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 |
|---|---|---|---|---|---|---|
12,400 | 65,574,711 | Is there a way to run posqresql queries in a pandas dataframe? | <p>I have pandas dataframe like this :</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th></th>
<th>created_at</th>
<th>lat</th>
<th>long</th>
<th>hex_ID</th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td>2020-10-13 15:12:18.682905</td>
<td>28.690628</td>
<td>77.323285</td>
<td>883da1ab0bfffff</t... | <p>just use groupy in df.</p>
<pre class="lang-py prettyprint-override"><code># 2020-10-13 15:12:18.682905 -> 2020-10-13 15:00:00
df['created_at_n'] = df['created_at'].astype(str).str.split(':').str[0] + ':00:00'
df.groupby(['created_at_n', 'hex_id'])['lat'].count()
</code></pre> | sql|pandas|postgresql|time-series|pandas-groupby | 0 |
12,401 | 63,697,275 | Regex string for different versions | <p>I'm trying to isolate instances in a Pandas Dataframe where the version number is not equal to .0 —i.e., if there are versions 10.0, 10.1, and 10.2, I only want to select versions 10.1 and 10.2. Does anyone know the proper regex to accomplish this? Thanks!</p> | <ul>
<li>Use <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html" rel="nofollow noreferrer">Boolean indexing</a></li>
<li>Split the string on the <code>.</code> and look at the value at index 1</li>
<li>It's not clear if this is a column of <code>str</code> or <code>float</code> types.
<ul>
<... | python|regex|pandas | 1 |
12,402 | 63,581,028 | model.compile() fails with every but 'accuracy' metric | <p>I am working on a simple MLP, and coded this:</p>
<pre><code>from keras.models import Sequential
from keras.layers import Dense
from keras import Input
def get_stats_model():
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(D... | <p>From the comments</p>
<blockquote>
<ol>
<li>Creating a <code>Virtual Environment</code> and</li>
<li><strong><code>Importing Keras</code></strong> using the code, <code>from tensorflow import keras</code></li>
</ol>
<p>has resolved the issue. (paraphrased from drops and Aniket Bote).</p>
</blockquote> | python|tensorflow|keras | 0 |
12,403 | 63,570,028 | Training gets progressively slower over time | <p>This is the first time I'm experiencing this issue. I've been using this model for a while, but with less data. The problem is that in the first 3 epochs training took 11 sec/step (31k samples / 128 batch size) while in the 4-th epoch it took 18 sec/step. In the fifth it took about 45 sec/step. I'm using Keras and n... | <p><strong>Symptoms:</strong></p>
<p>This seems to be a memory issue due to a leak. First, you are able to run the model in constant epoch time for a small batch BUT with complete data, the epoch times increase progressively (with increasing time/step too!). I am assuming that as you run out of memory, it is causing in... | tensorflow|keras | 3 |
12,404 | 24,616,671 | numpy and statsmodels give different values when calculating correlations, How to interpret this? | <p>I can't find a reason why calculating the correlation between two series A and B using <code>numpy.correlate</code> gives me different results than the ones I obtain using <code>statsmodels.tsa.stattools.ccf</code></p>
<p>Here's an example of this difference I mention:</p>
<pre><code>import numpy as np
from matplo... | <p><code>statsmodels.tsa.stattools.ccf</code> is based on <code>np.correlate</code> but does some additional things to give the correlation in the statistical sense instead of the signal processing sense, see <a href="http://en.wikipedia.org/wiki/Cross-correlation">cross-correlation on Wikipedia</a>. What happens exact... | python|numpy|statsmodels|cross-correlation | 10 |
12,405 | 30,256,670 | Pandas plotting: ValueError: ordinal should be >= 1 | <p>I have the following Series, called <code>sr</code>.</p>
<pre><code>In [1]: sr
Out[1]: 0 0
1 0
2 0
3 0
4 0
5 1
6 2
7 4
8 7
9 4
10 3
11 2
12 1
13 2
14 ... | <p>If you run <code>plt.clf()</code> this will clear the plot in memory and may allow the plotting to proceed (worked for me when I encountered this error after interrupting the plotting routine).</p> | python|pandas|matplotlib | 4 |
12,406 | 53,365,640 | Conditional Average from Pandas DataFrame | <p>I have a dataframe with multiple columns of real estate sales data. I would like to find the average price-per-square-foot <code>'ppsf'</code> for all 1bed-1bath sales by zip code. Here is my attempt (each key in the dict is a zip code):</p>
<pre><code>bed1_bath1={}
for zip in zip_codes:
bed1_bath1[zip]= (df.lo... | <p><code>(df[(df['bed']==1) & (df['bath']==1) & (df['zip']==zip)])['ppsf'].mean()</code> would do it. You simply choose the column you are interested in before calculating the mean (so you will not even do the processing for the rest of the columns).</p> | python|pandas | 4 |
12,407 | 53,363,994 | convert PIL numpy 3d array to 2d luma values | <p>I've loaded an image using:</p>
<pre><code>import numpy as np
from PIL import Image
imag = Image.open("image.png")
I = np.asarray(imag)
</code></pre>
<p>Where the shape of <code>I</code> is <code>(951, 1200, 3)</code></p>
<p>But I would like to average each pixel roughly to it's luma values (<code>(r*g*b)/3</... | <p>I think the easiest thing is to use Pillow's built-in conversion to Luminance as follows:</p>
<pre><code>import numpy as np
from PIL import Image
# Load image and convert to luminance, and thence to Numpy array
imag = Image.open("image.png").convert('L')
I = np.asarray(imag)
</code></pre> | python|numpy|python-imaging-library | 0 |
12,408 | 53,590,379 | Convert multiple xlsm files automatically to multiple csv files by using pandas | <p>I have 300 raw datas (.xlsm) and wanne to extract useful datas and turn them to csv files as input for subsequent neural network, now i try to implement them with 10 datas as example, i have sucessfully extracted the informations what i need, but i dont know how to convert them to csv files with the same name, for ... | <p>The easiest way of doing this is to get the filename from the excel and then use the os.path.join() method to save it to the directory you want.</p>
<pre><code>directory = "C:/Test"
for files in excel_files:
csvfilename = (os.path.basename(file)[-1]).replace('.xlsm','.csv')
newtempfile=os.path.joi... | python|pandas | 0 |
12,409 | 17,416,669 | Measuring increase in heap size after loading large object | <p>I'm interested to find out the increase in the total size of python's heap when a large object is loaded. heapy seems to be what I need, but I don't understand the results.</p>
<p>I have a 350 MB pickle file with a pandas <code>DataFrame</code> in it, which contains about 2.5 million entries. When I load the file a... | <p>You could try <a href="http://pythonhosted.org/Pympler/classtracker.html#classtracker" rel="nofollow">pympler</a>, which worked for me the last time I checked. If you are just interested in the total memory increase and not for a specific class, you could you an OS specific call to get the total memory used. Eg, on ... | python|pandas|heapy | 1 |
12,410 | 20,341,911 | Numpy array __contains__ check | <p>I got an array of arrays:</p>
<pre><code>temp = np.empty(5, dtype=np.ndarray)
temp[0] = np.array([0,1])
</code></pre>
<p>I want to check if <code>np.array([0,1]) in temp</code>, which in the above example clearly is but the code returns false. I also tried <code>temp.__contains__(np.array([0,1]))</code> but also ... | <p>One thing you need to understand, in python in general, is that, semantically, <code>__contains__</code> is based on <code>__eq__</code>, i.e. it looks for an element which satisfies the <code>==</code> predicate. (Of course one can override the <code>__contains__</code> operator to do other things, but that's a dif... | python|numpy | 2 |
12,411 | 12,555,323 | How to add a new column to an existing DataFrame? | <p>I have the following indexed DataFrame with named columns and rows not- continuous numbers:</p>
<pre><code> a b c d
2 0.671399 0.101208 -0.181532 0.241273
3 0.446172 -0.243316 0.051767 1.577318
5 0.614758 0.075793 -0.451460 -0.012493
</code></pre>
<p>I would like to add a n... | <p><strong>Edit 2017</strong></p>
<p>As indicated in the comments and by @Alexander, currently the best method to add the values of a Series as a new column of a DataFrame could be using <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.assign.html" rel="noreferrer"><strong><code>assi... | python|pandas|dataframe|chained-assignment | 1,277 |
12,412 | 71,938,710 | Extract elements from tensorflow dataset | <p>I have a tensorflow dataset containing all my data and labels.
