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 |
|---|---|---|---|---|---|---|
361,700 | 62,945,213 | Trying to match values in one data frame to values in another data frame (python) | <p>I currently have a dataframe A consisting of a column (code1) of country codes such as CA, RU, US etc. I have another dataframe B that has 3 columns where the first column has all possible country codes, the second has a longitude value and the third has a latitude value. I'm trying to loop through the A, get the fi... | <p>use <code>pd.merge</code> and specify the <code>left_on</code> column to merge on as well as the <code>right_on</code> column, since the two column you want to merge have different column names. Then, <code>.drop</code> the excess column that you don't need.</p>
<pre><code>A = pd.merge(A,B,how='left',left_on='code1'... | python|pandas|dataframe|country-codes | 0 |
361,701 | 63,129,981 | The most efficient way of finding indices of element(s) in numpy 2D array | <p>Out of huge matrix in numpy (currently <code>1000x1000</code>) only a few elements are relevant for me. Say these elements are <code>>1000</code> in value and others are way lower. I need to find indices of all such elements in the most efficient way because the search will be repeated often and the matrix can be... | <p>You can try this, the filter directly included in the numpy array!</p>
<pre><code>import numpy as np
arr = np.array([998, 999, 1000, 1001])
filter_arr = arr > 999
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
</code></pre>
<p><a href="https://www.w3schools.com/python/numpy_array_filter.asp" rel="n... | python|arrays|numpy | 0 |
361,702 | 63,193,247 | Pandas dataframe - create multiple columns based on multiple conditions calculations | <p>I am learning python so please excuse me if my question is too basic. Actually I need to create multiple columns on my pandas dataframe based on different conditions. I can do this in R using data.table. I am pasting below my code with sample data from R-</p>
<pre><code>library(data.table)
cr=4
phi=1.85
colA <-... | <p>You cannot use min to directly compare 2 columns. It needs to be applied at the element level.
Can you please check if this breakdown does the job..</p>
<pre><code>import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.uniform(0,100,size=(100, 6)), columns=list(['colA','colB','colC','colD','SALES','VALU... | python|r|pandas|numpy|data.table | 1 |
361,703 | 62,917,785 | Too many indices in numpy array when calculating size | <pre><code>def reg_interval_size(self, prediction, y, significance):
idx = int(significance * 100 - 1)
prediction = prediction[:, idx]
prediction_size = prediction[:, 1] - prediction[:, 0]
return prediction_size
</code></pre>
<p>This is the error I am getting when applying the function:</p>
<p... | <p><code>idx</code> is an <code>int</code> so <code>idx[0]</code> makes no sense.</p>
<p><code>prediction</code> is a 2d array so <strong>you can't</strong> access it with 3 indices like this:</p>
<pre class="lang-py prettyprint-override"><code>prediction = prediction[:, :, idx] # error
</code></pre>
<p>I don't know wh... | python|numpy|indexing | 1 |
361,704 | 63,239,374 | Does anyone see any possible way to slice this? (python) | <p>I've been trying to speed up some numpy arrays in Python and I know for loops are really bad so you should slice them but I just can't see anyway to slice this. Maybe there's some smart trick? I am pretty inexperienced in this so would appreciate any help!</p>
<pre><code> def propind(in1, in2):
return in1... | <p>Just wanted to give the solution I got using the comments to this question. It is about 20 times faster so thank you!</p>
<pre><code>tempH0s2 = []
for z in range(M):
tempL = np.zeros([M,N,N])
tempL[z] = 1
tempH0s2.append(tempH0s*tempL)
tempH0s3 = np.stack(tempH0s2, axis=2)
ret = np.reshape(tempH0s3, (... | python|numpy|numpy-slicing | 1 |
361,705 | 67,680,215 | Converting non numeric columns to numeric columns | <p>My imports are:</p>
<pre><code>import pandas as pd
import numpy as np
from pandas.api.types import is_numeric_dtype
</code></pre>
<p>I created a pandas dataframe (named df) that looks like this:</p>
<pre><code> state initial_temp final_temp
0 Cold 48.0 88.1
1 hot 80.7 30.0... | <p>You could do</p>
<pre><code>df.transform(pd.to_numeric, errors = 'ignore')
</code></pre> | python|python-3.x|pandas|dataframe|numpy | 1 |
361,706 | 68,010,844 | Out-file a CSV-Like instead of TXT | <p>I have some piece of code that write a txt file like this:</p>
<pre><code>f2.csv,val,2
f2.csv,val,5
f2.csv,new,234
f2.csv,new,432
f2.csv,old,3
f2.csv,old,437
f2.csv,val,2
f2.csv,val,9
</code></pre>
<p>But I'd like to have something like this:</p>
<pre><code>f2.csv,val,new,old
f2.csv,2,234,3
f2.csv,5,432,437
f2.csv,2... | <p>Pandas has a built in writer:</p>
<pre class="lang-py prettyprint-override"><code>df.to_csv(open("f2.csv","w"), header=True)
</code></pre> | python|pandas | 1 |
361,707 | 67,892,894 | Observing varying model performance in different machines while training an activity recognition model | <p>I am finding that my model has different performance (train and validation accuracy) on two separate machines (Laptop and PC). The code and data used are the same.</p>
<p>So:</p>
<ul>
<li>Train and Validate on Laptop (val accuracy ~91%)</li>
<li>Moved the same jupyter notebook and data to PC via (manually via Box, w... | <p>First and foremost, in order to obtain the same results you should use the same <code>tf</code> and <code>keras</code> library versions in both machines; it would be impossible to track the changes otherwise. Secondly, the GPU computation generally uses different data sizes and it impacts accuracy; you can either u... | keras|deep-learning|tensorflow2.0|activity-recognition|mobilenet | 0 |
361,708 | 67,881,744 | Update certain columns of dataframe with other dataframe based on condition in python | <p>I am trying to update certain columns of one dataframe with other dataframe based on condition but it is not updating. Could you please tell me what I am doing wrong?</p>
<p>Example:</p>
<pre><code>The below columns need to be updated-
column_list = ['File Name', 'File Type', 'Published Date', 'Program Name', 'Link'... | <p>There are duplicates in <code>PDF_File_Name</code>, so for me working <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.combine_first.html" rel="nofollow noreferrer"><code>DataFrame.combine_first</code></a> for replace missing values with convert to default index by <a href="http://... | python|python-3.x|pandas|dataframe | 0 |
361,709 | 67,817,583 | How can I create a datframe column which counts the occurrence of each value in anopther column? | <p>I am trying to add a column to my dataframe, which will hold a value which represents the number of times a unique value has appeared in another column.</p>
<p>For example , I haver the following dataframe:</p>
<p>Date|Team|Goals|</p>
<p>22.08.20|Team1|4|</p>
<p>22.08.20|Team2|3|</p>
<p>22.08.20|Team3|1|</p>
<p>22.0... | <p>TRY:</p>
<pre><code>df['Count'] = df.groupby('Team').cumcount().add(1)
</code></pre>
<p>OUTPUT:</p>
<pre><code> Date Team Goals Count
0 22.08.20 Team1 4 1
1 22.08.20 Team2 3 1
2 22.08.20 Team3 1 1
3 22.09.20 Team1 4 2
4 22.09.20 Team3 5 2
</code></... | pandas|dataframe | 2 |
361,710 | 67,849,179 | Results mismatch between convolution algorithms in Tensorflow/CUDA | <p>I'm training a convolutional autoencoder and noticed this warning:</p>
<pre><code>Tensorflow: 2.5-gpu from pip
Driver: 460.80
cuda: 11.2.2
cudnn: 8.1.1
XLA: Yes
Mixed precision: Yes
</code></pre>
<pre><code>26/27 [===========================>..] - ETA: 0s - loss: 1.0554 - pre_dense_out_loss: 0.9997 - de_conv1dtra... | <p>This could be the effect of accumulation with a low precision (e.g. FP16) data type.</p>
<p>Which data types are you using? And which algorithms?</p>
<p>From: <a href="https://docs.nvidia.com/deeplearning/cudnn/developer-guide/index.html" rel="nofollow noreferrer">https://docs.nvidia.com/deeplearning/cudnn/developer... | tensorflow|deconvolution | 0 |
361,711 | 67,795,442 | how to split a mixed data column to separate int and str columns in python | <p>so I have a column which has data like mileage and data as 24.5 km 11.3 km .I want to separate integer value and string value and make 2 diff columns. how to do it.?</p>
<p>I have mileage</p>
<pre><code> 11.5km
21.4km
</code></pre>
<p>I want integer</p>
<pre><code> 11.5
21.4
STR... | <p>Try:</p>
<pre class="lang-py prettyprint-override"><code>df[["integer", "string"]] = df["mileage"].str.extract(pat=r"(\d+\.?\d*)(.*)")
print(df)
</code></pre>
<p>Prints:</p>
<pre class="lang-none prettyprint-override"><code> mileage integer string
0 11.5km 11.5 km
1 ... | python|pandas|dataframe | 2 |
361,712 | 67,605,913 | Python: Concatenation of elements in different matrices | <p>I have some issues in trying to concatenate strings in two different matrices between each other.
