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 |
|---|---|---|---|---|---|---|
356,500 | 58,295,207 | Why not apply one hot encoder , knowing that there is no error during running file? | <p>I want to apply one hot encoding to one column which is "drive_wheels"
However, on running there is no error and no change to the dataset!
Is there any error in the code?</p>
<pre><code>import pandas as pd
import numpy as np
df = pd.read_csv('onehotencoding.csv')
df.head()
obj_df = df.select_dtypes(include=['o... | <p><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html#pandas-get-dummies" rel="nofollow noreferrer">pd.get_dummies()</a> doesn't have an <code>inplace</code> switch. Therefore, you need to join the resulting DataFrame to your original:</p>
<pre><code>dummies = pd.get_dummies(ob... | python|pandas | 1 |
356,501 | 58,252,742 | Re-Assign Maximum Values in a Pandas DataFrame | <p>I have a DataFrame that looks like this:</p>
<pre><code>id OUTCOME
A 0
A 1
A 0
B 0
B 0
B 0
C 0
C 1
C 1
</code></pre>
<p>How can I re-assign the outcome values so that they are equal to the maximum value for each group? In other words, the outcome should look like this:</p>
<pre><code>id... | <p>You can first group by the <code>'id</code>' column, and then perform a <code>.transform(..)</code> on the <code>OUTCOME</code> column:</p>
<pre><code>df['OUTCOME'] = df.groupby('id')['OUTCOME'].transform('max')
</code></pre>
<p>We then obtain:</p>
<pre><code>>>> df
id OUTCOME
0 A 1
1 A ... | python|pandas|pandas-groupby | 0 |
356,502 | 58,326,360 | Split a column in pandas dataframe based on dot | <p>I went through similar questions but could not solve my problem. A part of my dataframe looks like this:</p>
<pre><code> Index Character Top 10 by edits Top 10 by added text
780 NaN Viradha David G Brault · 8 (40%) David G Brault · 1,915 (81.4%)
781 NaN Viradha Wiki-uk ·... | <p>The dot in the 'Top 10 by added text' column is not a period but is rather a dot character whereas you are trying to split by a period in your code. Try changing one or the other to match. </p> | python|pandas|split | 2 |
356,503 | 58,434,832 | Aggregate pandas Series/DataFrame with MultiLevel Index And Insert Result | <p>Given a pandas <code>Series</code> (or <code>DataFrame</code>) with a multi-level index:</p>
<pre><code>name month
A 2019-05 8
2019-06 8
2019-07 3
2019-08 4
2019-09 7
B 2019-06 ... | <p>Here is one way use <code>unstack</code> </p>
<pre><code>s=df['count'].unstack()
s['sum']=s.sum(1)
s=s.stack()
name month
A 2019-05 8.0
2019-06 8.0
2019-07 3.0
2019-08 4.0
2019-09 7.0
sum 30.0
B 2019-06 10.0
2019-07 5.0
2019-08 ... | python|pandas|dataframe | 1 |
356,504 | 58,190,542 | Can someone help me understand what .index is doing in this code? | <p>I have the following code:</p>
<pre><code>print(df.drop(df[df['Quantity'] == 0].index).rename(columns={'Weight': 'Weight (oz.)'}))
</code></pre>
<p>I understand what query is trying to do, but I'm lost at why you need to add the " .index " portion?</p>
<p>What is .index doing in this particular code? </p>
<p>For... | <p>The <code>DataFrame.index</code> is the index of each record in your dataframe. It is unique to each row even if two rows have the same data in each column. <code>DataFrame.drop</code> takes the <code>index : single label or list-like</code> and drops those rows that match the index.</p>
<p>So from the code above, ... | python|pandas|indexing | 0 |
356,505 | 58,298,981 | Most efficient way to generate a large array of (x,y,z) coordinates | <p>I'm generating coordinates of a bi-cone model in spherical coordinates. I'm using a series of nested for loops, as show here:</p>
<pre><code>theta_in = 30.0 * np.pi/180.0
theta_out = 60.0 * np.pi/180.0
phi = 2*np.pi # rotation
R = 1.0
sampling = 100
theta = np.linspace(theta_in,theta_out,sampling)
phi = ... | <p>Create open grids off those inputs and then perform the same operations -</p>
<pre><code>RI,PI,TI = np.ix_(r,phi,theta) # get open grids
X = RI*np.cos(PI)*np.sin(TI)
Y = RI*np.sin(PI)*np.sin(TI)
Z = np.repeat(RI*np.cos(TI),sampling,axis=1)
</code></pre>
<p>Alternative #1 : Those open grids could also be... | python|arrays|numpy | 3 |
356,506 | 58,288,465 | The equivalent of tf.contrib.image.transform in tensorflow 2.0? | <p>What is the equivalent of <code>tf.contrib.image.transform</code> in <code>tensorflow 2.0</code>? When I use the <code>tf_upgrade_v2</code> conversion script, I get the error:</p>
<pre><code>ERROR: Using member tf.contrib.image.transform in deprecated module tf.contrib. tf.contrib.image.transform cannot be converte... | <p>There is no equivalent. Some of the ops are implemented in the tensorflow addons though which is only available in Linux not Windows for now.</p>
<p>Here is the link to tensorflow addones:
<a href="https://github.com/tensorflow/addons" rel="nofollow noreferrer">https://github.com/tensorflow/addons</a></p>
<p>Here ... | python|tensorflow|tensorflow2.0 | 0 |
356,507 | 58,326,771 | How to sum the time-differenced values in a dataframe without compromissing the format HH:MM:SS? | <p><a href="https://stackoverflow.com/questions/2780897/python-summing-up-time">Python summing up time</a> - In this link, the answers are long coded and also using a small list of time values. However, I would like to learn a pythonic way to sum the time values in a data-frame but i can't seem to figure out yet. Can s... | <p>I don't see why your solution wouldn't work. Maybe you haven't converted the datetime to the proper type:</p>
<pre><code>df['in'] = pd.to_datetime(df['in'], format="%Y/%m/%d %H:%M") # format needs adjustment
</code></pre>
<p>If it still doesnt work, please provide the rest of the code or some raw data.</p> | python|python-3.x|pandas|datetime|pandas-groupby | 0 |
356,508 | 58,441,694 | Most efficient way to split and perform function on pandas data frame | <p>I have been given a data frame that contains two measurements of a value (A and B) in rows and each column represents the measurements for sample.</p>
<p>Example below: </p>
<pre><code>ID S1 S2 S3
M1_A 1 2 3
M1_B 3 2 1
M2_A 1 2 3
M2_B 3 2 1
</code></pre>
<p>I need to calculate the ratio of B to A+B [i.e. (B/... | <p>This sounds like a classic groupby-aggregate problem. Pandas can handle the underscore in the ID column easily as well.</p>
<pre><code>df['ID'] = df['ID'].str.split('_').str[0]
df = df.groupby('ID').agg(lambda x: x.values[-1]/x.sum())
print(df)
S1 S2 S3
ID
M1 0.75 0.5 0.25
M2 0.75 ... | python|pandas|dataframe | 2 |
356,509 | 58,404,293 | Python DBSCAN clustering with periodic boundary conditions | <p>Im a noob, probably im doing things too big for me, but i need this for my tesis, please forgive my ignorance.
My goal is to do clustering on 3D points, using sklearn.cluster.DBSCAN, and implement periodic boundary condition only on x,y.
