from_df1 Measurement_from_df2
0 1 2 18.3 ... | python|pandas|dataframe|join | 0 |
369,857 | 68,244,304 | pandas: instead of applying the function to df get the result as a list from the function | <p>I have a dataframe like the following:</p>
<pre><code>df = pd.DataFrame({ 'text':['the weather is nice though', 'How are you today','the beautiful girl and the nice boy'],
'pos':["['DET', 'NOUN', 'VERB','ADJ', 'ADV']","['QUA', 'VERB', 'PRON', 'ADV']", "['DET', 'ADJ', 'NOUN','CON','DET', 'ADJ... | <p>Here's how you could use the function to get a list back, based on your DataFrame:</p>
<pre class="lang-py prettyprint-override"><code>from typing import List
df = pd.DataFrame({ 'text':['the weather is nice though', 'How are you today','the beautiful girl and the nice boy'],
'pos':[['DET', 'NOUN', 'VERB','ADJ', 'A... | python|pandas|append|refactoring | 2 |
369,858 | 68,055,535 | check for value in pandas Dataframe cell which has a list | <p>I have the following df:</p>
<pre><code>df = pd.DataFrame(columns=['Place', 'PLZ','shortName','Parzellen'])
new_row1 = {'Place':'Winterthur', 'PLZ':[8400, 8401, 8402, 8404, 8405, 8406, 8407, 8408, 8409, 8410, 8411], 'shortName':'WIN', 'Parzellen':[]}
new_row2 = {'Place':'Opfikon', 'PLZ':[8152], 'shortName':'OPF', '... | <p>try with boolean masking and <code>map()</code> method:</p>
<pre><code>df[df['PLZ'].map(lambda x:8405 in x)]
</code></pre>
<p>OR</p>
<p>via boolean masking and <code>agg()</code> method:</p>
<pre><code>df[df['PLZ'].agg(lambda x:8405 in x)]
#you can also use apply() in place of agg
</code></pre>
<p>output of above co... | python|pandas|dataframe | 2 |
369,859 | 68,164,092 | Filtering rows in a data frame based on value in the last column | <p>I want to remove any rows in a data frame if a cell in the last column is empty(Nan). More columns will be added to the data overtime so I just want it to look at the last column.</p>
<p>Here is the dataframe
0 1 2 3 4
aa bb cc dd 1
ae we df gh Nan
wr th fg rg Nan</p>
<p>And the expected result
0... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing" rel="nofollow noreferrer"><code>boolean indexing</code></a> with test if no missing value in last column by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.notna.html" rel="nofollow noreferre... | python|pandas | 0 |
369,860 | 68,416,878 | How to sort multiindex column month names? | <p>I have this multiindex <code>df</code>:</p>
<pre><code> YEARS_TMAX TMAX YEARS_TMAX TMAX YEARS_TMAX
MONTH April April August August December .....
CODE NAME
000130 RICA PLAYA 21.0 31.5 21.0 21.5 ... | <p>Use a <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html" rel="nofollow noreferrer">CategoricalDtype</a> by creating an ordered dtype from <a href="https://docs.python.org/3/library/calendar.html#calendar.month_name" rel="nofollow noreferrer">calendar.month_name</a> this will ensure th... | python|pandas | 1 |
369,861 | 68,074,556 | Changing column based on multiple conditions and previous rows values pandas | <p>I have this dataframe. I need to replace NaNs in column <strong>rank</strong> to a value based on multiple conditions. If column <strong>min</strong> is higher than 3 previous rows of <strong>max</strong> column then <strong>rank</strong> equals to <strong>min</strong>. Otherwise, I need to copy the previous value o... | <p>IIUC, here's one way:</p>
<pre><code>df['rank'].mask(pd.concat([df['min'].shift(i) for i in range(3)], 1).apply(
lambda x: x < df['min']).all(1), df['min']).ffill()
</code></pre>
<h5>OUTPUT:</h5>
<pre><code> max min rank
0 128.20 117.87 117.87
1 132.72 122.29 122.29
2 138.07 124.89 124.89... | python|pandas|dataframe | 1 |
369,862 | 68,268,987 | Splitting dataframe by date periods | <p>I wonder if you could point me in the right direction?</p>