Example:</p>
<ul>
<li>The first matrix is dat defines as follows</li>
</ul>
<pre><code>
dat = array([['data 1:1 ', 'data 2:2 '],
['data 1:1 ', 'data 2:2 '],
['data 1:1 ', 'data 2:2 ']], dtype='<U9')
</c... | <p>This question contains mistakes only you can fix, such as missing brackets in the result.</p>
<p>Moreover, I don't understand why use numpy instead of native Python lists or pandas in this case.</p>
<hr />
<p>Not arguing with that, here is a solution</p>
<pre><code>import numpy as np
dat = np.array([['data 1:1 ', '... | python|string|numpy|matrix|concatenation | 0 |
361,713 | 67,704,772 | edit columnnames that include duplicate special characters | <p>I have some column names that include two question marks at different spaces e.g. 'how old were you? when you started university?' - i need to identify which columns have two question marks in. any tips welcome! thanks</p>
<p><strong>data</strong></p>
<pre><code>df = pd.DataFrame(data={'id': [1, 2, 3, 4, 5], 'how ol... | <p>One idea with list comprehension:</p>
<pre><code>df = df[[c for c in df.columns if c.count("?") < 2]]
print (df)
id how old were you when you finished university?
0 1 1
1 2 2
2 3 ... | python|pandas|duplicates|columnname|drop | 4 |
361,714 | 67,913,076 | How can i get the location of certain rows by using the index of a series with Pandas? | <p>I want to use the high_accidents(Pandas Series) index which is a list of cities to get the rows of the dataframe that match the df["City"] value.</p>
<pre><code>count_city = df["City"].value_counts()
high_accidents = count_city[count_city >= 1000]
new_df = df.loc[df["City"].values ... | <p>Here df['City'].values will give your an array of cities and high_accidents.index is of pandas.index type. So the error message is shown that length must match to compare.</p>
<p>To get your desired result you can modify the code as following:-</p>
<pre><code>count_city = df["City"].value_counts()
high_ac... | python|pandas|dataframe | 0 |
361,715 | 67,878,928 | Gradient with respect to the parameters of a specific layer in Pytorch | <p>I am building a model in pytorch with multiple networks. For example let's consider <code>netA</code> and <code>netB</code>. In the loss function I need to work with the composition <code>netA(netB)</code>. In different parts of the optimization I need to calculate the gradient of <code>loss_func(netA(netB))</code> ... | <p>The gradients are properties of <em>tensors</em> not <em>networks</em>.<br />
Therefore, you can only <code>.detach</code> a tensor.</p>
<p>You can have different optimizers for each network. This way you can compute gradients for all networks all the time, but only update weights (calling <code>step</code> of the r... | neural-network|pytorch|gradient-descent|detach | 1 |
361,716 | 67,856,595 | How to convert sentence to category? | <p>I'm working on NLP problem.
The target column contain 5 types of sentences:</p>
<pre><code>"Extremely Positive", "Positive", "Neutral", "Negative", "Extremely Negative"
</code></pre>
<p>I want to convert those sentences to number [5,4,3,2,1].</p>
<p>Is there a build ... | <p>You probably want to use an Encoder from the sklearn library.</p>
<p>LabelEncoder can be used to transform categorical data into integers:</p>
<pre><code> from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
x = ['Positive', 'Neutral', 'Positive', 'Negative']
encoded = label_e... | python|tensorflow|keras | 5 |
361,717 | 67,776,511 | How to assign ids to sets of coordinates? -python | <p>I am working with a <code>GeoDataFrame (gdf)</code> containing a road network (Lines) that looks like the following:</p>
<pre class="lang-py prettyprint-override"><code> id_road speed geometry
0 1 50.00 LINESTRING (a_lon a_lat, b_lon b_lat)
1 2 50.00 LINESTRING (b_lon b_lat, c_lon c_lat)
2 ... | <p>This is a scrappy implementation, but let me know if it helps:</p>
<p>To begin you likely need some way of transforming coordinate pairs to a list of pairs from which you can index:</p>
<pre><code>coordinate_pairs = df['geometry'].apply(lambda g: [g.coords[0], g.coords[-1]])
coordinates = [p for pair in coordinate_p... | python|geopandas | 1 |
361,718 | 67,929,356 | How to convert the table in pandas? | <p>I have the dataframe below:</p>
<pre class="lang-py prettyprint-override"><code>df = pd.DataFrame(
[
['A11', 'One', 'Person1', 'Yes'],
['A11', 'One', 'Person2', 'No'],
['B22', 'Two', 'Person3', 'Yes'],
['B22', 'Two', 'Person1', 'No'],
['B22', 'Two', 'Person4', 'No'],
... | <p>Try:</p>
<pre class="lang-py prettyprint-override"><code>from string import ascii_uppercase
l = (
df.groupby(["Code", "Name"])
.agg(list)
.apply(lambda x: list(zip(x["Person"], x["Valid"])), axis=1)
)
data = []
for a in l:
data.append({})
for i, (b, c) in... | python|pandas|dataframe | 1 |
361,719 | 67,942,180 | PyTorch - Tensors multiplication along new dimension | <p>Sorry if already asked, but I can't find the words to look for on Google.</p>
<p>Let's say I have a tensor <code>t1</code> of size <code>[a,b]</code> and a tensor <code>t2</code> of size <code>[c]</code>.</p>
<p>How can I output a tensor <code>t3</code> of size <code>[a,b,c]</code>, so that:</p>
<pre><code>t3[0, :, ... | <p>Using <a href="https://pytorch.org/docs/stable/generated/torch.kron.html" rel="nofollow noreferrer">torch.kron</a> will give a tensor a x b*c, then using <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor.reshape" rel="nofollow noreferrer">torch.Tensor.reshape</a> could map to a tensor a x b x c :</p... | python|pytorch | 2 |
361,720 | 67,678,869 | Collapse Pandas rows to elliminate NaN entries | <p>Let's consider the following DataFrame</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>Name</th>
<th>A</th>
<th>B</th>
<th>C</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<td>tom</td>
<td>10.0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<td>tom</td>
<td>NaN</td>
<td>15.0</td>
<t... | <p>You can <code>.groupby</code> + <code>.transform</code> (where you "move" the values up). Then drop rows which contain all <code>NaN</code> values:</p>
<pre class="lang-py prettyprint-override"><code>print(
df.set_index("Name")
.groupby(level=0)
.transform(lambda x: sorted(x, key=lamb... | python|pandas|dataframe|numpy|collapse | 2 |
361,721 | 67,985,204 | Python: Converting a list of strings into pandas data-frame with two columns a and b, corresponding to odd and even strings respectively | <p>I have this kind of input as below. It is a list of strings, every odd string is a number starting with MR and every even string is some mixed text. I need to convert this list of strings to a pandas data-frame which strictly has two columns, but because some of the MR numbers are present several times paired with d... | <p>try:</p>
<pre><code>df=pd.DataFrame(lst) #here lst is your list...Don't assign anything to list function
c=df.index%2==0 #checking if the index is even bcz the values are in consicutive order
out=pd.concat((df.loc[c,0].str.strip(':').reset_index(drop=True),df[~c].reset_index()),axis=1).drop('index',1)
#seperating... | python|pandas | 1 |
361,722 | 67,955,410 | How to plot element count and add annotations | <p>I was working "globalterrorism.csv" and wanted to visualise terrorist attacks in each country. I did this for the same:</p>
<pre><code>len(gt['country_txt'].unique())
</code></pre>
<p>And I got 205 unique countries.</p>
<pre><code>labels = gt.groupby(["country_txt"]).count()['eventid'].index
x ... | <ul>
<li>Using the data from <a href="https://data.world/data-society/global-terrorism-data" rel="nofollow noreferrer">data.world: Global Terrorism Data</a>. Select a file to download, and then choose the option to <strong>Download all files</strong>. Extract the files into the default folder name.
<ul>
<li>The total d... | python|pandas|matplotlib|bar-chart | 0 |
361,723 | 67,784,745 | pandas-dev installation (How to install Pandas 1.3.0) | <p>I've seen that on Pandas version 1.3.0.dev0+1779.gdcc2a8f801 there is a new implemented method (read_xml) and I would like to use it. The problem is that I have not found a way to install a development version of Pandas. i am currently using Python3 and pip and have tried from its source repository (<a href="https:/... | <p>You can use pip:</p>
<pre><code>pip install git+https://github.com/pandas-dev/pandas.git
</code></pre>
<p>If you are using a jupyter notebook, just run:</p>
<pre><code>!pip install git+https://github.com/pandas-dev/pandas.git
</code></pre>
<p>it will install the last version:</p>
<pre><code>Collecting git+https://gi... | python|pandas | 2 |
361,724 | 67,872,803 | Huggingface SciBERT predict masked word not working | <p>I am trying to use the pretrained SciBERT model (<a href="https://huggingface.co/allenai/scibert_scivocab_uncased" rel="nofollow noreferrer">https://huggingface.co/allenai/scibert_scivocab_uncased</a>) from Huggingface to predict masked words in scientific/biomedical text. This produces errors, and not sure how to ... | <p>As the error message tells you, you need to use <a href="https://huggingface.co/transformers/model_doc/auto.html?highlight=automodelformaskedlm#transformers.AutoModelForMaskedLM" rel="nofollow noreferrer">AutoModelForMaskedLM</a>:</p>
<pre class="lang-py prettyprint-override"><code>from transformers import pipeline,... | python|bert-language-model|huggingface-transformers | 1 |
361,725 | 68,000,761 | PyTorch DDP: Finding the cause of "Expected to mark a variable ready only once" | <p>I'm extending a complex model (already with <code>DistributedDataParallel</code> with <code>find_unused_parameters</code> set to <code>True</code>) in PyTorch on <code>detectron2</code>.</p>
<p>I've added a new layer generating some additional output to the original network - initially, that layer was frozen (<code>... | <p>With the help of the PyTorch community, I moved forward (see the original discussion <a href="https://discuss.pytorch.org/t/finding-the-cause-of-runtimeerror-expected-to-mark-a-variable-ready-only-once/" rel="nofollow noreferrer">here</a>).</p>
<p>I updated my PyTorch to 1.9.0 (was using 1.7.0 before). Now I the err... | pytorch | 1 |
361,726 | 67,855,643 | What is the meaning of HIGH CORRELATION in pandas profiling? | <p>I'm trying to use <code>pandas profiling</code> on titanic dateset.