The easiest way that I have found is to use the scipy function <em>pdist</em> o... | <p>Squreform produces a condensed distance matrix in a one-dimensional array. That is a more memory efficient representation - but only if you use it from the beginning, not convert to it later.</p>
<p>Anyway, this form is only used by scipy, not by sklearn. But because python does not have a strong type system, it ca... | python|numpy|scikit-learn|cluster-analysis|dbscan | 0 |
356,510 | 58,219,683 | How to recognize two different objects with the similar shape, but different size | <p>I am using Mask-RCNN neural network. I retrained my network to detect and mask wheels of die-cast toy cars. I am using images, which present the side of the car (left or right).</p>
<p>Sometimes the cars have different sizes of the wheels like presented on the image below. The front wheels are much smaller than rea... | <p>Mask-RCNN can segment each instance of object separately irrespective of size of object. It does not classify object based on perspective, it will classify both wheels as wheels.</p>
<p>If you train model with two classes like front and rear wheel it will work fine when the condition is true, but when the wheels wi... | tensorflow|machine-learning|keras|neural-network|computer-vision | 0 |
356,511 | 58,399,766 | Python/Pandas - Rearrange string from column value | <p>I would need to rearrange the column value on column: "Quarter" . Expected output should be as in column:"new_Quarter"</p>
<p><a href="https://i.stack.imgur.com/Dok9i.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Dok9i.png" alt="enter image description here"></a></p>
<p>I got the column:"Quart... | <p>You may use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.strftime.html" rel="nofollow noreferrer"><code>strftime</code></a></p>
<pre><code>df['new_Quarter'] = df.Quarter.dt.strftime('Q%q %y')
</code></pre>
<p>As a side note, the column <code>Quarter</code> can also be simply... | python|pandas|date | 3 |
356,512 | 58,253,003 | Deriving the structure of a pytorch network | <p>For my use case, I require to be able to take a pytorch module and interpret the sequence of layers in the module so that I can create a “connection” between the layers in some file format. Now let’s say I have a simple module as below</p>
<pre><code>class mymodel(nn.Module):
def __init__(self, input_channels):... | <p>The information you are looking for is not stored in the <code>nn.Module</code>, but rather in the <code>grad_fn</code> attribute of the output tensor:</p>
<pre class="lang-py prettyprint-override"><code>model = mymodel(channels)
pred = model(torch.rand((1, channels))
pred.grad_fn # all the information is in the c... | python|neural-network|pytorch|tensor | 2 |
356,513 | 58,387,271 | Is numpy.array() equivalent to numpy.stack(..., axis=0)? | <p>I made my self an example:</p>
<pre><code>import numpy as np
arrays = [np.random.rand(3,4) for _ in range(10)]
arr1 = np.array(arrays)
print(arr1.shape)
arr2 = np.stack(arrays, axis=0)
print(arr2.shape)
</code></pre>
<p>I found that arr1 and arr2 have the same shape and content. So are these two methods (np.array(... | <p>In general, you should get something similar from the two, but there will be some edge cases. For example, passing ragged lists to <code>np.array</code> will give an <code>np.array</code> of lists, but <code>np.stack</code> will raise an exception:</p>
<pre><code>In [119]: np.stack([[1,2], [4,5,6]], axis=0)
-------... | python|arrays|numpy | 3 |
356,514 | 58,567,446 | get_weights is slow with every iteration | <p>I'm computing gradients from a private network and applying them to another master network. Then I'm copying the weights for the master to the private (it sounds redundant but bear with me). The problem is that with every iteration get_weights becomes slower and I even run out of memory. </p>
<pre><code> def wo... | <p>This would typically happen when you dynamically add new nodes to the graph. Example situation:</p>
<pre><code>while True:
grad_op = optimizer.get_gradients()
session.run([gradients])
</code></pre>
<p>Where get_gradients will add new operations to the graph. Operations returned by get_gradients would not c... | tensorflow|keras|python-3.7|tensorflow2.0 | 2 |
356,515 | 58,567,884 | How to handle pandas KeyError in a for loop? | <p>I wrote the following code in a for loop to handle the pandas KeyError I met, but it seemed that I couldn't use a continue statement and except keyword in this block. How could I fix it? </p>
<p>Originally, I would like to raise an exception to show those keys that are not in the table and let the for loop continue... | <p>If you raise an error, execution will stop. You can put your continue statement in the except block. That will allow you to continue going through the loop. Just make sure to print out/log out whatever information you need before hitting the continue statement.</p>
<pre><code>for i in range(1000):
# do somethin... | python|pandas|dataframe | 1 |
356,516 | 58,471,669 | SUMIFS formula in Pandas Python | <p>I work in a logistics company and we do B2C deliveries for our client. So we have a rate card in a form of a table and list of deliveries/ transaction, the weight of the package and the location where it was delivered. </p>
<p>I have seen a lot of SUMIFS question being answered here but is very different from the o... | <p>This is <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html" rel="nofollow noreferrer"><code>merge</code></a> on <code>category</code> and <code>island</code> and then <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html" rel="nof... | python|pandas|data-analysis | 2 |
356,517 | 58,357,486 | tflearn with tensorflow 2.0 | <p>I can't import tflearn with TensorFlow 2.0</p>
<pre><code>Python 3.7.4 (v3.7.4:e09359112e, Jul 8 2019, 14:54:52)
[Clang 6.0 (clang-600.0.57)] on darwin
Type "help", "copyright", "credits" or "license()" for more information.
>>> import tflearn
Traceback (most recent call last):
File "/Library/Framework... | <p>As of today, <a href="https://pypi.org/project/tflearn/" rel="nofollow noreferrer"><code>tflearn</code></a> (v0.3.2) is not TensorFlow 2.0 ready, and specifically requires TF 1.x. I'm sure it will get updated at some point but for now, if you need tflearn, use TF 1.</p>
<hr />
<p>I get a different error: <code>Modul... | python|python-3.x|tensorflow|tflearn | 6 |
356,518 | 58,438,243 | Python Pandas : Return column header/name where values equal the other in the dataframe | <p>I am trying to get the column header location(s) where the value in the last column equals a value in any of the other columns. This should be appended as a new column. Assuming I have the dataframe:</p>
<pre><code> category color size max_value
a [2, 1] [1,1,1] [1,1,1] ... | <p>You can do the comparison after dropping down to <code>numpy</code></p>
<pre><code>m = df.iloc[:, :-1].to_numpy() == df.max_value.to_numpy()[:, None]
#array([[False, True, True],
# [ True, False, False],
# [False, True, False]])
df['matched_cols'] = [', '.join(df.columns[:-1][x]) for x in m]
# cate... | python|pandas | 2 |
356,519 | 58,538,831 | 1D CNN in Keras: Flattening from pooled features to dense layer raises ValueError | <p>I have the following CNN model defined. it is expecting a 1D vector input of length 501.</p>
<pre><code>model = ml.models.Sequential()
model.add(ml.layers.Conv1D(filters=NUMBER_OF_FILTERS, kernel_size=KERNEL_SIZE, activation=ACTIVATION, input_shape=(None, 501)))
model.add(ml.layers.MaxPooling1D(pool_size=POOL_SIZE,... | <p>I have figured out the solution. I was not correctly defining the input_shape of the Conv1D Layer, it should instead be:</p>
<pre><code>model.add(ml.layers.Conv1D(filters=NUMBER_OF_FILTERS, kernel_size=KERNEL_SIZE, activation=ACTIVATION, input_shape=(501, 1)))
</code></pre> | python|tensorflow|keras | 0 |
356,520 | 58,305,598 | Using tensorflow_transform with tensorflow 2.0 | <p>After installing tensorflow 2 and tensorflow_transform, when importing tensorflow_transform I get the error:
<code>from tensorflow.contrib.boosted_trees.python.ops import gen_quantile_ops
ModuleNotFoundError: No module named 'tensorflow.contrib'</code></p>
<p>So it seems that tensorflow_transform is not yet on... | <p>All currently released versions of tensorflow-transform don't support TF 2.0.
See this table here: <a href="https://github.com/tensorflow/transform/blob/master/README.md#compatible-versions" rel="nofollow noreferrer">https://github.com/tensorflow/transform/blob/master/README.md#compatible-versions</a></p>
<p>The la... | tensorflow2.0|tensorflow-transform | 0 |
356,521 | 58,578,641 | panda: csv to dictionary | <p>I have a csv file in following format:</p>
<blockquote>
<p>id, category</p>
<p>1, apple</p>
<p>2, orange</p>
<p>3, banana</p>
</blockquote>
<p>I need to read this file and populate a dictionary which has ids as key and categories as value.</p>
<p>I am trying to use panda, but its to_dict function us returning a dict... | <p>You can create index by first column and then convert Series <code>df['category']</code> to dictionary:</p>