The first 20 elements are extracted into another dataset using following code:</p>
<pre><code>train_dataset = big_dataset.take(20)
</code></pre>
<p>But how do i extract for example the last 20 elements from big_dataset into a new dataset?</p>
<p>Thanks i... | <p>Try using <code>skip</code>. For example, suppose you have 120 data samples and a batch_size of 1 and you have not shuffled your data, then you can try something like the following:</p>
<pre><code>train_dataset = big_dataset.skip(100).take(20)
</code></pre>
<p>For your specific dataset, try:</p>
<pre><code>import te... | python|tensorflow|keras|tensorflow2.0|tf.keras | 0 |
12,413 | 71,833,955 | I need help working with pandas dataframe | <p>I have a big dataframe of items which is simplified as below. I am looking for good way to find the the item(A, B, C) in each row which is repeated more than or equal to 2 times.<br />
for example in row1 it is A and in row2 result is B.</p>
<p>simplified df:</p>
<pre><code>df = pd.DataFrame({'C1':['A','B','A','A','... | <p>Like mozway suggested, we don't know what will be your output. I will assume you need a list.</p>
<p>You can try something like this.</p>
<pre><code>import pandas as pd
from collections import Counter
holder = []
for index in range(len(df)):
temp = Counter(df.iloc[index,:].values)
holder.append(','.join([k... | python|pandas|pivot-table | 0 |
12,414 | 72,113,963 | Keras model.fit() runs faster on GPU when the CPU is loaded with a heavy multiprocessing script | <p>I wasn't expecting this to happen. The relevant code pieces are:</p>
<pre><code>import os
import tensorflow as tf
os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
...
csv_logger = CSVLogger(out_dir + 'log.csv', append = True, separator = '|')
for epoch in range(epochs):
np_i... | <p>After more research, empirical results show that limiting CPU parallelism indeed accelerates my model.fit().</p>
<p>First, I've found:</p>
<pre><code>config = tf.compat.v1.ConfigProto(intra_op_parallelism_threads = 4,
inter_op_parallelism_threads = 1,
allow_soft_place... | tensorflow|keras|parallel-processing | 0 |
12,415 | 72,018,721 | Pandas select rows by multiple conditions on columns | <p>I would like to reduce my code. So instead of 2 lines I would like to select rows by 3 conditions on 2 columns.
My DataFrame contains Country's population between 2000 and 2018 by granularity (Total, Female, Male, Urban, Rural)</p>
<pre><code> Zone Granularity Year Value
0 Afghanistan Total 2000... | <p>Very likely, you're using the wrong type for the year. I imagine these are integers.</p>
<p>You should try:</p>
<pre><code>df.loc[(df['Granularity'].isin(['Total', 'Urban'])) & df['Year'].eq(2017)]
</code></pre>
<p>output (for the Year 2018 as 2017 is missing from the provided data):</p>
<pre><code> Zo... | python|pandas|dataframe | 2 |
12,416 | 16,839,840 | Numpy/Scipy Sparse vs dense multiplication | <p>There appears to be some discrepancy between the scipy sparse matrix types and the normal numpy matrix type</p>
<pre><code>import scipy.sparse as sp
A = sp.dia_matrix(tri(3,4))
vec = array([1,2,3,4])
print A * vec #array([ 1., 3., 6.])
print A * (mat(vec).T) #matrix([[ 1.],
... | <p>In the <code>spmatrix</code> class (which you can check in scipy/sparse/base.py) the <code>__mul__()</code> there is a set of "ifs" that can answer your question:</p>
<pre><code>class spmatrix(object):
...
def __mul__(self, other):
...
M,N = self.shape
if other.__class__ is np.ndarra... | python|numpy|scipy|sparse-matrix|matrix-multiplication | 3 |
12,417 | 18,879,272 | Why does sum() operation on numpy masked_array change fill value to 1e20? | <p>Is this a feature or a bug? Can someone explain to me this behavior of a numpy masked_array? It seems to change the fill_value after applying the sum operation, which is confusing if you intend to use the filled result.</p>
<pre><code>data=ones((5,5))
m=zeros((5,5),dtype=bool)
"""Mask out row 3"""
m[3,:]=True
arr=... | <p>The array returned by <code>arr.sum</code> is a new array which does not inherit the fill_value of <code>arr</code> (though I agree that might be a nice improvement to <code>np.ma</code>). As a workaround, you could use</p>
<pre><code>In [18]: farr.filled(arr.fill_value)
Out[18]: array([ 5., 5., 5., nan, 5.... | python|numpy | 2 |
12,418 | 22,268,509 | fitting an image with 2D equation in python | <p>I have an image and I want to fit it to 2D equation in order to extract nx and ny parameters. First I defined 2D function and residuals from fit then I read the image file and then I tried to fit it using leastsq method, this is my code: </p>
<pre><code>#!/usr/bin/python
import pyfits
import numpy as np
import... | <p>Simply replace <code>residuals()</code> with the following should solve your problem:</p>
<pre><code>def residuals(p,y,nx,ny):
nx,ny = p
err = y-fun(nx,ny)
return err.flatten()
</code></pre>
<p>Basically I suspect the function call of <code>residuals(p0, meas, nx, ny)</code> would return a <code>2d arr... | python|numpy|matplotlib|scipy|pyfits | 1 |
12,419 | 8,794,610 | Neighbourhood of Scipy Labels | <p>I've got an array of objects labeled with <code>scipy.ndimage.measurements.label</code> called <code>Labels</code>. I've got other array <code>Data</code> containing stuff related to <code>Labels</code>. How can I make a third array <code>Neighbourhoods</code> which could serve to map <b>the nearest label to <i>x,y<... | <p>As suggested by David Zaslavsky, this is the job for a voroni diagram. Here is a numpy implementation: <a href="http://blancosilva.wordpress.com/2010/12/15/image-processing-with-numpy-scipy-and-matplotlibs-in-sage/" rel="nofollow">http://blancosilva.wordpress.com/2010/12/15/image-processing-with-numpy-scipy-and-matp... | python|numpy|scipy | 2 |
12,420 | 55,169,540 | Pandas Plot: scatter plot with index | <p>I am trying to create a scatter plot from pandas dataframe, and I dont want to use matplotlib plt for it. Following is the script </p>
<pre><code>df:
group people value
1 5 100
2 2 90
1 10 80
2 20 40
1 7 10
</code></pre>
<p>I want to create a scatter plot with index on x ... | <p>You can try and use:</p>
<pre><code>df.reset_index().plot.scatter(x = 'index', y = 'value')
</code></pre>
<p><a href="https://i.stack.imgur.com/noyod.png" rel="noreferrer"><img src="https://i.stack.imgur.com/noyod.png" alt="enter image description here"></a></p> | python|pandas|matplotlib|plot | 12 |
12,421 | 55,316,502 | What is the best way to fill each row of a column based on a condition of another cell, within the same row? | <p>I am attempting to fill a dataframe column 'Classification' with strings which indicate whether the value falls within the 200 lowest, or 200 highest values of a column titled, 'Valence_mean'.</p>
<p>So, if a value of a cell within the 'Valence_mean' column is in the 200 lowest values of the column's values, the la... | <pre><code>df.loc[df.nsmallest(200,'Valence_mean').index.values,["Classification"]]="Low_valence"
</code></pre>
<p>You can get index values and change the values</p> | python|pandas|dataframe | 0 |
12,422 | 55,398,498 | Get a new column (consensus of element in others) with pandas | <p>I need some help using pandas data frames.