Under the overview section there are some features with caption "<code>HIGH CORRELATION</code>"</p>
<ul>
<li>I know what is the meaning of correlation, but the caption doesn't tell which feature is correlated to this feature ?</li>
<li>So ... | <p>If you click on the <code>Warnings</code> tab it will tell what other feature the features are correlated with as seen in this <a href="https://pandas-profiling.github.io/pandas-profiling/examples/master/census/census_report.html" rel="nofollow noreferrer">example</a>. Can see the same thing in the <a href="https://... | pandas|pandas-profiling | 1 |
361,727 | 67,613,525 | How to select the specific class in cifar-10 | <p>I would like to know how to select the specific class in cifar-10. For example, I want 7, "horse" class in cifar-10. And I wrote the below code. But the obtained data is not what I want because it's wrong shape.</p>
<p>Please enlighten me on the specifics.</p>
<pre class="lang-py prettyprint-override"><cod... | <p>For the slicing, do something like:</p>
<pre><code>X_train = X_train[filter[0], ...]
Y_train = Y_train[filter[0], ...]
</code></pre>
<p>And the shapes would be</p>
<pre><code>X_train shape: (5000, 32, 32, 3), Y_train shape: (5000, 1)
</code></pre> | python|numpy | 0 |
361,728 | 67,945,968 | How to apply TF-IDF on pandas column based on colon delimiter in text data | <p>I have a column in pandas dataframe where I capture a visitor's journey. I want to implement TF-IDF on this text column. Here is the sample data -</p>
<pre><code>df = pd.DataFrame({'id': [10, 11, 12]
, 'pagename': ['home:cart:checkout:buy:home','home:cart:cart:home','home:account:home']})
</code><... | <p>I think I figured it out -</p>
<p>I had to use a custom tokenizer to solve this problem -</p>
<p>Here is my code that works now -</p>
<pre><code>def tokens(x): return x.split(':')
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf_vect= TfidfVectorizer( tokenizer=tokens
,u... | python|pandas|tfidfvectorizer | 1 |
361,729 | 67,908,812 | Issues with regex to match JSON-like string with optionally missing [] brackets around lists | <p>The following string is a typical example of the format of JSON input strings that I need to convert to a pandas DataFrame. My attempted work flow is to:</p>
<ol>
<li>split String into List (see String below, note this represents an individual row)</li>
<li>Convert each list to a dictionary</li>
<li>Convert dictiona... | <p>Your string is a valid JSON without braces. Add the braces and use <code>json.loads</code> to get the JSON object.</p>
<p>Next, just iterate the object, and if the current key contains a list of strings, join them:</p>
<pre class="lang-py prettyprint-override"><code>import json
s='"PN_#":9999,"Item&qu... | python|regex|pandas | 1 |
361,730 | 68,008,800 | Is this a valid way to filter to Unique Values? | <p>I have inherited a legacy piece of code and do not understand why this code bloc is throwing an Assertion Error, any help is really appreciated!</p>
<pre><code># rename columns and filter out null values
df_cipSP_PP = cipSP_PP_raw.rename(columns={'Asset/Tag Number': 'cipSP UID'})
df_cipSP_PP_select = df_cipSP_PP.loc... | <p>df['column_name'].unique()</p>
<p>This will return all the unique values</p>
<p>df['column_name'].unique().tolist() to have all unique values in a list</p> | python|pandas | 0 |
361,731 | 67,903,185 | How to fetch the data from next column in a dataframe | <p>I want to fetch the data of the column "Examples" with respect to column " Category"</p>
<p><a href="https://i.stack.imgur.com/7KtCO.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/7KtCO.png" alt="enter image description here" /></a></p>
<pre><code>Output:
Fruits [Apple,Mango,O... | <p>If I understood correct, you want to unpack each list that contains a
few lists in the <code>Example</code> column.</p>
<p>One way is to use numpy's <code>ravel</code> function. Assuming your dataframe is <code>df</code>:</p>
<pre><code>import numpy as np
df["Examples"] = df["Examples"].apply(lam... | python|pandas|dataframe|numpy | 1 |
361,732 | 67,752,969 | How to predict while training? | <p>I'm working on a reinforcement learning project where the agent is a load balancer observes service request and servers' status. The agent is supposed to do some batch train after accumulating some observation/action(allocating request to service server)/reward(whether the request is well handled i.e. timely/correct... | <p>Don't know why, but importing keras separately in each process solves the issue.</p>
<p>It's mentioned here.
<a href="https://stackoverflow.com/questions/56344611/how-can-take-advantage-of-multiprocessing-and-multithreading-in-deep-learning-us">How can take advantage of multiprocessing and multithreading in Deep lea... | python|tensorflow|machine-learning|keras|multiprocessing | 0 |
361,733 | 67,910,380 | Clubbing a dataframe column values based on a specific condition and storing it in a new column | <p>Trying to club the occurrences of col1 and storing it in a new column as col1_occurences, but unable to do so, please help</p>
<p>Input df:</p>
<pre><code>col1 col2
sheet1 john
sheet2 harry
sheet3 john
sheet4 mark
sheet5 mark
</code></pre>
<p>Expected Output</p>
<pre><code>col1 col2 col1_o... | <p>Try with <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.transform.html#pandas-core-groupby-dataframegroupby-transform" rel="nofollow noreferrer"><code>groupby transform</code></a>:</p>
<pre><code>df['col1_occurrences'] = df.groupby('col2')['col1'].transform('... | python|dataframe|pandas-groupby | 1 |
361,734 | 67,891,211 | Is it possible to find similarities between rows in a matrix without loop? | <p>i have a 2D numpy array. I'm trying to compute the similarities between rows and put it into a <code>similarities</code> array. Is this possible without loop? Thanks for your time!</p>
<pre><code># ratings.shape = (943, 1682)
arri = np.zeros(943)
arri = np.where(arri == 0)[0]
arrj = np.zeros(943)
arrj = np.where(a... | <p>The problem is how numpy iterates through the array when indexing a two-dimentional array with two arrays.</p>
<hr />
<p>First some setup:</p>
<pre class="lang-py prettyprint-override"><code>import numpy;
ratings = numpy.arange(1, 6)
indicesX = numpy.indices((ratings.shape[0],1))[0]
indicesY = numpy.indices((ratin... | python|arrays|numpy|broadcasting | 0 |
361,735 | 67,850,870 | Finding the smallest number not smaller than x for a rolling data of a dataframe in python | <p>Let suppose i have data in rows for a column(O) : 1,2,3,4,5,6,7,8,9,10.
Its average is 5.5.