<pre><code>df = pd.read_csv('file.csv', index_col=0)
d = df['category'].to_dict()
</code></pre>
<p>Or if only 2 columns csv is possible create Series by <code>index_col=0</code> and <code>squeeze=True</code... | python|pandas | 2 |
356,522 | 58,311,944 | Python: Locate the max value of a column and pick up the value of other column in the same row | <p>I have next data frame with two columns:</p>
<pre><code>Idx =[1,2,3,4,5]
Values =[7,-2,-1,10,5]
lists = list(zip(Idx, Values))
dfsr = pd.DataFrame(lists, columns = ['Idx', 'Values'])
dfsr
</code></pre>
<p>This generates the next DataFrame </p>
<pre><code> Idx Values
0 1 7
1 2 -2
2 3 -1
3 4 10... | <p>Try:</p>
<pre class="lang-py prettyprint-override"><code>>>> MVI = dfsr.loc[dfsr.Values == dfsr.Values.max(), 'Idx']
>>> MVI
3 4
Name: Idx, dtype: int64
</code></pre>
<p>Alternatively, if you just want the object itself (not <code>pandas.Series</code> object):</p>
<pre class="lang-py prettypr... | python-3.x|pandas | 3 |
356,523 | 58,308,175 | I want to create a pandas DF based on 2 np.ranges tied together | <p>I want to create a pandas DF with 2 columns based on 2 np.arrays.</p>
<p>in the end it it should look like a dissolved x-y-matrix, since i have to test all X and Y combinations</p>
<p>example should be a DF with columns "X" and "Y"</p>
<pre><code> X Y
----------
-5 -3
0 -3
5 -3
-5 0
0 ... | <p>Can you use <code>itertools.product</code> and <code>from_records</code>:</p>
<pre><code>from itertools import product
</code></pre>
<p><strike> df = pd.DataFrame.from_records([i for i in product(a,b)])</strike><br>
Actually, you don't need the list comprehension </p>
<pre><code>df = pd.DataFrame.from_rec... | python|pandas|numpy | 1 |
356,524 | 58,546,991 | Set Field in Column to 0 Based on Two Column Values Being Equal Pandas | <p>I have a df using pandas with a list of permits and then a list of subpermits. I need to compare the Parent and Sub Permit columns, and if the Parent Permit is equal to the sub permit, set the Value total field to 0. The BLD-00045 row needs to retain the 70000 value essentially, but the ELE and PM need to be set to ... | <p>Reading between the lines of your data, I am guessing that in reality, there is some kind of hierarchical, tree-like structure of permits, and you are interested in assigning costs to only certain levels.</p>
<p>Based on your example, it sounds like you want to identify rows where the Sub Permit is equal to <em>any... | python|pandas | 3 |
356,525 | 58,560,271 | How to sum values in a column dataframe base on values in another column | <p>I have a data set which has video games, their sales, and the year the game was released. I am only looking for the game sales per year, not the game sales per title per year.</p>
<p>I am using a pandas Dataframe. I have tried a groupby method. I have tried a loop with .unique() values. </p>
<pre><code>df = df[["Y... | <p>You can use</p>
<pre><code>df.groupby('Year', as_index=False)['NA_Sales'].sum()
</code></pre> | python|pandas|dataframe | 1 |
356,526 | 58,565,037 | Date formatting to month | <p>I want to change the format of "Date" column from 10/15/2019 to m/d/y format.</p>
<pre><code>tax['AsOfdate']= pd.to_datetime(tax['date'])
</code></pre>
<p>How do I do it?</p> | <p>like this, and here is the <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html" rel="nofollow noreferrer">documentation</a>. </p>
<pre class="lang-py prettyprint-override"><code>tax['AsOfdate']= pd.to_datetime(tax['date'], format="%m/%d/%Y" )
</code></pre> | python|pandas|datetime | 1 |
356,527 | 58,486,475 | How to put tag my duplicate values in particular way | <p>I have below .csv. I am trying to make a dataframe where I can find the duplicates and I need to find one more column where first value will be always </p>
<pre><code>Name = [('Hello'),
('Spider'),
('Captain'),
('Superman'),
('Hello'),
('Superman')]
dfName = pd.DataFrame(Name, column... | <p>Remove <code>keep=False</code> for default <code>keep='first'</code> parameter:</p>
<pre><code>dfName['un_dup_hel'] = np.where(dfName['Name'].duplicated(),'duplicate', 'unique')
print (dfName)
Name un_dup_hel
0 Hello unique
1 Spider unique
2 Captain unique
3 Superman unique
4 H... | python|pandas | 2 |
356,528 | 58,533,080 | Solving Shrodinger's equation for a particle in a harmonic potential well | <p>Hello (this is my first time posting in stack overflow), I am trying the calculate the first 3 energy levels of a particle in a harmonic potential using the shooter method</p>
<p>The code is adapted from a script in Computational Physics by Mark Newman, this script calculated the ground state for a particle in a bo... | <p>Yep! You are right about the values being too close to each other. Your code returns a <code>nan</code>. It is because of the division by zero. </p>
<p>I would suggest using a correction factor. Something like <code>max(delta, (psi2-psi1))</code> in the denominator where <code>delta</code> can still be a very small... | python|numpy | 2 |
356,529 | 58,454,612 | Binning a column in a DataFrame into 10 percentiles | <p>I am looking to qcut or cut my "Amount" column into bins of 10 percentiles. Basically the describe() feature but with 0-10%, 11-20%, 21-30%, 31-40%, 41-50%, 51-60%, 61-70%, 71-80%, 81-90%, 91-100% instead.</p>
<p>After the binning i'd like to create a column that shows 1-10 indicating the bin that particular amount... | <p>Use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.qcut.html" rel="nofollow noreferrer"><code>pd.qcut</code></a>:</p>
<pre><code># Sample data
size = 100
df = pd.DataFrame({
'Amount': np.random.randint(5000, 20000, size),
'CustomerType': np.random.choice(['New', 'Repeat'], size)
... | python|pandas|numpy | 1 |
356,530 | 58,510,980 | add left and right portions of the array symmetrically - python | <pre><code>a = np.array([[[ 1, 11],
[ 3, 13],
[ 5, 15],
[ 7, 17],
[ 9, 19]],
[[ 2, 12],
[ 4, 14],
[ 6, 16],
[ 8, 18],
[10, 20]]])
</code></pre>
<p>I'm trying to add the left portion to... | <p>Looks like you may invert the array, add and cut to the <code>a.shape[1]//2</code> middle</p>
<pre><code>(a + a[:,::-1,:])[:, :a.shape[1]//2, :]
</code></pre>
<hr>
<pre><code>array([[[10, 30],
[10, 30]],
[[12, 32],
[12, 32]]])
</code></pre> | python|arrays|numpy | 2 |
356,531 | 58,223,402 | Pandas Rolling: Return Min and Max Dates, Sum of Exposure | <p>I am trying to implement a rolling window and am struggling with the very last part. As you can see below, the code returns the sum of exposure, attached to the last date in the rolling window. I also want a column that has the first date in the window as well. (they are ordered by date but I am ultimately after ... | <p>Operations on rolling windows are actually limited to aggregation functions
and must be performed on <strong>numbers</strong>, not on <strong>dates</strong>.</p>
<p>To circumvent this limitation, notice that the Date from the beginning of
your rolling window of size 12 is actually the Date from 11-th row before.</p... | python|pandas|rolling-computation | 0 |
356,532 | 58,513,084 | Subtract values for all columns in df1 by values in one column in df2 | <p>Assuming I have the following dataframe <code>df1</code>:</p>
<pre><code> a b c d
10 15 20 25
8 18 28 38
20 25 30 35
</code></pre>
<p>And for simplicity, assuming I have a dataset <code>df2</code>:</p>
<pre><code> y
1
2
3
</code></pre>
<p>I want to subtract, row-wise, values in ... | <p>Use <code>sub</code> and <code>axis=0</code> for a vectorized solution</p>
<pre><code>df.sub(df2.values, axis=0)
</code></pre>
<hr />
<pre><code> a b c d
0 9 14 19 24
1 6 16 26 36
2 17 22 27 32
</code></pre>
<hr />
<h3><code>Timings</code></h3>
<p>For a small number of columns:</p>
<pre><code>... | python|pandas | 5 |
356,533 | 58,599,623 | merging arrays from n file into a new file in python | <p>I have three files each containing columns that are a mixture of ints and floats for example the first line from each of the files is:</p>
<pre><code>file1
1.0000,0,0,1,1,0,0,0,0,0,0.0000,0.0000,0.0000,0,0,0,8.7129,-102.3384,142.2611,0
</code></pre>
<pre><code>file2
1640 3110 1780
</code></pre>
<pre><code>file3
... | <p>Hi I was able to solve this, the problem was with the way the square brackets were positioned. The right way to write the code was</p>
<pre><code>tbl_out=rfn.merge_arrays([tbl1[['CCC']],tbl3[['Bin','NA8']],\
tbl1[['pIndex']],\
tbl3[['NA9','NA10','NA11','NA12','NA13'... | python|python-3.x|numpy|array-merge | 0 |
356,534 | 58,397,340 | Plotting by Index with different labels | <p>I am using pandas and matplotlib to generate some charts.</p>
<p>My DataFrame:</p>
<pre><code> Journal Papers per year in journal
0 Information and Software Technology 4
1 2012 International Conference on Cyber Securit... 4
2 Journal of Net... | <p>I found a solution, based on this question <a href="https://stackoverflow.com/questions/11927715/how-to-give-a-pandas-matplotlib-bar-graph-custom-colors">here</a>.
SO, the dataframe needs to be transformed into a matrix, were the values exist only on the main diagonal.