<p>I have a dataframe with a dateindex and corresponding values:</p>
<hr />
<h2>date|value</h2>
<p>2010-10-16 | 485</p>
<p>2010-10-17 | 486</p>
<p>... ...</p>
<p>2013-10-12 | 8588</p>
<p>2013-10-12 | 8589</p>
<hr />
<p>and I want to split this dataframe ... | <p>I prefer here dictionary of DataFrames:</p>
<pre><code>start_date = df['date'].min()
end_date = df['date'].max()
months = (end_date.year - start_date.year) * 12 + (end_date.month - start_date.month)
</code></pre>
<hr />
<pre><code>dfs = {f'period_{i+1}': df[df['date'].between(start_date + pd.DateOffset(months=i),
... | python|pandas|dataframe|date|time | 0 |
369,863 | 68,176,249 | Pandas. Check if any of the split strings from one column are in another | <p>I have a data frame with 2 columns with strings comma-separated.<br />
I'm trying to make speed-efficient solution to calculate 3-d column indicating if any of split strings from column <code>A</code> present in column <code>B</code>.<br />
For example:</p>
<pre><code>df = pd.DataFrame({'A':['apple', 'cucamber', 'to... | <p>One idea with lsit comprehension and <code>any</code> for test if match at least one string:</p>
<pre><code>df['C'] = [any(z in y for z in x.split(',')) for x, y in df[['A','B']].to_numpy()]
df['C'] = df['C'].astype(int)
print (df)
A B C
0 apple apple,banana 1
1 ... | python|pandas | 2 |
369,864 | 68,272,050 | dataframe value diagonal shift row | <p>I would like to have left shift for each row in <code>df</code>, like a diagonal shift.
I have df like that:</p>
<pre><code> a1 a2 a3 a4
row1 1 5 5 3
row2 0 4 1 4
row3 0 0 7 6
row4 0 0 0 2
</code></pre>
<p>and would like to have it like:</p>
<pre><code> a1 a2 a3 ... | <p>IIUC, here's one way:</p>
<pre><code>df1 = df.mask(df.eq(0)).apply(lambda x: pd.Series(sorted(x, key=pd.isnull)), axis = 1).fillna(0, downcast='infer')
df1.columns = df.columns
</code></pre> | python|pandas|dataframe|shift | 1 |
369,865 | 68,388,894 | Value Error with numpy when installed TensorFlow | <p>I am running into this error when i import this</p>
<p><code>from gensim.models import KeyedVectors</code></p>
<p>the error is</p>
<pre><code> File "c:\Users\frase\eg1.py", line 11, in <module>
from gensim.models import KeyedVectors
File "C:\Users\frase\AppData\Local\Programs\Python\Pytho... | <p>Solutions.</p>
<ol>
<li><p>Downgrade python environment to 3.8/3.7</p>
</li>
<li><p>uninstall Numpy and upgrade to Latest NumPy version</p>
</li>
</ol>
<p>Reference- <a href="https://stackoverflow.com/questions/66060487/valueerror-numpy-ndarray-size-changed-may-indicate-binary-incompatibility-exp">ValueError: numpy... | python|numpy|tensorflow|keras|neural-network | 0 |
369,866 | 68,028,490 | Android Studio: import org.tensorflow.Operation does not seem to work | <p>I am using the latest Android Studio to create an image recognition project. I am using a .pb file downloaded from Github.</p>
<p>I have added "<code>implementation 'org.tensorflow:tensorflow-android:1.5.0'</code>" in build.gradle file.</p>
<p>When I go to the java file, the following two import statements... | <p>Please ignore this question. I misunderstood what "unused" means.</p> | android-studio|tensorflow|image-recognition | 0 |
369,867 | 68,446,439 | How can I scrape multiple pages with scrapy in my python code? | <p>So I am currently making a scraper project for this one website: <a href="https://www.datacenters.com/locations?page=1&per_page=40&query=&withProducts=false&showHidden=false&nearby=false&radius=0&bounds=&circleBounds=&polygonPath=" rel="nofollow noreferrer">https://www.datacenters... | <p>If you view the page in a browser, and log your network traffic while clicking through the result pages, you'll notice an XHR HTTP GET request being made to a REST API endpoint, the response of which is JSON and contains a lot of information for all warehouse locations for a given page of 40 results. You can imitate... | python|pandas|web-scraping|scrapy|web-crawler | 0 |