Here is the data frame:</p>
<pre><code>group col1 col2 name
1 dog 40 canidae
1 dog 40 canidae
1 dog 40 canidae
1 dog 40 canidae
1 dog 40
1 dog 40 canidae
1 dog 40 ... | <p>You'll need to define your own function. Make sure to replace the empty strings with <code>NaN</code> so they are never considered. <code>transform</code> can get tricky with calculations based on multiple columns, so instead groupby and map the result back to the original.</p>
<pre><code>import numpy as np
def my... | python|python-3.x|pandas|pandas-groupby | 4 |
12,423 | 55,326,761 | Iterator protocol within numpy | <p>Is there a way to work with iterators instead of (for example) <code>numpy.ndarray</code> in numpy? </p>
<p>For example, imagine I have a 2D-array and I want to know if there is a row that only contain even numbers: </p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
x = np.array([[1, 2], [2, ... | <p>The number of temporary arrays may be more than you realize:</p>
<pre><code>In [224]: x = np.array([[1, 2], [2, 4], [3, 6]])
In [225]: x % 2
Out[225]:
array([[1, 0],
[0, 0],
[1, 0]])
In [226]: _ == 0 ... | python|numpy|iterator | 1 |
12,424 | 56,813,590 | Finding where each unique subarray occurs | <p><strong>Situation:</strong></p>
<p>I'm filling a narray of shape (2N, 2N), where N is close to 8000, call it A, with values I get from a function by using nested for loops to call a function that takes as argument subarrays of shape (2,) from the last dimension of an array of shape (N,N,2), call it B. </p>
<p>This... | <p>Vectoring is often better. a slight rearrangement of your function facilitate the job :</p>
<pre><code>import numpy as np
def average_lat_pos(a,b,x,y): # all arguments are scalars
return a*x+2*b*y # as example
n=1000
B=np.random.rand(n,n,2)
def loops():
A=np.empty((2*n,2*n))
for i in ... | python|arrays|numpy|vectorization | 0 |
12,425 | 56,598,674 | How to install older version of pytorch | <p>Following this <a href="https://pytorch.org/get-started/previous-versions/#via-pip" rel="noreferrer">https://pytorch.org/get-started/previous-versions/#via-pip</a></p>
<pre><code>pip install torch==0.2.0_4 -f https://download.pytorch.org/whl/cpu/stable
Collecting torch==0.2.0_4
Could not find a version that satis... | <pre><code>pip install torch==
Collecting torch==
</code></pre>
<blockquote>
<p>ERROR: Could not find a version that satisfies the requirement torch== (from versions: 0.1.2, 0.1.2.post1, 0.4.1, 1.0.0, 1.0.1, 1.0.1.post2, 1.1.0)
ERROR: No matching distribution found for torch==</p>
</blockquote>
<p>This means... | python|pip|pytorch | 5 |
12,426 | 25,857,555 | Trim Pandas DataFrame based on values in list | <p>I'm trying to trim a DataFrame based on an input list, but I need to check if the items in the list are in some of the frame's columns. </p>
<p>(data below is random)</p>
<p>The frame I'd like to trim looks like this:</p>
<pre><code> UID S1 S2 ElementHID n1 n2 n3 n4
0 88.340153 -88.... | <p>you can do something like:</p>
<pre><code>>>> i = element_frame[['n1', 'n2', 'n3', 'n4']].isin(node_list).any(axis=1)
</code></pre>
<p>and then, <code>i</code> would be the boolean indexer:</p>
<pre><code>>>> element_frame[i]
</code></pre> | python|pandas | 2 |
12,427 | 26,343,815 | Converting non-numeric integers in column that also contains strings | <p>I have a dataframe that looks like the junk column below:</p>
<pre><code>d = {'Junk Column' : ['1', '2', '3', '4', '5', '6', '7', 'J', 'K'],
'Good Column' : [1, 2, 3, 4, 5, 6, 7, 'J', 'K']}
df = pd.DataFrame(d)
Good Column Junk Column
0 1 1
1 2 2
2 3 3
3 4 4
4 5 5
5 6... | <p>This may be faster:</p>
<pre><code>>>> df
good junk
0 1 1.25 # a float
1 2 2 # already an int
2 3 +3
3 4 -4 # signed
4 5 5 # leading/trailing space
5 6 6
6 7 7
7 J J 3
8 K K5
>>> df['junk'].values
array([1.... | python|pandas|dataframe | 0 |
12,428 | 26,034,805 | Condition on numpy arrays | <p>I have two arrays with the same number of elements</p>
<pre><code>X = [1,2,3,4,5,6,7,8,9]
Y = [10,4,3,7,7,3,1,8,98]
</code></pre>
<p>I would like to keep the elements of X and Y such as <code>2<X<7</code>. How can I do?</p>
<p>Ok it works well with </p>
<pre><code> Y = Y[np.logical_and(X>2, X<5)]
X ... | <p>You can use <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.logical_and.html" rel="nofollow"><code>numpy.logical_and</code></a>:</p>
<pre><code>>>> X = np.array([1,2,3,4,5,6,7,8,9])
>>> X[np.logical_and(X>2, X<7)]
array([3, 4, 5, 6])
</code></pre> | python|arrays|numpy | 2 |
12,429 | 66,958,951 | Convert DataFrame to a multi polygon DataFrame, multiple data point - python | <p>I have a DataFrame as below, I want to convert data to a multi polygon DataFrame, because I want to plot each multi polygon on a map.</p>
<p>I know how to convert if I have two data point, but with 6 data point, I don't know how to convert it. can anyone help me please.</p>
<pre><code>
geometry = [Point(xy) for xy ... | <p>Try this, assuming the 'lan' is latitude.</p>
<pre><code>import geopandas as gpd
from shapely.geometry import Polygon
import numpy as np
import pandas as pd
import folium
# ....
def addpolygeom(row):
row_array = np.array(row)
# split dataframe row to a list of tuples (lat, lon)
coords = [tuple(i)[::-1]... | python|dataframe|polygon|geopandas | 1 |
12,430 | 67,078,196 | Score function from RandomizedSearchCV gives different results on the same data set | <p>I'm running a RandomizedSearchCV using several pipelines (scaling, imputing, one-hot-encoding) to perform hyperparameter optimization for a random forest. I fit the model on my training data set and have been then using the <code>model.score()</code> function to assess its performance. Strangely, every time I run th... | <p>After some digging I discovered the part of the code that was responsible for the strange behaviour I was observing. It turns out the argument <code>sample_posterior = True</code> in the IterativeImputer was causing the issues. From <a href="https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeI... | python|pandas | 0 |
12,431 | 66,874,237 | Having trouble making update in gradient descent implementation? | <p>Hi I am working on implementing gradient descent with backtracking line search. However when I try to update f(x0) the value doesn't change. Could it be something going on with the lambda expression, I am not too familiar with them?</p>
<pre><code>import numpy as np
import math
alpha = 0.1
beta = 0.6
f = lambda x: ... | <p><strong>Update for new code</strong></p>
<p>OK, your numbers now change too much!</p>
<p>When writing these routines stepping through the code with a debugger is really useful to check that the code is doing what you want. In this case you'll see that on the second pass through the loop <code>x0 = [-1.32e+170, 3.96e... | python|numpy|convex-optimization | 1 |
12,432 | 66,910,391 | Normalization python | <p>I'm trying to normalize a numpy array but I'm not getting the expected values( from 0 to 1).</p>
<p>Here how I approached the problem:</p>
<p>Suppose <code>a</code> is a numpy array</p>
<pre><code>result = a - np.mean(a) / np.sqrt(np.sum((a-np.mean(a) ** 2) / (len(a)-1)
</code></pre> | <p>Normalization doesn't mean you get values from 0 to 1, it just adjusts scales to comparable magnitudes and/or removes bias. If you want to normalize to the 0-1 range you have to subtract <code>np.min(a)</code> and divide by <code>np.max(a)-np.min(a)</code>.</p>
<pre><code>a = (a - np.min(a))/(np.max(a)-np.min(a))
</... | python|numpy | 2 |
12,433 | 66,829,282 | how to clip pandas for a multiple column in a data frame | <p>Here is the df:</p>
<pre><code>{'Type 1': {1: 123.0,
2: 123.0,
3: 123.0,
4: 123.0,
5: 123.0,
6: 45.0,
7: 45.0,
8: 45.0,
9: 45.0,
10: 9.5,
11: 9.5,
12: 9.5,
13: 2.34,
14: 2.34,
15: 2.34},
'Type 2': {1: 0,
2: 0,
3: -90,
4: -90,
5: -90,
6: -90,
7: -90,
8: -270,
9: -270,
10... | <p>From <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.clip.html" rel="nofollow noreferrer">documentation</a>, <code>lower</code> and <code>upper</code> must be <code>float</code> or <code>array-like</code>, not <code>Series</code>.</p>
<p>You could do</p>
<pre><code>lower_limit = [3,-90,0,0,0,1... | python|pandas | 3 |
12,434 | 47,500,081 | Appending a new row to an existing dataframe | <p>I am trying to insert a new record to a data frame and am getting this error - <code>DataFrame constructor not Properly called</code>.</p>
<p>This is my code,</p>
<pre><code>import pandas as pd
#defining the dataframe
dfHeader = { 'Name': [], 'Daily': [],'Weekly': [],'Monthly': [],'Yearly': [],
'Area... | <p>If you need to add a single row:</p>
<pre><code>In [136]: dFrame.loc[len(dFrame)] = row
In [137]: dFrame
Out[137]:
Amenity Area Daily Latitude Longitude Monthly Name Weekly Yearly
0 NA NA NA NA NA NA NA NA NA
</code></pre>
<p>NOTE: usually it's much better, faster, less mem... | python|pandas|dataframe | 1 |
12,435 | 47,152,923 | assign value to new column [Python pandas] | <p>I have a scenario where I am running two functions in a script:</p>
<p>test.py :</p>
<pre><code>def func1():
df1=pd.read_csv('test1.csv')
val1=df['col1'].mean().round(2)
return va11
def func2():
df2=pd.read_csv('test2.csv')
val2=df['col1'].mean().round(2)
return val2
def func3():
data... | <p>You need pass variables as parameters in function <code>func3</code>, and if only difference in <code>func1</code> and <code>func2</code> is file name, create only one function with parameetr .</p>
<p>Thanks for idea <a href="https://stackoverflow.com/questions/47152923/assign-value-to-new-column-python-pandas/4715... | python|python-2.7|python-3.x|pandas|csv | 2 |
12,436 | 47,338,052 | Nested range() in range() in Python | <p>Can you nested the range in range? Use variable in range? Because I would like to get some effect. To illustrate the problem I have the following pseudocode:</p>
<pre><code>for i in range(str(2**i) for i in range(1,2)):
print (str(i*0.01))
</code></pre>
<p>At the exit I would like to receive:</p>
<pre><code>0... | <p>For this specific task you'll want to nest them like this:</p>
<pre><code>for i in range(1,3):
for j in range(2**i):
print(i * 0.01)
</code></pre>
<p>which will print what you want. What this is doing is taking a number <code>i</code> in <code>range(1,3) #[1,2]</code> and then print <code>i * 0.01</cod... | python|numpy | 4 |
12,437 | 47,539,271 | Multiple conditional selection using list of variables | <p>I'm cleaning up a data set and would like to filter it using a list of variables that satisfy a condition. Such as</p>
<pre><code>import pandas as pd
import numpy as np
data = {"var1": [0,1,0,0,0],
"var2": [0,0,0,0,0],
"var3": [0,0,0,0,1],
'var4': [0,0,0,0,0],
'var5': [1,2,3,4,5]
... | <p><code>isin</code> +<code>any(1)</code></p>
<pre><code>df[['var1','var2','var3','var4']].isin([1]).any(1)
Out[538]:
0 False
1 True
2 False
3 False
4 True
dtype: bool
</code></pre> | python|pandas | 1 |
12,438 | 68,195,301 | DF - Sorting specific columns based on character code values | <p>I have below dataframe and I need to perform sort based on 6th, 3rd and 4th column values.
sorting should be based on ASCII character code corresponding to the column values.