First, I save the column <code>journals</code>... | python|python-3.x|pandas|matplotlib | 0 |
356,535 | 58,268,840 | Hyperparameter tuning with ml-engine returns State: failed | <p>I'm trying to get my models hyperparameters tuned with the ml-engine but i'm not quite sure if its working or not.</p>
<p>I'm not specifying the <code>algorithm</code> tag in <code>HyperparameterSpec</code>, which should default to Bayesian optimization method according to the documentation. Im also not setting <co... | <p>The problem could be solved by using the python package <code>cloudml-hypertune</code> with the following code:</p>
<pre><code>self.hpt.report_hyperparameter_tuning_metric(
hyperparameter_metric_tag=hypeparam_metric_name,
metric_value=value,
global_step=step)
</code></pre>
<p>An... | tensorflow|google-cloud-ml|hyperparameters | 1 |
356,536 | 58,586,007 | Selecting random windows from numpy arrays greater than 2 dimensions | <p>How can I select a random window from a numpy array greater than 2 dimensions wherein the window is random with respect to 2 different dimensions? </p>
<p>I'd like to do something similar to the answer in this post but in 3 dimensions, not 2:
<a href="https://stackoverflow.com/questions/47982894/selecting-random-wi... | <p>We can leverage <a href="http://www.scipy-lectures.org/advanced/advanced_numpy/#indexing-scheme-strides" rel="nofollow noreferrer"><code>np.lib.stride_tricks.as_strided</code></a> based <a href="http://scikit-image.org/docs/dev/api/skimage.util.html#skimage.util.view_as_windows" rel="nofollow noreferrer"><code>sciki... | python|arrays|numpy|vectorization | 0 |
356,537 | 58,226,184 | How to assign each row in a numpy array into keys in dictionary python | <p>I have a rather large numpy array. I'd like to take each row in my array and assign it to be a key in a dictionary I created. For example, I have a short 2-dimensional array:</p>
<pre><code>my_array = [[5.8 2.7 3.9 1.2]
[5.6 3. 4.5 1.5]
[5.6 3. 4.1 1.3]]
</code></pre>
<p>and I'd like to c... | <p>If a tuple will do, then:</p>
<pre><code>import numpy as np
my_array = np.array([[5.8, 2.7, 3.9, 1.2],
[5.6, 3., 4.5, 1.5],
[5.6, 3., 4.1, 1.3]])
d = { k : None for k in map(tuple, my_array)}
print(d)
</code></pre>
<p><strong>Output</strong></p>
<pre><code>{(5.8, 2.7,... | python|numpy|dictionary | 2 |
356,538 | 58,602,442 | How to save data in .csv file in row and column form using numpy | <p>I am trying to read and image using OpenCV and after reading that image I have got some data which I have to save in a CSV file using numpy. Here is the program:-</p>
<pre><code>import cv2 as cv
import numpy as np
import os
img1 = cv.imread('C:/Users/sbans/Pictures/bird.jpg')
dataA1 = os.path.basename('C:/Users/sb... | <p>You could simplify the code a bit by defining a function </p>
<pre><code>def get_array(file):
img = cv.imread(file)
basename = os.path.basename(file)
height, width, channels = img.shape
h = int(height/2)
w = int(width/2)
px = img[h,w]
return np.array([basename, height, width, channels, ... | python|numpy|csv | 1 |
356,539 | 58,477,696 | Plotly Choroplethmapbox not showing all polygons | <p>I'm having an odd issue with Plotly, the image below will give some context:</p>
<p><a href="https://i.stack.imgur.com/L5PKR.png" rel="nofollow noreferrer">This is the map made with Bokeh</a></p>
<p><a href="https://i.stack.imgur.com/l82ql.png" rel="nofollow noreferrer">This is the map made with Plotly</a></p>
<p... | <p>I had a similar issue. That is a slice of my geopandas dataframe looked like -</p>
<pre><code> province_id geometry
0 1 POLYGON (x1, y1)
1 1 POLYGON (x2, y2)
2 1 POLYGON (x3, y3)
</code></pre>
<p>I used <code>province_id_data.dissolve(by='province_id', aggfunc='first')</code> t... | python|plotly|mapbox|geopandas|choropleth | 1 |
356,540 | 58,368,601 | RuntimeError: size mismatch, m1: [32 x 1], m2: [32 x 9] | <p>I'm building a CNN and training it on hand sign gesture classification for letters A through I (9 classes), each image is RGB with 224x224 size.</p>
<p>Not sure which matrix I need to transpose and how. I have managed to match the inputs and outputs of layers, but that matrix multiplication thing, not really sure h... | <p>You don't need <code>x=x.view(-1,1)</code> and <code>x = x.squeeze(1)</code> in your <code>forward</code> function. Remove these two lines. Your output shape would be <code>(batch_size, 9)</code>.</p>
<p>Also, you need to convert <code>labels</code> to one-hot encoding, which is in shape of <code>(batch_size, 9)</c... | python|neural-network|deep-learning|conv-neural-network|pytorch | 2 |
356,541 | 58,587,685 | Protection against "index 0 is out of bounds for axis 0 with size 0" error in Python | <p>I have a code in which I get a specific distribution of points on the graph of the function <code>tan()</code>
limited from the bottom and top by straight lines:</p>
<pre><code>import matplotlib.pyplot as plt
import numpy as np
import sys
import itertools
import multiprocessing
import tqdm
ic = range(1,10)
jc = r... | <p>Where does this error occur? That's a fundamental piece of information - for us, but especially for you!</p>
<p>@edison says it's in the <code>argwhere</code> expression. I'll try to recreate that step, starting with a guess as to what <code>diffs</code> looks like:</p>
<pre><code>In [8]: x = np.ones(5)*.1 ... | python|python-3.x|numpy|matplotlib | 2 |
356,542 | 58,413,499 | Create a list of random numbers and filter the list to only have numbers larger than 50 | <p>I am using list comprehension to create a list of random numbers with numpy. Is there a way to check if random number generated is larger than 50 and only then append it to the list.</p>
<p>I know I can simply use:</p>
<pre><code>numbers = [np.random.randint(50,100) for x in range(100)]
</code></pre>
<p>and that ... | <p>You use numpy, so we can leverage indexing method.</p>
<pre class="lang-py prettyprint-override"><code>my_array = np.random.randint(1, 100, size=100)
mask = my_array > 50
print(my_array[mask]) # Contain only value greater than 50
</code></pre>
<p>But of course, the best way to do what you want is that. </p>
<p... | python|numpy|random | 4 |
356,543 | 69,061,733 | Counting selected dataframe columns according to condition | <p>Although this question seems somewhat similar to previous ones, I could not have it solved with previous answers and I need help from experts.</p>
<p>I am trying to create a column (e.g. 'Result') with the count of other columns with labels that start with 'X_', given a condition (eg. column element >1).</p>
<pre... | <p>We can <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.filter.html" rel="nofollow noreferrer"><code>filter</code></a> the DataFrame for columns that start with <code>X_</code> test which values are <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ge.html" rel="nofollow no... | python|pandas|dataframe | 2 |
356,544 | 68,900,171 | Counting occurrence of string value in df1 if other column date value in df1 is between two dates in df2 | <pre><code> df1 = pandas.DataFrame( {
"ID" : ["11", "11", "11", "11"] ,
"updated_date" : ["2019/04/03", "2019/05/02", "2019/05/20", "2019/03/03"],
"other_date" : ["2019/04/09&q... | <ol>
<li><p><strong>merge</strong>:</p>
<p><strong>df3=pd.merge(df1,df2,how='left',on='ID')</strong></p>
</li>
</ol>
<p>then you got a new table:</p>
<pre><code>ID updated_date other_date new_date characteristic
0 11 2019/04/03 2019/04/09 2019/04/02 T
1 11 2019/04/03 2019/04/09 2019/05/03 T
2 11 ... | python|pandas|dataframe|count|conditional-statements | 0 |
356,545 | 68,989,800 | Parsing XML and converting to CSV python | <p>I'm having some trouble with parsing an XML.
After having a search on here I've got close to getting what I need but I'm having issues with unnesting some deeper data.</p>
<p>this is my xml data.</p>
<pre><code>xml = """
<instance>
<ID>1</ID>
<start>0</start>
... | <p>You're almost there, just a few hiccups. Try chainging your <code>for</code> loop to</p>
<pre><code>for i in root:
#no change in the first 4 items:
ID = i.find("ID").text
Start = i.find("start").text
End = i.find("end").text
Player= i.find("code").text
... | python|pandas|xml|csv | 2 |
356,546 | 69,014,545 | formatting row output from python pandas dataframe iterrows() | <p>I've got a python pandas dataframe (<code>my_df</code>). I'd like to extract the rows using <code>iterrows()</code>, then turn the rows into lists, and finally append the rows-turned-lists to a list of lists (<code>my_list</code>).</p>
<pre><code>import pandas as pd
# DATA
data = {'a': [8, 8, 8, 7],
'b': [7, 8... | <p>Just use <code>tolist</code>:</p>
<pre><code>my_list = []
for index, row in my_df.iterrows():
my_list.append(row.tolist())
print(my_list)
</code></pre>
<p>Output:</p>
<pre><code>[[8, 7, 7, 7], [8, 8, 7, 7], [8, 8, 8, 7], [7, 8, 8, 7]]
</code></pre> | python|pandas|data-science | 1 |
356,547 | 69,285,931 | Adding multiple new columns to an existing dataframe base on a given condition | <p>I have a Dataframe with the below column names, and I want to create new columns(<strong>n_1, n_2, n_3 n_4, n_5, n_6, n_7, n_8</strong>) off the original Dataframe based on a given condition. The condition is to create new columns for each unique <strong>EVENT_ID</strong> in the Dataframe. check for rows in the ... | <p>You can use a pivot table and add prefixes once that's done.</p>
<pre><code>df.pivot_table(index='EVENT_ID',columns='SELECTION_TRAP',values='BSP').add_prefix('n_')
</code></pre>
<p>Output</p>
<pre><code>SELECTION_TRAP n_1 n_2 n_3 n_4 n_5 n_6 n_7 n_8
EVENT_ID ... | python|pandas|dataframe | 2 |
356,548 | 69,280,640 | Unstack columns in pandas-python | <p>I would like to Unpivot/unstack my dataframe. My df is like:</p>
<pre><code>a=pd.DataFrame(columns=['Name','Title','Status'],data=[['John','Course1','Finished'],['Mike','Course2','Accepted'],['Jim','Course1','Accepted'],['Jhonny','Course3','Rejected'],['Jhonny','Course3','Accepted']])
</code></pre>
<p>And I need it ... | <p>You could use <code>pivot_table()</code></p>
<pre><code>a.pivot_table(index='Name', columns=['Title'], values='Status', aggfunc='first')
</code></pre>
<p>prints:</p>
<pre><code> Course1 Course2 Course3
Name
Jhonny Accepted NaN Rejected
Jim Accepted NaN ... | python|pandas | 0 |
356,549 | 68,995,072 | Plotting data grouped by labels | <p>I am training a neural network with different hyper-parameters and would like to plot the different results in order to compare which ones perform better.</p>
<p>I currently have a plugging to do this but would like to do it myself with <code>matplotlib</code>. I would like to replicate the following image.</p>
<p><... | <ul>
<li><p><a href="https://seaborn.pydata.org/generated/seaborn.stripplot.html" rel="nofollow noreferrer"><strong><code>seaborn.stripplot</code></strong></a> with <code>jitter</code> disabled</p>
<pre class="lang-py prettyprint-override"><code>import seaborn as sns
sns.stripplot(data=df, x='Activation', y='Accuracy',... | python|pandas|matplotlib | 1 |
356,550 | 69,052,249 | Text Detection using tensorflowjs | <p>I want to do text detection in an image using only tensorfow.js or opencv.js, i have already build a EAST model on keras and converted to tensorflowjs model</p>
<p>can anyone help me with this, any resource will be great</p>
<p>Thanks.</p> | <p>So, initially you need to download the East frozen model and then conver it to tensorflow.js model by using the below command</p>
<pre><code>tensorflowjs_converter --input_format=tf_frozen_model --output_node_names='feature_fusion/Conv_7/Sigmoid,feature_fusion/concat_3' /path_to_model /path_to_where_you_want_save_c... | tensorflow.js|tensorflowjs-converter | 2 |
356,551 | 68,902,688 | Nested Json in Pandas Column | <p>I have a dataframe with nested json as column.</p>
<pre><code>df.depth
0 {'buy': [{'quantity': 51, 'price': 2275.85, 'o...