369,868 | 68,360,411 | Pandas data frame column containing list of dicts | <p>Hi I have the following pandas data frame:</p>
<pre><code>df = pd.DataFrame({'info':[1.4,3.6,6.5], 'new':[[{'score':0.998, 'letters':'C', 'temp':1}, {'score':1.343, 'letters':'B', 'temp':0}, {'score':2.323, 'letters':'F', 'temp':1}], [{'score':2.532, 'letters':'D', 'temp':1}, {'score':2.123, 'letters':'G', 'temp':1}... | <p>Use list with dict comprehension for new columns names with <code>enumerate</code>:</p>
<pre><code>d = [{f'{k}{i}': v for i,y in enumerate(x, 1) for k,v in y.items()} for x in df['new']]
df = pd.DataFrame(d, index=df.index).sort_index(axis=1)
print (df)
letters1 letters2 letters3 score1 score2 score3 temp1 t... | python|pandas | 2 |
369,869 | 68,206,280 | Split tuple of two elements and add to pandas dataframe | <p>I have a list of tuple in python:<code>[(3, 0), (3, 6), (9, 6), (9, 9), (13, 10), (13, 1), (16, 8), (12, 17), (20, 18), (10, 21), (24, 17), (8, 25), (28, 25), (18, 31), (32, 8), (19, 33), (29, 33), (34, 37), (34, 19), (33, 37), (35, 40), (40, 24), (40, 50), (46, 40), (40, 40), (11, 43), (43, 47), (43, 26), (35, 46),... | <pre><code>data = [(3, 0), (3, 6), (9, 6), (9, 9), (13, 10), (13, 1), (16, 8), (12, 17), (20, 18), (10, 21), (24, 17), (8, 25), (28, 25), (18, 31), (32, 8), (19, 33), (29, 33), (34, 37), (34, 19), (33, 37), (35, 40), (40, 24), (40, 50), (46, 40), (40, 40), (11, 43), (43, 47), (43, 26), (35, 46), (42, 49), (52, 44), (46... | python|pandas|dataframe|tuples | 2 |
369,870 | 68,075,343 | Python - How to export monthly data into excel based on month? | <p>I have dataframe that contain two columns. Date from 2018 until now and Orders with order count for each day.</p>
<pre><code>Date Orders
0 2018-01-01 57
1 2018-01-02 324
2 2018-01-03 54
3 2018-01-04 677
4 2018-01-05 234
5 2018-01-06 54
6 2018-01-07 234
7 2018-01-08 65
8 2018-01-09 234
9... | <p>You might want to have a look <a href="https://stackoverflow.com/a/59604826/12661819">here</a>. You can use simple integers to address the month, so you should be able to iterate like this (not tested):</p>
<pre><code>for month in range(1, 13):
df_per_month = df[df['Date'].dt.month == month]
df_per_month.to_... | python|python-3.x|pandas | 1 |
369,871 | 68,395,571 | Pandas Series - force dtype in Series constructor | <p>I have this very simple series.</p>
<pre><code>pd.Series(np.random.randn(10), dtype=np.int32)
</code></pre>
<p>I want to force a dtype, but pandas will overrule my initial setup:</p>
<pre><code>Out[6]:
0 0.764638
1 -1.451616
2 -0.318875
3 -1.882215
4 1.995595
5 -0.497508
6 -1.004066
7 -1.641371
8 ... | <p>You can use this:</p>
<pre><code>>>> pd.Series(np.random.randn(10).astype(np.int32))
0 0
1 1
2 1
3 1
4 0
5 0
6 -1
7 0
8 0
9 0
dtype: int32
</code></pre>
<p>Pandas infers data type correctly. You can force your datatype with one exception. If your data is <code>float</code> and y... | python|pandas|series | 1 |
369,872 | 68,060,507 | Adding holidays column to pandas dataframe | <p>I have a pandas dataframe object <code>df</code> for the following dates:</p>
<pre><code>>> df.index
DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',
'2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08',
'2015-01-09', '2015-01-10'],
dtype='da... | <p>Try this solution using <code>lambda</code> function:</p>
<pre><code>df['hols'] = pd.Series(df.index).apply(lambda x: holidays.CountryHoliday('AUS',prov='NSW').get(x)).values
</code></pre>
<p>Method <code>get()</code> should receive only one value, not entire index or array.