I need to find the smallest number which is larger than the average 5.5 :- i.e. '6'</p>
<p>Here is what I have tried soo far.</p>
<p>method 1:</p>
<pre><code>df["test1"] = df["O"].shift().rolling(min_perio... | <p>Mask the Series when it is greater than the mean, then sort, then take the first row.</p>
<pre><code>import pandas as pd
df = pd.DataFrame([1,2,3,4,5,6,7,8,9,10], columns=("vals",))
df[df.vals > df.vals.mean()].sort_values("vals").head(1)
# > vals
# 5 6
</code></pre> | python|pandas|dataframe|rolling-computation | 4 |
361,736 | 67,902,307 | How to crate new column based on time interval? | <p>I want to create a new column, based on time interval of 6hours from datetime column how can I do that?</p>
<p><a href="https://i.stack.imgur.com/a65yc.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/a65yc.png" alt="enter image description here" /></a></p>
<pre><code> C/A UNIT SCP ... | <p>pandas has <code>floor</code> function for time</p>
<pre><code>df['DATETIME'].dt.floor('6H')
</code></pre>
<p>this column needs to be datetime type</p>
<pre><code> 0 1
0 2021-06-06 00:00:00 2021-06-06 00:00:00
1 2021-06-06 01:00:00 2021-06-06 00:00:00
2 2021-06-06 02:00:00 2021-06-06 00:00:00
... | python|pandas | 1 |
361,737 | 67,824,840 | How to I join two pandas DataFrames that have the same multiple columns? I don't want duplicates such as Date.a | Date.b | <p>I have two pandas DataFrames of Date | Region | District. I want to combine these two dataframes so there is not Date.a | Date.b | Region.a | District.a Region.b | District.b</p> | <p>Merge columns must be present in both dataframes</p>
<pre class="lang-py prettyprint-override"><code>df = df1.merge(df2, on=["Date", "Region", "Disctric"])
</code></pre> | python|pandas | 0 |
361,738 | 67,837,710 | How to solve cannot assign to function call in this Python code | <pre><code>#Start cleaning loop through all the pings
for P in Pings:
#All beams for current ping
print("Cleansing completed", round(P/len(Pings)*100,1),"%")
Slice_one = df[(df.P==P)&(df.Bm>0)&(df.Bm<257)].copy()
model = LinearRegression().fit(Slice_one.B... | <p>you have to use square brackets [ ], <code>Slice_one["dZ"] = abs(Slice_one.Z_1 - Slice_one.Z)</code></p> | python|python-3.x|pandas|function|linear-regression | 0 |
361,739 | 68,006,587 | Pandas groupby and then find max value per group in another column | <p><a href="https://i.stack.imgur.com/DvGFH.png" rel="nofollow noreferrer">This is not the whole dataset, just .head(10)</a></p>
<p>I want a dataframe with 3 columns: groupby user_id</p>
<ol>
<li><p>’user_id’</p>
</li>
<li><p>The ‘product_id’ that is most ordered per ‘user_id’ (max in ‘uxp_total_bought' per ‘user_id’)<... | <p>I think the following is gonna work.</p>
<pre><code>test = your_dataset.groupby('product_id')['uxp_total_bought'].max()
test = test.reset_index()
test = your_dataset.loc[uxp.groupby("user_id")["uxp_total_bought"].idxmax()]
del test["uxp_total_bought"]
test.rename(columns = {"produc... | pandas|dataframe|pandas-groupby | 0 |
361,740 | 67,985,433 | Pandas: Fill na with mode of a group | <p>I have a <code>df</code> with multiple columns.</p>
<pre class="lang-py prettyprint-override"><code>df = pd.DataFrame({'Store':['M1','M2','M3','M1','M1','M2','M2','M3','M3'],
'Category':['A','A','A','B','B','B','C','C','C'],
'Price_Category':[np.nan,X,np.nan,np.nan,Y,Y,Z,np.nan,... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.iat.html" rel="nofollow noreferrer"><code>Series.iat</code></a> for first value of <code>Series</code> by position:</p>
<pre><code>f = lambda x: x.fillna(x.mode().iat[0])
df['Price_Category'] = df.groupby('Category')['Price_Category... | python|pandas|mode|fillna | 3 |
361,741 | 67,991,822 | Updating Entry in Table - Python | <p>I have a table like the one below, which I used a double for loop to calculate. The problem is, I'm getting different results for rows <code>test1 vs test2</code> and <code>test2 vs test1</code> (see intersection value, rate, and percentage columns in table). They should be the same. For example, <code>test1 vs test... | <p>Create a sort key from <code>Param -a</code> and <code>Param -b</code> to match the same records and group them. You can copy the first value of the group using <code>transform</code>:</p>
<pre class="lang-py prettyprint-override"><code>cols = ['Intersection.Value', 'Rate', 'Percentage']
key = df[['Param -a', 'Param... | python|pandas | 0 |
361,742 | 67,917,626 | group by id1 and id2 and apply a function using another dataframe and dates | <p>My problem is the following:
I have a dataframe <strong>DF1</strong> of car accidents (<strong>id_accident</strong>) and PASSENGER victims (<strong>id_victim</strong>) and the date of the accident (<strong>date1</strong>).</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>id_accident</th>... | <p>This looks like classic SQL question. What kind of output format do you need?</p>
<p>I had to change first date if DF2 to <code>2020/20/01</code> to make pandas recognize it.
Below is the complete example using <code>pd.merge</code></p>
<pre><code>import pandas as pd
import numpy as np
from io import StringIO
df1 ... | python|pandas|function|date | 1 |
361,743 | 67,676,636 | Why my function that creates a pandas dataframe changes the dtype to none when called | <p>I'm working on processing csv files, I was writing my code without functions and it worked, albeit some problems when trying to fillna with a string, before I did a try and except.
For some reason it didn't work before creating the while loop.
My question is why a dataframe object created inside of a function by rea... | <p>Well sorry guys, I figure it out by myself:
I was missing the scope, sorry again for the newb stuff. I just started coding in Python a few months ago(last December) and I'm learning in the process.</p>
<p>What worked for me was to add the scope Global, within the function, seriously I didn't know dataframes behaved ... | python-3.x|pandas|dataframe|function|dtype | 0 |
361,744 | 67,725,175 | How to access pytorch embeddings lookup table as a tensor | <p>I want to show my embeddings with the tensorboard projector. I would like to access the embeddings matrix (lookup table) of one of my layers so I can write it to the logs.</p>
<p>I instantiate my layer as this:</p>
<p><code>self.embeddings_user = torch.nn.Embedding(30,300)</code></p>
<p>And I'm looking for the tenso... | <p>Embeddings layers have weight attributes corresponding to the lookup table. You can access it as follows.</p>
<pre class="lang-py prettyprint-override"><code>vectors = self.embeddings_user.weight
</code></pre>
<p>So now you can visualize it with tensorboard.</p>
<pre><code>import numpy as np
import tensorflow as tf
... | tensorflow|machine-learning|pytorch|tensorboard | 0 |
361,745 | 67,766,734 | How do I reverse all lists in a pandas dataframe column? | <p>I have a dataframe with a column that is a filled with different lists. I want to reverse every list in the column.</p>
<p>Example of what I want:</p>
<p>I want this df:</p>
<pre><code> index x_val
1 [1,2,3,4,5]
2 [2,3,4,5,6]
</code></pre>
<p>to become this:</p>
<pre><code> index x_... | <p><strong>Two Cases:</strong></p>
<h3>Case 1</h3>
<p>If <code>List</code> is of <code>int type</code> forming the <code>object type</code> column <code>x_val</code></p>
<pre><code>df = pd.DataFrame({
'index':[1,2],
'x_val':[[1,2,3,4,5], [2,3,4,5,6]]
})
</code></pre>
<p><strong>Code</strong></p>
<pre><code>df['... | python|pandas | 1 |
361,746 | 67,858,524 | How to append a 2d array to 3d array? | <p>I have a 3D nested list constructed like this:</p>
<pre><code>[[[1,1],[2,1],[2,2],[1,2]],[[-1,-1],[-2,-1],[-2,-2],[-1,-2]]]
</code></pre>
<p>and I want to concat to that <code>[[0,0]]</code> to get this result:</p>
<pre><code>[[[1,1],[2,1],[2,2],[1,2]],[[-1,-1],[-2,-1],[-2,-2],[-1,-2]],[[0,0]]]
</code></pre>
<p>I tr... | <p>The problem is that once you have a <code>numpy</code> array with shape <code>(2, 4, 2)</code>, you cannot really append another array with shape <code>(1, 1, 2)</code> unless you treat lists as objects (i.e. you are appending an element to an array of size <code>2</code> so that in the end you have an array of size... | python|list|numpy | 1 |
361,747 | 67,808,562 | Read multiple files from Unix folder and extract key value pair using Python | <p>Reading multiple files from Unix directory. I am trying to read multiple files stored in Unix folder and extract Key Value Pair after this bit of text "input1".</p>
<p>Each Files consist data in below formats:-</p>
<p>input1 = {'hostname' : 'host', 'port' : '22', 'basedn' : 'CN=Users', 'bindusername' : 'ad... | <p>Here try this?</p>
<pre><code>import pandas as pd
dic1={'xyz':'123' , 'abc':'456','def':'765'}
data = pd.DataFrame()
data['Col1']=[val for val,key in dic1.items()] #=== All keys in dic1
data['Col2']=[key for val,key in dic1.items()] #=== All Values in dic1
print(data)
</code></pre>
<p>prints:</p>
<pre><code> Col1 ... | python|python-3.x|pandas|dictionary|tuples | 0 |
361,748 | 67,840,158 | Numpy Array Local variable reference Python | <p>I'm getting an error with <code>npArray</code> however <code>listvals</code> works just fine. How would I be able to fix it so that the numpy array works just like <code>listvals</code>?</p>
<p>Code:</p>
<pre><code>import numpy as np
listvals=[]
npArray=np.array([])
def Run():
for n in range(5):
listva... | <p>very simple, All you have to do is pass it as a function</p> | python|python-3.x|list|function|numpy | 1 |