1 {'buy': [{'quantity': 1, 'price': 2275.85, 'or...
2 {'buy': [{'quantity': 1, 'price': 2275.85, 'or..
</code></pre>
<p>inside each row have 5 depths of buy sell</p>
<pre><code>df.de... | <p>You could try <code>concat</code>:</p>
<pre><code>df = pd.concat([pd.concat([pd.DataFrame(x, index=[0]) for x in i], axis=1) for i in pd.json_normalize(df['depth'])['buy'].tolist()], ignore_index=True)
print(df)
</code></pre>
<p>Output:</p>
<pre><code> quantity price orders quantity price orders ... quan... | python|json|pandas | 1 |
356,552 | 68,930,282 | Create a column with time elapsed (in seconds) since first date based on two conditions | <p>I have 3 cols:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>User_id</th>
<th>Country</th>
<th>Datetime</th>
</tr>
</thead>
</table>
</div>
<p><strong>Objective:</strong> I need to create a fourth column that is time elapsed in seconds based on user and country's first datetime</p>
<p>... | <p>I can't test the code right now, but I would do something like:</p>
<pre><code># ensure datetime type (optional if already right type)
df['Datetime'] = pd.to_datetime(df['Datetime'])
# get the first value per group:
df['first'] = df.groupby(['User_id', 'Country']).transform.min() # or first() if you want the first ... | python|pandas|datetime | 2 |
356,553 | 69,031,189 | How to use lambda functions with cross-index computation when iterating dataframes columns | <p>I have a pandas data frame, <code>df</code>, with one of the columns called <code>val</code> to which I apply a cross index computation:</p>
<pre><code>import pandas as pd
sensor_data = {'Sensor': ['A', 'B', 'C', 'D', 'E'], 'val': [20, 2 , 2, 19, 18]}
df = pd.DataFrame(sensor_data)
# Cross index computation:
cross_... | <p>What about <a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.shift.html" rel="nofollow noreferrer">shift</a> ?</p>
<pre><code>print((df['val'] * df['val'].shift(-1)).dropna().astype(int).to_list())
[40, 4, 38, 342]
</code></pre> | python|pandas|dataframe|lambda | 0 |
356,554 | 69,132,933 | Count unique values of a series based on condition - Pandas | <p>I have a dataframe like this.</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>booking_id</th>
<th>booking_category</th>
<th>vehicle_number</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>x</td>
<td>abc</td>
</tr>
<tr>
<td>2</td>
<td>x</td>
<td>def</td>
</tr>
<tr>
<td>3</td>
<td>y</td>
<t... | <p>Create a frequency table with <code>crosstab</code>, then check for the counts to make sure only <code>x</code> category has count greater than <code>0</code></p>
<pre><code>s = pd.crosstab(df['vehicle_number'], df['booking_category'])
m = s.pop('x').ge(1) & s.eq(0).all(1)
</code></pre>
<p>Details</p>
<pre><code... | python|pandas|dataframe | 0 |
356,555 | 68,881,498 | converting columns to rows in paython | <p>I have a dataframe like this,</p>
<pre><code>df1=
time asset_id sensor_01 sensor_02
0 2019-08-01 120 23 54
1 2019-08-02 125 45 38
2 2019-08-03 120 25 49
</code></pre>
<p>since number of sensors are variable, I decided to write them in rows like,</p>
<... | <p>Use <a href="https://pandas.pydata.org/docs/reference/api/pandas.melt.html" rel="nofollow noreferrer"><code>pandas.melt</code></a>:</p>
<pre><code>pd.melt(df1,
id_vars=['time', 'asset_id'], # variables to keep as columns
var_name='sensor_ID', # column name for the variable
value_name=... | python|pandas|dataframe | 1 |
356,556 | 68,890,495 | Merge two dataframes on DateTimeIndex ignoring the year | <p>I have two dataframes, one is a series of measurements,</p>
<pre><code>A = ID Value
2020-01-01 00:00:00 0.2
2020-01-01 01:00:00 0.2
...
2020-12-31 22:00:00 0.6
2020-12-31 23:00:00 0.5
2021-01-01 00:00:00 0.4
2021-01-01 01:00:00 0.3
...
202... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html" rel="nofollow noreferrer"><code>DataFrame.merge</code></a> with left join and helper column defined by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DatetimeIndex.strftime.html" rel="nofoll... | python|pandas|dataframe|merge | 3 |
356,557 | 68,933,462 | Calculate set of multilevel columns mean based on a lookup table in Pandas | <p>Given a two level columns as below. The first level can be group into <code>tech_one</code>, <code>tech_two</code>, <code>tech_three</code>, <code>tech_four</code>, <code>etc</code> and <code>mnt</code>. On the second level, the <code>ch</code> and <code>b</code> is separated by <code>_</code>, and <code>ch</code> c... | <p>Any reason in particular you need to deal with the MultiIndex? They are usually more of a headache than being helpful. Is your data structured in a way, that you can tranpose the table and make the index levels simple columns instead? Like this:</p>
<pre><code>df = df_cal.T.reset_index().rename(columns={"level_... | python|pandas | 1 |
356,558 | 68,893,910 | Create a "stacked" bar chart according to one boolean column | <p>I would like to create a single bar which is composed of multiple layers stacked on top of each other, where each layer is colored according to a boolean flag (contained for example in a dataframe).</p>
<p>The final effect should be something like the picture below, but I would be using a large set (40'000 entries).... | <p>You could convert the dataframe column to a 2D numpy array and use <code>sns.heatmap()</code>:</p>
<pre class="lang-py prettyprint-override"><code>import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
df = pd.DataFrame({'bool_val': np.random.randn(40000).cumsum() > 0})
ax =... | python|pandas|dataframe|matplotlib|seaborn | 1 |
356,559 | 69,168,432 | How to remove string None from a single string with commas and count the most common words in a row? | <p>I have rows in a df formed by a string that contains several elements separated by commas.</p>
<p>Among the rows, there are words of interest (eg. Car, Bus) and the word None. Also, there are rows that only have the word None.</p>
<p>Here is an example of df:</p>
<div class="s-table-container">
<table class="s-table... | <p>You can split the "Col" column with <code>.str.split(", ")</code>, filter out the <code>None</code> values, empty lists and count unique items with <code>.value_counts()</code>:</p>
<pre class="lang-py prettyprint-override"><code>df.Col = df.Col.str.split(", ").apply(lambda x: [v for v ... | python|pandas|dataframe|python-re | 1 |
356,560 | 68,921,691 | pyreadstat read and write spss without data loss | <p>To read an spss .sav file using pandas/pyreadstat, you use:</p>
<pre><code>df, meta = pyreadstat.read_sav()
</code></pre>
<p>to write a dataframe, you use:</p>
<pre><code>pyreadstat.write_sav(df)
</code></pre>
<p>How can I read, edit and write a .sav file without losing any meta data, like labels and other things th... | <p>Talk is cheap, here's the code. :-)</p>
<pre class="lang-py prettyprint-override"><code># using pyreadstat
from pyreadstat import write_sav
class TempFile(type(pathlib.Path())): # type: ignore
def __exit__(self, exc_type, exc_val, exc_tb):
filepath = str(self.absolute())
try:
os.rem... | python|pandas|dataframe|spss | 1 |
356,561 | 69,263,823 | How to put together all corelated values from different column | <p>I have 5 set (at max) of values:</p>
<pre><code>ID1 ID2 ID3 ID4 ID5
1 2 3 5 7
1 2
1 8
3 9
4 11 15
4 17
11 15
17 4 18
</code></pre>
<p>IF IDs are on the same row then they belong to a common group:</p>
<p>SO, I want to generate the groups:</p>
<p>In this example, I w... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.stack.html" rel="nofollow noreferrer"><code>DataFrame.stack</code></a> for <code>DataFrame</code> for <code>level_0</code> form indices and <code>val</code> column first:</p>
<pre><code>df = df.rename(index=str).stack().astype(in... | python|pandas | 3 |
356,562 | 68,897,195 | How to read csv data from kaggle in pycharm | <p>Hi there if anyone can answer this.