Result when apply to Your data is:</p>
<p... | python|pandas|dataframe | 2 |
369,873 | 68,095,618 | Importing JSON-like data into Python | <p>I have a file that is similar to JSON data but isn't formatted fully that I would like to parse into Pandas in Python. I could cycle through and manipulate the data until it's what I need it to be, but I wanted to see if there was a better solution than what I can think of.</p>
<p>Sample file:</p>
<pre><code>{"... | <p>first remove end comma.use sed to replace comma and write to another file</p>
<pre class="lang-sh prettyprint-override"><code>sed -r 's/\S*,\S*$//g' json_file > data.jl
</code></pre>
<pre class="lang-py prettyprint-override"><code>#use lines True to read json line file.
df = pd.read_json("data.jl",lines... | python|pandas | 0 |
369,874 | 68,262,769 | Getting the right values from the dictionary without duplication | <p>I am trying to match a random amount of employee records to an auditor. so I made a dictionary, the key being the name of the auditors and the values being the list of the employees' names. However, as the result of this, the dictionary values become cumulative and make duplicates of themselves as iteration for audi... | <p>To use the random sample method.
create list of auditors and a list of employees.</p>
<pre><code>emps = ['EMP_3201', 'EMP_4617', 'EMP_4332', 'EMP_3999', 'EMP_1386', 'EMP_3357', 'EMP_3584',
'EMP_4698', 'EMP_1484', 'EMP_4268', 'EMP_5368', 'EMP_1969', 'EMP_3398', 'EMP_1874']
auditors = ['auditor_A', 'auditor_B'... | python|python-3.x|pandas|dataframe | 0 |
369,875 | 68,295,817 | Optimal way to acquire percentiles of DataFrame rows | <h2>Problem</h2>
<p>I have a <code>pandas</code> DataFrame <code>df</code>:</p>
<pre><code>year val0 val1 val2 ... val98 val99
1983 -42.187 15.213 -32.185 12.887 -33.821
1984 39.213 -142.344 23.221 0... | <p>You can get use <code>.describe()</code> function like this:</p>
<pre><code># Create Datarame
df = pd.DataFrame(np.random.randn(5,3))
# .apply() the .describe() function with "axis = 1" rows
df.apply(pd.DataFrame.describe, axis=1)
</code></pre>
<p>output:</p>
<pre><code> count mean std m... | python|pandas|dataframe|percentile | 3 |
369,876 | 68,444,606 | Pandas groupby and count across multiple columns | <p>I have data ordered by ID, Year, and then a series of event flags indicating whether a thing did or did not happen for that ID in that year:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>ID</th>
<th>Year</th>
<th>x</th>
<th>y</th>
<th>z</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>2... | <p>You can use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html" rel="nofollow noreferrer"><code>.groupby()</code></a> + <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cumsum.html" rel="nofollow noreferrer"><code>.cumsum()... | python|pandas|dataframe|counter | 0 |
369,877 | 68,284,953 | How to add 1 to all numbers == to 0 in a pandas dataframe column python | <p>I am currently trying to calculate the length of disasters, measured in days, and then with this column that is the difference between the start date and end date, use groupby ( I think), in order to sum the length of disasters for each year, as my data set is from 1960 to present. Eventually, I'd like to also group... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.days.html" rel="nofollow noreferrer"><code>Series.dt.days</code></a> for convert tiemdeltas to integers and then use <code>replace</code>:</p>
<pre><code>df_time['Disaster_Length'] = (df_time.Start_Date_A - df_time.End_Date_A).dt... | python|pandas | 3 |
369,878 | 68,358,825 | How to wright a function to work with dictionary type Serires and a column in Dataframe? | <p>I am trying to wright a function that works with Series and Dataframe.</p>
<pre><code>dct= {10: 0.5, 20: 2, 30: 3,40:4}