361,749 | 67,714,936 | rotate diagonal of a 2d numpy array into row | <p>I have a 2d numpy array:</p>
<pre><code>A = array([[1, 7, 5, 0, 5],
[9, 1, 4, 6, 0],
[9, 6, 1, 0, 0],
[2, 5, 0, 0, 0],
[1, 0, 0, 0, 0]])
</code></pre>
<p>What I want to achieve is</p>
<pre><code>B = array([[1, 0, 0, 0, 0],
[9, 7, 0, 0, 0],
[9, 1, 5, 0... | <p>This is what I could come up with:</p>
<pre><code>B = np.empty_like(A)
for i in range(5):
pad_width = (0, 5 - len(np.diag(A[::-1], k=n))
B[i, :] = np.pad(np.diag(A[::-1], k=i-4), pad_width)
</code></pre>
<p>Here is the explanation:</p>
<ol>
<li>You can use <a href="https://numpy.org/doc/stable/reference/gene... | numpy | 0 |
361,750 | 67,705,320 | MACD stock indicator function using ewm() from pandas library | <p>Here is the test code for my macd function, however, the values I am getting are incorrect. I don't know if it is because my span is in days and my data is in 2 minute increments, or if it is a seperate issue. Any help would be much appreciated :)</p>
<pre><code>import yfinance as yf
import pandas as pd
import panda... | <p>Use min_periods instead adjust
code:</p>
<pre><code> import pandas as pd
import pandas_datareader as pdr
import matplotlib.pyplot as plt
df = pdr.DataReader('BTC-USD' , data_source='yahoo' , start='2020-01-01')
df
</code></pre>
<p>Function definition:</p>
<pre><code>def MACD(DF,a,b,c):
df=DF.copy()
... | python|pandas|yfinance | 0 |
361,751 | 67,731,499 | reading columns of csv file using pandas not working | <p>I am trying to read the following .csv file, but I want to read each column of it. However, usecols is not working as it is giving the following error:
<code>ValueError: Usecols do not match columns, columns expected but not found: ['sources', 'RMS']</code></p>
<p>this is how I am reading it:</p>
<pre><code>train=pd... | <p>If the order of the columns will always be the same, you can also use an integer-list with <code>usecols</code></p>
<pre><code>df = pd.read_csv('file.csv',usecols=[0,4] #this selects just 0 and 4
</code></pre> | python|pandas|csv|file | 0 |
361,752 | 31,764,579 | Numpy array scaling not returning proper values | <p>I have a numpy array that I want to alter by scaling all of the columns (e.g. all the values in a column are divided by the maximum value in that column so that all values are <1). </p>
<p>A sample output of the array is </p>
<p>[ 2. 0. 367.877 ..., -0.358 51.547 -32.633]</p>
<p>[ 2. 0... | <p>IIUC, it's not that the maximum value is shared between columns, it's that you probably want to divide by the maximum <em>absolute</em> value instead, because you have elements of both signs. 1 > -100, after all, and so if you divide by the <em>maximum</em> value of a column with [1, -100], nothing would change.</p>... | python|arrays|numpy | 2 |
361,753 | 31,889,441 | pandas: complex grouping and nests | <p>Here is a sample data set. Assume that there are many other records and many many more customer records.</p>
<pre><code> customers = ['a','a','a','a','b','b','b','c','c','c']
level = [10,15,30,49,12,15,49,9, 22, 49]
cars = ['bmw','audi','vw','mercedes','bmw','bmw','audi','audi', 'bmw', 'audi']
df = pd.DataFrame(... | <p>No promises that this is the slickest way, but I think you can get where you want to go with two groupbys, and a <code>cut</code> to get the levels:</p>
<pre><code>df["lev"] = pd.cut(df.levels, bins=range(0,100,10), right=False)
dc = df.groupby(["customers", "lev"]).size().reset_index(name="count")
dfinal = dc.grou... | pandas|grouping|nested | 1 |
361,754 | 31,785,594 | Python pandas: banal apply statements incredibly slow | <p>I have a pandas dataframe with ca. 250,000 rows. I am trying to create a new field as follows:</p>
<pre><code>df['new_field'] = df.apply( lambda x: x.field2 if x.field1 > 0 else 0, axis =1 )
</code></pre>
<p>this works, but the single line above takes about 15 seconds to run!</p>
<p>I optimised it this way:</p... | <p>You can use <code>np.where</code>:</p>
<pre><code>df['new_field'] = np.where(df['field1'] > 0, df['field2'], 0)
</code></pre>
<p>So the above tests your boolean condition and returns <code>df['field2']</code> when <code>True</code> else it returns <code>0</code></p>
<p>or in pandas style:</p>
<pre><code>df['n... | python|pandas|dataframe|numba | 4 |
361,755 | 32,078,737 | Create pandas dataframe manually without columns name | <p>I would like to create the following dataframe without columns names</p>
<pre><code>import pandas as pd
df = pd.DataFrame([0,423,2342],[12,123,1231], [1,3,5])
</code></pre>
<p>I get back an error</p>
<pre><code>TypeError: unhashable type: 'list'
</code></pre>
<p>What am I doing wrong?</p>
<p>I also tried <code>... | <p>The thing is you should pass your data as a 2D array, otherwise the constructor thinks you've passed several positional arguments. </p>
<pre><code>DataFrame([[0,423,2342],[12,123,1231], [1,3,5]])
</code></pre> | python|pandas|dataframe|multiple-columns|manual | 4 |
361,756 | 32,027,446 | Error in pandas while subtracting two values and storing them again | <pre><code>First.csv
LAC Reference_Count
1000 500
2222 1000
3333 500
5555 1000
9999 1500
Second.csv
LAC 10/08/15 00:00 10/08/15 01:00
1000 2000 2500
2222 3000 4000
</code></pre>
<p>I have two files first.csv and second.csv,in first.csv I have two headers LAC and reference count,second.csv ... | <p>Try converting everything to float as the error is saying that you have a string that you are trying to do math with. </p>
<p>Did you try:</p>
<pre><code>float(value) = float(tmp) - float(val)
</code></pre>
<p>Let me know if the error changes after that. Post the error too.</p> | python|csv|numpy|pandas | 0 |
361,757 | 31,843,008 | Transforming text data in sklearn pipeline | <p>Given an array of text data, </p>
<pre><code>X = np.array(['cat', 'dog', 'cow', 'cat', 'cow', 'dog'])
</code></pre>
<p>I would like to use an sklearn pipeline to produce output like </p>
<pre><code>np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [0, 0, 1], [0, 1, 0]])
</code></pre>
<p>My initial attempt</p... | <p>Use <code>LabelBinarizer</code>:</p>
<pre><code>import numpy as np
from sklearn import preprocessing
X = np.array(['cat', 'dog', 'cow', 'cat', 'cow', 'dog']) ... | python|pandas|scikit-learn | 3 |
361,758 | 32,002,260 | statsmodel armax: Cannot add integral value to Timestamp without offset | <p>I am trying to use a simple ARMAX model to predict a time series (GDP) based on another time series (covar). I keep getting: ValueError: Cannot add integral value to Timestamp without offset.</p>
<p>The GDP and covar time series are monthly data points from 201101 to 201505. I would like to predict GDP over all 12 ... | <p>According to the statsmodels documents, <code>ARMAResults.predict()</code> has four parameters, in which the third parameter <code>exog</code>, aslo known as independent variables, is not necessary to be given.</p>
<p>In your case, since the prediction time interval is from Jan 2014 to Dec 2014, it is part of your ... | pandas|statsmodels | 0 |
361,759 | 32,021,612 | Select and Count items datetime64[ns] in Pandas | <p>I have a pd as following:</p>
<pre><code>df=
CreationDate
0 2008-11-04 13:21:39
1 2008-11-24 23:50:29
2 2009-05-18 07:46:48
3 2009-09-22 06:03:34
4 2009-11-07 07:28:21
5 2009-12-08 14:29:56
6 2010-01-12 06:42:00
7 2010-05-20 17:56:01
8 2010-06-05 19:27:02
9 2010-07-16 19:52:22
10 2010-07-25 ... | <p>You need to first set <code>CreationDate</code> as index and then use the slice <code>'2008-11':'2009-12'</code> to select.</p>
<pre><code>print(df)
CreationDate A
0 2008-11-04 13:21:39 1.7641
1 2008-11-24 23:50:29 0.4002
2 2009-05-18 07:46:48 0.9787
3 2009-09-22 06:03:34 2.2409
4 2009-11-... | python|pandas | 1 |
361,760 | 31,765,123 | Pandas DataFrame.merge MemoryError | <h2>Goal</h2>
<p>My goal is to merge two DataFrames by their common column (gene names) so I can take a product of each gene score across each gene row. I'd then perform a <code>groupby</code> on patients and cells and sum all scores from each. The ultimate data frame should look like this:</p>
<pre><code> patie... | <p>Consider two workarounds:</p>
<p><strong>CSV By CHUNKS</strong></p>
<p>Apparently, <a href="https://stackoverflow.com/questions/11622652/large-persistent-dataframe-in-pandas">read_csv</a> can suffer performance issues and therefore large files must load in iterated chunks.</p>
<pre><code>cellsfilepath = 'C:\\Path... | python|pandas|dataframe|anaconda | 5 |
361,761 | 41,543,687 | TensorFlow: What is the purpose of endpoints for data parallelism when training across multiple machines? | <p>In the <code>TensorFlow-slim</code> source code, there was an endpoint indicated in the creation of its loss function:</p>
<pre><code>def clone_fn(batch_queue):
"""Allows data parallelism by creating multiple clones of network_fn."""
images, labels = batch_queue.dequeue()
logits, end_points = network_fn(image... | <p>The <code>endpoints</code> in this case just track the different outputs of the model. The <code>AuxLogits</code> one, for example, has the logits. </p> | python|machine-learning|tensorflow|deep-learning|tf-slim | 2 |
361,762 | 41,504,125 | How do I get the value of weight and bias from auto-encoder program | <p>I ran <a href="https://gist.github.com/tomokishii/7ddde510edb1c4273438ba0663b26fc6#file-mnist_ae1-py" rel="nofollow noreferrer">mnist_ae1.py</a>(very simple model of auto-encoder), and want to get value of <code>w_enc</code> and <code>b_enc</code>.