I am trying to read csv data from kaggle <a href="https://www.kaggle.com/stackoverflow/stack-overflow-2018-developer-survey" rel="nofollow noreferrer">https://www.kaggle.com/stackoverflow/stack-overflow-2018-developer-survey</a> in pycharm and online jupyter notebook but I can not... | <p>From the page you linked to, you have a couple of options.</p>
<ol>
<li>Create a notebook and the input files will be automatically included. Run the first cell that's generated for you and it will print out the paths to the input files. You can use Pandas <code>read_csv</code> in the notebook to load the data using... | python|pandas|csv|pycharm | 0 |
356,563 | 69,137,208 | How to interpolate only over a specific window? | <p>I have a dataset that follows a weekly indexation, and a list of dates that I need to get interpolated data for. For example, I have the following df with weekly aggregation:</p>
<pre><code>data value
1/01/2021 10
7/01/2021 10
14/01/2021 10
28/01/2021 10
</code></pre>
<p>and a list of... | <p>Use <code>interpolate</code> to get expected outcome but before you have to prepare your dataframe like below.</p>
<p>I slightly modify your input data to show you interpolation with datetimeindex (<code>method='time'</code>):</p>
<pre><code># Input data
df = pd.DataFrame({'data': ['1/01/2021', '7/01/2021', '14/01/2... | python|pandas | 0 |
356,564 | 69,233,353 | TypeError: 'Series' object cannot be interpreted as an integer | <p>Is there a way to use values from a dataframe in functions like <code>range</code> or compare the values to non-dataframe values? My code is:</p>
<pre><code>import pandas as pd
cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
'Qty': [20,34,12,43]
}
df = pd.DataFrame(cars, c... | <p>Not for sure what's your point.
But if you want to get a DataFrame where 'Qty' > 15, you can do it like this:</p>
<pre><code>df[df['Qty'] > 15]
</code></pre>
<p>Or you may want this:</p>
<pre><code>[item for item in df['Qty'] if item > 15]
</code></pre>
<p>It returns a list with elements which great than 15... | python|pandas|dataframe | 0 |
356,565 | 69,249,220 | Passing Dataframes in classes and functions | <p>I know this has probably be done to death, but im really struggling with the use of variables (dataframes) in classes and functions.
I have a created a small code example.
basically I want to</p>
<ol>
<li>read a csv to a Dataframe via a button</li>
<li>show a label when a dataframe is read</li>
<li>ignore the label ... | <p>Nevermind</p>
<p>it seems I didnt address the variable correctly as I had to include the classname before it.</p>
<p>in this case it was UI.df_data
I also didnt need to use different names as this name is valid throughout the class</p> | python|pandas|dataframe | 0 |
356,566 | 69,177,945 | Split a column by separating numbers and letters | <p>I have a dataframe with a column looking like this:</p>
<pre><code> X
1.6 aaa_2345
1.6 aaa_2345
Bbb 1.4t_2890
Bbb 1.4t_2891
1.2 ccc_4570
</code></pre>
<p>I would like to create a new column with only the float part i.e:</p>
<pre><code> X
1.6
1.6
1.4
1.4
1.2
</code></pre> | <p>You can use <code>extract</code>:</p>
<pre><code>df['X'].str.extract('([\d.]+)').astype(float)
</code></pre>
<p>output:</p>
<pre><code> 0
0 1.6
1 1.6
2 1.4
3 1.4
4 1.2
</code></pre> | python|pandas | 2 |
356,567 | 69,127,427 | How to merge matching indices with two pandas dataframes | <p>While this seemed like something that had been asked before, I have not found any information on best practices on how to perform this function.</p>
<p>Overview: I have two dataframes; the first is what I would call a FULL dataframe. It is the original source, so to speak. Then I have a dataframe that includes parts... | <p>You may use the <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.update.html" rel="nofollow noreferrer"><code>pandas.DataFrame.update</code></a> method.</p>
<pre><code>geoid = geoid.set_index('ID')
original = original.set_index('ID')
original.update(geoid)
</code></pre> | python|pandas | 4 |
356,568 | 69,201,063 | Can we use VGG19 with transferlearning and another image size? | <p>I've followed this really good example of how to use transfer learning with VGG19 and rock,paper,scissors image classification: <a href="https://github.com/Nithyashree-2022/VGG-19-for-Rock-Paper-and-Scissors-classification" rel="nofollow noreferrer">https://github.com/Nithyashree-2022/VGG-19-for-Rock-Paper-and-Sciss... | <p>Vgg will work with an image size other than 224 X22 X 3. Make sure you call
tf.keras.applications.vgg19.preprocess_input on your inputs before passing them to the model. vgg19.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset... | tensorflow|keras|transfer-learning|vgg-net|image-classification | 0 |
356,569 | 69,133,967 | Pandas map --- ValueError: Length mismatch | <p>I have two CSVs in memory stored as dataframes: df1 and df2</p>
<p>df1 has a column 'OOSCUSTID'
df2 has a column 'FORCUSTID'</p>
<p>For each row in df1:</p>
<p>Where the OOSCUSTID value in df1 == FORCUSTID value in df2, take the value from df2['KKLM'], and store it in df1['FOREIGN-KKLM'']</p>
<pre><code>df1:
NO. ... | <h2><strong><code>merge()</code></strong> method:</h2>
<pre><code>df1['FOREIGN-KKLM'] = df1.merge(df2, left_on='OOSCUSTID',
right_on='FORCUSTID',
how='left')['KKLM']
Print(df1)
NO. OOSCUSTID FOREIGN-KKLM
0 648500 -17 3.... | python|pandas | 1 |
356,570 | 68,936,835 | How to specify input sequence length for BERT tokenizer in Tensorflow? | <p>I am following this <a href="https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4" rel="nofollow noreferrer">example</a> to use BERT for sentiment classification.</p>
<pre><code>text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
preprocessor = hub.KerasLayer(
"https://tfhub.dev/tensorfl... | <p>Just going off the docs here (haven't tested this), but you might do :</p>
<pre><code>preprocessor = hub.load(
"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3")
text_inputs = [tf.keras.layers.Input(shape=(), dtype=tf.string)]
</code></pre>
<p>Doesn't look like you've tokenized your data ab... | tensorflow|keras|nlp|tokenize|bert-language-model | 0 |
356,571 | 68,940,201 | How to create list of coordinates for each row in pandas dataframe? | <p>I have a dataframe that contains coordinates of several 2d points in the sequence of frames. It looks like</p>
<pre><code>frame point_1_x point_2_x point_3_x point_1_y point_2_y point_3_y
1 0 1 1 2 3 1
2 2 3 5 1 2 3
3 ... | <p>rename the columns and then groupby</p>
<pre><code>df.columns = df.columns.str[:-2]
arr = df.stack().groupby(level=[1,0]).agg(tuple).values
array([(0, 2), (2, 1), (8, 4), (1, 3), (3, 2), (2, 5), (1, 1), (5, 3),
(3, 6)], dtype=object)
</code></pre> | python|pandas | -1 |
356,572 | 69,252,549 | Using Python Great Expectations to remove invalid data | <p>I just started with Great Expectations library and I want to know if it is possible to use it to remove invalidated data from Pandas DataFrame. And how I can do that if is possible ?
Also I want to insert invalid data to PostgreSQL database.</p>
<p>I didn't find anything about this in the documentation and on search... | <p><code>Great Expectations</code> is a powerful tool to validate data.<br />
Like all powerful tools, it's not that straightforward.</p>
<p>You can start from here:</p>
<pre><code>import great_expectations as ge
import numpy as np
import pandas as pd
# get some random numbers and create a pandas df
df_raw = pd.Da... | python|pandas|postgresql|great-expectations | 1 |
356,573 | 69,257,090 | python pandas is giving a keyerror for a column I group by, even though a boolean expression shows that the column is part of the dataframe | <p>I cannot seem to print the following line: <code>summarydata["Name"].groupby(["Tag"]).size()</code></p>
<p>without getting the error:</p>
<pre><code> File "C:\Users\rspatel\untitled0.py", line 76, in <module>
print(summarydata["Name"].groupby(["Tag"]).size... | <p>You are trying to group by a key on the column itself. Instead you want:</p>
<pre class="lang-py prettyprint-override"><code>summarydata["name"].groupby(summarydata["Tag"])
</code></pre>
<p>from the docs:</p>
<blockquote>
<p>by: (mapping, function, label, or list of labels)</p>
</blockquote>
<bl... | python|pandas|dataframe|pandas-groupby|keyerror | 2 |
356,574 | 68,944,416 | 'numpy.ndarray' object has no attribute 'reset_index' | <p>I have installed pandas but I still have trouble using reset_drop...any idea what the problem is?!