#Defining the function
def funtion_dict(row,dict1):
total_area=row['total_area']
if total_area.round(-1) in dict1:
return dict1.get(total_area.round(-1))*total_area
#checking ... | <p>This is the expected behavior because you are trying to use a "Series" as a lookup for a dictionary which is not allowed.</p>
<p>From your code,</p>
<pre><code>dct= {10: 0.5, 20: 2, 30: 3,40:4}
df = pd.DataFrame({
'total_area': [53, 14.8, 94, 77, 12],
'b': [5, 4, 3, 2, 1],
'c': ['X', 'Y', 'Y', ... | python-3.x|pandas|dataframe|dictionary|series | 1 |
369,879 | 68,403,286 | getting class layer error in Tensorflow model | <p>I am on the last step of training my model, and I am getting the further described error. How can I fix this? (this is an image classification model)</p>
<pre class="lang-py prettyprint-override"><code>from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import GlobalMaxPooling2... | <p>I am able to execute code by changing imports as shown below</p>
<pre><code>import tensorflow as tf
print(tf.__version__)
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import GlobalMaxPooling2D, Dense, Flatten, GlobalAveragePooling2D
image_size = 224
#Model definiti... | python|tensorflow|machine-learning|keras|image-classification | 0 |
369,880 | 68,163,992 | Refine duplicate selection with Pandas | <p>I have a dataframe with two columns 'text' and 'lang' and I need to extract the groups (unique) of 'text' values that have the same number N of languages.
For example:</p>
<p>For the following example dataframe:</p>
<pre><code>text lang
--------------
text_a en
text_b es
text_a es
text_a it
text_c de
t... | <h3>Approach 1</h3>
<p><code>Query</code> the dataframe to filter the rows where the corresponding language is one of <code>en</code>, <code>es</code>, then group the filtered dataframe on <code>text</code> and transform <code>lang</code> column using <code>nunique</code> to get the counts of unique values, now compare... | python|pandas | 1 |
369,881 | 68,044,853 | pandas readcsv ValueError: header must be integer or list of integers | <p>I'm a very beginning programmer. I've been trying to figure this out for a few days but can't seem to wrap my head around it.</p>
<p>I am working with txt files (they are generated by another program). The columns contain no titles (headers?). Values are seperated by tabs.</p>
<p>This is the base file:</p>
<pre><cod... | <p>You have to replace <code>'none'</code> (a string) by <code>None</code> (object)</p>
<pre><code>data = pandas.read_csv("q658.csv", sep= '\t', header=None)
</code></pre>
<p>Read this : <a href="https://docs.python.org/3/library/constants.html#None" rel="nofollow noreferrer">https://docs.python.org/3/library... | python|pandas | 1 |
369,882 | 68,305,116 | alternative way of filtering dataframe | <p>Community! It´s a long explanation but a 'simple' question! I have this following df:</p>
<pre><code>d = {'name': ['john', 'mary', 'james'], 'area':[['IT', 'Resources', 'Admin'], ['Software', 'ITS', 'Programming'], ['Teaching', 'Research', 'KS']]}
df = pd.DataFrame(data=d)
</code></pre>
<p><a href="https://i.stack.i... | <p>The problem is that you are returning the first element that is longer than 3. Try this:</p>
<pre><code>def f(x):
answer = []
for e in x:
if len(e)>3:
answer.append(e)
return answer
</code></pre>
<p>But even better, trying a more pythonic way:</p>
<pre><code>def f(x):
return [e... | python|pandas|function|loops | 1 |
369,883 | 68,154,556 | Printing months in the x axis with pyplot | <p>Data I'm working with: <a href="https://drive.google.com/file/d/1xb7icmocz-SD2Rkq4ykTZowxW0uFFhBl/view?usp=sharing" rel="nofollow noreferrer">https://drive.google.com/file/d/1xb7icmocz-SD2Rkq4ykTZowxW0uFFhBl/view?usp=sharing</a></p>
<p>Hey everyone,</p>
<p>I am a bit stuck with editing a plot.