So, I added some process as below.</p>
<pre><code># Train
init = tf... | <p>The second argument to <a href="https://www.tensorflow.org/api_docs/python/tf/Operation#run" rel="nofollow noreferrer">Operation.run</a> which you call in the <code>train_step.run</code> line is the session. By passing a dictionary there you confuse the runtime into treating that dictionary as a session. Try doing <... | python|python-2.7|tensorflow | 0 |
361,763 | 41,552,090 | How to efficiently add rows for those data points which are missing from a sequence using pandas? | <p>I have the following time series dataset of the number of sales happening for a day as a pandas data frame.</p>
<pre><code>date, sales
20161224,5
20161225,2
20161227,4
20161231,8
</code></pre>
<p>Now if I have to include the missing data points here(i. e. missing dates) with a constant value(zero) and want to make... | <p>You can first cast to <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html" rel="nofollow noreferrer"><code>to_datetime</code></a> column <code>date</code>, then <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.set_index.html" rel="nofollow noreferrer"... | python|pandas | 3 |
361,764 | 41,314,967 | How to combine multiple cells into a single text сell | <p>I have a dataframe like this</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'item': [1, 1,2,2],
'user': [1,2,2,1],
'appraisal': [4,2,1,3],
'feedback' : ['good', 'bad', 'bad', 'well']
})
names = ['item', 'user', 'appraisal', 'feedba... | <p>you can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.DataFrameGroupBy.agg.html" rel="nofollow noreferrer">GroupBy.agg()</a> method:</p>
<pre><code>In [4]: df.groupby('item').agg({'appraisal':'mean','feedback':' '.join})
Out[4]:
appraisal feedback
item
1 3... | python-3.x|pandas|text | 4 |
361,765 | 41,588,383 | How to run Keras on multiple cores? | <p>I'm using Keras with Tensorflow backend on a cluster (creating neural networks). How can I run it in a multi-threaded way on the cluster (on several cores) or is this done automatically by Keras? For example in Java one can create several threads, each thread running on a core.</p>
<p>If possible, how many cores sh... | <p>Tensorflow automatically runs the computations on as many cores as are available on a single machine. </p>
<p>If you have a distributed cluster, be sure you follow the instructions at <a href="https://www.tensorflow.org/how_tos/distributed/" rel="noreferrer">https://www.tensorflow.org/how_tos/distributed/</a> to co... | python|multithreading|python-3.x|tensorflow|keras | 18 |
361,766 | 41,328,122 | Creating a pandas DataFrame from a list followed by an array produces error | <p>When I try creating a pandas DataFrame with a list followed by an array, I get an error:</p>
<pre><code>print(DataFrame([[10,20,30],np.arange(3)]))
</code></pre>
<p>TypeError: Expected list, got numpy.ndarray</p>
<p>But if I reverse the order of the data, then the operation succeeds:</p>
<pre><code>print(DataFra... | <p>This can give you a rough idea. There are multiple checks on data argument. If data argument is a list of items then the below code will execute under the hood. The first element of list is checked. If it's a list then it proceeds into listlike block but if its ndarray it proceeds into ndarray block. The lists block... | pandas|dataframe | 1 |
361,767 | 41,645,209 | Select largest square chunk for data from numpy array with no data values | <p>I need to select the square section of data within an 2D numpy array with nan's as the no data value. Here is a simplified example:</p>
<pre><code>import numpy as np
#Fake Data
data =np.reshape(np.arange(100,dtype='float64'), (10,10))
extra_cols = np.zeros((1,10), dtype=data.dtype)
data = np.concatenate((data,ext... | <p>I am pretty sure I cracked it. A couple of days and too much coffee.</p>
<pre><code>import numpy as np
def crop_to_data(mask, im):
true_points = np.argwhere(mask)
top_left = true_points.min(axis=0)
# take the largest points and use them as the bottom right of your crop
bottom_right = true_points.m... | python|arrays|numpy|image-processing|subset | 0 |
361,768 | 41,638,369 | Pandas : Cannot select row from dataframe | <p>Here is my dataframe</p>
<pre><code> Word 1_gram-Probability
0 ('A',) 0.001461
1 ('45',) 0.000730
</code></pre>
<p>now i just want to select the row where <code>Word</code> is 45. i tried</p>
<pre><code>print(simple_df.loc[simple_df['Word']=='45'])
</co... | <p>It appears that you have the literal string value <code>"('45',)"</code> in the cell of your dataframe. You must select it exactly so.</p>
<pre><code>simple_df.loc[simple_df['Word']=="('45',)"]
</code></pre> | python-3.x|pandas|dataframe | 2 |
361,769 | 41,478,086 | Why ndim = 2 in this numpy array? | <pre><code>>import numpy as np
>a = np.arange(15).reshape(3, 5)
([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
> a.shape
(3, 5)
> a.ndim
2 /// how to calculate it for any narray
</code></pre> | <p><code>ndim</code> is the same as <code>len(a.shape)</code></p>
<p>in your case you have 2 dimensions, first of size 3 and second of size 5</p> | python|numpy | 2 |
361,770 | 41,268,347 | Pandas - Using a list of values to create a smaller frame | <p>I have a list of values that are found in a large pandas dataframe:</p>
<pre><code>value_list = [1, 4, 5, 6, 54]
</code></pre>
<p>Example DataFrame <code>df</code> is below:</p>
<pre><code> column x
0 1 3
1 4 6
2 5 8
3 6 19
4 8 21
5 12 97
6 54 102
</code></pre>
<p>I woul... | <p>You might be looking for <code>isin</code> operation.</p>
<pre><code>In [60]: df[df['column'].isin(value_list)]
Out[60]:
column x
0 1 3
1 4 6
2 5 8
3 6 19
6 54 102
</code></pre>
<p>Also, you can use <code>query</code> like</p>
<pre><code>In [63]: df.query('column in ... | python|pandas | 3 |
361,771 | 41,230,644 | Tensorflow matmul operation for rank>2 does not work | <p>I find the following on the Tensorflow documentation homepage for using the matmul operation when rank>2:</p>
<p><a href="https://www.tensorflow.org/api_docs/python/math_ops/matrix_math_functions#matmul" rel="nofollow noreferrer">https://www.tensorflow.org/api_docs/python/math_ops/matrix_math_functions#matmul</a><... | <p>Is your TensorFlow too old? Here's what I get in version 0.12rc0</p>
<pre><code>a = tf.constant(np.arange(1,13).astype(np.float32), shape=[2, 2, 3])
b = tf.constant(np.arange(13,25).astype(np.float32), shape=[2, 3, 2])
sess.run(tf.matmul(a, b)) =>
array([[[ 94., 100.],
[ 229., 244.]],
[[ 508.... | python|tensorflow|rank | 1 |
361,772 | 41,374,177 | pandas read_html does not storing complete data | <p>I am using read_html function in pandas to extract data from some html tables . But for some reason the output gets cut after a certain size : </p>
<p>example :</p>
<pre><code>0 RECKITT BENCKISER INDIA PRIVATE LIMITED Vs.ST...