recently I've been using and dataframing and I have trouble using reset_drop code the result is</p>
<pre><code>'numpy.ndarray' object has no attribute 'reset_index'
</code></pre> | <p>I don't think there is <code>reset_drop</code> in pandas, but if you want to reset the index you can use <code>df.reset_index(drop=True)</code>.</p> | python|pandas|machine-learning | 1 |
356,575 | 69,220,930 | How to delete common index values with Pandas? | <p>I have a pandas df sourced from a csv file. There is a common value within the index column for all entries. How can I remove this common value? The common value is '00:00:00'</p>
<pre><code> Date/Time
2021-01-04 00:00:00 Compost Maker
2021-01-05 00:00:00 Green Up Feed ... | <p>Try:</p>
<pre><code>df.index = pd.to_datetime(df.index).normalize()
</code></pre>
<p>Result:</p>
<pre><code>print(df)
Product
Date/Time
2021-01-04 Compost Maker
2021-01-05 Green Up Feed & Weed
2021-01-05 Nippon Mouse Trap in a Box... | python|pandas|jupyter-lab | 0 |
356,576 | 68,886,676 | why the output of model is different in pytorch | <p>I have a simple model, just only one linear layer.</p>
<pre><code>model = torch.nn.Linear(1,1).to(device)
x_train1 = torch.FloatTensor([[1], [2], [3]])
out = model(x_train1)
print(out)
</code></pre>
<p>But whenever I tried to run this code, the printed output is diffrent.</p>
<p>Also I set these random seeds.</p>
<p... | <p>You must set the seed every time you run the code if want to get the same result.</p>
<pre class="lang-py prettyprint-override"><code>import torch
def my_func(device: str, seed: int):
torch.manual_seed(seed)
model = torch.nn.Linear(1,1).to(device)
x_train1 = torch.FloatTensor([[1], [2], [3]])
out = ... | pytorch | 1 |
356,577 | 69,091,019 | How to print rows and columns of missing values using NaN | <pre><code>for i in range(19):
for j in range(5):
if df.iloc[i,j] == 'NaN':
print('Missing Value at (row,col): ({}, {}) '.format(i,j))
</code></pre> | <pre><code>You can try this, hope it helps:)
# importing pandas as pd
import pandas as pd
# importing numpy
import numpy as np
# dictionary of lists
dict = {'First Score':[100, 90, np.nan, 95],
'Second Score': [30, 45, 56, np.nan],
'Third Score':[np.nan, 40, 80, 98]}
# creating a dataframe using dict... | python|pandas|nan|locate | 1 |
356,578 | 68,938,545 | Is there a mean-variance normalization layer in PyTorch? | <p>I am new to PyTorch and I would like to add a mean-variance normalization layer to my network that will normalize features to zero mean and unit standard deviation. I got a bit confused reading the documentation, could anyone give me some leads?</p> | <p>As @Ivan commented, the normalization can be done on many levels. However, as You say</p>
<blockquote>
<p>normalize features to zero mean and unit standard deviation</p>
</blockquote>
<p>I suppose You just want to input unbiased data to the network. If that's the case, You should treat it as data preprocessing step ... | pytorch|conv-neural-network|normalization | 1 |
356,579 | 69,130,083 | How to move the first 2 rows of a file to the end with pandas | <p>I have a file with 2 columns and 10 rows:</p>
<pre><code>01/12/2019 234.75
02/12/2019 303.6666666666667
03/12/2019 213.29166666666663
04/12/2019 187.91666666666663
05/12/2019 191.875
06/12/2019 188.25
07/12/2019 208.5833333333333
08/12/2019 184.125
09/12/2019 210.16666666666663
10/12/2019 315.4166666666667... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html" rel="nofollow noreferrer"><code>concat</code></a> with selecting rows by positions:</p>
<pre><code>df1 = pd.concat([df.iloc[2:], df.iloc[:2]])
</code></pre>
<p>Or <a href="http://pandas.pydata.org/pandas-docs/stable/reference/... | python|pandas|row | 4 |
356,580 | 68,994,870 | Pandas tz.convert GMT and local time does not match | <p>I am trying to convert UTC data to local time Mozambique. For Mozambique the local time follows GMT+2 or Africa/Maputo. However, when using <code>.tz_localize('UTC').tz_convert(X)</code> where X can either be <code>= 'GMT+2'</code> or <code>= 'Africa/Maputo'</code> I get separate answers. As an example:</p>
<pre><co... | <p>The time zone conversion using etcetera works in reverse, and perhaps it should be deprecated altogether, considering the following observation on its <a href="https://opensource.apple.com/source/system_cmds/system_cmds-230/zic.tproj/datfiles/etcetera" rel="nofollow noreferrer">documentation</a>:</p>
<blockquote>
<p... | python|pandas|numpy|time-series | 0 |
356,581 | 69,083,832 | Filter and replace substring in Pandas | <p>How can I filter <code>df</code> rows where <code>name</code> contains <code>Al</code>, and replace <code>large</code> with <code>L</code> in <code>sport</code>?</p>
<p>Reproducible example:</p>
<pre><code>df = pd.DataFrame({'name': ['Bob', 'Jane', 'Alice'],
'sport': ['tennis small', 'football me... | <p>Try with <code>loc</code> and <code>str.contains</code> with <code>str.replace</code>:</p>
<pre><code>df.loc[df.name.str.contains('Al'), 'sport'] = df.sport.str.replace('large', 'L')
</code></pre>
<p>Example:</p>
<pre><code>>>> df.loc[df.name.str.contains('Al'), 'sport'] = df.sport.str.replace('large', 'L')... | python|pandas | 1 |
356,582 | 68,880,433 | How to read 24:00 hour? | <p>I have a csv file with 24:00 hour instead of 00:00 and try to read it with pandas. I found solution and try to adopt it. The problem is, I get an error and don't know how to fix it. Can someone help me?</p>
<p>My csv:</p>
<pre><code> Datetime Value
45 01.01.2021 23:00 2.7
46 01.01.2021 23:30 ... | <p>You can use <code>str.split()</code>+<code>pd.to_datetime()</code>+<code>pd.to_timedelta()</code>:</p>
<pre><code>s=df['Datetime'].str.replace('.','-').str.split(expand=True)
df['Datetime']=pd.to_datetime(s[0])+pd.to_timedelta(s[1]+':00')
</code></pre>
<p>OR</p>
<pre><code>df['Datetime']=pd.to_datetime(df['Datetime'... | python|pandas|dataframe|datetime | 2 |
356,583 | 69,057,220 | Is there a better way to group by a category, and then select values based on different column values in Pandas? | <p>I have an issue where I want to group by a date column, sort by a time column, and grab the resulting values in the values column.</p>
<p>The data that looks something like this</p>
<pre><code> time value date
0 12.850000 19.195359 08-22-2019
1 9.733333 13.519543 09-19-2019
2 14.08333... | <p>You can sort the data frame before calling <code>groupby</code>:</p>
<pre class="lang-py prettyprint-override"><code>first_of_day = df.sort_values('time').groupby('date').head(1)
</code></pre> | python|pandas|data-science|grouping|data-preprocessing | 0 |
356,584 | 69,289,726 | Lookup Values and sum values in cell pandas | <p>I have two dataframes:</p>
<pre><code>df1 = pd.DataFrame({'Code' : ['10', '100', '1010'],
'Value' : [25, 50, 75]})
df2 = pd.DataFrame({'ID' : ['A', 'B', 'C'],
'Codes' : ['10', '100;1010', '100'],
'Value' : [25, 125, 50]})
</code></pre>
<p>Column "C... | <ul>
<li><code>explode()</code> the list of <strong>Codes</strong></li>
<li><code>merge()</code> with <strong>df1</strong> and calculate total, grouping on the index of <strong>df2</strong></li>
<li>have created a new column with this calculated</li>
</ul>
<pre><code>df1 = pd.DataFrame({"Code": ["10"... | python|pandas|dataframe|vlookup | 0 |
356,585 | 69,019,207 | NumPy TypeError: only integer scalar arrays can be converted to a scalar index | <p>I want to create <code>linnerud_df</code> dataframe by appending the <code>physiological</code> class to the <code>linnerud</code> data.</p>
<pre><code>import numpy as np
import seaborn as sns; sns.set(style="ticks", color_codes=True)
import sklearn.datasets
import pandas as pd
linnerud = sklearn.datasets... | <pre><code>In [2]: import sklearn.datasets
In [3]: linnerud = sklearn.datasets.load_linnerud()
In [5]: linnerud.target_names
Out[5]: ['Weight', 'Waist', 'Pulse']
In [6]: linnerud.target
Out[6]:
array([[191., 36., 50.],
[189., 37., 52.],
[193., 38., 58.],
[162., 35., 62.],
[189., 3... | python|pandas|numpy|scikit-learn | 0 |
356,586 | 69,008,617 | How to extract value from dictionary into new colum? | <p>I have a dataframe that contains one column with format like {"orderNum":123456}
Here is the example:</p>
<pre><code>ActionTime Details OrderNumber
0 1/2/2021 17:21 {"orderNum":123456}
1 1/2/2021 20:16 {"orderNum":467899}
2 1/3/2021 8:38 {"orderNum&... | <p>If the <code>Details</code> column is a string representation of a dictionary, you could use regular expressions to extract the number:</p>
<pre class="lang-py prettyprint-override"><code>df = pd.DataFrame({"Action": [0, 1, 2], "Details": ['{"orderNum":123456}', '{"orderNum":4... | python-3.x|pandas|dataframe | 1 |
356,587 | 69,122,523 | Removing values in two columns of a Pandas dataframe if row above have the same values | <p>With this sample pandas df:</p>
<pre><code>ColA ColB ColC
Apple Fruit Food
Apple Fruit Pie
Apple Arrow Story
</code></pre>