Basically, I would lik... | <h3>Option 1 (Most Similar Approach)</h3>
<p>Change the index based on month abbreviations using <a href="https://pandas.pydata.org/docs/reference/api/pandas.Index.map.html" rel="nofollow noreferrer"><code>Index.map</code></a> and <a href="https://docs.python.org/3/library/calendar.html" rel="nofollow noreferrer"><code... | python|pandas|date|plot|series | 1 |
369,884 | 68,371,165 | Replace column values using a mapping-logic in pandas (problem with implementing a function) | <p>I have a dataframe as follows. What I would like is to generate another column (<code>freq</code>) where the rows will have values according to this logic:</p>
<ul>
<li><p>If <strong>Mode</strong> column value starts with a digit <code>m</code>, then fill-in digit <code>n</code> in the <strong>freq</strong> column.<... | <p>Is this what you are after?</p>
<p>print(df1)</p>
<pre><code> Mode
0 602
1 603
2 700
3 100
4 100
5 100
6 802
7 100
8 100
9 100
10 100
c=[df1['Mode'].astype(str).str.startswith('8'),df1['Mode'].astype(str).str.startswith('7'),df1['Mode'].astype(str).str.startswith('6'),df1['Mode... | python|pandas|dataframe|numpy|numpy-ndarray | 1 |
369,885 | 68,050,242 | How to multiply batches of image? N-D matrix multiplication of shape [batch, height, width] (dot product) | <p>Let us suppose I have a matrix with batch of 2 images or a matrix of 2 sentences where words are vectored for last dimension.</p>
<p>image = <code>[batch, width, height, channel]</code></p>
<p>words = <code>[batch, no of words in each sentence, vector length of each word]</code></p>
<p>What is the best way to multip... | <p>If I get your question right, you want to reduce two tensors of fixed orders but arbitrary shapes that agree only in their first dimension (the batch dimension).</p>
<p>The consistent way of doing so is the <a href="https://en.wikipedia.org/wiki/Einstein_notation" rel="nofollow noreferrer">Einstein summation notatio... | python|numpy|machine-learning|math|deep-learning | 0 |
369,886 | 68,234,760 | Find all groups of contiguous timestamp and assigns a unique id to each group in a data frame | <p>I want to write a function that assigns a unique ID to all groups of contiguous timeframe, where 'contiguous' means all the observation within the group are no more that 'max_time_gap' seconds apart.</p>
<p>e.g:
def assign_groups(df, max_time_gap, group_col_name):</p>
<p>Where df is the input data as a pandas data f... | <p>You can do it this way:</p>
<p><strong>1) Convert the column <code>timestamp</code> to datetime format if not already in that format</strong></p>
<pre><code>df['timestamp'] = pd.to_datetime(df['timestamp'])
</code></pre>
<p><strong>2) Define the function as follows:</strong></p>
<p>Use <a href="https://pandas.pydata... | python|pandas|datetime|pandas-groupby | 1 |
369,887 | 68,261,345 | Pandas drop and update rows and columns based on column value | <p>Here is sample csv file of cricket score:</p>
<pre><code>>>> df
venue ball run extra wide noball
0 a 0.1 0 1 NaN NaN
1 a 0.2 4 0 NaN NaN
2 a 0.3 1 5 5.0 NaN
3 a 0.4 1 0 NaN NaN
4 a 0.5 1 1 NaN 1.0
5 a 0.6 2 1 NaN... | <p>Alrighty. This was a fun one.</p>
<p>(I tried to add comments for clarity.)</p>
<p><strong>Note</strong>: "ball," "run," "extra," "wide," and "noball" are all <em>numeric</em> fields.</p>
<p><strong>Note</strong> <strong>Note</strong>: This all assumes your initial ... | python|pandas|dataframe|csv | 1 |
369,888 | 68,283,274 | python round numbers to specific value | <p>I would like to write a specif rounding logic.</p>
<pre><code>number = x
if x < 950:
# round number to and in steps of 50
elif x < 9000:
# round number to and in steps of 100
elif x < 100000:
# round number to and in steps of 250
else:
# round number to and in steps of 1000
</code></pre>
<p... | <p>Mostlikely not the cleanest way to do it but :
<code>(x + step/2)//step*step</code> should work.</p>
<p>Example :
<code>print((880+25)//50*50)</code> returns <code>900</code>.</p> | python|numpy|math|rounding|ceil | 3 |