1 SMT. SONY AND ANOTHER Vs. STATE OF UTTARAKHA...
2 BHATIA BHAWAN DHARAM... | <p>It is getting the complete text. It was not showing the full text due to limited column width.</p>
<p>Check this:</p>
<pre><code>import pandas as pd
pd.set_option('max_colwidth',400)
df=pd.read_html('http://pastebin.com/raw/p7vfb2JG')[0]
df.head()
</code></pre>
<p>Output:</p>
<p><a href="https://i.stack.imgur.c... | python|pandas|web-scraping | 0 |
361,773 | 41,580,143 | How do I replace the values in the second dataframe based on the values in the first dataframe | <p>I have two dataframes i.e df and df1,</p>
<p><code>df</code>:</p>
<pre><code>Product_name Name City
Rice Chetwynd Chetwynd, British Columbia, Canada
Wheat Yuma Yuma, AZ, United States
Sugar Dochra Singleton, New South Wales, Australia
Milk Ind... | <h1>Setup</h1>
<pre><code>from io import StringIO
import pandas as pd
df_txt = """Product_name Name City
Rice Chetwynd Chetwynd, British Columbia, Canada
Wheat Yuma Yuma, AZ, United States
Sugar Dochra Singleton, New South Wales, Australia
Milk I... | python|pandas | 2 |
361,774 | 41,271,425 | pycharm ipython package not loading? | <p>I am trying to work on a ipython notebook on pycharm but I am not being able to use the packages
<a href="https://i.stack.imgur.com/09pTh.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/09pTh.jpg" alt="enter image description here"></a></p>
<p>As you can see it says <code>pd</code> is not defined... | <p>Turns out I have to run the first cell first then the others. </p> | python|pandas|pycharm | 1 |
361,775 | 41,537,320 | curve fitting and parameter estimation in Python | <p>I am currently using Python to compare two different datasets (xDAT and yDAT) that are composed of 240 distance measurements taken over a certain amount of time. However, dataset xDAT is offset by a non-linear amount. This non-linear amount is equal to the width of a time-dependent, dynamic medium, which I call leve... | <p>The answer depends on what Level A is. If it is independent, your first line should be something like </p>
<p><code>coefs = np.polynomial.polynomial.polyfit(numpy.arange(xDAT.size), yDAT-xDAT, 5)</code></p>
<p>This will give a polyfit of an independent <code>A</code> as drawn, and then the corrected <code>x</code... | python|numpy|estimation|polynomials | 0 |
361,776 | 41,492,390 | How to create a new empty pandas columns with a specific dtype? | <p>I have a DataFrame <code>df</code> with columns <code>'a'</code>. How would I create a new column <code>'b'</code> which has <code>dtype=object</code>?</p>
<p>I know this may be considered poor form, but at the moment I have a dataframe <code>df</code> where the column <code>'a'</code> contains arrays (each element... | <p>As each row of your column is an array, it's better to use the standard <code>NumPy</code> mathematical functions for computing their element-wise logarithms to the base 10:</p>
<pre><code>df['log_a'] = df.a.apply(lambda x: np.log10(x))
</code></pre>
<p><a href="https://i.stack.imgur.com/K05Ni.png" rel="nofollow n... | python|pandas | 1 |
361,777 | 27,710,953 | Divide each plane of cube by its median without loop | <p>I need to normalize a numpy data cube say:</p>
<pre><code>cube = np.random.random(100000).reshape(10,100,100)
</code></pre>
<p>and then normalise each of the 10 resulting planes by the median. So, e.g. for the first plane</p>
<pre><code>cube[0, :, :] /= np.median(cube[0, :, :])
</code></pre>
<p>I just want to av... | <p>You can pass a list of axes to <code>np.median</code> and then expand via <code>None</code> (<code>np.newaxis</code>):</p>
<pre><code>>>> cube = np.random.random(100000).reshape(10,100,100)
>>> simple = cube / np.median(cube,axis=[1,2])[:,None,None]
>>>
>>> brute = cube.copy()
&... | python|numpy | 5 |
361,778 | 27,638,743 | Pandas - Replace outliers with groupby mean | <p>I have a pandas dataframe which I would like to split into groups, calculate the mean and standard deviation, and then replace all outliers with the mean of the group. Outliers are defined as such if they are more than 3 standard deviations away from the group mean.</p>
<pre><code>df = pandas.DataFrame({'a': ['A','... | <p>Try this:</p>
<pre><code>def replace(group):
mean, std = group.mean(), group.std()
outliers = (group - mean).abs() > 3*std
group[outliers] = mean # or "group[~outliers].mean()"
return group
df.groupby('a').transform(replace)
</code></pre>
<p>Note: If you want to eliminate the 100 in your... | python|pandas | 8 |
361,779 | 27,778,299 | Replace the zeros in a NumPy integer array with nan | <p>I wrote a python script below:</p>
<pre><code>import numpy as np
arr = np.arange(6).reshape(2, 3)
arr[arr==0]=['nan']
print arr
</code></pre>
<p>But I got this error:</p>
<pre><code>Traceback (most recent call last):
File "C:\Users\Desktop\test.py", line 4, in <module>
arr[arr==0]=['nan']
ValueError:... | <p><code>np.nan</code> has type <code>float</code>: arrays containing it must also have this datatype (or the <code>complex</code> or <code>object</code> datatype) so you may need to cast <code>arr</code> before you try to assign this value. </p>
<p>The error arises because the string value <code>'nan'</code> can't be... | python|arrays|numpy|nan | 52 |
361,780 | 27,621,904 | Create a single-file dataset out of _many_ b/n GIFs | <p>I have many 100x100px black/white GIF images.
I want to use them in Numpy to train a machine learning algorithm, but I would like to save them in a single file that is easily readable in Python/Numpy.
By saying many I mean several hundred thousands, so I would like to take advantage of the images carrying only 1 bit... | <p>PyTables seems like a good option here. Something like this might work:</p>
<pre><code>import numpy as np
import tables as tb
nfiles = 100000 #or however many files you have
h5file = tb.openFile('data.h5', mode='w', title="Test Array")
root = h5file.root
x = h5file.createCArray(root,'x',tb.Float64Atom(),shape=(100,... | python|numpy | 0 |
361,781 | 27,672,556 | pandas asfreq returns NaN if exact date DNE | <p>Let's say I have financial data in a <code>pandas.Series</code>, called <code>fin_series.</code></p>
<p>Here's a peek at <code>fin_series</code>.</p>
<pre><code>In [565]: fin_series
Out[565]:
Date
2008-05-16 1000.000000
2008-05-19 1001.651747
2008-05-20 1004.137434
...
2014-12-22 1158.085200
2014-12-2... | <p>Assuming the input is a <strike>dataframe</strike> <code>Series</code> , first do </p>
<pre><code>import pandas as pd
fin_series.resample("q",pd.Series.last_valid_index)
</code></pre>
<p>to get a series with the last non-NA index for each quarter. Then</p>
<pre><code>fin_series.resample("q","last")
</code></pre>
... | python|datetime|pandas | 1 |
361,782 | 61,375,125 | Keras tuner: mismatch between number of layers used and number of layers reported | <p>Using example from Keras Tuner website, I wrote simple tuning code</p>
<pre><code>base_model = tf.keras.applications.vgg16.VGG16(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')
base_model.trainable = False
de... | <p>Any hyperparameter seen so far will be displayed in the summary, meaning that once a trial containing three layers has been run, all subsequent summaries will contain three layer sizes. It does not mean it uses all three layers, which is indicated by by the <code>num_layers: 1</code> print for this particular trial.... | python|tensorflow|keras|deep-learning|keras-tuner | 1 |
361,783 | 61,339,327 | How to draw a Frequency plot using the output? | <p>Using below code i listed the top 10 most frequent words with its count now i need to put it in a frequency plot .</p>
<pre><code>freq6000.sort_values(by=['Word Frequency'],ascending=False).head(10)
Word Frequency
data 124289
experience 59135
business 33528
work 28146
science 268... | <p>Select column <code>Word Frequency</code> for <code>Series</code> and then use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.bar.html" rel="nofollow noreferrer"><code>Series.plot.bar</code></a>:</p>
<pre><code>(freq6000.sort_values(by=['Word Frequency'],ascending=False)
... | python-3.x|pandas | 1 |
361,784 | 61,334,090 | Is it safe to always use torch.tensor or torch.FloatTensor? Or do I need to treat Ints with care? | <blockquote>
<p><a href="https://pytorch.org/docs/stable/tensors.html" rel="nofollow noreferrer">https://pytorch.org/docs/stable/tensors.html</a></p>
</blockquote>
<p>I am trying to understand the difference between <code>tensor, FloatTensor, IntTensor</code> - and am wondering if I can just always stick to <code>te... | <p><code>CrossEntropyLoss</code> (or <code>NLLLoss</code>) expect the <code>target</code> type to be <code>Long</code>. For example, the code below raises a <code>RuntimeError</code>:</p>
<pre class="lang-py prettyprint-override"><code>import torch.nn
criterion = torch.nn.CrossEntropyLoss()
predicted = torch.rand(1... | pytorch | 1 |
361,785 | 61,292,529 | Python, store the number of training and validation images obtained from ImageDataGenerator in variables for use later on | <p>I have the following python code which I run in Google Colab Notebooks which uses ImageDataGenerator to split out a development set into training and validation. </p>
<pre><code>datagen = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1./255,
validation_split=0.35)
train_data_gen = datagen.flow_... | <p>You cannot retrieve those numbers as a result from <code>flow_from_directory()</code>.</p>
<p>What you can do instead (since I presume you want to feed <code>steps_per_epoch = training_set_length // batch_size</code> and the same for <code>validation_steps</code>), you can write some arbitrary Python code to solve ... | python|tensorflow|keras | 0 |
361,786 | 61,512,551 | Function to get row Value of a dataframe using str Index | <p>Sample df:</p>
<pre><code>Student Marks
Avery 70
Joe 80
John 75
Jordan 90
</code></pre>
<p>I want to use a function as below to return marks when a student name is passed. </p>
<pre><code>def get_marks(student):
return *something*
</code></pre>
<p>Expected Output: get_marks('Joe') ==> 80</... | <p>I think the following might work.</p>
<pre class="lang-python prettyprint-override"><code>def get_marks(student):
p = df.index[df['Student'] == student].tolist()
p = p[0]
return df['Marks'][p]
</code></pre>
<p>What I have done is I have first to get the index of row of the Student and then simply retur... | python|pandas | 0 |
361,787 | 61,332,910 | by changing dataframe some columns are duplicated | <p>I have dataset:</p>
<pre><code>,target,text
0,0,awww thats bummer shoulda got david carr third day
1,0,upset cant update facebook texting might cry result school today also blah
2,0,dived many times ball managed save 50 rest go bounds
3,0,whole body feels itchy like fire
4,0,behaving im mad cant see
5,0,whole crew
... | <p>Create a copy and specify which column is your index when reading the CSV file:</p>
<pre><code># ...