""ASCII Character code is Digits are the lowest value characters and followed by uppercase letter, followed by lowercase letters&quo... | <p>Unless I'm missing something this is as simple as using <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html" rel="nofollow noreferrer">.sort_values</a></p>
<pre class="lang-py prettyprint-override"><code>df1.sort_values(["6","3","4"])
</... | python|pandas|dataframe|sorting | 0 |
12,439 | 68,289,209 | How to use constants in loss function? | <p>I know this is dumb, but I need the equivalent of <code>np.sqrt(2.0*np.pi)</code> in my loss function. How can I get it? Statements like this give error: 'float object has no attribute dtype':</p>
<pre><code>pi = np.pi
def myLoss(...):
k = K.sqrt(2.0*pi)
...
</code></pre>
<p>Even <code>K.sqrt(2.0*3.14159)</co... | <p>Use it like this:</p>
<pre><code>k = K.sqrt(tf.constant([2.0*np.pi]))
</code></pre>
<p>Since, it accepts an object which has dtype. One option is a Tensor.</p>
<p>Another option is to not using keras backend, but using numpy:</p>
<pre><code>k = np.sqrt(2.0*np.pi)
</code></pre> | tensorflow|keras | 0 |
12,440 | 68,415,512 | Update array while inside for loop over arrays | <p>I have a Numpy array and can successfully update all its elements with one line:</p>
<pre class="lang-py prettyprint-override"><code>array_1 = np.array([1, 2, 3, 4])
array_1 = array_1 / 10.0
print(array_1)
# [0.1 0.2 0.3 0.4] -- Success!
</code></pre>
<p>However, when I have a list of Numpy arrays and iterate over ... | <p>You can make a shallow copy of the target array inside the for loop to edit the original.</p>
<pre><code>for array in [array_1,array_2,array_3]:
array[:] = array / 10.0
</code></pre>
<p>EDIT With Explanation---</p>
<p>In the for loop the control variable is its own object that deep copies the item being iterated... | python|numpy | 2 |
12,441 | 68,119,256 | Keras: Does model.predict() require normalized data if I train the model with normalized data? | <p>After completing model training using Keras I am trying to use Keras' <code>model.predict()</code> in order to test the model on novel inputs.</p>
<p>When I trained the model, I normalized my training data with Scikit Learn's <code>MinMaxScaler()</code>.</p>
<p>Do I need to normalize the data as well when using <cod... | <p>Yes. You need. Because your model has learned from data with a specific scale, so, it's better to convert your data to the same scale as your model works and then let it predict.</p>
<p>For example, you may use the Scikitlearn library to normalize and standardize the data:</p>
<pre><code>x_scaler = StandardScaler()
... | python|tensorflow|machine-learning|keras|scikit-learn | 4 |
12,442 | 59,441,811 | How to convert this forloop to pandas lambda function, to increase speed | <p>This forloop will take 3 days to complete. How can I increase the speed?</p>
<pre><code>for i in range(df.shape[0]):
df.loc[df['Creation date'] >= pd.to_datetime(str(df['Original conf GI dte'].iloc[i])),'delivered'] += df['Sale order item'].iloc[i]
</code></pre>
<p>I think the forloop is enough to understan... | <p>Convert values to numpy arrays by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.to_numpy.html" rel="nofollow noreferrer"><code>Series.to_numpy</code></a>, compare them with broadcasting, match <code>order</code> values by <a href="https://docs.scipy.org/doc/numpy/reference/generate... | python|pandas | 3 |
12,443 | 59,319,399 | Can I check pandas dataframe index is end? | <p>I use while loop for show data in dataframe</p>
<pre><code>while True:
last_id = get_last_id()
res = df.iloc[last_id + 1]
</code></pre>
<p>when last_id end of data and still use last_id + 1 it show error </p>
<pre><code>IndexError: single positional indexer is out-of-bounds
</code></pre>
<p>Can I check i... | <p>Why not just remove <code>+1</code> from the last line:</p>
<pre><code>while True:
last_id = get_last_id()
res = df.iloc[last_id]
</code></pre> | python|python-3.x|pandas|dataframe | 7 |
12,444 | 45,036,303 | How to apply default and None operation names when constructing operations with Java Tensorflow API? | <p>Many tf operations have optional/default 'name' argument, but it seems
there is no way to use the default value or avoid specifying it when
constructing operations with Java API. So I have two questions:</p>
<ol>
<li>Is it possible to use default operation name when building it? If so, what should I pass to <code>o... | <p>By "many tf operations have optiona/default 'name' argument", I take it to mean that you're talking about the Python API for TensorFlow, where functions like <a href="https://www.tensorflow.org/api_docs/python/tf/add" rel="nofollow noreferrer"><code>tf.add</code></a> take a 'name' argument.</p>
<p>The default in th... | java|tensorflow | 1 |
12,445 | 44,937,860 | Faster apply method in pandas | <p>I have a function that I'm trying to apply to a dataframe of locations. Specifically, I want to append a new column that contains the 10 closest sites to each site. The following seems to work, but it is excruciatingly slow. </p>
<pre><code>def distance(first_lat, first_lon, second_lat, second_lon):
return ((fi... | <p>Notice that your code has a time complexity of O(n^2): In this case, you're computing 30k*30k=900 million distances within an apply function that's in a for loop, i.e. pure Python.</p>
<p>Vector operations in pandas are implemented in C, so you would get a relative speedup if you calculated all the distances in a s... | python|pandas | 2 |
12,446 | 44,963,135 | Index out of bounds / IndexError | <p>I am trying to move a kernel around an array of an image to create a gaussian filter. I am getting an IndexError, and Idk why. This is the code: error at line 34</p>
<pre><code>import numpy as np
import scipy
from scipy import misc
import matplotlib.pyplot as plt
imagen_nueva = np.empty((1931, 1282))
imagen = sci... | <p>With some minor modifications to your code, such as fixing indentation and using an open source image i do not get any error. So it seems like an indentation errror.</p>
<p>See working code below:</p>
<pre><code>import numpy as np
import scipy
from scipy import misc
import matplotlib.pyplot as plt
imagen_nueva = ... | python|arrays|numpy|scipy|bounds | 1 |
12,447 | 57,055,774 | Pass Input to tensorflow lite model in Android | <p>I have created a neural network that take numerical data as input and saved it as tensorflow lite model using python.
I am trying to pass input to the model in Android.