369,889 | 68,080,345 | ImportError: cannot import name 'LayerNormalization' from 'tensorflow.python.keras.layers.no rmalization' | <p>In running a python project I get the following error. I installed various versions of tensorflow (from 2.2.3 to 2.4.1), but the problem is there... I don't know what I should change or what is the mismatch. It previously was working... please help if you know tensroflow</p>
<pre><code>File "/home/pouramini/seq... | <p>I uninstalled <code>tensorflow</code> both by <code>conda remove tensorflow</code> and <code>pip uninstall tensorflow</code> and even removed the folder manually from <code>miniconda3/lib/python3.7/site-packages/</code></p>
<p>Then installed tensorflow (I tried 2.3.0) by <code>pip install tensorflow==2.3.0</code></p... | python|python-3.x|tensorflow|keras | 1 |
369,890 | 68,298,877 | parse datetime which stores ms.us | <p>I am attempting to parse some logfiles stored from a piece of test equipment (CANbus logger)
Within the exported TSV file they unfortunately store the absolute time in a <code>HH:MM:SS.XXX.YYY</code> format</p>
<p>where <strong>XXX is in ms</strong> and <strong>YYY is in µs</strong> eg: <code>13:58.06.286.591</code>... | <p>you can write a simple formatter that removes the rightmost dot, so you can parse with <code>%f</code>.</p>
<p><em><strong>Ex:</strong></em></p>
<pre><code>formatter = lambda x: x[::-1].replace('.', '', 1)[::-1]
s = "13:58.06.286.591"
print(formatter(s))
# 13:58.06.286591
</code></pre>
<p>Now you could i... | python|numpy|datetime | 1 |
369,891 | 68,040,059 | Conditionally Create Temp Table in SQL from Python | <p>In a large set of queries, I'm trying to create a temp table in SQL, if it doesn't already exist. Obviously, you could remove the 2nd <code>CREATE TABLE</code> statement. However, the queries I'm building are dynamic and may, or may not, have the 1st <code>CREATE TABLE</code> statement present.</p>
<p>I can get th... | <p>This is a batch compilation error. When you remove the <code>GO</code>, which you must to to get this to compile, then there are two <code>CREATE TABLE</code> statements for the same temp table, which won't parse and compile. EG this batch generates the same error:</p>
<pre><code>CREATE TABLE #temp_sample (id int)... | python|sql|pandas|odbc|temp-tables | 0 |
369,892 | 68,191,448 | Unknown image file format. One of JPEG, PNG, GIF, BMP required | <p>I built a simple CNN model and it raised below errors:</p>
<pre><code>Epoch 1/10
235/235 [==============================] - ETA: 0s - loss: 540.2643 - accuracy: 0.4358
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call las... | <p>Some of your files in the validation folder are not in the format accepted by Tensorflow ( <code>JPEG, PNG, GIF, BMP</code>), or may be corrupted. The extension of a file is indicative only, and does not enforce anything on the content of the file.</p>
<p>You might be able to find the culprit using the <a href="http... | python|tensorflow | 10 |
369,893 | 68,381,803 | Cumulative sum but conditionally excluding earlier rows | <p>I have a DataFrame like this:</p>
<pre class="lang-py prettyprint-override"><code>df = pd.DataFrame({
'val_a': [3, 3, 3, 2, 2, 2, 1, 1, 1],
'val_b': [3, np.nan, 2, 2, 2, 0, 1, np.nan, 0],
'quantity': [1, 4, 2, 8, 5, 7, 1, 4, 2]
})
</code></pre>
<p>It looks like this:</p>
<pre><code>| | val_a | val_b | ... | <p>With the help of NumPy:</p>
<pre><code># sum without conditions
raw_sum = df.groupby("val_a", sort=False).quantity.sum().cumsum()
# comparing each `val_b` against each unique `val_a` via `gt.outer`
sub_mask = np.greater.outer(df.val_b.to_numpy(), df.val_a.unique())
# selecting values to subtract from `qu... | python|pandas|dataframe | 2 |
369,894 | 68,380,947 | Get all rows that have a column "Message" where at least one of the words is in Array | <p>Consider the code:</p>
<pre><code>df = pd.read_csv('...csv')
array = [.....,....,....]