df_neg = df[data_neg].copy()
df_neg.to_csv("negative.csv")
# For reading it
df_neg = pd.read_csv("negative.csv", index_col=0)
</code></pre> | python|pandas|dataframe | 0 |
361,788 | 61,595,221 | How to groupby values in pandas but using lists as an index? | <p>I have dataframe like and i need to groupby it based on fruit and value but i need to index it based on lists</p>
<pre><code> Date ID Age Value Fruits
1.1.19 1 50 2 Apple
2.1.19 1 50 5 Mango
2.1.19 1 50 8 ... | <p>Fix your code with <code>reindex</code> </p>
<pre><code>df.groupby(['Fruits', 'Date'])['Value'].mean().unstack(fill_value=0).\
reindex(columns=date_list,index=fruits_list,fill_value=0).\
round().reset_index()
Out[172]:
Date Fruits 1.1.19 2.1.19 3.1.19 4.1.19 5.1.19 6.1.19
0 Apple 2 ... | python|pandas|group-by | 2 |
361,789 | 61,447,570 | How to perform this copying operation using numpy? | <p>I've been working on a basic simulation for "diffusion monte carlo" to find the ground state energy of the hydrogen molecule. There's a critical piece of the algorithm which is slowing my code down painfully, and I'm not sure how to fix it. </p>
<p>This is what the code is doing. I have a 6 by N numpy array called ... | <p>let M be an N x 1 array of the m values for each random walker.</p>
<p>let X be your original 6 x N data array</p>
<pre><code># np.where returns a list of indices where the condition is satisfied
zeros = np.where(M == 0) # don't actually need this variable, I just did it for completeness
ones = np.where(M == 1... | python|numpy|physics | 2 |
361,790 | 61,532,297 | Why does the optimize.curve_fit not work on smaller datasets? | <p>I have performed a piecewise linear fit for my data <code>H2O</code> and <code>CO2</code>. It works perfectly fine for a dataset of <code>288</code> data points but not for a dataset of <code>144</code> data points. My code is as following:</p>
<pre><code>#Piecewiselinear fit
x = np.array(H2O)
y = np.array(CO2)
p ,... | <p>I ended up changing the approach from the common</p>
<pre><code>def piecewise_linear(x, x0, y0, k1, k2):
return np.piecewise(x, [x < x0], [lambda x:k1*x + y0-k1*x0, lambda x:k2*x + y0-k2*x0])
</code></pre>
<p>to </p>
<pre><code>my_pwlf = pwlf.PiecewiseLinFit(x, y)
breaks = my_pwlf.fit(2) #if you want multi... | python|numpy|statistics|linear-regression|piecewise | 0 |
361,791 | 61,489,306 | Filter Pandas Dataframe by number of list entries and rearrange output by pairs | <p>I'm working with a csv file in the format like the below created by using df.groupby to filter which ids where publicly sharing which links.</p>
<pre><code> url id
bbc.com ['183','194','101']
cnn.com ['182', '193', '103']
google.com ['131']
</code></pre>
<p>I'm now trying to turn this into a new... | <p>I guess you need to use itertools.combinations(x, k). Here is example:</p>
<pre><code>import pandas as pd
import numpy as np
import itertools
df = pd.DataFrame({ 'url': ['bbc.com', 'cnn.com', 'google.com'],
'id' : [['183','194','101'], ['182', '193', '103'], ['131'] ]})
df
url id
0 ... | python|pandas|pandas-groupby | 0 |
361,792 | 61,557,536 | How Tensorflow & Keras go from one-hot encoded outputs to class predictions for calculating the accuracy? | <p>I'm wondering how the Accuracy metrics in TensorFlow/Keras calculates if a given input matches the expected prediction, or, in other words, how it determines the predicted number of the net.</p>
<hr>
<p><strong>Example 1:</strong></p>
<p>Output: <code>[0, 0, 0.6]</code>, expected output: <code>[0, 0, 1]</code> </... | <p>There are several issues with your question.</p>
<p>To start with, we have to clarify the exact setting; so, in <em>single-label multi-class</em> classification (i.e. a sample can belong to one and only one class) with one-hot encoded samples (and predictions), all the examples you show here are <strong>invalid</st... | python|tensorflow|machine-learning|keras | 4 |
361,793 | 61,396,850 | Pandas apply function to multindexed columns that takes columns (Series) as arguments | <p>I need to apply a function that takes subcolumns (aka Series) of multiindexed columns as arguments. I have come up with a solution that works, but I was curious if there was a more pythonic/proper pandas way to do this.</p>
<p>Let's say we have a function that takes two series as arguments and performs some user-de... | <p>You can <code>groupby</code> over the columns axis. Your function requires a <code>Series</code> so we'll need to <code>squeeze</code> if we want to select by label.</p>
<pre><code>(df.groupby(level=0, axis=1)
.apply(lambda gp: user_defined_function(gp.xs('sub_col_1', level=1, axis=1).squeeze(),
... | python|pandas | 2 |
361,794 | 61,562,326 | Interpreting ANN results -> MSE, MAE and undisplayed epochs results | <p>Im trainning an ANN model with just a few samples (10) to predict 45 targets with 38 inputs. I cannot figure iut why the results per epoch are not being displayed, any idea?
Also, the overall MAE and MSE I get is 0.5252 and 0.6234, respectively. I'm not sure how to interpretate such values as my dataset was scaled. ... | <p>You should pass verbose=2 in model.fit() to get results for every epoch.
MAE and MSE values depend on your data. MSE is more sensitive to large error values, in general high MSE means you probably get large errors for few of your samples, while high MAE means you are getting smaller error values, but for many of you... | python|tensorflow|neural-network | 0 |
361,795 | 61,564,786 | How to set initial zoom of bokeh box chart of pandas group with a large number of categories | <p>I'm plotting covid-19 data for countries grouped by World Bank regions using pandas and Bokeh.</p>
<pre><code>from bokeh.io import output_file, show
from bokeh.palettes import Spectral5
from bokeh.plotting import figure
from bokeh.transform import factor_cmap
group = data.groupby(["region", "CountryName"])
index_... | <p>You should be able to accomplish this with the x_range parameter. In this example, the plot's x range would be the first 20 countries. You can adjust as needed. You might also have to mess around a bit to get the group_cn_list correct. It's hard to say without seeing your data. If you can post a df example for repro... | python|pandas|bokeh | 0 |
361,796 | 61,389,654 | Converting day count to date time | <p>I've seen many examples of the reverse (date time --> day count), but can't seem to figure out how to convert day counts to date times.</p>
<p>I have a df that looks like this:</p>
<pre><code>day person var
1 1 a
2 1 b
3 1 a
1 2 b
2 2 b
3 2 b
1 3 a
2 3 a
3 ... | <p>Here's a pure pandas solution:</p>
<pre><code>start_date = pd.to_datetime('2019-01-01')
df['date'] = pd.to_timedelta(df['day']-1, unit='D') + start_date
</code></pre>
<p>output:</p>
<pre><code> day person var date
0 1 1 a 2019-01-01
1 2 1 b 2019-01-02
2 3 1 a 2019-01-03
3... | python|python-3.x|pandas | 2 |
361,797 | 61,446,009 | Python pandas - what is the proper way to NaN all zeros before first non-zero value in multiple columns? | <p>I have a <code>df</code> with columns <code>date</code>, <code>a</code>, <code>b</code> and an <code>id</code>. The <code>id</code> is grouping and the <code>date</code> values repeat when going to a new <code>id</code>. In column <code>a</code> and <code>b</code> I want to replace 0 with <code>nan</code> <em>before... | <p>use 2 masks with <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.cummax.html" rel="nofollow noreferrer">cummax</a> and <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.transform.html" rel="nofollow noreferrer">transform</a> then <code>df.where</code></p>
<pre><code>m1 = d... | python|pandas | 1 |
361,798 | 61,601,893 | Encode folder labels stored in a numpy array in Python | <p>I'm working on a Parkinson dataset.
In my dataset folder, there are two folders : <a href="https://i.stack.imgur.com/WDPZF.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/WDPZF.png" alt=""></a></p>
<p>In two of each, there are two other folders but that's really a detail:<a href="https://i.stack.... | <p>3 numbers, in case its not either :)</p>
<pre><code>x = imagePath.split(os.path.sep)[-2]
label = '0' if x == 'healthy' else '1' if x == 'parkinsons' else '-1'
</code></pre> | python|numpy | 0 |
361,799 | 61,465,541 | When training a GAN, should dropout be disabled in discriminator when training is disabled? | <p>I'm doing a basic GAN implementation in keras. The training is in phases, first training the discriminator alone, then training the generator as part of a combined model (generator plus discriminator) with the training disabled for the discriminator. If the discriminator has dropout in it, it seems to me that it sho... | <p>You are right, dropout should be disabled for generator while training the discriminator or at any testing stage. And good thing is that keras does this by default <a href="https://github.com/keras-team/keras/blob/master/keras/layers/core.py#L81" rel="nofollow noreferrer">link</a>.</p>
<p>So looking at your scenari... | tensorflow|keras|generative-adversarial-network | 2 |
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