Shape of ndarray is 1*3 </p>
<p>Sample of the input in python is as follows</p>
<pre><code>np.array([[-0.276786765 ,8.41897583008 ,-0.022201538... | <p>Assuming you're using <a href="https://www.tensorflow.org/lite/guide/inference#java" rel="nofollow noreferrer">TensorFlow Lite</a>, you can provide a 1x3 input using:</p>
<pre><code>float[] innerInput = {-0.276786765 ,8.41897583008 ,-0.0222015380859
float[][] input = {innerInput};
interpreter.run(input, output);
<... | python|tensorflow|neural-network|tensorflow-lite | 0 |
12,448 | 57,064,375 | Pandas str.replace method regex flag raises inconsistent exceptions | <p>When I use the <code>regex=[True|False]</code> flag in the <code>pd.Series.str.replace()</code> method, I get contradictory exceptions:</p>
<ul>
<li><code>repl</code> is a dictionary => it says <code>repl must be a string or callable</code></li>
<li><code>repl</code> is a callable => it says <code>Cannot use a call... | <p>If you look at the documentation for <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.replace.html" rel="nofollow noreferrer"><code>pandas.Series.str.replace</code></a> you will see that the <code>repl</code> argument can be a <em>string or callable</em>, but a <code>dict</code> ... | python|pandas | 2 |
12,449 | 57,016,929 | Sort trainable parameters in Keras | <p>When I have some trainable parameters, say <code>layer.trainable_weights</code>. I want to sort those weights before feed into other operations, is it possible for me to do that? Can I use something like </p>
<pre class="lang-py prettyprint-override"><code>import tensorflow as tf
p = layer.trainable_weights
p = tf.... | <p>Of course, you can do it. Since you have not stated clearly what these downstreaming operations are, it is more difficult to answer your question. </p>
<p>If you only want to do something to monitor the training process, e.g. monitoring a custom metric to measure the cumulative distribution function of the weight m... | python|tensorflow|machine-learning|keras|deep-learning | 0 |
12,450 | 56,913,326 | how to create a function in python that gives elements of an array? | <p>I have an array. Let's say a=array([[10, 2, 13, 55]])
I want to create a function that gives me the 1st element for t=0, the second element for t=1... </p>
<p>I have tried the following:</p>
<pre><code>def a(t):
return a[t]
</code></pre> | <p>You can do it like this :</p>
<pre class="lang-py prettyprint-override"><code>a=array([[10, 2, 13, 55]])
def get_value(t):
return a[t]
get_value(0) #results [10, 2, 13, 55]
</code></pre>
<p>Since your example data is 2D , if we want to access each of them we must pass 2 numbers as index.</p>
<pre class="la... | python|arrays|function|numpy | 1 |
12,451 | 56,915,156 | add a column based on the values of another column of dataframe in pandas | <p>this is my dataframe:</p>
<pre><code>df = pd.DataFrame({'symbol': ['msft', 'amd', 'bac', 'citi'], 'close': [100, 30, 70, 80]})
</code></pre>
<p>I want to add another column called <code>sector</code> that checks the values of <code>symbol</code> and add the values that I want (<code>tech</code> for <code>amd</code... | <p>In case the sector-symbol relation is a straightforward lookup, you may use something like:</p>
<pre class="lang-py prettyprint-override"><code>symbol_sector = {
'amd': 'tech',
'msft': 'tech',
'bac': 'bank',
'citi': 'bank'
}
df['sector'] = df['symbol'].map(symbol_sector)
</code></pre>
<p>If your r... | python|pandas | 3 |
12,452 | 45,952,729 | Can we create a jumbled table using pandas dataframe? | <p>Tables are usually having m rows and n columns. But can we create jumbled table in python ?</p>
<p>Like:</p>
<pre><code>1 2 3
4 5
6 7 8 9
</code></pre>
<p>R programming language have a functionality which automatically filled the blank or uninitialized cell with 'NA'. For example, If we make above table in ... | <p>Yes, pandas does the same thing. For instance, here's an example of concatenating two dataframes with different lengths column-wise:</p>
<pre><code>>>> import pandas as pd
>>> df = pd.DataFrame({"A":[1,2,3],"B":[2,65,4]})
>>> df
A B
0 1 2
1 2 65
2 3 4
>>> df1 = pd.... | r|python-3.x|pandas|dataframe | 2 |
12,453 | 45,799,550 | Pandas Row Date Conditional Filter Prior to Groupby - MAXIFS/MINIFS | <p>I am trying to do MAXIFS style calculations in Pandas</p>
<p>I am trying to add a column containing the next (if exists) & last (if exists) flagged date for each unique ID</p>
<p>Sample Table: (Trying to get the Next Flag & Last Flag Columns)</p>
<p>Edit: To form a more generic case, what if you wanted to... | <p>Try this ? I break down the steps , Assuming you already <code>sort_values</code> by column <code>Id</code> and <code>Date</code></p>
<pre><code>df['Next Flag']=np.nan
df['Last Flag']=np.nan
df.loc[(df.Flag==1).shift().fillna(False),'Next Flag']=df.Date.shift()
df.loc[(df.Flag==1).fillna(False),'Last Flag']=df.Date... | python|pandas|pandas-groupby | 1 |
12,454 | 45,831,857 | Transforming a CSV from wide to long format | <p>I have a csv like this:</p>
<pre><code>col1,col2,col2_val,col3,col3_val
A,1,3,5,6
B,2,3,4,5
</code></pre>
<p>and i want to transfer this csv like this :</p>
<pre><code>col1,col6,col7,col8
A,Col2,1,3
A,col3,5,6
</code></pre>
<p>there are col3 and col3_val so i want to keep col3 in col6 and values of col3 in col7 ... | <p>I think what you're looking for is <code>df.melt</code> and <code>df.groupby</code>:</p>
<pre><code>In [63]: df.rename(columns=lambda x: x.strip('_val')).melt('col1')\
.groupby(['col1', 'variable'], as_index=False)['value'].apply(lambda x: pd.Series(x.values))\
.add_prefix('value')\
... | python|pandas|csv|dataframe | 2 |
12,455 | 11,691,981 | Matlab VS Python - eig(A,B) VS sc.linalg.eig(A,B) | <p>I have the following matrices sigma and sigmad:</p>
<p>sigma:</p>
<pre><code> 1.9958 0.7250
0.7250 1.3167
</code></pre>
<p>sigmad:</p>
<pre><code> 4.8889 1.1944
1.1944 4.2361
</code></pre>
<p>If I try to solve the generalized eigenvalue problem in python I obtain:</p>
<pre><code> d,V =... | <p>Any (nonzero) scalar multiple of an eigenvector will also be an eigenvector; only the direction is meaningful, not the overall normalization. Different routines use different conventions -- often you'll see the magnitude set to 1, or the maximum value set to 1 or -1 -- and some routines don't even bother being inte... | python|matlab|numpy|scipy|eigenvalue | 11 |
12,456 | 28,726,839 | Removing rows below first line that meets threshold in pandas dataframe | <p>I have a df that looks like:</p>
<pre><code>import pandas as pd
import numpy as np
d = {'Hours':np.arange(12, 97, 12),
'Average':np.random.random(8),
'Count':[500, 250, 125, 75, 60, 25, 5, 15]}
df = pd.DataFrame(d)
</code></pre>
<p>This df has a decrease number of cases for each row. After the count drop... | <p>We can use the index generated from the boolean index and slice the df using <code>iloc</code>:</p>
<pre><code>In [58]:
df.iloc[:df[df.Count < 10].index[0]]
Out[58]:
Average Count Hours
0 0.183016 500 12
1 0.046221 250 24
2 0.687945 125 36
3 0.387634 75 48
4 0.167491 ... | python|python-2.7|pandas | 2 |
12,457 | 50,841,239 | How to separate a list item in to separate variables | <p>I'm working on a project for web scraping.</p>
<p>I run into an issue where I run a for loop to iterate through a list but it brings it back as one.</p>
<p>My aim is to separate each item inside the list and save it as a variable displaying it in a data frame, however, I'm faced with a block of text instead.</p>
... | <p>i think what would do the job is</p>
<pre><code>df1 = df.Specs.str.split(pat='\n', expand=True)
df1 = df1.replace('',np.nan)
df1 = df1.dropna(axis=1, how='all')
df1.columns = ['Spec_' + str(x) for x in list(df1)]
df1
Spec_1 Spec_2 Spec_3 Spec_4 Spec_5 Spec_6 Spec_7
0 2008 (08 reg) Coupe ... | list|pandas|web-scraping|python-requests | 1 |
12,458 | 20,633,506 | How to solve the Pandas issue related to Series.fillna()? | <p>I just upgrade from Pandas 0.11 to 0.13.0rc1. The upgration caused one error related to Series.fillna().</p>
<pre><code>>>> df
sales net_pft
STK_ID RPT_Date
600809 20060331 5.8951 1.1241
20060630 8.3031 1.5464
20060930 11.9084 2.2990
200... | <p>There was a recent discussion on this, and it is fixed in pandas master: <a href="https://github.com/pydata/pandas/issues/5703" rel="nofollow">https://github.com/pydata/pandas/issues/5703</a> (after the release of 0.13rc1, so it will be fixed in final 0.13).</p>
<p>Note: the behaviour changed! This was not supporte... | python|pandas | 3 |