results = df[df.Message.isin(array).fillna(False)]
</code></pre>
<p>The column <code>Message</code> contains more than one word.</p>
<p>How can we get all rows that have the column "Message" where at least one of the wor... | <p>Maybe something like this (in a single line without loops):</p>
<pre><code>import pandas as pd
data = [['Client','Message','City','Phone'],
['Jackson','I will back soon','Rome',1111],
['Cole','Please try to be patient','Cairo',2222 ],
['Rains','Sure anything you want , anything','Paris',3333 ]]
Array = ['try', 'a... | python|python-3.x|pandas|string|dataframe | 1 |
369,895 | 68,141,489 | Failed to convert a NumPy array to a Tensor (Unsupported object type dict) | <p>my method i thought that the problem from it is</p>
<pre><code> history = model.fit_generator(train_generator, epochs=epochs, steps_per_epoch=train_steps, verbose=1, callbacks=[checkpoint], validation_data=val_generator, validation_steps=val_steps)
def data_generator(descriptions, photos, tokenizer, max_length, img... | <p><strong>This error indicates some values or all values in your data does not have a valid data type to convert.</strong></p>
<hr />
<p><strong>Reason</strong>:</p>
<p>Common reason for this error is that values of the array are not of given dtype in graph mode. It may be because some values are <code>NaN</code> or <... | python|tensorflow|keras|lstm|tensor | 0 |
369,896 | 68,332,561 | Filtering a pandas data frame | <p>Suppose we have a pandas data frame <code> df </code> with a column <code> id </code> with about 5 rows. In the following code below, why do I still get the length of the filtered data frame to be 5:</p>
<pre><code>import pickle
import gzip
import bz2
import pandas as pd
import os
import _pickle as cPickle
import bz... | <p>I figured it out. The filtered data frame would have the same dimensions as the original one because they are equal. If I had put a different id, then the dimension of the filtered data frame would have been different.</p> | python|pandas | 0 |
369,897 | 68,314,841 | Teradata - An illegally formed character string was encountered during translation | <p>I am fetching tweets via Twitter API in pandas dataframe and writing the data to teradata database. However, unlike other tweets one cell has specific tweet which contains data in bold. When I try to insert it in database, it pops up the following error:</p>
<pre><code>OperationalError: [Version 17.0.0.4] [Session 3... | <p>To store or retrieve arbitrary Unicode code points, use the Unicode Pass-Through feature both for loading and querying sessions.</p>
<pre><code>SET SESSION CHARACTER SET UNICODE PASS THROUGH ON;
</code></pre>
<p>For the specific example given, you might find it useful to "normalize" the Unicode text, e.g. ... | python|pandas|twitter|teradata|twitterapi-python | 1 |
369,898 | 68,122,925 | Subtraction of matrices with different dimensions based on the index | <p>I have an array where each element of the array is an array of points given by pairs of coordinates.</p>
<p>For example:</p>
<pre><code>x = array([[[1, 2],
[3, 4]],
[[22, 4],
[ 9, 10]]])
</code></pre>
<p>On the other hand I have a list whose length matches the first dimension of the previous matrix where e... | <p>Your original example has a 2 element array:</p>
<pre><code>In [294]: x = np.array([np.array([[1, 2],
...: [3, 4]]),
...: np.array([[22, 4],
...: [ 9, 10],
...: [ 3, 2]])], dtype=object)
...:
In [295]: x.shape
Out[295]: (2,)
In [296]: y = [[1,2],[7,8]]
In [297]: len(y)
Out[297... | python|numpy | 0 |
369,899 | 68,393,758 | How to shift numpy slices? | <p>I have a class like this</p>
<pre class="lang-py prettyprint-override"><code>class A:
def __init__(self):
self.top_left = (1,2)
self.arr = np.reshape(np.arange(100), (10,10))
def __setitem__(self, key, val):
return self.arr[shifted(key, self.top_left)] = val
</code></pre>
<p>I want al... | <p>Numpy array operate on builtin python <code>slice</code> or <code>tuple</code>.</p>
<p><code>shifter</code> function decides what kind of index you passed.</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
class A:
def __init__(self):
self.top_left = (1,2)
self.arr = np.resh... | python|numpy | 0 |
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