<p>I am attempting to roll through the dataframe and if the values in ColA and ColB are the same in the current row as in the previous row, delete the current rows values for those two... | <p>Try:</p>
<pre><code>df[["ColA", "ColB"]] = df[["ColA", "ColB"]].where(~df.duplicated(["ColA", "ColB"]), "")
>>> df
ColA ColB ColC
0 Apple Fruit Food
1 Pie
2 Apple Arrow Story
</code></pre>
<p>If your data ... | python|pandas | 1 |
356,588 | 69,032,087 | Combining the Same Column in Python | <p>I want to combine the same columns. Here is an example:</p>
<pre><code> Name X Name Y Name Z
0 Jack 5 Maria 8 John 12
1 Celine 14 Andrew 14 Jonathan 21
</code></pre>
<p>In the above example, I want to combine "<em><strong>Name</strong></em>" columns. It wil... | <p>Not the prettiest solution probably, but does the job. Open to improvements.</p>
<pre><code>>>> df
Name X Name Y Name Z
0 Jack 5 Maria 8 John 12
1 Celine 14 Andrew 14 Jonathan 21
>>> pd.concat([df.iloc[:, i:i+2] for i in range(0, df.shape[1], 2)])
Nam... | python|pandas|list|dataframe|datatable | 0 |
356,589 | 68,974,288 | Group rows in Pandas dataframe, apply custom function and store results in a new dataframe as rows | <p>I have a pandas dataframe <strong>df_org</strong> with three columns - Index (integer), Titles (string) and Dates (date).</p>
<p><a href="https://i.stack.imgur.com/L0Eqd.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/L0Eqd.png" alt="enter image description here" /></a></p>
<p>I have a method <str... | <p>You can use the <code>DataFrame.explode</code> method, followed by <code>groupby</code> and <code>size</code>:</p>
<p>I am going to just use a simple <code>.str.split</code> instead of your function, as I don't know where <code>word_tokenize</code> comes from.</p>
<pre class="lang-py prettyprint-override"><code>In [... | python|pandas|dataframe|nltk|data-analysis | 1 |
356,590 | 69,271,491 | Pandas replace values with dictionary values using python built in map() function | <p>I have a dictionary like this <code>{'Note1':'Desc1','Note2':'Desc2','Note3':'Desc3'}</code>
and a dataframe with values like this:</p>
<pre><code>{0: 'Note1',
1: 'Note1',
2: 'Note1',
3: 'Note2',
4: 'Note2',
5: 'Note2;Note3',
6: 'Note2;Note3',
7: 'Note3',
</code></pre>
<p>I want to have a new column where the... | <p>Figured it out thanks to <a href="https://stackoverflow.com/questions/33078554/mapping-dictionary-value-to-list">this</a> post.</p>
<pre><code>def swap(x):
x = x.split(';')
x = [*map(notelist.get, x)]
x = ",".join(x)
return x
df['noteText'] = df['NoteRef'].apply(swap)
</code></pre> | python|pandas|dictionary | 0 |
356,591 | 68,947,262 | Errror when trying to find index of list variables: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() | <p>The following is my code where I get the value error stated above.</p>
<pre><code>citiesR = [melbourneR, perthR, brisbaneR]
print(citiesR.index(melbourneR))
print(citiesR.index(perthR))
ValueError Traceback (most recent call last)
<ipython-input-43-517b86a5b2b0> in <mo... | <blockquote>
<p>Printing melbourneR, the first element of the list gives me the index value correctly</p>
</blockquote>
<p>That's because it is identified by object identity.</p>
<blockquote>
<p>but trying to print PerthR gives me this error and I can't work out why.</p>
</blockquote>
<p>Because it's a different object... | python|numpy | 1 |
356,592 | 69,170,921 | Adding a list as a column to a Data Frame on python | <p>I have a data frame and lists I generated from some for loops using the values on the data frame. However I would like this lists to become columns of the data frame.</p>
<pre><code>archivo=pd.read_csv('winequalityN.csv') #this is my file
Y=archivo['quality'] #a column from the data frame
y1=[]
for y in Y:
i... | <p>to simplify the data, say data is the 3 lists of numbers</p>
<pre><code>data = [[1,2,3,4],[5,6,7,8],[9,10,11,12]]
</code></pre>
<p>would be same as saying <code>data = [y1,y2,y3]</code>. To pivot that is fast with this method.</p>
<pre><code>>>> data = [[1,2,3,4],[5,6,7,8],[9,10,11,12]]
>>> [list(x... | python|pandas|dataframe|for-loop | 0 |
356,593 | 68,956,247 | Plotting geopandas changes figure size in matplotlib | <p>So I create a matplotlib figure, and then add 3 (germany, slovakia, czech) countries via shape files.</p>
<p>I explictly set the figsize as <code>(15, 15)</code>. <code>germany</code>, <code>czech</code>, <code>slovakia</code> are the read shape files, and finally <code>germany_pipe</code> is a <code>GeoDataFrame</c... | <p>Plotting several <code>geodataframes</code> on a common <code>ax</code> axis correctly requires all of them to have CRS (coordinate reference system) set properly. Preferably, all of them should have the same CRS for easy operation without (unnecessary) specifying coordinate transformation in the plotting instructio... | python|matplotlib|size|geopandas|figure | 0 |
356,594 | 69,042,540 | Split data frame in python based on one parameter shape | <p>I have a data frame which is like the following :</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import csv
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
df_input = pd.read_csv('combine_input.csv', delimite... | <p><strong>Note: InPlace of target you have to write time as your column name Is time,or change column name to target</strong></p>
<pre><code>def calRows(df,x,y):
#df For consideration
df1 = pd.DataFrame(df.target[df.target<=x])
minCount = len(df1)
targets = df1.target.unique()
for i in targets:
count = int(df1[... | python|pandas|dataframe|csv|split | 0 |
356,595 | 69,046,534 | docker stops when importing tensorflow | <p>I have a problem when building a docker container using tensorflow. Container gets build fine but when it runs the script 'ai_app.py' and reaches the <code>import tensorflow as tf</code> line the container immediately stops. It does not show me any error or something, it is like if i were using ctrl + c inside the d... | <p>I figured it out, the container stops because AVX support is not enabled</p> | python|docker|tensorflow | 0 |
356,596 | 68,875,227 | How to subtract two dataframes with duplicate first column? | <p>So, I have the following two dataframes and my ideal output is to get open_orders reduced by cancel_orders so I know how many open_orders I have.</p>
<p>Desired Output:</p>
<pre><code>df_total_orders
order_id business_symbol open_orders
0 a1b2c3111111 AA 0.0
1 4kl3l2242244... | <pre><code>df_total_orders =df_add_orders.merge(df_cancel_orders,
how = 'left',
on = 'order_id)
</code></pre>
<p>will get you a dataframe with the data from the two original dataframes. You can then do</p>
<pre><code>df_total_orders['open_orders'] =
df_total_orders['open_orders']-
df_total_orders['ca... | python|pandas|dataframe | 1 |
356,597 | 68,906,112 | How to get an exact representation of floats during `DataFrame.to_json`? | <p>I observed the following behavior with <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html" rel="noreferrer"><code>DataFrame.to_json</code></a>:</p>
<pre class="lang-py prettyprint-override"><code>>>> df = pd.DataFrame([[eval(f'1.12345e-{i}') for i in range(8, 20)]])
>>... | <p>I'm not sure on achieving this with <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html" rel="nofollow noreferrer">pd.DataFrame.to_json</a>, but we can use <a href="https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.to_dict.html" rel="nofollow noreferrer">pd.Data... | python|json|pandas|floating-point | 1 |
356,598 | 69,010,397 | Concatenate n number of numpy arrays columnwise in python | <p>I know that we concatenate two 2-D numpy
arrays named <code>arr1</code> and <code>arr2</code> with same number of rows with the help of following command:</p>
<pre><code>np.concatenate((arr1,arr2),axis=1)
</code></pre>
<p>But I have n number of numpy arrays (I haven't done global variable name assignment to these a... | <p>Just a side note,</p>
<p>Concatenating with <a href="https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html" rel="nofollow noreferrer"><code>np.concatenate</code></a> on <code>axis=1</code> is equivalent to a horizontal stack: <a href="https://numpy.org/doc/stable/reference/generated/numpy.hstack.ht... | python|arrays|numpy|arraylist | 1 |
356,599 | 69,024,042 | seasonal WindRose subplots | <p>I'm trying to make WindRoses for the four seasons of the year on the same plot. I tried to follow the method from <a href="https://stackoverflow.com/questions/42733194/subplot-of-windrose-in-matplotlib">Subplot of Windrose in matplotlib</a> but the method did not work for me.</p>
<p>I also tried the following from <... | <p>I managed to reproduce your question. Please keep in mind @mozway suggestion about <a href="/help/mcve">mcve</a> for your next questions.</p>
<h1>Prepare data</h1>
<p>I downloaded locally your data in my working direction.</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import numpy as np
fro... | python|pandas|matplotlib|subplot|windrose | 1 |
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