12,459 | 33,110,533 | When I try to drop a single row in a pandas dataframe with datetimeindex, it shifts the index | <p>I have a dataframe with a datetimeindex index. When i try and drop a single row by its index value, the number of rows become N-1 correctly, but the times in the index shift. In fact, a large chunk of rows is chopped from the start, and then a chunk of rows with Nan values is added to the end. The size of this 'c... | <p>You should indicate you are using 0.17.0.</p>
<pre><code>In [13]: import psycopg2
In [14]: df = DataFrame(np.arange(10),index=pd.date_range('20130101 09:00:00',periods=10,tz=psycopg2.tz.FixedOffsetTimezone(offset=-480, name=None),freq='H'),columns=['value'])
In [15]: df
Out[15]:
value
... | python|pandas|datetimeindex | 1 |
12,460 | 66,377,901 | Keras model ValueError: Can not squeeze dim[1], expected a dimension of 1, got 90 | <p>My current model is:</p>
<pre><code># from tensorflow.keras.layers import InputLayer
model_training = Sequential()
# input_layer = keras.Input(shape=(300,1))
model_training.add(InputLayer(input_shape=(300,1)))
model_training.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='tanh'))
model_training.add... | <p>Squeeze your labels before training:</p>
<pre class="lang-py prettyprint-override"><code>train_labels = tf.squeeze(train_labels, axis=-1)
</code></pre>
<p>It seems like the shape of your labels is the problem. The model will output a shape of <code>(batch, 90)</code>, but you are providing <code>(batch, 90, 1)</code... | python|tensorflow|keras | 2 |
12,461 | 66,342,074 | Calculate date difference of dataframe groups | <p>I have a dataframe where I need to calculate the length of time (in years) between dates of groups. For example, I want the difference between the <em>first</em> time a <code>Name</code>-<code>ID</code> group appeared (identified by <code>%_chng=New</code>), and the date in the <code>Date</code> column.</p>
<pre><co... | <p>Let us do <code>cumsum</code> create the additional <code>groupby</code> key then do <code>transform</code></p>
<pre><code>df.Date = pd.to_datetime(df.Date)
s = df['%_chng'].eq('New').iloc[::-1].cumsum()
datediff = df.groupby([df['Name'],df['ID'],s])['Date'].transform('last')
df['date_length'] = (df['Date'] - datedi... | python|pandas | 1 |
12,462 | 66,555,744 | ValueError: Cannot assign to variable conv1_conv/kernel:0 due to variable shape (7, 7, 1, 64) and value shape (64, 3, 7, 7) are incompatible | <p>I am facing an issue using Resnet, Since i am new to this model it is a bit hard to find what might have gone wrong. Initially i tried to use the input shape as (10, 224, 224, 1) but this works only for 2d cnn or 3d cnn models but not for Resnet. Is there a workaround or i have to use only CNN models?</p>
<p>Please ... | <p>I got this error when I was using python3.8 with tensorflow. When I changed back to python3.6 with tensorflow, it works well with no errors.</p> | python|tensorflow|deep-learning|neural-network | 0 |
12,463 | 16,246,643 | Adding records to a numpy record array | <p>Let's say I define a record array</p>
<pre><code>>>> y=np.zeros(4,dtype=('a4,int32,float64'))
</code></pre>
<p>and then I proceed to fill up the 4 records available. Now I get more data, something like</p>
<pre><code>>>> c=('a',7,'24.5')
</code></pre>
<p>and I want to add this record to <code>... | <p>You can use <code>numpy.append()</code>, but as you need to convert the new data into a record array also:</p>
<pre><code>import numpy as np
y = np.zeros(4,dtype=('a4,int32,float64'))
y = np.append(y, np.array([("0",7,24.5)], dtype=y.dtype))
</code></pre>
<p>Since ndarray can't dynamic change it's size, you need t... | python|numpy|concatenation|record | 26 |
12,464 | 57,509,069 | Convert models( ?weights ) downloaded using applications module to tflite | <p>I am trying to convert mobilenet model downloaded using applications module in tf.keras to tensorflow lite format. TensorFlow version I am using is 1.31. I don't know whether model is actually stored weights only or weights+architecture+optimizer_state. When I tried the conversion command :</p>
<pre><code>from tens... | <p>How did you saved your model,maybe you have saved only weights not model and you are trying to call load model which is not present.</p>
<p>If this is not the problem try to clear session.</p>
<pre><code>from keras.backend import clear_session
clear_session()
</code></pre>
<p>I convert the model in this way</p>
... | python|tensorflow|keras|tf.keras | 0 |
12,465 | 57,717,961 | converting the values in a text file and making new text file in python | <p>I have a text file like this example:</p>
<p>example:</p>
<pre><code>"class" "Name" "Access" "CF33456_12.RCC" "CF33457_05.RCC" "CF33458_04.RCC"
"ff" "edi" "ff" "kju" 2444.91910958478 1669.55827263364 699.627215729572
"gg" "edi" "gg" "uhy" 2002.95278984564 369.565070720533 ... | <p>In your example the original dataframe (which has the structure of the input table) can be changed using this code:</p>
<pre><code> df = pd.read_table("myfile.txt", index_col=0)
import numpy as np
df2 = df.iloc[:, [3:5]]
df3 = np.array(df2)
df4 = np.log2(df3)
df.iloc[:, [3:5]] = df4
final... | python-3.x|pandas|file | 0 |
12,466 | 57,519,243 | access pyodbc object in dataframe | <p>Probably a naive question but any pointers would be appreciated. </p>
<p>I am trying to connect to my database and then trying to put the data in the pandas dataframe. However I am not able to achieve the same. </p>
<p>Here is the code that I am trying : </p>
<pre><code>import pandas as pd
import pyodbc
server = ... | <p><code>df = pd.Dataframe(cursor.fetchall(),columns=resoverall.keys())</code></p> | python|pandas|pyodbc | 0 |
12,467 | 24,259,988 | Change formatting on datetime ticks when plotting daily mean with Pandas/matplotlib | <p>I'm calculating the daily mean with the standard deviation as a bar plot. My dataframe looks like this:</p>
<pre><code> Ozone
2014-06-10 41.958586
2014-06-11 32.747222
2014-06-12 35.781250
2014-06-13 28.623264
2014-06-14 31.160764
2014-06-15 30.494444
2014-06-16 35.666667
[7 rows x 1 columns]... | <p>One possiblity is that as Pandas/Matplotlib is taking the dates as <code>datetime</code> values if you convert them to strings then you can control the format by using the <code>datetime.strftime</code> method.</p> | python|matplotlib|plot|pandas | 2 |
12,468 | 72,907,288 | Pandas str.extract() a number that ends in a letter | <p>I have a pandas column like below:</p>
<pre><code> df['description']
0. PRAIRIE HIGHLANDS SIXTH PLAT Lt: 156 PIN# DP73770000 0156 312 ABC
1. PRAIRIE VILLAGE PIN# OP55000034 0020A Rmrk: PT OF
2. Sub: HOLLY GREEN Lt: 14 Bl: 1 PIN# DP34500001 0D14
3. FAIRWAY PIN# GP20000006 0029 Rmrk: W
</code></pre>
<... | <p>I would use <code>str.extract</code> as follows:</p>
<pre class="lang-py prettyprint-override"><code>df["PIN"] = df["description"].str.extract(r'PIN#((?: [A-Z0-9]*[0-9][A-Z0-9]*)*)')
</code></pre>
<p>Here is a link to a running regex <a href="https://regex101.com/r/Ep2R8h/1" rel="nofollow norefer... | python|regex|pandas | 1 |
12,469 | 72,946,256 | How to concatenate two csv files horizontally to one dataframe? | <p>Suppose I have two csv files / pandas data_frames</p>
<pre><code>file1.csv ->
file A
---------
0 K0 E1
1 K0 E2
2 K0 E3
3 K1 W1
4 K2 W2
file2.csv ->
file B
--------
0 K0 E3
1 K0 W3
2 K1 E4
3 K1 W4
4 K3 W5
</code></pre>
<p>How to merge/concatenate them to get a resul... | <p>You can try 'np.concatenate((a,b), axis=0)'</p> | python|pandas|dataframe|csv | 0 |
12,470 | 73,039,047 | List param into sheet_name pandas read_execel() | <p>Im trying to send a list in sheet_name for access more then one sheet from .csv file and when i print df "<code>df = pd.read_excel( "https://www.football-data.co.uk/mmz4281/2122/all-euro-data-2021-2022.xlsx?raw=true", sheet_name=liga)</code>" works, he print me two sheets but in next line he said... | <p><code>pd.read_excel</code> returns a dictionary like</p>
<pre class="lang-py prettyprint-override"><code>{'D1': dataframe1, 'D2': dataframe2}
</code></pre>
<p>You need get the dataframe with dictionary key like</p>
<pre><code> d = pd.read_excel(
"https://www.football-data.co.uk/mmz4281/2122/all-euro-... | python|pandas | 0 |
12,471 | 70,391,141 | Change certain categorical variables to a unified entry | <p>Let's say I have have a dataframe with a column called animals. The entries look as followed:</p>
<pre><code>'A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'E', 'F', 'G', 'H', 'I'.
</code></pre>
<p>I want to change the entries 'E', 'F', 'G', 'H' and 'I' to another unified entry called 'D'. What is the best way to transform... | <p>You can create a <code>list</code> of the entries you want to change, and then you can assign 'D' for them using <code>loc</code> to spot them, and <code>isin</code> to evalute if your condition is satisfied:</p>
<pre><code>li = ['E','F','G','H','I']
df.loc[df.animals.isin(li), 'animals'] = 'D'
</code></pre>
<p>An a... | python|pandas|dataframe|categorical-data | 2 |
12,472 | 70,612,777 | Dataclass and Callable Initialization Problem via Classmethods | <p>I found this weird behaviour where I don't know if I am the problem or if this is a python / dataclass / callable bug.</p>
<p>Here is a minimal working example</p>
<pre><code>from dataclasses import dataclass
from typing import Callable
import numpy as np
def my_dummy_callable(my_array, my_bool):
return 1.0
... | <p>The <code>@dataclass</code> decorator by default supplies an <code>__init__()</code> method to a class. This method turns type annotated class variables into attributes of instances of the class. This mechanism is used in the case of the class <code>MySecondDataClassDummy</code>. In effect, every instance of this cl... | python|python-3.x|numpy|callable|python-dataclasses | 1 |
12,473 | 70,539,124 | Alternatively assign values to two column | <p>I have a dataset that looks as follows</p>
<pre><code> Datetime Message
0 2021-12-20 09:50:08.819 Current sidewing pressure: 3362
1 2021-12-20 09:50:08.820 Current sidewing pressure: 3303
2 2021-12-20 09:50:08.839 Current sidewing pressure: 3398
3 2021-12-20 09:50:08.839 Current sidewin... | <p>You can use <code>iloc[::2]</code> to extract every other value (<code>::2</code> for even indices values, and <code>1::2</code> for odd indices values) and assign to a column:</p>
<pre><code>vals = df.Message.str.extract('(\d+)$')
df['Right'] = vals.iloc[::2]
df['Left'] = vals.iloc[1::2]
df
Datet... | python|pandas|dataframe | 2 |
12,474 | 42,627,091 | pandas parse csv with left and right quote chars | <p>I am trying to read a file in pandas which is structured as follows</p>
<pre><code><first>$$><$$<second>$$><$$<first>$$>
<foo>$$><$$<bar>$$><$$<baz>$$>
</code></pre>
<p>using <code>pd.read_csv('myflie.csv', encoding='utf8', sep='$$><$$', decim... | <p>You need escape <code>$</code> by <code>\</code>, because it is read as regex (end of string):</p>
<blockquote>
<p>(separators > 1 char and different from '\s+' are interpreted as regex)</p>
</blockquote>
<pre><code>import pandas as pd
from pandas.compat import StringIO
temp=u"""<first>$$><$$<se... | python|csv|parsing|pandas | 3 |
12,475 | 42,983,799 | What is wrong with this pandas code? | <pre><code>import pandas as pd
s1 = pd.Series([1, 2, 3])
s2 = pd.Series([4, 5, 6])
s1.append(s2)
print(s1)
</code></pre>
<p>Such a simple thing but Its not appending. Out up is :
0 1
1 2
2 3
dtype: int64
It just prints s1. Its not appending? What silly mistake am I doing here?</p> | <p>Because <code>.append</code> returns a new series, it doesn't mutate in place (like <code>list.append</code>). Try:</p>
<pre><code>import pandas as pd
s1 = pd.Series([1, 2, 3])
s2 = pd.Series([4, 5, 6])
s3 = s1.append(s2)
print(s3)
</code></pre> | python|pandas|series | 2 |
12,476 | 42,686,065 | Iteratively subsetting pandas chunks with .duplicated() gives me empty arrays | <p>I am reading in a large csv in chunks with Pandas. I subset each chunk to see if there are duplicated timestamps:</p>
<pre><code>for c in chunks:
dups= c.duplicated(subset='Timestamp')
dups= dups[dups==True]
print(dups)
</code></pre>
<p>When I print dups, I get the following:</p>
<pre><code>255 Tr... | <p>In your loop, the line <code>dups= dups[dups==True]</code> returns an empty <code>Series</code> if <code>dups</code> is all <code>False</code>. If you don't want to print it when it's empty you could include a check for <code>len(dups) > 0</code>:</p>
<pre><code>for c in chunks:
dups= c.duplicated(subset='Ti... | python|pandas|bigdata | 0 |
12,477 | 27,010,793 | How to make this rounding function faster? | <p>I am trying to write a function to round values to the nearest valid odds in a list from here:
<a href="https://api.developer.betfair.com/services/webapps/docs/display/1smk3cen4v3lu3yomq5qye0ni/Betfair+Price+Increments" rel="nofollow noreferrer">https://api.developer.betfair.com/services/webapps/docs/display/1sm... | <p>You can get a speed increase in numpy by creating a magnitude array and then doing the rounding all at the end with the magnitude array.</p>
<pre><code>def nclosest_valid_odds_3( x ):
magnitudes = np.empty_like(x)
magnitudes[x < 1] = np.nan
magnitudes[(1 <= x) & (x <= 2)] = 0.01
v = ... | python|multithreading|numpy|multiprocessing|numba | 2 |
12,478 | 27,108,850 | Tuples of closed continuous intervals | <p>Say I have the following list of numbers:</p>
<pre><code>my_array = [0, 3, 4, 7, 8, 9, 10, 20, 21, 22, 70]
</code></pre>
<p>I would like to find every closed interval containing <strong>consecutive integers without gaps</strong> in this list. If for any number in the list there are multiple such intervals, we <str... | <p>I don't have a numpy install handy, but this is the approach that I would take. First handle the case of an empty array separately. Sort the array if it isn't already sorted and use <code>np.diff</code> to compute the differences.</p>
<pre><code>0, 3, 4, 7, 8, 9, 10, 20, 21, 22, 70
3 1 3 1 1 1 10 1 1... | python|algorithm|numpy | 3 |
12,479 | 25,113,682 | Acces all off diagonal elements of boolean numpy matrix | <p>Suppose there is a diagonal matrix M:</p>
<pre><code>#import numpy as np
M = np.matrix(np.eye(5, dtype=bool))
</code></pre>
<p>Does anybody know a simple way to access all off diagonal elements, meaning all elements that are <code>False</code>? In <code>R</code> I can simply do this by executing </p>
<pre><code>... | <p>You need the bitwise not operator:</p>
<pre><code>M[~M]
</code></pre> | python|numpy|matrix | 8 |
12,480 | 39,378,535 | Changing data in a DataFrame column (Pandas) with a For loop | <p>I'm trying to take the data from "Mathscore" and convert the values into numerical values, all under "Mathscore."</p>
<p>strong =1
Weak = 0</p>
<p>I tried doing this via the function below using For loop but I can't get the code to run. Is the way I'm trying to assign data incorrect?</p>
<p>Thanks! </p>
<pre><c... | <p>you can <a href="http://pandas.pydata.org/pandas-docs/stable/categorical.html" rel="nofollow">categorize</a> your data:</p>
<pre><code>In [23]: df['Mathscore'] = df.Mathscore.astype('category').cat.rename_categories(['1','0'])
In [24]: df
Out[24]:
Id_Student Mathscore
0 1 1
1 2 ... | python|pandas|dataframe | 3 |
12,481 | 39,128,145 | Average of numpy array ignoring specified value | <p>I have a number of 1-dimensional numpy ndarrays containing the path length between a given node and all other nodes in a network for which I would like to calculate the average. The matter is complicated though by the fact that if no path exists between two nodes the algorithm returns a value of 2147483647 for that ... | <p>Why not using your usual numpy filtering for this?</p>
<pre><code>m = my_array[my_array != 2147483647].mean()
</code></pre>
<p>By the way, if you really want speed, your whole algorithm description seems certainly naive and could be improved by a lot.</p>
<p>Oh and I guess that you are calculating the mean becaus... | python|arrays|performance|numpy|graph-tool | 5 |
12,482 | 39,357,882 | Pandas DENSE RANK | <p>I'm dealing with pandas dataframe and have a frame like this:</p>
<pre><code>Year Value
2012 10
2013 20
2013 25
2014 30
</code></pre>
<p>I want to make an equialent to DENSE_RANK () over (order by year) function. to make an additional column like this:</p>
<pre><code> Year Value Rank
2012 10 1
... | <p>Use <code>pd.Series.rank</code> with <code>method='dense'</code></p>
<pre><code>df['Rank'] = df.Year.rank(method='dense').astype(int)
df
</code></pre>
<p><a href="https://i.stack.imgur.com/67n7I.png" rel="noreferrer"><img src="https://i.stack.imgur.com/67n7I.png" alt="enter image description here"></a></p> | python|sql|pandas | 21 |
12,483 | 39,376,770 | Comparing 3d tensor and 4d tensor Tensorflow | <p>I have the following U-Net which I use to segment grayscale PNG images. </p>
<pre><code>import cv2
import os
from sklearn.utils import shuffle
import tensorflow as tf
import numpy as np
OVERALLSIZE = int(float(input('Choose the number of images you want (<5635) : ')))
PATH = input('give absolute path to image'... | <p>stick a tf.newaxis into the tensor.</p>
<pre><code>x = x[:,:, :, tf.newaxis]
</code></pre>
<p>or use tf.squeeze to get rid of the extra axis in y.</p>
<pre><code>y = tf.squeeze(y, axis=-1)
</code></pre> | python|tensorflow | 0 |
12,484 | 29,334,205 | PYTHON - Error while using numpy genfromtxt to import csv data with multiple data types | <p>I'm working on a kaggle competition to predict restaurant revenue based on multiple predictors. I'm a beginner user of Python, I would normally use Rapidminer for data analysis. I am using Python 3.4 on the Spyder 2.3 dev environment.</p>
<p>I am using the below code to import the training csv file. </p>
<pre><cod... | <p>[Solved].</p>
<p>I just chucked numpy's genfromtext and opted to use read_csv from pandas since it gives the option to import text in 'utf-8' encoding. </p> | python|csv|python-3.x|numpy|data-mining | 0 |
12,485 | 33,594,894 | Adding scikit-learn (sklearn) prediction to pandas data frame | <p>I am trying to add a sklearn prediction to a pandas dataframe, so that I can make a thorough evaluation of the prediction. The relavant piece of code is the following:</p>
<pre><code>clf = linear_model.LinearRegression()
clf.fit(Xtrain,ytrain)
ypred = pd.DataFrame({'pred_lin_regr': pd.Series(clf.predict(Xtest))})
<... | <p>You're correct with your second line, <code>df_total["pred_lin_regr"] = clf.predict(Xtest)</code> and it's more efficient.</p>
<p>In that one you're taking the output of <a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.predict... | python|numpy|pandas|scikit-learn | 9 |
12,486 | 33,919,875 | Interpolate irregular 3d data from a XYZ file to a regular grid | <p>I have a xyz file containing a lot of 3D coordinates like so:</p>
<pre><code> 370373.771 6535261.431 2.908
370373.788 6535261.441 2.911
370373.787 6535261.442 2.909
370373.809 6535261.449 2.908
370373.810 6535261.439 2.909
370373.743 ... | <p>The <code>coord_z</code> argument passed in must also be an array:</p>
<pre><code>grid = griddata(np.array(coord_xy), np.array(coord_z), (X, Y), method='nearest')
</code></pre> | python|numpy|grid|interpolation | 4 |
12,487 | 22,537,354 | error when installing pandas package: no module named numpy | <p>I have a big solutions with multiple projects inside. I use <code>virtualenv</code> for that.
So for one of my projects in solution I already install the stuff I need, including <code>numpy</code> and <code>pandas</code></p>
<p>but when I I executing something like that:</p>
<pre><code>cd ../project2
sudo python s... | <p>I recently had this error while trying to update Pandas from version 0.23.1 to 0.24.1. </p>
<p>What solved my problem was to first update pip by executing:</p>
<pre><code>python -m pip install --upgrade pip
</code></pre>
<p>And then updating the desired library.</p> | python|numpy|pandas | 7 |
12,488 | 62,323,162 | Get datetime64[ns] between two datetimes pandas | <p>I'm trying to extract the rows within a certain datetime. What am I doing wrong?</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'year': [2015, 2016, 2017, 2016],
'month': [2, 3, 4, 6],
'day': [4, 5, 4, 3]})
df = pd.to_datetime(df)
df = df.to_frame(name='test')
start_d... | <p>There missing <code>()</code> becauase priority of operators:</p>
<pre><code>print (df[(df['test'] > start_date) & (df['test'] < end_date)])
</code></pre>
<p>Or use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.between.html" rel="nofollow noreferrer"><code>Series.betwee... | pandas|datetime | 1 |
12,489 | 62,281,578 | How to merge multiple pandas dataframes into one original dataframe in the most efficient way? | <p>How to merge 4 pandas dataframes into one original dataframe in the most efficient way?
Below shows the original dataframe <code>df</code> whose 4 columns <code>CC1</code>, <code>CC2</code>, <code>CC3</code> and <code>CC4</code> need to be updated with the respective columns from <code>df1</code>, <code>df2</code>, ... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html" rel="nofollow noreferrer"><code>concat</code></a> in list comprehension for create <code>MultiIndex</code> by <code>TD</code> and <code>PD</code> used for outer join by <a href="http://pandas.pydata.org/pandas-docs/stable/refe... | python|pandas|dataframe | 2 |
12,490 | 62,203,593 | Averaging rows from one pandas df to to another as mean (using two keys) | <p>I have two dataframes.</p>
<p>DF1 looks like:</p>
<p><a href="https://i.stack.imgur.com/SNCAH.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/SNCAH.png" alt="mock up of 4 column table, two id columns, two data columns representing averages"></a></p>
<p>DF2 looks like:</p>
<p><a href="https://i... | <p>Looks like you can try <code>groupby()</code> then <code>merge</code>:</p>
<pre><code>df1.merge(df2.groupby(['ID_1','ID_2']).mean().add_suffix('_Mean'),
on=['ID_1','ID_2'])
</code></pre> | python|pandas|dataframe | 0 |
12,491 | 62,369,319 | what could be the reason for the importerror | <p>I am trying to train a model of CNN. <br>
When I run the code, it returns following error:</p>
<pre><code>Traceback (most recent call last):
File "train_network.py", line 5, in <module>
import matplotlib
File "/home/kaustubhj/.virtualenvs/dl4cv/lib/python3.7/site-packages/matplotlib/__init__.py", line... | <p><strong>ImportError</strong> generally refers to an import library required for execution is not present within executing systems.</p>
<p>Few possible cases to look for.</p>
<ol>
<li><p>In a general case, your system where py is running is missing this package.</p></li>
<li><p>In Spark(general-purpose computing cl... | python|numpy|matplotlib | 1 |
12,492 | 62,359,037 | .eq() method is not giving same result as [ == ] | <p>I am having a hard time understanding why the results are not the same for following code.</p>
<p>I am trying to find the accuracy of a model but the first item gives a result of tensor(66.), and second item gives a result of tensor(105).</p>
<pre><code>(y_test[y_test==y_predicted_cls].sum(), y_predicted_cls.eq(y_... | <p>The statement <code>y_test[y_test==y_predicted_cls].sum()</code> gives the sum for the <code>y_test</code> list/array while, <code>y_predicted_cls.eq(y_test).sum()</code> gives the sum for <code>y_predicted_cls</code>, and in the first case, if both the arrays are same, it yields:</p>
<pre><code>y_test[1].sum()
</c... | python|pytorch | 0 |
12,493 | 62,067,400 | Understanding accumulated gradients in PyTorch | <p>I am trying to comprehend inner workings of the gradient accumulation in <code>PyTorch</code>. My question is somewhat related to these two:</p>
<p><a href="https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch#">Why do we need to call zero_grad() in PyTorch?</a></p>
<p><a href="... | <p>You are not actually accumulating gradients. Just leaving off <code>optimizer.zero_grad()</code> has no effect if you have a single <code>.backward()</code> call, as the gradients are already zero to begin with (technically <code>None</code> but they will be
automatically initialised to zero).</p>
<p>The only differ... | python|deep-learning|pytorch|gradient-descent | 44 |
12,494 | 62,073,469 | Array out of bounds by checking elements | <p>My for loop is always going out of bounds. It keeps checking whether the element is > 0. I tried plenty of restrictions, but none of them worked. Do you have any suggestions?</p>
<pre><code>###Creation of the graph in a method
def graph(self):
###Creation of the given array I added zero rows and columns because... | <p>To answer the question of why the restrictions don't work, let's examine one of them for example (substituing in the <code>d[0]</code> value, since the <code>d</code> list isn't actually helping us either with solving the problem or simplifying the code):</p>
<pre><code>a[r-1, c] > 0 and r >= 0
</code></pre>
... | python|arrays|numpy | 0 |
12,495 | 51,425,729 | What is the best way to multiply arrays? in Python | <p>I have two arrays. </p>
<pre><code>Array1
[[-0.23, 0.11],
[0.29, -0.37]]
Array2
([5.28, 4.40])
</code></pre>
<p>I want to do sum the multiplication of one array by the other</p>
<p>Example </p>
<ul>
<li><p>sum(5.28 *-0.23 + 4.40 * 0.11) = ind1</p></li>
<li><p>sum(5.28 *-0.29 + 4.40 * -0.37) = ind2</p></li>... | <p>Are you familiar with how to <a href="https://docs.scipy.org/doc/numpy-1.13.0/user/basics.creation.html" rel="nofollow noreferrer">create numpy arrays</a> and <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.matmul.html" rel="nofollow noreferrer">multiply</a> them?</p>
<pre><code>numpy.matmul(Arr... | python|numpy | 1 |
12,496 | 51,364,975 | Datatype in NumPy | <p>I have come across the following statement in numpy:</p>
<pre><code>x=numpy.zeros((2,2),dtype=[('x','i4'),('y','i4')])
</code></pre>
<p>and the output is like this:</p>
<pre><code>[[(0,0)(0,0)]
[(0,0)(0,0)]]
</code></pre>
<p>What is the meaning of <code>[('x','i4'),('y','i4')]</code>? Please explain.</p> | <p>This is how the elements of the array are given a name and datatype.</p>
<p>In this case, the names of the first elements of each entry in the array can be accessed using <code>'x'</code> and the second elements can be accessed using <code>'y'</code>:</p>
<pre><code>>>> x['x']
array([[0, 0],
[0, 0]... | python|numpy | 4 |
12,497 | 51,490,965 | Pytorch Torch.save FileNotFoundError | <p>When I try to call "torch.save" to save my model in a "tmp_file", it rises a <code>FileNotFoundError</code>. the trace back is as follow:</p>
<blockquote>
<p>Traceback (most recent call last):
File “C:/Users/Haoran/Documents/GitHub/dose-response/python/simulations/hdr.py”, line 234, in
test_hdr_continuous()
... | <p>As <a href="https://stackoverflow.com/questions/51490965/pytorch-torch-save-filenotfounderror#comment89950953_51490965">shmee</a> observed, you are trying to write to <code>/tmp/[...]</code> on a <em>Windows</em> machine. Therefore you get <code>FileNotFoundError</code>.<br>
To make your code OS agnostic, you may fi... | python|pytorch|torch | 0 |
12,498 | 51,369,303 | Density of distribution | <p>I want to implement function func() which completes following task:
the average weight of car is <code>m</code> kg, with a standard deviation of <code>s</code> kg. What part of all cars would you expect to have weight more than <code>k</code> kg (probability must be <code><1</code>)?</p>
<pre><code>import scipy.... | <p><code>norm_rv.cdf(k)</code> returns the probability that the random variable takes on a value <em>less than or equal to</em> <code>k</code>.</p>
<p>Your implementation should be</p>
<pre><code>import scipy.stats as sts
def func(m, s, k):
norm_rv = sts.norm(loc=m, scale=s)
return round(1 - norm_rv.cdf(k)... | python|numpy|scipy|probability|distribution | 1 |
12,499 | 48,118,809 | Add different color markers by day of week to a Pandas time series plot | <p>I made a time-series plot as below with customized x axis:</p>
<pre><code>import matplotlib.pyplot as plt
import matplotlib.dates as mdates
df = pd.DataFrame({'points': np.random.randint(1,100, 61)},
index=pd.date_range(start='11-1-2017', end='12-31-2017', freq='D'))
df['dow'] = df.index.dayofweek
fig, ax = plt.... | <p>The only way I have seen lines plotted with different color markers is by plotting the markers as a scatter plot and then plotting the line. In this situation I would plot the dates with the marker <code>-</code> and then make a scatter plot over the top like so:</p>
<pre><code>import matplotlib.pyplot as plt
impor... | python|pandas|matplotlib|timeserieschart | 1 |
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